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  • 301.
    Huang, Xiaomei
    et al.
    School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China. School of Mathematics and Statistics, Jiangxi Normal University, Nanchang 330022, China.
    Liao, Guoqiong
    School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China.
    Xiong, Naixue
    Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA.
    Vasilakos, Athanasios V.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. School of Electrical and Data Engineering, University of Technology Sydney, New South Wales, NSW 2007, Australia. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China.
    Lan, Tianming
    School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China.
    A Survey of Context-Aware Recommendation Schemes in Event-Based Social Networks2020Ingår i: Electronics, E-ISSN 2079-9292, Vol. 9, nr 10, artikel-id 1583Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In recent years, Event-based social network (EBSN) applications, such as Meetup and DoubanEvent, have received popularity and rapid growth. They provide convenient online platforms for users to create, publish, and organize social events, which will be held in physical places. Additionally, they not only support typical online social networking facilities (e.g., sharing comments and photos), but also promote face-to-face offline social interactions. To provide better service for users, Context-Aware Recommender Systems (CARS) in EBSNs have recently been singled out as a fascinating area of research. CARS in EBSNs provide the suitable recommendation to target users by incorporating the contextual factors into the recommendation process. This paper provides an overview on the development of CARS in EBSNs. We begin by illustrating the concept of the term context and the paradigms of conventional context-aware recommendation process. Subsequently, we introduce the formal definition of an EBSN, the characteristics of EBSNs, the challenges that are faced by CARS in EBSNs, and the implementation process of CARS in EBSNs. We also investigate which contextual factors are considered and how they are represented in the recommendation process. Next, we focus on the state-of-the-art computational techniques regarding CARS in EBSNs. We also overview the datasets and evaluation metrics for evaluation in this research area, and discuss the applications of context-aware recommendation in EBSNs. Finally, we point out research opportunities for the research community.

  • 302.
    Idowu, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hagos, Desta Haileselassie
    Tesfay, Welderufael Berhane
    Famurewa, Abiola
    Rana, Juwel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Synnes, Kåre
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    NexTrend: Context-aware music-relay corridors using NFC tags2013Ingår i: 7th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing: IMIS 2013, Taichung, Taiwan; 3 July 2013 - 5 July 2013, Piscataway, NJ: IEEE Computer Society Press , 2013, s. 573-578Konferensbidrag (Refereegranskat)
    Abstract [en]

    The rise of pervasive computing presents unique opportunities due to increasing availability of smart devices such as mobile phones and tablets equipped with various sensors enabling Near Field Communication (NFC) technologies. The growth of mobile computing has led to an increase in access to digital music. With the growth of digital music, the development of music information sharing services for users becomes important. The existing sharing methods are based on the users’ social network and preferences in music. However, sometimes, sharing music according to location and time is needed.This paper presents work on smart spaces equipped with NFC tags, deployed at different locations in hallways for discovering and sharing new music experiences. This concept provides a new way of interaction between passers-by for discovering music in relation to location. For example, the hallway locations use sensing devices to provide an automatic means of exchanging music information among the passers-by.We utilized NFC tags as Music-Relay hot spots. The hot spot retrieves information about the music a user is playing on her/his device while s/he is passing by the hot spot. The work contributes to a pervasive service that equips an environment with music context intelligence about a passer-bys choice of music and allows users to feel the musical presence of other users who have been in the same location at previous point in time. In general, this paper proposes a new music information sharing service using the music information captured from users at a specific location in time.

  • 303.
    Idowu, Samuel O.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Applied Machine Learning in District Heating System2018Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and the increasingly accepted need for green energy is gradually challenging energy supplies and the efficiency in its consumption.

    This research, carried out within the area of pervasive computing, aims to explore the application of machine learning and its effectiveness in the energy sector with dependency on sensing devices. The target application area readily falls under a multi-domain energy grid which provides a system across two energy utility grids as a combined heat and power system. The multi-domain aspect of the target system links to a district heating system network and electrical power from a combined heat and power plant. This thesis, however, focuses on the district heating system as the application area of interest while contributing towards a future goal of a multi-domain energy grid, where improved efficiency level, reduction of overall carbon dioxide footprint and enhanced interaction and synergy between the electricity and thermal grid are vital goals. This thesis explores research issues relating to the effectiveness of machine learning in forecasting heat demands at district heating substations, and the key factors affecting domestic heat load patterns in buildings.

    The key contribution of this thesis is the application of machine learning techniques in forecasting heat energy consumption in buildings, and our research outcome shows that supervised machine learning methods are suitable for domestic thermal load forecast. Among the examined machine learning methods which include multiple linear regression, support vector machine,  feed forward neural network, and regression tree, the support vector machine performed best with a normalized root mean square error of 0.07 for a 24-hour forecast horizon. In addition, weather and time information are observed to be the most influencing factors when forecasting heat load at heating network substations. Investigation on the effect of using substation's operational attributes, such as the supply and return temperatures, as additional input parameters when forecasting heat load shows that the use of substation's internal operational attributes has less impact.

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  • 304.
    Idowu, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Applied Machine Learning: Forecasting Heat Load in District Heating System2016Ingår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 133, s. 478-488Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Forecasting energy consumption in buildings is a key step towards the realization of optimized energy production, distribution and consumption. This paper presents a data driven approach for analysis and forecast of aggregate space and water thermal load in buildings. The analysis and the forecast models are built using district heating data unobtrusively collected from ten residential and commercial buildings located in Skellefteå, Sweden. The load forecast models are generated using supervised machine learning techniques, namely, support vector machine, regression tree, feed forward neural network, and multiple linear regression. The model takes the outdoor temperature, historical values of heat load, time factor variables and physical parameters of district heating substations as its input. A performance comparison among the machine learning methods and identification of the importance of models input variables is carried out. The models are evaluated with varying forecast horizons of every hour from 1 up to 48 hours. Our results show that support vector machine, feed forward neural network and multiple linear regression are more suitable machine learning methods with lower performance errors than the regression tree. Support vector machine has the least normalized root mean square error of 0.07 for a forecast horizon of 24 hour.

