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  • 1.
    Ahmed, Muhammad
    et al.
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany.
    Hashmi, Khurram Azeem
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany .
    Pagani, Alain
    German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Stricker, Didier
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany .
    Afzal, Muhammad Zeshan
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany.
    Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 15Article, review/survey (Refereed)
    Abstract [en]

    Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of deep learning-based object detection in challenging environments. However, there is no consolidated reference to cover the state of the art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present a quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

  • 2.
    Akbar, Mariam
    et al.
    COMSATS Institute of Information Technology, Islamabad.
    Javaid, Nadeem
    COMSATS Institute of Information Technology, Islamabad.
    Kahn, Ayesha Hussain
    COMSATS Institute of Information Technology, Islamabad.
    Imran, Muhammad Al
    College of Computer and Information Sciences, Almuzahmiyah, King Saud University.
    Shoaib, Muhammad
    College of Computer and Information Sciences, Almuzahmiyah, King Saud University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility2016In: Sensors, E-ISSN 1424-8220, Vol. 16, no 3, article id 404Article in journal (Refereed)
    Abstract [en]

    Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), i.e., an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability

  • 3.
    Al-Azzawi, Sana Sabah
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. College of Engineering, University of Information Technology and Communications, Baghdad 10013, Iraq.
    Khaksar, Siavash
    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia.
    Hadi, Emad Khdhair
    Rehabilitation Medical Center and Joint Diseases, Baghdad 10001, Iraq.
    Agrawal, Himanshu
    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia.
    Murray, Iain
    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia.
    HeadUp: A Low-Cost Solution for Tracking Head Movement of Children with Cerebral Palsy Using IMU2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 23, article id 8148Article in journal (Refereed)
    Abstract [en]

    Cerebral palsy (CP) is a common reason for human motor ability limitations caused before birth, through infancy or early childhood. Poor head control is one of the most important problems in children with level IV CP and level V CP, which can affect many aspects of children's lives. The current visual assessment method for measuring head control ability and cervical range of motion (CROM) lacks accuracy and reliability. In this paper, a HeadUp system that is based on a low-cost, 9-axis, inertial measurement unit (IMU) is proposed to capture and evaluate the head control ability for children with CP. The proposed system wirelessly measures CROM in frontal, sagittal, and transverse planes during ordinary life activities. The system is designed to provide real-time, bidirectional communication with an Euler-based, sensor fusion algorithm (SFA) to estimate the head orientation and its control ability tracking. The experimental results for the proposed SFA show high accuracy in noise reduction with faster system response. The system is clinically tested on five typically developing children and five children with CP (age range: 2-5 years). The proposed HeadUp system can be implemented as a head control trainer in an entertaining way to motivate the child with CP to keep their head up.

  • 4.
    Alhashimi, Anas
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors2015In: Sensors, E-ISSN 1424-8220, Vol. 15, no 12, p. 31205-31223Article in journal (Refereed)
    Abstract [en]

    We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.

  • 5.
    Ali, Bako
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Faculty of Engineering, Al Azhar University.
    Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes2018In: Sensors, E-ISSN 1424-8220, Vol. 18, no 3, article id 817Article in journal (Refereed)
    Abstract [en]

    The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or “things” to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.

  • 6.
    Al-Turjman, Fadi M.
    et al.
    Department of Computer Engineering, Middle East Technical University, Northern Cyprus Campus.
    Imran, Muhammad
    College of Computer and Information Sciences, King Saud University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Value-Based Caching in Information-Centric Wireless Body Area Networks2017In: Sensors, E-ISSN 1424-8220, Vol. 17, no 1, article id 181Article in journal (Refereed)
    Abstract [en]

    We propose a resilient cache replacement approach based on a Value of sensed Information (VoI) policy. To resolve and fetch content when the origin is not available due to isolated in-network nodes (fragmentation) and harsh operational conditions, we exploit a content caching approach. Our approach depends on four functional parameters in sensory Wireless Body Area Networks (WBANs). These four parameters are: age of data based on periodic request, popularity of on-demand requests, communication interference cost, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These parameters are considered together to assign a value to the cached data to retain the most valuable information in the cache for prolonged time periods. The higher the value, the longer the duration for which the data will be retained in the cache. This caching strategy provides significant availability for most valuable and difficult to retrieve data in the WBANs. Extensive simulations are performed to compare the proposed scheme against other significant caching schemes in the literature while varying critical aspects in WBANs (e.g., data popularity, cache size, publisher load, connectivity-degree, and severe probabilities of node failures). These simulation results indicate that the proposed VoI-based approach is a valid tool for the retrieval of cached content in disruptive and challenging scenarios, such as the one experienced in WBANs, since it allows the retrieval of content for a long period even while experiencing severe in-network node failures.

  • 7.
    Bezerra, Nibia Souza
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    de Sousa Jr., Vicente A.
    Federal University of Rio Grande do Norte (UFRN).
    Temperature Impact in LoRaWAN: A Case Study in Northern Sweden2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 20, article id 4414Article in journal (Refereed)
    Abstract [en]

    LoRaWAN has become popular as an IoT enabler. The low cost, ease of installation and the capacity of fine-tuning the parameters make this network a suitable candidate for the deployment of smart cities. In northern Sweden, in the smart region of Skellefteå, we have deployed a LoRaWAN to enable IoT applications to assist the lives of citizens. As Skellefteå has a subarctic climate, we investigate how the extreme changes in the weather happening during a year affect a real LoRaWAN deployment in terms of SNR, RSSI and the use of SF when ADR is enabled. Additionally, we evaluate two propagation models (Okumura-Hata and ITM) and verify if any of those models fit the measurements obtained from our real-life network. Our results regarding the weather impact show that cold weather improves the SNR while warm weather makes the sensors select lower SFs, to minimize the time-on-air. Regarding the tested propagation models, Okumura-Hata has the best fit to our data, while ITM tends to overestimate the RSSI values.

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  • 8.
    Chaudhry, Muhammad Hamid
    et al.
    Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia. Centre of GIS, University of the Punjab, Lahore 54590, Pakistan.
    Ahmad, Anuar
    Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia.
    Gulzar, Qudsia
    Centre of GIS, University of the Punjab, Lahore 54590, Pakistan.
    Farid, Muhammad Shahid
    Punjab University College of Information Technology, University of the Punjab, Lahore 54590, Pakistan.
    Shahabi, Himan
    Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran.
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Assessment of DSM Based on Radiometric Transformation of UAV Data2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 5, article id 1649Article in journal (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy. 

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  • 9.
    Cheng, Haibo
    et al.
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China. University of Chinese Academy of Sciences, Beijing 100049, China.
    Yu, Haibin
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
    Zeng, Peng
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Li, Shichao
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland.
    Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 19, article id 5659Article in journal (Refereed)
    Abstract [en]

    Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

  • 10.
    Cichoń, Krzysztof
    et al.
    Institute of Radiocommunications, Poznan University of Technology, Poznań, 60-965, Poland.
    Nikiforuk, Maciej
    Tietoevry, al. Piastów 30, Szczecin, 71-064, Poland.
    Kliks, Adrian
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Institute of Radiocommunications, Poznan University of Technology, Poznań, 60-965, Poland.
    Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands2024In: Sensors, E-ISSN 1424-8220, Vol. 24, no 10, article id 3190Article in journal (Refereed)
    Abstract [en]

    As fixed wireless access (FWA) is still envisioned as a reasonable way to achieve communications links, foliage attenuation becomes an important wireless channel impairment in the millimeter-wave bandwidth. Foliage is modeled in the radiative transfer equation as a medium of random scatterers. However, other phenomena in the wireless channel may also occur. In this work, vegetation attenuation measurements are presented for a single tree alley for 26–32 GHz. The results show that vegetation loss increases significantly after the second tree in the alley. Measurement-based foliage losses are compared with model-based, and new tuning parameters are proposed for models.

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  • 11.
    Cleland, Ian
    et al.
    School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim, Northern Ireland BT37 0QB, UK.
    Kikhia, Basel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Nugent, Chris
    School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim, Northern Ireland BT37 0QB, UK.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    McClean, Sally
    Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, Northern Ireland BT52 1SA, UK.
    Finlay, Dewar
    School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim, Northern Ireland BT37 0QB, UK.
    Optimal Placement of Accelerometers for the Detection of Everyday Activities2013In: Sensors, E-ISSN 1424-8220, Vol. 13, no 7, p. 9183-9200Article in journal (Refereed)
    Abstract [en]

    This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.

  • 12.
    Cruciani, Frederico
    et al.
    Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK.
    Cleland, Ian
    Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK.
    Nugent, Chris
    Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK.
    McCullagh, Paul
    Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Automatic annotation for human activity recognition in free living using a smartphone2018In: Sensors, E-ISSN 1424-8220, Vol. 18, no 7, article id 2203Article in journal (Refereed)
    Abstract [en]

    Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).

  • 13.
    Damigos, Gerasimos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindgren, Tore
    Ericsson Research, Laboratoriegränd 11, 977 53 Luleå, Sweden.
    Sandberg, Sara
    Ericsson Research, Laboratoriegränd 11, 977 53 Luleå, Sweden.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Performance of Sensor Data Process Offloading on 5G-Enabled UAVs2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 2, article id 864Article in journal (Refereed)
    Abstract [en]

    Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing worldwide. Thus, there is a strong interest in using UAVs for applications requiring wide-area connectivity coverage. Such applications might be power line inspection, road inspection, offshore site monitoring, wind turbine inspections, and others. The utilization of cellular networks, such as the fifth-generation (5G) technology, is often considered to meet the requirement of wide-area connectivity. This study quantifies the performance of 5G-enabled UAVs when sensor data throughput requirements are within the 5G network’s capability and when throughput requirements significantly exceed the capability of the 5G network, respectively. Our experimental results show that in the first case, the 5G network maintains bounded latency, and the application behaves as expected. In the latter case, the overloading of the 5G network results in increased latency, dropped packets, and overall degradation of the application performance. Our findings show that offloading processes requiring moderate sensor data rates work well, while transmitting all the raw data generated by the UAV’s sensors is not possible. This study highlights and experimentally demonstrates the impact of critical parameters that affect real-life 5G-enabled UAVs that utilize the edge-offloading power of a 5G cellular network.

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  • 14.
    Dong, Pingping
    et al.
    College of Information Science and Engineering, Hunan Normal University, Changsha, China. Hunnan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China.
    Gao, Kai
    College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China. Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha, China.
    Xie, Jingyun
    College of Information Science and Engineering, Hunan Normal University, Changsha, China. Hunnan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China.
    Tang, Wensheng
    College of Information Science and Engineering, Hunan Normal University, Changsha, China. Hunnan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China.
    Xiong, Naixue
    College of Intelligence and Computing, Tianjin University, Tianjin, China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Receiver-Side TCP Countermeasure in Cellular Networks2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 12, article id 2791Article in journal (Refereed)
    Abstract [en]

    Cellular-based networks keep large buffers at base stations to smooth out the bursty data traffic, which has a negative impact on the user’s Quality of Experience (QoE). With the boom of smart vehicles and phones, this has drawn growing attention. For this paper, we first conducted experiments to reveal the large delays, thus long flow completion time (FCT), caused by the large buffer in the cellular networks. Then, a receiver-side transmission control protocol (TCP) countermeasure named Delay-based Flow Control algorithm with Service Differentiation (DFCSD) was proposed to target interactive applications requiring high throughput and low delay in cellular networks by limiting the standing queue size and decreasing the amount of packets that are dropped in the eNodeB in Long Term Evolution (LTE). DFCSD stems from delay-based congestion control algorithms but works at the receiver side to avoid the performance degradation of the delay-based algorithms when competing with loss-based mechanisms. In addition, it is derived based on the TCP fluid model to maximize the network utility. Furthermore, DFCSD also takes service differentiation into consideration based on the size of competing flows to shorten their completion time, thus improving user QoE. Simulation results confirmed that DFCSD is compatible with existing TCP algorithms, significantly reduces the latency of TCP flows, and increases network throughput.

  • 15.
    Drobnič, Miha
    et al.
    Faculty of Sport, University of Ljubljana, 1000 Ljubljana, Slovenia.
    Verdel, Nina
    Department of Health Sciences, Mid Sweden University, 83125 Östersund, Sweden.
    Holmberg, Hans-Christer
    Luleå University of Technology, Department of Health, Education and Technology, Health, Medicine and Rehabilitation. School of Kinesiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
    Supej, Matej
    Faculty of Sport, University of Ljubljana, 1000 Ljubljana, Slovenia; Department of Health Sciences, Mid Sweden University, 83125 Östersund, Sweden.
    The Validity of a Three-Dimensional Motion Capture System and the Garmin Running Dynamics Pod in Connection with an Assessment of Ground Contact Time While Running in Place2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 16, article id 7155Article in journal (Refereed)
    Abstract [en]

    A three-dimensional motion capture system (MoCap) and the Garmin Running Dynamics Pod can be utilised to monitor a variety of dynamic parameters during running. The present investigation was designed to examine the validity of these two systems for determining ground contact times while running in place by comparing the values obtained with those provided by the bilateral force plate (gold standard). Eleven subjects completed three 20-s runs in place at self-selected rates, starting slowly, continuing at an intermediate pace, and finishing rapidly. The ground contact times obtained with both systems differed significantly from the gold standard at all three rates, as well as for all the rates combined (p < 0.001 in all cases), with the smallest mean bias at the fastest step rate for both (11.5 ± 14.4 ms for MoCap and −81.5 ± 18.4 ms for Garmin). This algorithm was developed for the determination of ground contact times during normal running and was adapted here for the assessment of running in place by the MoCap, which could be one explanation for its lack of validity. In conclusion, the wearables developed for monitoring normal running cannot be assumed to be suitable for determining ground contact times while running in place.