  • 305.
    Idowu, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Forecasting Heat Load for Smart District Heating Systems: A Machine Learning Approach2014Ingår i: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm 2014): Venice, 3-6 Nov. 2014, Piscataway, NJ: IEEE Communications Society, 2014, s. 554-559Konferensbidrag (Refereegranskat)
    Abstract [en]

    The rapid increase in energy demand requires effective measures to plan and optimize resources for efficient energy production within a smart grid environment. This paper presents a data driven approach to forecasting heat load for multi- family apartment buildings in a District Heating System (DHS). The forecasting model is built using six and eleven weeks of data from five building substations. The external factors and internal factors influencing the heat load in substations are parameters used as our model’s input. Short-term forecast models are generated using four supervised Machine Learning (ML) techniques: Support Vector Regression (SVR), Regression Tree, Feed Forwards Neural Network (FFNN) and Multiple Linear Regression (MLR). Performance comparison among these ML methods was carried out. The effects of combining the internal and external factors influencing heat load at substations was studied. The models are evaluated with varying horizon up to 24-hours ahead. The results show that SVR has the best accuracy of 5.6% MAPE for the best-case scenario.

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  • 306.
    Idowu, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Machine learning in district heating system energy optimization2014Ingår i: 2014 IEEE International Conference on Pervasive Computing and Communications workshops: PERCOM WORKSHOPS 2014, Budapest, Hungary; 24-28 March 2014, Piscataway, NJ: IEEE Communications Society, 2014, s. 224-227, artikel-id 6815206Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper introduces a work in progress, where we intend to investigate the application of Reinforcement Learning (RL) and online Supervised Learning (SL) to achieve energy optimization in District-Heating (DH) systems. We believe RL is an ideal approach since this task falls under the control-optimization problem where RL has yielded optimal results in previous work. The magnitude and scale of a DH system complexity incurs the curse of dimensionalities and model, hereby making RL a good choice since it provides a solution for the problem. To assist RL even further with the curse of dimensionalities, we intend to investigate the use of SL to reduce the state space. To achieve this, we shall use historical data to generate a heat load sub-model for each home. We believe using the output of these sub-models as feedback to the RL algorithm could significantly reduce the complexity of the learning task. Also, it could reduce convergence time for the RL algorithm. The desired goal is to achieve a realtime application, which takes operational actions when it receives new direct feedback. However, considering the dynamics of DH system such as large time delay and dissipation in DH network due to various factors, we hope to investigate things such as the appropriate data sampling rate and new parameters / sensors that could improve knowledge about the state of the system, especially on the consumer side of the DH network.

  • 307.
    Idowu, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Brännström, Robert
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Machine Learning in Pervasive Computing2013Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Increase in data quantities and number of pervasive systems has resulted in many decision-making systems. Most of these systems employ Machine Learning (ML) in various practical scenarios and applications. Enormous amount of data generated by sensors can be useful in decision-making systems. The rising number of sensor driven pervasive systems presents interesting research areas on how to adapt and apply existing ML techniques effectively to the domain of pervasive computing. In the face of data deluge, ML has proved viable in many application areas such as data mining and self-customizing programs and could bring about great impact in the field of pervasive computing.The objective of this study is to give the underlying concepts of ML techniques that can be applied to problems in the domain of pervasive and mobile computing. The scope of this study covers the three primary types of ML, supervised, unsupervised and reinforcement learning methods. In the process of providing the fundamental knowledge of ML, we present some conceptual terms of ML and the steps required in developing ML system with a great impact on domains outside ML scope.Our findings show that previous works in the area of ubiquitous computing have successfully applied supervised learning and reinforcement learning methods. Hence, this study focuses more on supervised learning and reinforcement learning. In conclusion, we discuss some basic performance evaluation metrics and methods for obtaining reliable classifiers estimates, such as cross-validation and leave-one-out validation.

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  • 308.
    Imran, Muhammad
    et al.
    King Saud University, Riyadh 11543, Saudi Arabia.
    Ullah, Sana
    Polytechnic Institute of Porto, 4200-465 Porto, Portugal.
    Yasar, Ansar-Ul-Haque
    Hasselt University, 3500 Hasselt, Belgium.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hussain, Sajid
    Fisk University, Nashville, TN 37208, USA.
    Enabling Technologies for Next-Generation Sensor Networks: Prospects, Issues, Solutions, and Emerging Trends2015Ingår i: International Journal of Distributed Sensor Networks, ISSN 1550-1329, E-ISSN 1550-1477, Vol. 2015, artikel-id 634268Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    This paper firstly investigates the problem of uplink power control in cognitive radio networks (CRNs) with multiple primary users (PUs) and multiple second users (SUs) considering channel outage constraints and interference power constraints, where PUs and SUs compete with each other to maximize their utilities. We formulate a Stackelberg game to model this hierarchical competition, where PUs and SUs are considered to be leaders and followers, respectively. We theoretically prove the existence and uniqueness of robust Stackelberg equilibrium for the noncooperative approach. Then, we apply the Lagrange dual decomposition method to solve this problem, and an efficient iterative algorithm is proposed to search the Stackelberg equilibrium. Simulation results show that the proposed algorithm improves the performance compared with those proportionate game schemes.

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  • 309.
    Islam, Md. Saiful
    et al.
    Department of Computer Science and Engineering, Port City International University, Chittagong 4202, Bangladesh.
    Hossain, Emam
    Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.
    Rahman, Abdur
    Department of Computer Science and Engineering, Port City International University, Chittagong 4202, Bangladesh.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Review on Recent Advancements in FOREX Currency Prediction2020Ingår i: Algorithms, E-ISSN 1999-4893, Vol. 13, nr 8, artikel-id 186Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.