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  • 16.
    Düking, Peter
    et al.
    University of Würzburg, Würzburg, Germany.
    Achtzehn, Silvia
    German Sport University, Cologne, Germany.
    Holmberg, Hans-Christer
    Mittuniversitetet, Avdelningen för hälsovetenskap.
    Sperlich, Billy
    University of Würzburg, Würzburg, Germany.
    Integrated framework of load monitoring by a combination of smartphone applications, wearables and point-of-care testing provides feedback that allows individual responsive adjustments to activities of daily living2018In: Sensors, E-ISSN 1424-8220, Vol. 18, no 5, article id 1632Article in journal (Refereed)
    Abstract [en]

    Athletes schedule their training and recovery in periods, often utilizing a pre-defined strategy. To avoid underperformance and/or compromised health, the external load during training should take into account the individual’s physiological and perceptual responses. No single variable provides an adequate basis for planning, but continuous monitoring of a combination of several indicators of internal and external load during training, recovery and off-training as well may allow individual responsive adjustments of a training program in an effective manner. From a practical perspective, including that of coaches, monitoring of potential changes in health and performance should ideally be valid, reliable and sensitive, as well as time-efficient, easily applicable, non-fatiguing and as non-invasive as possible. Accordingly, smartphone applications, wearable sensors and point-of-care testing appear to offer a suitable monitoring framework allowing responsive adjustments to exercise prescription. Here, we outline 24-h monitoring of selected parameters by these technologies that (i) allows responsive adjustments of exercise programs, (ii) enhances performance and/or (iii) reduces the risk for overuse, injury and/or illness.

  • 17.
    Esmaeili, Chakavak
    et al.
    School of Chemical Sciences and Food Technology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi.
    Abdi, Mahnaz M.
    University Putra Malaysia, Department of Chemistry, Faculty of Science, University Putra Malaysia.
    Mathew, Aji P.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Jonoobi, Mehdi
    Department of Wood and Paper Science and Technology, Faculty of Natural Resources, University of Tehran.
    Oksman, Kristiina
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Rezayi, Majid
    Chemistry Department, Faculty of Science, University Malaya.
    Synergy Effect of Nanocrystalline Cellulose for the Biosensing Detection of Glucose2015In: Sensors, E-ISSN 1424-8220, Vol. 15, no 10, p. 24681-24697Article in journal (Refereed)
    Abstract [en]

    Integrating polypyrrole-cellulose nanocrystal-based composites with glucose oxidase (GOx) as a new sensing regime was investigated. Polypyrrole-cellulose nanocrystal (PPy-CNC)-based composite as a novel immobilization membrane with unique physicochemical properties was found to enhance biosensor performance. Field emission scanning electron microscopy (FESEM) images showed that fibers were nanosized and porous, which is appropriate for accommodating enzymes and increasing electron transfer kinetics. The voltammetric results showed that the native structure and biocatalytic activity of GOx immobilized on the PPy-CNC nanocomposite remained and exhibited a high sensitivity (ca. 0.73 μA·mM(-1)), with a high dynamic response ranging from 1.0 to 20 mM glucose. The modified glucose biosensor exhibits a limit of detection (LOD) of (50 ± 10) µM and also excludes interfering species, such as ascorbic acid, uric acid, and cholesterol, which makes this sensor suitable for glucose determination in real samples. This sensor displays an acceptable reproducibility and stability over time. The current response was maintained over 95% of the initial value after 17 days, and the current difference measurement obtained using different electrodes provided a relative standard deviation (RSD) of 4.47%.

  • 18.
    Esmaeili Kelishomi, A.
    et al.
    Xi'an Jiaotong University, Xi'an, China.
    Garmabaki, Amir Soleimani
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Bahaghighat, M.
    Raja University, Qazvin, Iran.
    Dong, J.
    Xi'an Jiaotong University, Xi'an, China.
    Mobile User Indoor-Outdoor Detection Through Physical Daily Activities2019In: Sensors, E-ISSN 1424-8220, no 3, article id 511Article in journal (Refereed)
    Abstract [en]

    An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.

  • 19.
    Farooq, Umer
    et al.
    Department of Cyber Security, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.
    Asim, Muhammad
    Department of Cyber Security, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.
    Tariq, Noshina
    Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan.
    Baker, Thar
    Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17551, United Arab Emirates; Faculty of Engineering, Al-Azhar University, Qena P.O. Box 83513, Egypt; Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, UK.
    Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 12, article id 4539Article in journal (Refereed)
    Abstract [en]

    Recently, the Internet of Things (IoT) has emerged as an important way to connect diverse physical devices to the internet. The IoT paves the way for a slew of new cutting-edge applications. Despite the prospective benefits and many security solutions offered in the literature, the security of IoT networks remains a critical concern, considering the massive amount of data generated and transmitted. The resource-constrained, mobile, and heterogeneous nature of the IoT makes it increasingly challenging to preserve security in routing protocols, such as the routing protocol for low-power and lossy networks (RPL). RPL does not offer good protection against routing attacks, such as rank, Sybil, and sinkhole attacks. Therefore, to augment the security of RPL, this article proposes the energy-efficient multi-mobile agent-based trust framework for RPL (MMTM-RPL). The goal of MMTM-RPL is to mitigate internal attacks in IoT-based wireless sensor networks using fog layer capabilities. MMTM-RPL mitigates rank, Sybil, and sinkhole attacks while minimizing energy and message overheads by 25–30% due to the use of mobile agents and dynamic itineraries. MMTM-RPL enhances the security of RPL and improves network lifetime (by 25–30% or more) and the detection rate (by 10% or more) compared to state-of-the-art approaches, namely, DCTM-RPL, RBAM-IoT, RPL-MRC, and DSH-RPL. 

  • 20.
    Forsberg, Karin
    et al.
    Luleå University of Technology, Department of Health, Education and Technology, Health, Medicine and Rehabilitation.
    Jirlén, Johan
    Luleå University of Technology, Department of Health, Education and Technology, Health, Medicine and Rehabilitation.
    Jacobson, Inger
    Luleå University of Technology, Department of Health, Education and Technology, Health, Medicine and Rehabilitation.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health, Education and Technology, Health, Medicine and Rehabilitation.
    Concurrent Validity of Cervical Movement Tests Using VR Technology—Taking the Lab to the Clinic2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 24, article id 9864Article in journal (Refereed)
    Abstract [en]

    Reduced cervical range of motion (ROM) and movement velocity are often seen in people with neck pain. Objective assessment of movement characteristics is important to identify dysfunction, to inform tailored interventions, and for the evaluation of the treatment effect. The purpose of this study was to investigate the concurrent validity of a newly developed VR technology for the assessment of cervical ROM and movement velocity. VR technology was compared against a gold-standard three-dimensional optical motion capture system. Consequently, 20 people, 13 without and 7 with neck pain, participated in this quantitative cross-sectional study. ROM was assessed according to right/left rotation, flexion, extension, right/left lateral flexion, and four diagonal directions. Velocity was assessed according to fast cervical rotation to the right and left. The correlations between VR and the optical system for cervical ROM and velocity were excellent, with intraclass correlation coefficient (ICC) values > 0.95. The mean biases between VR and the optical system were ≤ 2.1° for the ROM variables, <12°/s for maximum velocity, and ≤3.0°/s for mean velocity. In conclusion, VR is a useful assessment device for ROM and velocity measurements with clinically acceptable biases. It is a feasible tool for the objective measurement of cervical kinematics in the clinic.

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  • 21.
    Ghasemian, Bahareh
    et al.
    Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran.
    Shahabi, Himan
    Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran.
    Shirzadi, Ataollah
    Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran.
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Jaafari, Abolfazl
    Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran 1496813111, Iran.
    Kress, Victoria R.
    Department of Ecosystem Science and Management, University of Northern British Columbia, 3333 University Way, Prince George, BC V2N 4Z9, Canada.
    Geertsema, Marten
    Research Geomorphologist, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, 499 George Street, Prince George, BC V2L 1R5, Canada.
    Renoud, Somayeh
    Data Mining Laboratory, Department of Engineering, College of Farabi, University of Tehran, Tehran 1417935840, Iran.
    Ahmad, Anuar
    Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia.
    A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 4, article id 1573Article in journal (Refereed)
    Abstract [en]

    We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.

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  • 22.
    Ghulam, Muhammad
    et al.
    Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh .
    Alhamid, Mohammed F
    Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh .
    Hossain, M. Shamim
    Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh .
    Almogren, Ahmad S.
    Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh .
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix2017In: Sensors, E-ISSN 1424-8220, Vol. 17, no 2, article id 267Article in journal (Refereed)
    Abstract [en]

    large number of the population around the world suffers from various disabilities. Disabilities affect not only children but also adults of different professions. Smart technology can assist the disabled population and lead to a comfortable life in an enhanced living environment (ELE). In this paper, we propose an effective voice pathology assessment system that works in a smart home framework. The proposed system takes input from various sensors, and processes the acquired voice signals and electroglottography (EGG) signals. Co-occurrence matrices in different directions and neighborhoods from the spectrograms of these signals were obtained. Several features such as energy, entropy, contrast, and homogeneity from these matrices were calculated and fed into a Gaussian mixture model-based classifier. Experiments were performed with a publicly available database, namely, the Saarbrucken voice database. The results demonstrate the feasibility of the proposed system in light of its high accuracy and speed. The proposed system can be extended to assess other disabilities in an ELE.

  • 23.
    Granlund, Daniel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Holmlund, Patrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Opportunistic Mobility Support for Resource Constrained Sensor Devices in Smart Cities2015In: Sensors, E-ISSN 1424-8220, Vol. 15, no 3, p. 5112-5135Article in journal (Refereed)
    Abstract [en]

    A multitude of wireless sensor devices and technologies are being developed and deployed in cities all over the world. Sensor applications in city environments may include highly mobile installations that span large areas which necessitates sensor mobility support. This paper presents and validates two mechanisms for supporting sensor mobility between different administrative domains. Firstly, EAP-Swift, an Extensible Authentication Protocol (EAP)-based sensor authentication protocol is proposed that enables light-weight sensor authentication and key generation. Secondly, a mechanism for handoffs between wireless sensor gateways is proposed. We validate both mechanisms in a real-life study that was conducted in a smart city environment with several fixed sensors and moving gateways. We conduct similar experiments in an industry-based anechoic Long Term Evolution (LTE) chamber with an ideal radio environment. Further, we validate our results collected from the smart city environment against the results produced under ideal conditions to establish best and real-life case scenarios. Our results clearly validate that our proposed mechanisms can facilitate efficient sensor authentication and handoffs while sensors are roaming in a smart city environment.

  • 24.
    Gustafsson, Jonas
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kyusakov, Rumen
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Mäkitaavola, Henrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Application of service oriented architecture for sensors and actuators in district heating substations2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 8, p. 15553-15572Article in journal (Refereed)
    Abstract [en]

    Hardwired sensor installations using proprietary protocols found in today’s district heating substations limit the potential usability of the sensors in and around the substations. If sensor resources can be shared and re-used in a variety of applications, the cost of sensors and installation can be reduced, and their functionality and operability can be increased. In this paper, we present a new concept of district heating substation control and monitoring, where a service oriented architecture (SOA) is deployed in a wireless sensor network (WSN), which is integrated with the substation. IP-networking is exclusively used from sensor to server; hence, no middleware is needed for Internet integration. Further, by enabling thousands of sensors with SOA capabilities, a System of Systems approach can be applied. The results of this paper show that it is possible to utilize SOA solutions with heavily resource-constrained embedded devices in contexts where the real-time constrains are limited, such as in a district heating substation.

  • 25.
    Hashmi, Khurram Azeem
    et al.
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Pagani, Alain
    German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Stricker, Didier
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Afzal, Muhammad Zeshan
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 10, article id 3703Article in journal (Refereed)
    Abstract [en]

    In recent years, due to the advancements in machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvements in deep learning, traditional approaches, such as sliding windows and manual feature selection techniques, have been replaced with deep learning techniques. However, object detection algorithms face a problem when performed in low light, challenging weather, and crowded scenes, similar to any other task. Such an environment is termed a challenging environment. This paper exploits pixel-level information to improve detection under challenging situations. To this end, we exploit the recently proposed hybrid task cascade network. This network works collaboratively with detection and segmentation heads at different cascade levels. We evaluate the proposed methods on three complex datasets of ExDark, CURE-TSD, and RESIDE, and achieve a mAP of 0.71, 0.52, and 0.43, respectively. Our experimental results assert the efficacy of the proposed approach.

  • 26.
    Hayajneh, Thaier
    et al.
    School of Engineering and Computing Sciences, New York Institute of Technology.
    Mohd, Bassam Jamil
    Computer Engineering Department, Hashemite University.
    Imran, Muhammad Al
    College of Computer and Information Sciences, Almuzahmiyah, King Saud University.
    Almashaqbeh, Ghada
    Computer Science Department, Columbia University, New York.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Secure Authentication for Remote Patient Monitoring with Wireless Medical Sensor Networks2016In: Sensors, E-ISSN 1424-8220, Vol. 16, no 4, article id 424Article in journal (Refereed)
    Abstract [en]

    There is broad consensus that remote health monitoring will benefit all stakeholders in the healthcare system and that it has the potential to save billions of dollars. Among the major concerns that are preventing the patients from widely adopting this technology are data privacy and security. Wireless Medical Sensor Networks (MSNs) are the building blocks for remote health monitoring systems. This paper helps to identify the most challenging security issues in the existing authentication protocols for remote patient monitoring and presents a lightweight public-key-based authentication protocol for MSNs. In MSNs, the nodes are classified into sensors that report measurements about the human body and actuators that receive commands from the medical staff and perform actions. Authenticating these commands is a critical security issue, as any alteration may lead to serious consequences. The proposed protocol is based on the Rabin authentication algorithm, which is modified in this paper to improve its signature signing process, making it suitable for delay-sensitive MSN applications. To prove the efficiency of the Rabin algorithm, we implemented the algorithm with different hardware settings using Tmote Sky motes and also programmed the algorithm on an FPGA to evaluate its design and performance. Furthermore, the proposed protocol is implemented and tested using the MIRACL (Multiprecision Integer and Rational Arithmetic C/C++) library. The results show that secure, direct, instant and authenticated commands can be delivered from the medical staff to the MSN nodes

  • 27.
    Häggström, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Hultqvist, Tobias
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Energy harvesting from raceway strain in rolling element bearingsIn: Sensors, E-ISSN 1424-8220Article in journal (Refereed)
    Abstract [en]

    This paper presents how strain in rolling element bearings can be utilized to power embedded systems. Mechanical strain can be converted to the electrical domain by using piezoelectric materials; here, we present how piezoelectric patches should be dimensioned and mounted to optimize power output. Previous work has not addressed how repetitive strain in bearings can be used to harvest energy. Simulation data from the SKFtool BEAST are analyzed together with linear piezoelectricity to extract the power output. In the simulated case, results show that piezoelectric patches can be used to power embedded systems and that sensory data can be extracted to monitor the bearings.