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  • 310.
    Islam, Md. Zahirul
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation2019Ingår i: Joint 2019 8th International Conference on Informatics, Electronics and Vision (ICIEV) & 3rd International Conference on Imaging, Vision & Pattern Recognition (IVPR) with International Conference on Activity and Behavior Computing (ABC), IEEE, 2019, s. 324-329Konferensbidrag (Refereegranskat)
    Abstract [en]

    Computer is a part and parcel in our day to day life and used in various fields. The interaction of human and computer is accomplished by traditional input devices like mouse, keyboard etc. Hand gestures can be a useful medium of human-computer interaction and can make the interaction easier. Gestures vary in orientation and shape from person to person. So, non-linearity exists in this problem. Recent research has proved the supremacy of Convolutional Neural Network (CNN) for image representation and classification. Since, CNN can learn complex and non-linear relationships among images, in this paper, a static hand gesture recognition method using CNN was proposed. Data augmentation like re-scaling, zooming, shearing, rotation, width and height shifting was applied to the dataset. The model was trained on 8000 images and tested on 1600 images which were divided into 10 classes. The model with augmented data achieved accuracy 97.12% which is nearly 4% higher than the model without augmentation (92.87%).

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  • 311.
    Islam, Mohammad A.
    et al.
    Florida International University, Miami.
    Ren, Shaolei
    University of California, Riverside, CA.
    Quan, Gang
    Florida International University, Miami.
    Shakir, Muhammad Zeeshan
    Texas A&M University, Qatar.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Water-Constrained Geographic Load Balancing in Data Centers2017Ingår i: IEEE Transactions on Cloud Computing, ISSN 2168-7161, Vol. 5, nr 2, s. 208-220, artikel-id 7152842Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Spreading across many parts of the world and presently hard striking California, extended droughts could even potentially threaten reliable electricity production and local water supplies, both of which are critical for data center operation. While numerous efforts have been dedicated to reducing data centers’ energy consumption, the enormity of data centers’ water footprints is largely neglected and, if still left unchecked, may handicap service availability during droughts. In this paper, we propose a water-aware workload management algorithm, called WATCH (WATer-constrained workload sCHeduling in data centers), which caps data centers’ long-term water consumption by exploiting spatio-temporal diversities of water efficiency and dynamically dispatching workloads among distributed data centers. We demonstrate the effectiveness of WATCH both analytically and empirically using simulations: based on only online information, WATCH can result in a provably-low operational cost while successfully capping water consumption under a desired level. Our results also show that WATCH can cut water consumption by 20 percent while only incurring a negligible cost increase even compared to state-of-the-art cost-minimizing but water-oblivious solution. Sensitivity studies are conducted to validate WATCH under various settings.

  • 312.
    Islam, Raihan Ul
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System2017Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people.

    This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed.

    Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis.

    Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed.

    In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.

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  • 313.
    Islam, Raihan Ul
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Luleå tekniska universitet, Institutionen för system- och rymdteknik, CDT.
    Hossain, Mohammad Shahadat
    University of Chittagong.
    A Web Based Belief Rule Based Expert System to Predict Flood2015Ingår i: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services (iiWAS2015) / [ed] Maria Indrawan-Santiago; Matthias Steinbauer; Ismail Khalil; Gabriele Anderst-Kotsis, New York: Association for Computing Machinery (ACM), 2015, s. 19-26, artikel-id 3Konferensbidrag (Refereegranskat)
    Abstract [en]

    Natural calamity disrupts our daily life and brings many sufferings in our life. Among the natural calamities, flood is one of the most catastrophic. Predicting flood helps us to take necessary precautions and save human lives. Several types of data (meteorological condition, topography, river characteristics, and human activities) are used to predict flood water level in an area. In our previous works, we proposed a belief rule based flood prediction system in a desktop environment. In this paper, we propose a web-service based flood prediction expert system by incorporating belief rule base with the capability of reading sensor data such as rainfall, river flow on real time basis. This will facilitate the monitoring of the various flood-intensifying factors, contributing in increasing the flood water level in an area. Eventually, the decision makers would able to take measures to control those factors and to reduce the intensity of flooding in an area.

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  • 314.
    Islam, Raihan Ul
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering,University of Chittagong Chittagong, Bangladesh..
    Network Intelligence for Enhanced Multi-Access Edge Computing (MEC) in 5G2019Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    5G networks will enable people and machines to communicate at high speeds and very low latencies, in a reliable way. This opens up opportunities for totally new usage patterns and the fully connected Industry 4.0-enabled enterprise covering the entire value chain from design, production, deployment, to usage of products. 5G will be rolled out across the whole world, including Sweden where the first 5G test network was launched late 2018. One important new feature in 5G is the emerging edge computing capabilities, where users can easily offload computational tasks to the network’s edge very close to the user. At the same time, computational tasks traditionally performed in central nodes can be offloaded from remotely located data centres to the network’s edge. Multi-access Edge Computing (MEC) is a promising network architecture delivering solutions along these lines offering a platform for applications with requirements on low latencies and high reliability. This paper targets this environment with a novel Belief-rule-based (BRB) unsupervised learning algorithm for clustering helping 5G applications to take intelligent decisions on software deployment. The scenarios consist of different combinations of numbers of users and connections and mobility patterns. The target environment is built up using a three-tier structure with a container-based solution where software components can easily be spread around the network.

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  • 315.
    Islam, Raihan Ul
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, University-4331, Bangladesh.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Deep Learning Inspired Belief Rule-based Expert System2020Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 190637-190651Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recent technological advancements in the area of the Internet of Things (IoT) and cloud services, enable the generation of large amounts of raw data. However, the accurate prediction by using this data is considered as challenging for machine learning methods. Deep Learning (DL) methods are widely used to process large amounts of data because they need less preprocessing than traditional machine learning methods. Various types of uncertainty associated with large amounts of raw data hinder the prediction accuracy. Belief Rule-Based Expert Systems (BRBES) are widely used to handle uncertain data. However, due to their incapability of integrating associative memory within the inference procedures, they demonstrate poor accuracy of prediction when large amounts of data is considered. Therefore, we propose the integration of an associative memory based DL method within the BRBES inference procedures, allowing to discover accurate data patterns and hence, the improvement of prediction under uncertainty. To demonstrate the applicability of the proposed method, which is named BRB-DL, it has been fine tuned against two datasets, one in the area of air pollution and the other in the area of power generation. The reliability of the proposed BRB-DL method, has also been compared with other DL methods such as Long-Short Term Memory and Deep Neural Network, and BRBES by taking into account of the air quality dataset from Beijing city and the power generation dataset of a combined cycle power plant. BRB-DL outperforms the above-mentioned methods in terms of prediction accuracy. For example, the Mean Square Error value of BRB-DL is 4.12 whereas for Long-Short Term Memory, Deep Neural Network, Fuzzy Deep Neural Network, Adaptive Neuro Fuzzy Inference System and BRBES it is 18.66, 28.49, 17.05, 16.37 and 38.15 for combined cycle power plant respectively, which are significantly higher.