  • 28.
    Israel Nazarious, Miracle
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
    Zorzano, María-Paz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology. Centro de Astrobiología (CSIC-INTA), Torrejon de Ardoz, 28850 Madrid, Spain. School of Geosciences, University of Aberdeen, Meston Building, King’s College, Aberdeen AB24 3UE, UK.
    Martín-Torres, Javier
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology. School of Geosciences, University of Aberdeen, Meston Building, King’s College, Aberdeen AB24 3UE, UK. Instituto Andaluz de Ciencias de la Tierra (CSIC-UGR), 18100 Granada, Spain.
    Metabolt: An In-Situ Instrument to Characterize the Metabolic Activity of Microbial Soil Ecosystems Using Electrochemical and Gaseous Signatures2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 16, article id 4479Article in journal (Refereed)
    Abstract [en]

    Metabolt is a portable soil incubator to characterize the metabolic activity of microbial ecosystems in soils. It measures the electrical conductivity, the redox potential, and the concentration of certain metabolism-related gases in the headspace just above a given sample of regolith. In its current design, the overall weight of Metabolt, including the soils (250 g), is 1.9 kg with a maximum power consumption of 1.5 W. Metabolt has been designed to monitor the activity of the soil microbiome for Earth and space applications. In particular, it can be used to monitor the health of soils, the atmospheric-regolith fixation, and release of gaseous species such as N2, H2O, CO2, O2, N2O, NH3, etc., that affect the Earth climate and atmospheric chemistry. It may be used to detect and monitor life signatures in soils, treated or untreated, as well as in controlled environments like greenhouse facilities in space, laboratory research environments like anaerobic chambers, or simulating facilities with different atmospheres and pressures. To illustrate its operation, we tested the instrument with sub-arctic soil samples at Earth environmental conditions under three different conditions: (i) no treatment (unperturbed); (ii) sterilized soil: after heating at 125 °C for 35.4 h (thermal stress); (iii) stressed soil: after adding 25% CaCl2 brine (osmotic stress); with and without addition of 0.5% glucose solution (for control). All the samples showed some distinguishable metabolic response, however there was a time delay on its appearance which depends on the treatment applied to the samples: 80 h for thermal stress without glucose, 59 h with glucose; 36 h for osmotic stress with glucose and no significant reactivation in the pure water case. This instrument shows that, over time, there is a clear observable footprint of the electrochemical signatures in the redox profile which is complementary to the gaseous footprint of the metabolic activity through respiration.

  • 29.
    Javaid, Nadeem
    et al.
    COMSATS Institute of Information Technology, Islamabad.
    Shah, Mehreen
    Allama Iqbal Open University, Islamabad.
    Ahmad, Ashfaq
    COMSATS Institute of Information Technology, Islamabad.
    Imran, Muhammad Al
    College of Computer and Information Sciences, Almuzahmiyah, King Saud University.
    Khan, Majid Iqbal
    COMSATS Institute of Information Technology, Islamabad.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks2016In: Sensors, E-ISSN 1424-8220, Vol. 16, no 4, article id 487Article in journal (Refereed)
    Abstract [en]

    This paper presents two new energy balanced routing protocols for Underwater Acoustic Sensor Networks (UASNs); Efficient and Balanced Energy consumption Technique (EBET) and Enhanced EBET (EEBET). The first proposed protocol avoids direct transmission over long distance to save sufficient amount of energy consumed in the routing process. The second protocol overcomes the deficiencies in both Balanced Transmission Mechanism (BTM) and EBET techniques. EBET selects relay node on the basis of optimal distance threshold which leads to network lifetime prolongation. The initial energy of each sensor node is divided into energy levels for balanced energy consumption. Selection of high energy level node within transmission range avoids long distance direct data transmission. The EEBET incorporates depth threshold to minimize the number of hops between source node and sink while eradicating backward data transmissions. The EBET technique balances energy consumption within successive ring sectors, while, EEBET balances energy consumption of the entire network. In EEBET, optimum number of energy levels are also calculated to further enhance the network lifetime. Effectiveness of the proposed schemes is validated through simulations where these are compared with two existing routing protocols in terms of network lifetime, transmission loss, and throughput. The simulations are conducted under different network radii and varied number of nodes.

  • 30.
    Javed, Saleha
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Usman, Muhammad
    Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot, Pakistan.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Mokayed, Hamam
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 20, article id 8427Article in journal (Refereed)
    Abstract [en]

    The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device&apos;s message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.

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  • 31.
    Jing, Xu
    et al.
    College of Information Engineering, Northwest A & F University, Yangling.
    Hu, Hanwen
    College of Information Engineering, Northwest A & F University, Yangling.
    Yang, Huijun
    College of Information Engineering, Northwest A & F University, Yangling.
    Au, Man Ho
    Department of Computing, The Hong Kong Polytechnic University.
    Li, Shuqin
    College of Information Engineering, Northwest A & F University.
    Xiong, Naixue
    Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK.
    Imran, Muhammad
    College of Computer and Information Sciences, King Saud University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks2017In: Sensors, E-ISSN 1424-8220, Vol. 17, no 3, article id 642Article in journal (Refereed)
    Abstract [en]

    The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider's server contains a lot of valuable resources. LoBSs' users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs' risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs' risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing.

  • 32.
    Kabir, Sami
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science & Engineering, University of Chittagong, Chattogram, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 7, p. 1-25, article id 1956Article in journal (Refereed)
    Abstract [en]

    Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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  • 33.
    Kebande, Victor R.
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. Department of Computer Science (DIDA), Blekinge Institute of Technology, 37179 Karlskrona, Sweden.
    Awaysheh, Feras M.
    Institute of Computer Science, Data Systems Research Group, Tartu University, 51009 Tartu, Estonia.
    Ikuesan, Richard A.
    Cyber and Network Security Department, Community College Qatar, Doha 00974, Qatar.
    Alawadi, Sadi A.
    Department of Information Technology, Uppsala University, 75236 Uppsala, Sweden.
    Alshehri, Mohammad Dahman
    Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
    A Blockchain-Based Multi-Factor Authentication Model for a Cloud-Enabled Internet of Vehicles2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 18, article id 6018Article in journal (Refereed)
    Abstract [en]

    Continuous and emerging advances in Information and Communication Technology (ICT) have enabled Internet-of-Things (IoT)-to-Cloud applications to be induced by data pipelines and Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet of Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or ineffective authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected edge to cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication were proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on the Dolev–Yao adversarial model while satisfying aspects of the Confidentiality, Integrity, and Availability (CIA) triad.

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  • 34.
    Kerrouche, Abdelfateh
    et al.
    School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.
    Najeh, Taoufik
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Jaen-Sola, Pablo
    School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.
    Experimental Strain Measurement Approach Using Fiber Bragg Grating Sensors for Monitoring of Railway Switches and Crossings2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 11, article id 3639Article in journal (Refereed)
    Abstract [en]

    Railway infrastructure plays a major role in providing the most cost-effective way to transport freight and passengers. The increase in train speed, traffic growth, heavier axles, and harsh environments make railway assets susceptible to degradation and failure. Railway switches and crossings (S&C) are a key element in any railway network, providing flexible traffic for trains to switch between tracks (through or turnout direction). S&C systems have complex structures, with many components, such as crossing parts, frogs, switchblades, and point machines. Many technologies (e.g., electrical, mechanical, and electronic devices) are used to operate and control S&C. These S&C systems are subject to failures and malfunctions that can cause delays, traffic disruptions, and even deadly accidents. Suitable field-based monitoring techniques to deal with fault detection in railway S&C systems are sought after. Wear is the major cause of S&C system failures. A novel measuring method to monitor excessive wear on the frog, as part of S&C, based on fiber Bragg grating (FBG) optical fiber sensors, is discussed in this paper. The developed solution is based on FBG sensors measuring the strain profile of the frog of S&C to determine wear size. A numerical model of a 3D prototype was developed through the finite element method, to define loading testing conditions, as well as for comparison with experimental tests. The sensors were examined under periodic and controlled loading tests. Results of this pilot study, based on simulation and laboratory tests, have shown a correlation for the static load. It was shown that the results of the experimental and the numerical studies were in good agreement.

  • 35.
    Khan, Muhammad Ahmed Ullah
    et al.
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Nazir, Danish
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Pagani, Alain
    German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Mokayed, Hamam
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Stricker, Didier
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Afzal, Muhammad Zeshan
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    A Comprehensive Survey of Depth Completion Approaches2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 18, article id 6969Article, review/survey (Refereed)
    Abstract [en]

    Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the earlier approaches focused on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have divided the literature into two major categories; unguided methods and image-guided methods. The latter is further subdivided into multi-branch and spatial propagation networks. The multi-branch networks further have a sub-category named image-guided filtering. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review in detail different state-of-the-art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.

  • 36.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Simon, Miguel Gomez
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jimenez, Lara Lorna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Karvonen, Niklas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Analyzing Body Movements within the Laban Effort Framework using a Single Accelerometer2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 3, p. 5725-41Article in journal (Refereed)
    Abstract [en]

    This article presents a study on analyzing body movements by using a single accelerometer sensor. The investigated categories of body movements belong to the Laban Effort Framework: Strong - Light, Free – Bound and Sudden - Sustained. All body movements were represented by a set of activities used for data collection. The calculated accuracy of detecting the body movements was based on collecting data from a single wireless tri-axial accelerometer sensor. Ten healthy subjects collected data from three body locations (chest, wrist and thigh) simultaneously in order to analyze the locations comparatively. The data was then processed and analyzed using Machine Learning techniques. The wrist placement was found to be the best single location to record data for detecting (Strong – Light) body movements using the Random Forest classifier. The wrist placement was also the best location for classifying (Bound – Free) body movements using the SVM classifier. However, the data collected from the chest placement yielded the best results for detecting (Sudden – Sustained) body movements using the Random Forest classifier. The study shows that the choice of the accelerometer placement should depend on the targeted type of movement. In addition, the choice of the classifier when processing data should also depend on the chosen location and the target movement.

  • 37.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Stavropoulos, Thanos G.
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Andreadis, Stelios
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Karvonen, Niklas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kompatsiaris, Ioannis
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Sävenstedt, Stefan
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Pijl, Marten
    Personal Health Solutions, Philips Research.
    Melander, Catharina
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia2016In: Sensors, E-ISSN 1424-8220, Vol. 16, no 12, article id 1989Article in journal (Refereed)
    Abstract [en]

    Stress is a common problem that affects most people with dementia and their caregivers. Stress symptoms for people with dementia are often measured by answering a checklist of questions by the clinical staff who work closely with the person with the dementia. This process requires a lot of effort with continuous observation of the person with dementia over the long term. This article investigates the effectiveness of using a straightforward method, based on a single wristband sensor to classify events of "Stressed" and "Not stressed" for people with dementia. The presented system calculates the stress level as an integer value from zero to five, providing clinical information of behavioral patterns to the clinical staff. Thirty staff members participated in this experiment, together with six residents suffering from dementia, from two nursing homes. The residents were equipped with the wristband sensor during the day, and the staff were writing observation notes during the experiment to serve as ground truth. Experimental evaluation showed relationships between staff observations and sensor analysis, while stress level thresholds adjusted to each individual can serve different scenarios.

  • 38.
    Knoepp, F.
    et al.
    Excellence-Cluster Cardio-Pulmonary System (ECCPS), Universities of Giessen and Marburg Lung Center (UGMLC), Member of the German Center for Lung Research (DZL), Justus-Liebig University Giessen, Giessen, Germany.
    Wahl, Joel
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Andersson, Anders G.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Borg, J.
    CMS, Imperial College, London, UK.
    Weissmann, N.
    Excellence-Cluster Cardio-Pulmonary System (ECCPS), Universities of Giessen and Marburg Lung Center (UGMLC), Member of the German Center for Lung Research (DZL), Justus-Liebig University Giessen, D-35392 Giessen, Germany.
    Ramser, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Development of a Gas-Tight Microfluidic System for Raman Sensing of Single Pulmonary Arterial Smooth Muscle Cells Under Normoxic/Hypoxic Conditions2018In: Sensors, E-ISSN 1424-8220, Vol. 10, article id 3238Article in journal (Refereed)
    Abstract [en]

    Acute hypoxia changes the redox-state of pulmonary arterial smooth muscle cells (PASMCs). This might influence the activity of redox-sensitive voltage-gated K⁺-channels (Kv-channels) whose inhibition initiates hypoxic pulmonary vasoconstriction (HPV). However, the molecular mechanism of how hypoxia-or the subsequent change in the cellular redox-state-inhibits Kv-channels remains elusive. For this purpose, a new multifunctional gas-tight microfluidic system was developed enabling simultaneous single-cell Raman spectroscopic studies (to sense the redox-state under normoxic/hypoxic conditions) and patch-clamp experiments (to study the Kv-channel activity). The performance of the system was tested by optically recording the O₂-content and taking Raman spectra on murine PASMCs under normoxic/hypoxic conditions or in the presence of H₂O₂. Oxygen sensing showed that hypoxic levels in the gas-tight microfluidic system were achieved faster, more stable and significantly lower compared to a conventional open system (1.6 ± 0.2%, respectively 6.7 ± 0.7%, n = 6, p < 0.001). Raman spectra revealed that the redistribution of biomarkers (cytochromes, FeS, myoglobin and NADH) under hypoxic/normoxic conditions were improved in the gas-tight microfluidic system (p-values from 0.00% to 16.30%) compared to the open system (p-value from 0.01% to 98.42%). In conclusion, the new redox sensor holds promise for future experiments that may elucidate the role of Kv-channels during HPV.