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  • 316.
    Islam, Raihan Ul
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Learning Mechanism for BRBES Using Enhanced Belief Rule-Based Adaptive Differential Evolution2020Ingår i: 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), IEEE, 2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    Nowadays, belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. Learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. Therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. This is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space. In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered. The reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. This comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. The comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms. Contribution-An enhanced differential evolution algorithm has been proposed in this paper, which is later used as a novel optimal training procedure for BRBES.

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  • 317.
    Islam, Raihan Ul
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A novel anomaly detection algorithm for sensor data under uncertainty2018Ingår i: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 22, nr 5, s. 1623-1639Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned.The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications. 

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  • 318.
    Islam, Raihan Ul
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Inference and Multi-level Learning in a Belief Rule-Based Expert System to Predict Flooding2020Ingår i: 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), IEEE, 2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    Floods are one of the most dangerous catastrophic events. By the year 2050 flooding due to rise of ocean level  may  cost  one  trillion USD to coastal cities. Since flooding involves multi-dimensional elements, its accurate prediction is difficult. In addition, the elements cannot be measured with 100% accuracy. Belief rule-based expert systems (BRBESs) can be considered as an appropriate approach to handle  this  type  of  problem  because they are capable of addressing  uncertainty. However, BRBESs need to be equipped with the capacity to handle multi- level learning and inference to improve its accuracy of flood prediction. Therefore, this paper proposes a new learning and inference mechanism, named joint optimization using belief rule- based adaptive differential evolution (BRBaDE) for multi-level BRBES, which has the capability to handle multi-level learning and inference. Various machine learning methods, including Artificial Neural Networks (ANN), Support Vector Machine (SVM), Linear Regression and Long Short Term Memory have been compared with BRBaDE. The result exhibits that our proposed learning mechanism performs betters than learning techniques as mentioned above in terms of accuracy in flood prediction.

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  • 319.
    Islam, Raihan Ul
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Ruci, Xhesika
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Kor, Ah-Lian
    School of Computing, Creative Technologies and Engineering Leeds Beckett University, Leeds, UK.
    Capacity Management of Hyperscale Data Centers Using Predictive Modelling2019Ingår i: Energies, E-ISSN 1996-1073, Vol. 12, nr 18, artikel-id 3438Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

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  • 320.
    Islam, Raihan Ul
    et al.
    NEC Europe Ltd, Kurfürsten-Anlage 36, 69115 Heidelberg, Germany.
    Schmidt, Mischa
    NEC Europe Ltd, Kurfürsten-Anlage 36, 69115 Heidelberg, Germany.
    Kolbe, Hans-Joerg
    NEC Europe Ltd, Kurfürsten-Anlage 36, 69115 Heidelberg, Germany.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Nomadic mobility between smart homes2012Ingår i: Proceedings of the 2012 IEEE Globecom Workshops (GC Wkshps), IEEE Communications Society, 2012, s. 1062-1067Konferensbidrag (Refereegranskat)
    Abstract [en]

    Powerful, user-friendly mobile devices and cost-efficient wireless access technologies have lately changed the landscape for smart home environments to a large extent. Developments in the media landscape with large flat screens, new capturing devices, and large digital media libraries have also changed the way smart home environments are used. This paper presents and evaluates an architecture for nomadic mobility in such environments where end-users, by authenticating their terminals with a node in the home or visited environment using the infrastructure provided by the operator, easily can gain access to various types of resources at home while roaming to other people's home networks.

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  • 321.
    Islam, Raihan Ul
    et al.
    NEC Europe Ltd..
    Schmidt, Mischa
    NEC Europe Ltd..
    Kolbe, Hans-Joerg
    NEC Europe Ltd..
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Luleå tekniska universitet, Institutionen för system- och rymdteknik, CDT.
    Secure and scalable multimedia sharing between smart homes2014Ingår i: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 5, nr 3, s. 79-93, artikel-id 6Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The smartphone revolution together with cost-efficient wireless access technologies have lately changed the landscape for smart home environments to a large extent. Moreover, large flat screens, new capturing devices, and large digital media libraries have also changed the way smart home environments are used. We present and evaluate an architecture for multimedia sharing in such environments. End-users can, by authenticating their terminals with a node in the home or visited environment easily gain access to various types of resources at home while roaming to other people's home networks. This is achieved by using the infrastructure provided by the operator.

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  • 322.
    James, Nord
    et al.
    Luleå tekniska universitet.
    Synnes, Kåre
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Parnes, Peter
    An architecture for location aware applications2002Ingår i: Proceedings of the 35th Annual Hawaii International Conference on System Sciences: 7 - 10 January 2001 [i.e. 2002], Big Island, Hawaii / [ed] Ralph H. Sprague, Los Alamitos, Calif: IEEE Communications Society, 2002, s. 3805-3810Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents an architecture for location aware applications, where positioning sources such as GPS, WaveLAN and Bluetooth devices can be seamlessly interchanged and combined to achieve a more accurate positioning service with a higher availability. The architecture also supports peer-to-peer communication to allow clients to interchange position information over a network such as Bluetooth or WaveLAN. This enables a user to use other users position sources if their clients are close enough. The position information can be used directly by an application or be combined with habitual and other contextual information to achieve more personalized applications. A generic positioning protocol for interchange of position information between position sources and client applications, and different techniques for merging of position information are presented. The interfaces to the platform are also discussed. The paper finally touches on privacy issues and outlines a schema for handling positioning information by user controlled contracts

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  • 323.
    Jamil, Mohammad Newaj
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Belief Rule Based Expert System for Evaluating Technological Innovation Capability of High-Tech Firms Under Uncertainty2019Ingår i: Joint 2019 8th International Conference on Informatics, Electronics and Vision (ICIEV) & 3rd International Conference on Imaging, Vision & Pattern Recognition (IVPR) with International Conference on Activity and Behavior Computing (ABC), IEEE, 2019, s. 330-335Konferensbidrag (Refereegranskat)
    Abstract [en]