  • 39.
    Kułacz, Łukasz
    et al.
    Institute of Radiocommunications, Poznan University of Technology, 60-965 Poznan, Poland.
    Kliks, Adrian
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Institute of Radiocommunications, Poznan University of Technology, 60-965 Poznan, Poland.
    Federated Learning-Based Spectrum Occupancy Detection2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 14, article id 6436Article in journal (Refereed)
    Abstract [en]

    Dynamic access to the spectrum is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is most often effective spectrum occupancy detection. In many cases, machine learning algorithms improve this detection’s effectiveness. Given the recent trend of using federated learning, we present a federated learning algorithm for distributed spectrum occupancy detection. This idea improves overall spectrum-detection effectiveness, simultaneously keeping a low amount of data that needs to be exchanged between sensors. The proposed solution achieves a higher accuracy score than separate and autonomous models used without federated learning. Additionally, the proposed solution shows some sort of resistance to faulty sensors encountered in the system. The results of the work presented in the article are based on actual signal samples collected in the laboratory. The proposed algorithm is effective (in terms of spectrum occupancy detection and amount of exchanged data), especially in the context of a set of sensors in which there are faulty sensors.

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  • 40.
    Li, Yuhong
    et al.
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China. Department of Computer and Systems Sciences, Stockholm University, 16407 Stockholm, Sweden.
    Su, Xiang
    Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland. Center for Ubiquitous Computing, University of Oulu, FI-90014 Oulu, Finland.
    Ding, Aaron Yi
    Department Engineering Systems and Services, Delft University of Technology, 2628BX Delft, The Netherlands.
    Lindgren, Anders
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. RISE Research Institutes of Sweden, 16440 Kista, Sweden.
    Liu, Xiaoli
    Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland.
    Prehofer, Christian
    DENSO Automotive Germany GmbH, 85386 Eching, Germany. Department of Informatics, Technical University of Munich, 80333 München, Germany.
    Riekki, Jukka
    Center for Ubiquitous Computing, University of Oulu, FI-90014 Oulu, Finland.
    Rahmani, Rahim
    Department of Computer and Systems Sciences, Stockholm University, 16407 Stockholm, Sweden.
    Tarkoma, Sasu
    Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland.
    Hui, Pan
    Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
    Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 12, article id 3459Article, review/survey (Refereed)
    Abstract [en]

    The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

  • 41.
    Ma, Mujia
    et al.
    School of Sport Science, Beijing Sport University, Beijing 100084, China.
    Zhao, Shuang
    China Institute of Sport and Health Science, Beijing Sport University, Beijing 100084, China; Dalian Fast Move Technology Co., Ltd., Dalian 116033, China.
    Long, Ting
    Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand.
    Song, Qingquan
    School of Strength and Conditioning Training, Beijing Sport University, Beijing 100084, China.
    Holmberg, Hans-Christer
    Department of Health Sciences, Mid Sweden University, 831 25 Östersund, Sweden.
    Liu, Hui
    School of Sport Science, Beijing Sport University, Beijing 100084, China;China Institute of Sport and Health Science, Beijing Sport University, Beijing 100084, China.
    Comparative Analysis of the Diagonal Stride Technique during Roller Skiing and On-Snow Skiing in Youth Cross-Country Skiers2024In: Sensors, E-ISSN 1424-8220, Vol. 24, no 5, article id 1412Article in journal (Refereed)
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  • 42.
    Mohammadi, Ayub
    et al.
    Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran.
    Karimzadeh, Sadra
    Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran. Institute of Environment, University of Tabriz, Tabriz 5166616471, Iran; Department of Architecture and Building Engineering, Tokyo Institute of Technology, Yokohama 226-8502, Japan.
    Jalal, Shazad Jamal
    College of Engineering, University of Sulaimani, Sulaimani 46001, Iraq; Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia.
    Kamran, Khalil Valizadeh
    Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran.
    Shahabi, Himan
    Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran; Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 6617715175, Iran.
    Homayouni, Saeid
    Centre Eau Terre Environnement, Institute National de la Recherche Scientifique, Quebec, QC G1K 9A9, Canada.
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 24, article id 7214Article in journal (Refereed)
    Abstract [en]

    Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely available DEMs from various sources, many researchers generate this information for their areas from various observations. Sentinal-1 synthetic aperture radar (SAR) images are among the best Earth observations for DEM generation thanks to their availabilities, high-resolution, and C-band sensitivity to surface structure. This paper presents a comparative study, from a hydrological point of view, on the quality and reliability of the DEMs generated from Sentinel-1 data and DEMs from other sources such as AIRSAR, ALOS-PALSAR, TanDEM-X, and SRTM. To this end, pair of Sentinel-1 data were acquired and processed using the SAR interferometry technique to produce a DEM for two different study areas of a part of the Cameron Highlands, Pahang, Malaysia, a part of Sanandaj, Iran. Based on the estimated linear regression and standard errors, generating DEM from Sentinel-1 did not yield promising results. The river streams for all DEMs were extracted using geospatial analysis tool in a geographic information system (GIS) environment. The results indicated that because of the higher spatial resolution (compared to SRTM and TanDEM-X), more stream orders were delineated from AIRSAR and Sentinel-1 DEMs. Due to the shorter perpendicular baseline, the phase decorrelation in the created DEM resulted in a lot of noise. At the same time, results from ground control points (GCPs) showed that the created DEM from Sentinel-1 is not promising. Therefore, other DEMs’ performance, such as 90-meters’ TanDEM-X and 30-meters’ SRTM, are better than Sentinel-1 DEM (with a better spatial resolution).

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  • 43.
    Mukherjee, Moumita
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Banerjee, Avijit
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Papadimitriou, Andreas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A decentralized sensor fusion scheme for multi sensorial fault resilient pose estimation2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 24, article id 8259Article in journal (Refereed)
    Abstract [en]

    This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

  • 44.
    Muralidhara, Shishir
    et al.
    Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, Kaiserslautern, Germany.
    Hashmi, Khurram Azeem
    Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Pagani, Alain
    German Research Institute for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Stricker, Didier
    Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Afzal, Muhammad Zeshan
    Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Attention-Guided Disentangled Feature Aggregation for Video Object Detection2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 21, article id 8583Article in journal (Refereed)
    Abstract [en]

    Object detection is a computer vision task that involves localisation and classification of objects in an image. Video data implicitly introduces several challenges, such as blur, occlusion and defocus, making video object detection more challenging in comparison to still image object detection, which is performed on individual and independent images. This paper tackles these challenges by proposing an attention-heavy framework for video object detection that aggregates the disentangled features extracted from individual frames. The proposed framework is a two-stage object detector based on the Faster R-CNN architecture. The disentanglement head integrates scale, spatial and task-aware attention and applies it to the features extracted by the backbone network across all the frames. Subsequently, the aggregation head incorporates temporal attention and improves detection in the target frame by aggregating the features of the support frames. These include the features extracted from the disentanglement network along with the temporal features. We evaluate the proposed framework using the ImageNet VID dataset and achieve a mean Average Precision (mAP) of 49.8 and 52.5 using the backbones of ResNet-50 and ResNet-101, respectively. The improvement in performance over the individual baseline methods validates the efficacy of the proposed approach.