    Technological innovation capability (TIC) is a complicated and subtle concept which is based on multiple quantitative and qualitative criteria. The cores of a firm’s long-term competitive dominance are defined by technological innovation capability which is the incentive for a firm’s innovation. Various types of uncertainty can be noticed while considering multiple criteria for evaluating TIC. In order to evaluate TIC in a reliable way, a Belief Rule Base (BRB) Expert System can be used to handle both quantitative and qualitative data and their associated uncertainties. In this paper, a RESTful API-based BRB expert system is introduced to evaluate technological innovation capability by taking uncertainties into consideration. This expert system will facilitate firms’ managers to obtain a recapitulation of the TIC evaluation. It will help them to take essential steps to ensure corporate survival and strengthen their weak capabilities continuously to facilitate a competitive advantage. Other users can also use this API to apply BRB for a different domain. However, a comparison between the knowledge-driven approach (BRBES) and several data-driven models has been performed to find out the reliability in evaluating TIC. The result shows that the outcome of BRBES is better than other data-driven approaches.

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  • 324.
    Jamil, Mohammad Newaj
    et al.
    Dept of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh.
    Hossain, Mohammad Shahadat
    Dept of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh.
    Islam, Raihan Ul
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Technological Innovation Capability Evaluation of High-Tech Firms Using Conjunctive and Disjunctive Belief Rule-Based Expert System: A Comparative Study2020Ingår i: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 11, nr 3, s. 29-49Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Technological Innovation Capability (TIC) is an intricate concept which defines the essence of a firm’s influence in the long run. It is associated with multiple quantitative and qualitative criteria, and various types of uncertainty can be seen while measuring these criteria. Therefore, to address this issue, a Belief Rule-Based Expert System (BRBES) can be employed with the capability of handling multiple criteria and their associated uncertainties in an integrated framework. In this article, two web-based BRBESs, namely conjunctive BRBES, and disjunctive BRBES, have been developed which are capable of reading data and producing web-based output by taking uncertainties into consideration. Then a comparison has been performed between them to determine the reliability of TIC evaluation. The results show that the performance of conjunctive BRBES is promising than disjunctive BRBES for technological innovation capability evaluation. In addition, a new learning mechanism, namely Belief Rule-Based Adaptive Particle Swarm Optimization (BRBAPSO), has been developed to support learning in BRBES and a comparison between trained conjunctive and trained disjunctive BRBES has also been carried out to evaluate TIC, where trained conjunctive BRBES is found effective than trained disjunctive BRBES.

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  • 325.
    Jangirala, Srinivas
    et al.
    Jindal Global Business School, O. P. Jindal Global University, Haryana, India.
    Das, Ashok Kumar
    Center for Security, Theory and Algorithmic Research, International Institute of Information Technology Hyderabad, Hyderabad, Telangana, India .
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Designing Secure Lightweight Blockchain-Enabled RFID-Based Authentication Protocol for Supply Chains in 5G Mobile Edge Computing Environment2020Ingår i: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, nr 11, s. 7081-7093Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Secure real-time data about goods in transit in supply chains needs bandwidth having capacity that is not fulfilled with the current infrastructure. Hence, 5G-enabled Internet of Things (IoT) in mobile edge computing is intended to substantially increase this capacity. To deal with this issue, we design a new efficient lightweight blockchain-enabled RFID-based authentication protocol for supply chains in 5G mobile edge computing environment, called LBRAPS. LBRAPS is based on bitwise exclusive-or (XOR), one-way cryptographic hash and bitwise rotation operations only. LBRAPS is shown to be secure against various attacks. Moreover, the simulation-based formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool assures that LBRAPS is secure. Finally, it is shown that LBRAPS has better trade-off among its security and functionality features, communication and computation costs as compared to those for existing protocols.

  • 326.
    Jasim, Al-Hussein Hameed
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Ögren, Niclas
    InfoVista Sweden AB, SE-931 62, Skelleftea, Sweden.
    Minovski, Dimitar
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. InfoVista Sweden AB, SE-931 62, Skelleftea, Sweden.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Packet Probing Study to Assess Sustainability in Available Bandwidth Measurements: Case of High-Speed Cellular Networks2020Ingår i: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 11, nr 2, s. 106-125Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Facts and figures indicate that the latest generations of cellular networks are likely to become the dominant medium of the global data exchange. This may pose a challenge to service providers trying to improve the Quality of Service (QoS) provided that is usually specified in Service Level Agreement (SLA), which, in turn, has to be practically verified. It requires methods and tools to measure the adopted QoS metrics such as bandwidth and Round-Trip Time (RTT). For this research, the InfoVista’s proprietary tool (Blixt™) was used. The research discusses how the probing packet parameters play a vital role in determining the accuracy of the measurements, the level of intrusiveness in a shared-resources network, and its implications for sustainability for this application. The experiments were carried out in a live commercial network and the performance was also compared with other cutting-edge available bandwidth measurement tools in a multi-carrier scenario.

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  • 327.
    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 tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks2016Ingår i: Sensors, E-ISSN 1424-8220, Vol. 16, nr 4, artikel-id 487Artikel i tidskrift (Refereegranskat)
    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.

  • 328.
    Jayaraman, Prem
    et al.
    RMIT University, Melbourne.
    Mitra, Karan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Saguna, Saguna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Shah, Tejal
    School of Computer Science and Engineering, University of New South Wales.
    Georgeakopoulos, Dimitros
    Royal Melbourne Institute of Technology, Melbourne.
    Ranjan, Rajiv
    School of Computing Science, Newcastle University, Newcastle upon Tyne.
    Orchestrating Quality of Service in the Cloud of Things Ecosystem2015Ingår i: 2015 IEEE International Symposium on Nanoelectronic and Information Systems: Indore, 21-23 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, s. 185-190, artikel-id 7434422Konferensbidrag (Refereegranskat)
    Abstract [en]

    Cloud of Things (CoT) is a vision inspired by Internet of Things (IoT) and cloud computing where the IoT devices are connected to the clouds via the Internet for datastorage, processing, analytics and visualization. CoT ecosystem will encompass heterogeneous clouds, networks and devices to provide seamless service delivery, for example, in smart cites. To enable efficient service delivery, there is a need to guarantee a certain level of quality of service from both cloud and networksperspective. This paper discusses the Cloud of Things, cloud computing, networks and new quality of service management research issues arising due to realisation of CoT ecosystem vision.