  • 45.
    Najeh, Taoufik
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kerrouche, Abdelfateh
    School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.
    Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 15, article id 5217Article in journal (Refereed)
    Abstract [en]

    The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

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  • 46.
    Nazir, Danish
    et al.
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany.
    Afzal, Muhammad Zeshan
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Pagani, Alain
    German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Stricker, Didier
    Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
    Contrastive Learning for 3D Point Clouds Classification and Shape Completion2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 21, article id 7392Article in journal (Refereed)
    Abstract [en]

    In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as classification, segmentation, detection, and other related applications. Our idea is to add contrastive learning into AutoEncoders to encourage global feature learning of the point cloud classes. It is performed by optimizing triplet loss. Furthermore, local feature representations learning of point cloud is performed by adding the Chamfer distance function. To evaluate the performance of our approach, we utilize the PointNet classifier. We also extend the number of classes for evaluation from 4 to 10 to show the generalization ability of the learned features. Based on our results, embeddings generated from the contrastive AutoEncoder enhances shape completion and classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

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  • 47.
    Nilsson, Jacob
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Javed, Saleha
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Albertsson, Kim
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    AI Concepts for System of Systems Dynamic Interoperability2024In: Sensors, E-ISSN 1424-8220, Vol. 24, no 9, article id 2921Article in journal (Refereed)
    Abstract [en]

    Interoperability is a central problem in digitization and sos engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cps at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as sos, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish sos with purposeful cps communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with ai-enabled solutions in relation to sos interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex sos, and realistic testbeds are needed.

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  • 48.
    Ponzoni, Andrea
    et al.
    University of Brescia, CNR IDASC SENSOR Lab.
    Comini, Elisabetta
    University of Brescia, CNR IDASC SENSOR Lab.
    Concina, Isabella
    SENSOR Lab, Department of Chemistry and Physics, Brescia University and CNR-IDASC.
    Ferroni, Matteo
    Consiglio Nazionale delle Ricerche, Pisa, Universita Degli Studi di Brescia, Dipartimento di Economia Aziendale.
    Falasconi, Matteo
    CNR IDASC SENSOR Lab, University of Brescia.
    Gobbi, Emanuela
    CNR IDASC SENSOR Lab, University of Brescia.
    Sberveglieri, Veronica
    CNR IDASC SENSOR Lab, University of Brescia.
    Sberveglieri, Giorgio
    Consiglio Nazionale delle Ricerche, Pisa, Universita Degli Studi di Brescia, Dipartimento di Economia Aziendale , SENSOR Lab, Department of Information Engineering, University of Brescia.
    Nanostructured Metal Oxide Gas Sensors, a Survey of Applications Carried out at SENSOR Lab, Brescia (Italy) in the Security and Food Quality Fields2012In: Sensors, E-ISSN 1424-8220, Vol. 12, no 12, p. 17023-17045Article in journal (Refereed)
    Abstract [en]

    In this work we report on metal oxide (MOX) based gas sensors, presenting the work done at the SENSOR laboratory of the CNR-IDASC and University of Brescia, Italy since the 80s up to the latest results achieved in recent times. In particular we report the strategies followed at SENSOR during these 30 years to increase the performance of MOX sensors through the development of different preparation techniques, from Rheotaxial Growth Thermal Oxidation (RGTO) to nanowire technology to address sensitivity and stability, and the development of electronic nose systems and pattern recognition techniques to address selectivity. We will show the obtained achievement in the context of selected applications such as safety and security and food quality control.

  • 49.
    Reetz, Susanne
    et al.
    Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig, 38108, Germany.
    Najeh, Taoufik
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Groos, Jörn
    Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig, 38108, Germany.
    Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations2024In: Sensors, E-ISSN 1424-8220, Vol. 24, no 2, article id 477Article in journal (Refereed)
    Abstract [en]

    Switches are an essential, safety-critical part of the railway infrastructure. Compared to open tracks, their complex geometry leads to increased dynamic loading on the track superstructure from passing trains, resulting in high maintenance costs. To increase efficiency, condition monitoring methods specific to railway switches are required. A common approach to track superstructure monitoring is to measure the acceleration caused by vehicle track interaction. Local interruptions in the wheel–rail contact, caused for example by local defects or track discontinuities, appear in the data as transient impact events. In this paper, such transient events are investigated in an experimental setup of a railway switch with track-side acceleration sensors, using frequency and waveform analysis. The aim is to understand if and how the origins of these impact events can be distinguished in the data of this experiment, and what the implications for condition monitoring of local track discontinuities and defects with wayside acceleration sensors are in practice. For the same experimental configuration, individual impact events are shown to be reproducible in waveform and frequency content. Nevertheless, with this track-side sensor setup, the different types of track discontinuities and defects (squats, joints, crossing) could not be clearly distinguished using characteristic frequencies or waveforms. Other factors, such as the location of impact event origin relative to the sensor, are shown to have a much stronger influence. The experimental data suggest that filtering the data to narrow frequency bands around certain natural track frequencies could be beneficial for impact event detection in practice, but differentiating between individual impact event origins requires broadband signals. A multi-sensor setup with time-synchronized acceleration sensors distributed over the switch is recommended.

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  • 50.
    Riliskis, Laurynas
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Maestro: an orchestration framework for large scale WSN simulations2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 3, p. 5392-5414Article in journal (Refereed)
    Abstract [en]

    Contemporary wireless sensor networks (WSNs) have evolved into large and complex systems and are one of the main technologies used in cyber-physical systems and the Internet of Things. Extensive research on WSNs has led to the development of diverse solutions at all levels of software architecture, including protocol stacks for communications. This multitude of solutions is due to the limited computational power and restrictions on energy consumption that must be accounted for when designing typical WSN systems. It is therefore challenging to develop, test and validate even small WSN applications, and this process can easily consume significant resources. Simulations are inexpensive tools for testing, verifying and generally experimenting with new technologies in a repeatable fashion. Consequently, as the size of the systems to be tested increases, so does the need for large-scale simulations. This article describes a tool called Maestro for the automation of large-scale simulation and investigates the feasibility of using cloud computing facilities for such task. Using tools that are built into Maestro, we demonstrate a feasible approach for benchmarking cloud infrastructure in order to identify cloud Virtual Machine (VM)instances that provide an optimal balance of performance and cost for a given simulation.

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