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  • 329.
    Jayaraman, Prem Prakash
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Cost efficient data collection of sensory originated data using context-aware mobile devices2008Ingår i: 2008 Ninth International Conference on Mobile Data Management workshops: MDMW ; Beijing, China, 27 - 30 April 2008, Piscataway, NJ: IEEE Communications Society, 2008, s. 190-197Konferensbidrag (Refereegranskat)
    Abstract [en]

    Sensory originated data collection and processing has always been a big challenge in wireless sensor networks (WSN). WSN represent a distributed producer of large amount of valuable data required by varied number of applications. In this paper we propose the use of context aware data mules (CADAMULE) as a solution for smart data collection within sensor networks. We present an extension to Context Spaces modelling theory by incorporating context discovery at runtime. This facilitates our system to discover new context attributes by looking into previous situations and events when pre-defined context is not sufficient for the reasoning process. We use this model as a base to provide contextual information to the mobile data mule whose spare capacity for communication and processing can be used to collect and process sensor data. The focus of the paper is to propose and evaluate a cost-efficient data collection technique which uses a cost formula computed from the context information obtained by the system. We validate our system by a simulation in which we try to reason out and identify the best and also the most cost efficient data mule. The context aware data mule negotiates with the sensor node collecting and delivering the data to the sink

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  • 330.
    Jayaraman, Prem Prakash
    et al.
    Caulfield School of Information Technology, Monash University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Cost-efficient data collection approach using K-nearest neighbors in a 3D sensor network2010Ingår i: 11th IEEE International Conference on Mobile Data Management, MDM 2010, Piscataway, NJ: IEEE Communications Society, 2010, s. 183-188Konferensbidrag (Refereegranskat)
    Abstract [en]

    Sensor networks represent an important component of distributed infrastructure supplying raw data to various applications from military to healthcare. A key challenge is costefficient collection of distributed data streaming from those sensor networks. In this paper we propose the use of mobile data collectors that employ K-NN queries as a cost-efficient approach to collect data within the sensor network. We investigate a 3D sensor network and propose a cost-efficient 3D-KNN algorithm that uses minimal energy and communication overheads to compute k-nearest neighbors. The 3D-KNN algorithm uses a 3 dimensional plane rotation algorithm that maps sensor nodes on a 3D plane to a reference plane identified by the mobile data collector. We propose a cost-efficient KNN boundary estimation algorithm that computes KNN boundary based on network density. We also propose a neighbor prediction algorithm that uses distance, signal to noise ratio and mobile data collector's trajectory information to identify sensor nodes along the mobile data collector's path. We simulate the proposed 3D-KNN algorithm using GlomoSim and validate its cost efficiency by evaluating its energy efficiency and query latency. Lessons and results of extensive simulation conclude the paper

  • 331.
    Jayaraman, Prem Prakash
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Coverage area computation on the run for efficient sensor data collection2008Ingår i: New technologies, mobility and security: proceedings of NTMS '2008 conference and workshops ; [held in Tangier, Morocco during November 5 - November 7 2008] / [ed] Akshai Aggarwal, Piscataway, NJ: IEEE Communications Society, 2008Konferensbidrag (Refereegranskat)
    Abstract [en]

    Wireless sensor networks have emerged as a key area of research in recent years. With the dominance of ubiquitous and pervasive era of computing, these networks present a rich infrastructure for valuable information. In this paper we focus on efficient data collection in sensor networks by proposing an algorithm to compute a coverage (collection) area using smart mobile objects. The algorithm proposed computes a collection area dynamically covering nodes around the mobile objects current location. It uses a weighed graph technique to identify nodes from which data can be collected efficiently by the mobile object discarding the rest. The proposed algorithm computes the collection area using Voronoi Diagrams and Delaunay triangle. We validate the proposed algorithm by simulating the algorithm over a Bluetooth based sensor network. We also evaluate the algorithms efficiency to compute the coverage area by changing the mobile objects context parameters.

  • 332.
    Jayaraman, Prem Prakash
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Intelligent mobile data mules for cost-efficient sensor data collection2010Ingår i: International Journal of Next-Generation Computing, ISSN 2229-4678, E-ISSN 0976-5034, Vol. 1, nr 1Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Sensor networks represent an important component of distributed pervasive infrastructure. A key challenge facing sensor networks is cost-effcient collection of data streaming from these distributed data sites. In this paper, we present a mobile data mule-based sensor data collection approach employing K-Nearest Neighbours queries. We propose a novel 3D-KNN algorithm that dynamically computes nearest sensors spread within a 3D environment around the data mule. The 3D-KNN algorithm incorporates a novel boundary estimation and neighbour selection algorithm to compute the nearest neighbour set. Further, we propose a neighbour prediction algorithm that computes sensor locations within the vicinity of the data mules' trajectory. We simulate the proposed 3D-KNN algorithm using GlomoSim validating its cost-effciency by extensive evaluations. Results of our simulations conclude the paper.

  • 333.
    Jayaraman, Prem Prakash
    et al.
    Caulfield School of Information Technology, Monash University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Intelligent processing of K-nearest neighbors queries using mobile data collectors in a location aware 3D wireless sensor network2010Ingår i: Trends in applied intelligent systems: 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain, June 1 - 4, 2010 ; proceedings, part III / [ed] Nicolás García-Pedrajas; Francisco Herrera; Colin Fyfe; José Manuel Benítez; Moonis Ali, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2010, s. 260-270Konferensbidrag (Refereegranskat)
    Abstract [en]

    The increased acceptance of sensor networks into everyday pervasive environments has lead to the creation of abundant distributed resource constrained data sources. In this paper, we propose an intelligent mobile data collector-based K-Nearest Neighbor query processing algorithm namely 3D-KNN. The K-Nearest Neighbor query is an important class of query processing approach in sensor networks. The proposed algorithm is employed over a sensor network that is situated within a 3 dimensional space. We propose a novel boundary estimation algorithm which computes an energy efficient sensor boundary that encloses at least k nearest nodes. We then propose a 3D plane rotation algorithm that maps selected sensor nodes on different planes onto a reference plane and a novel k nearest neighbor selection algorithm based on node distance and signal-to-noise ratio parameters. We have implemented the 3D-KNN algorithm in GlomoSim and validate the proposed algorithm's cost efficiency by extensive performance evaluation over well defined system criteria

  • 334.
    Jayaraman, Prem Prakash
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    On-the-fly situation composition within smart spaces2009Ingår i: Smart Spaces and Next Generation Wired/Wireless Networking: 9th International Conference, NEW2AN 2009 and Second Conference on Smart Spaces, ruSMART 2009, St. Petersburg, Russia, September 15-18, 2009. Proceedings / [ed] Sergey Balandin; Dmitri Moltchanev; Yevgeni Koucheryavy, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2009, s. 52-65Konferensbidrag (Refereegranskat)
    Abstract [en]

    Advances in pervasive computing systems have made smart computing spaces a reality. These smart spaces are sources of large amount of data required for context aware pervasive applications to function autonomously. In this paper we present a situation aware reasoning system that composes situations at runtime based on available  information from the smart spaces. Our proposed system R-CS uses situation composition on-the-fly to compute temporal situations that best represent the real world situation (contextual information). Our proposed situation composition algorithm is dependent on underlying sensor data (hardware and software). These sensory data are prone to errors like inaccuracy, old data, data ambiguity etc. R-CS proposes algorithms that incorporate sensor data errors estimation techniques into our proposed dynamic situation composition based reasoning system. R-CS is built as an extension to Context Spaces, a fixed situation set based reasoning system. We implement R-CS dynamic situation composition algorithms over context spaces and validate our proposed R-CS model against context spaces' fixed situation reasoning model.

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  • 335.
    Jayaraman, Prem Prakash
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Sensor data collection using heterogeneous mobile devices2007Ingår i: 2007 IEEE International Conference on Pervasive Services: [ICPS '07] ; Istanbul, Turkey, 15 - 20 July 2007, Piscataway, NJ: IEEE Communications Society, 2007, s. 161-164Konferensbidrag (Refereegranskat)
    Abstract [en]

    Data collection has always been a major challenge in sensor networks and various techniques have been proposed to enable efficient data collection. One such methodology is the use of mobile elements within the existing infrastructure to enable data collection. The paper proposes the use of existing mobile elements like mobile phones which have enough spare capacity to act as data carriers within a sensor network to carry sensor data. With advent of technology, mobile devices have become so powerful that they can work in a pervasive environment and make decisions based on context information like presence, location etc. Our proposal is an intelligent heterogeneous network in which the sensor nodes act as the data accumulators and the context-aware mobile phones act as data carriers of the sensed data. A framework that enables the mobile node and sensor node communication over Bluetooth is proposed and a p implementation is presented.

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  • 336.
    Jayaraman, Prem Prakash
    et al.
    Caulfield School of Information Technology, Monash University.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Smart sensing and sensor data collection on the move for modelling intelligent environments2008Ingår i: Next Generation Teletraffic and Wired/Wireless Advanced Networking: 8th International Conference, NEW2AN and 1st Russian Conference on Smart Spaces, ruSMART 2008, St. Petersburg, Russia, September 3-5, 2008. Proceedings / [ed] Sergey Balandin; Dimitri Moltchanov; Yevgeni Koucheryavy, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2008, s. 306-317Konferensbidrag (Refereegranskat)
    Abstract [en]

    With advent of pervasive computing and considerable acceptance of sensor networks, smart sensing techniques and data collection have been topics of interest. This paper presents a smart sensing and data collection technique from sensor networks using context aware high powered mobile objects within the environment. The paper proposes CAM-R a context aware robot that can move within smart environments sensing new sensor sources and collecting sensory originated data efficiently. Based on these sensed data sources, we propose an extension to context spaces model that builds a virtual model of the intelligent environment. This intelligent environment model built using extended context spaces can be used by number of context aware applications to efficiently query and retrieve data from the sensor network using CAM-R based data collection approach. We also present a prototype implementation of CAM-R built using off-the-shelf hardware and a context based cost function used to compute data collection decisions. We validate our system by implementing the virtual modelling of the intelligent environment based on simulated input obtained from CAM-R and sensors. We also evaluate CAM-Rby simulating and comparing the energy spent by the sensor nodes during data collection process using our proposed approach and traditional fixed sink based approach.

  • 337.
    Jayaraman, Prem
    et al.
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Delsing, Jerker
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Dynamic situation modeling and reasoning under uncertainty2009Ingår i: AUPC '09: ICPS 2009 & co-located workshops compilation proceedings, July 13-16, 2009, Imperial College, London, UK, New York: ACM Digital Library, 2009, s. 113-122Konferensbidrag (Refereegranskat)
    Abstract [en]

    Reasoning under uncertainty is a key challenge in context aware pervasive systems. In this paper we propose R-CS a situation based context reasoning model that employs ranking technique to rank and order context attributes. Using the proposed ranking technique and available context information, we compute dynamic situation spaces (a collection of contextual attributes that best represent a real world situation) We also propose and incorporate multilevel hierarchical contextual regions into R-CS that enables situation reasoning to be based on one or more dependent context attributes. We present a theoretical approach to compute importance and relevance of newly discovered context attributes which are not defined within the situation space definition by employing the approach of investigating similar neighboring situation spaces. R-CS builds on context spaces theory, a context model based on situation reasoning. We have implemented the proposed algorithms/approaches into R-CS and have validated them by evaluating against context spaces reasoning model.

  • 338.
    Jiau, Mingkai
    et al.
    Department of Electronic Engineering, National Taipei University of Technology.
    Huang, Shihchia
    Department of Electronic Engineering, National Taipei University of Technology.
    Hwang, Jenqneng
    Department of Electrical Engineering, University of Washington, Seattle.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Multimedia Services in Cloud-Based Vehicular Networks2015Ingår i: IEEE Intelligent Transportation Systems Magazine (ITSM), ISSN 1939-1390, Vol. 7, nr 3, s. 62-79, artikel-id 7166430Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Research into the requirements for mobile services has seen a growing interest in the fields of cloud technology and vehicular applications. Integrating cloud computing and storage with vehicles is a way to increase accessibility to multimedia services, and inspire myriad potential applications and research topics. This paper presents an overview of the characteristics of cloud computing, and introduces the basic concepts of vehicular networks. An architecture for multimedia cloud computing is proposed to suit subscription service mechanisms. The tendency to equip vehicles with advanced and embedded devices such as diverse sensors increases the capabilities of vehicles to provide computation and collection of multimedia content in the form of the vehicular network. Then, the taxonomy of cloud-based vehicular networks is addressed from the standpoint of the service relationship between the cloud computing and vehicular networks. In this paper, we identify the main considerations and challenges for cloud based vehicular networks regarding multimedia services, and propose potential research directions to make multimedia services achievable. More specifically, we quantitatively evaluate the performance metrics of these researches. For example, in the proposed broadcast storm mitigation scheme for vehicular networks, the packet delivery ratio and the normalized throughput can both achieve about 90%, making the proposed scheme a useful candidate for multimedia data exchange. Moreover, in the video uplinking scenarios, the proposed scheme is favorably compared with two well-known schedulers, M-LWDF and EXP, with the performance much closer to the optimum

  • 339.
    Jimenez, Lara Lorna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Decentralized Location-aware Orchestration of Containerized Microservice Applications: Enabling Distributed Intelligence at the Edge2020Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Services that operate on public, private, or hybrid clouds, should always be available and reachable to their end-users or clients. However, a shift in the demand for current and future services has led to new requirements on network infrastructure, service orchestration, and Quality-of-Service (QoS). Services related to, for example, online-gaming, video-streaming, smart cities, smart homes, connected cars, or other Internet-of-Things (IoT) powered use cases are data-intensive and often have real-time and locality requirements. These have pushed for a new computing paradigm, Edge computing, based on moving some intelligence from the cloud to the edge of the network to minimize latency and data transfer. This situation has set new challenges for cloud providers, telecommunications operators, and content providers. This thesis addresses two issues in this problem area that call for distinct approaches and solutions. Both issues share the common objectives of improving energy-efficiency and mitigating network congestion by minimizing data transfer to boost service performance, particularly concerning latency, a prevalent QoS metric. The first issue is related to the demand for a highly scalable orchestrator that can manage a geographically distributed infrastructure to deploy services efficiently at clouds, edges, or a combination of these. We present an orchestrator using process containers as the virtualization technology for efficient infrastructure deployment in the cloud and at the edge. The work focuses on a Proof-of-Concept design and analysis of a scalable and resilient decentralized orchestrator for containerized applications, and a scalable monitoring solution for containerized processes. The proposed orchestrator deals with the complexity of managing a geographically dispersed and heterogeneous infrastructure to efficiently deploy and manage applications that operate across different geographical locations — thus facilitating the pursuit of bringing some of the intelligence from the cloud to the edge, in a way that is transparent to the applications. The results show this orchestrator’s ability to scale to 20 000 nodes and to deploy 30 000 applications in parallel. The resource search algorithm employed and the impact of location awareness on the orchestrator’s deployment capabilities were also analyzed and deemed favorable. The second issue is related to enabling fast real-time predictions and minimizing data transfer for data-intensive scenarios by deploying machine learning models at devices to decrease the need for the processing of data by upper tiers and to decrease prediction latency. Many IoT or edge devices are typically resource-scarce, such as FPGAs, ASICs, or low-level microcontrollers. Limited devices make running well-known machine learning algorithms that are either too complex or too resource-consuming unfeasible. Consequently, we explore developing innovative supervised machine learning algorithms to efficiently run in settings demanding low power and resource consumption, and realtime responses. The classifiers proposed are computationally inexpensive, suitable for parallel processing, and have a small memory footprint. Therefore, they are a viable choice for pervasive systems with one or a combination of these limitations, as they facilitate increasing battery life and achieving reduced predictive latency. An implementation of one of the developed classifiers deployed to an off-the-shelf FPGA resulted in a predictive throughput of 57.1 million classifications per second, or one classification every 17.485 ns.

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  • 340.
    Jimenez, Lara Lorna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Simon, Miguel Gomez
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Kristiansson, Johan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Synnes, Kåre
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Åhlund, Christer
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    CoMA: Resource Monitoring of Docker Containers2015Ingår i: Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER 2015), SCITEPRESS Digital Library , 2015, Vol. 1, s. 145-154Konferensbidrag (Refereegranskat)
    Abstract [en]

    This research paper presents CoMA, a Container Monitoring Agent, that oversees resource consumption of operating system level virtualization platforms, primarily targeting container-based platforms such as Docker. The core contribution is CoMA, together with a quantitative evaluation verifying the validity of the measurements reported by the agent for three metrics: CPU, memory and block I/O. The proof-of-concept is implemented for Docker-based systems and consists of CoMA, the Ganglia Monitoring System and the Host sFlow agent. This research is in line with the rising trend of container adoption which is due to the resource efficiency and ease of deployment. These characteristics have set containers in a position to topple virtual machines as the reigning virtualization technology in data centers.

  • 341.
    Jiménez, Lara Lorna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    DOCMA: A Decentralized Orchestrator for Containerized Microservice Applications2019Ingår i: 2019 3rd IEEE International Conference on Cloud and Fog Computing Technologies and Applications: IEEE Cloud Summit 2019, Washington, D.C., USA, USA: IEEE, 2019, s. 45-51Konferensbidrag (Refereegranskat)
    Abstract [en]

    The advent of the Internet-of-Things and its associated applications are key business and technological drivers in industry. These pose challenges that modify the playing field for Internet and cloud service providers who must enable this new context. Applications and services must now be deployed not only to clusters in data centers but also across data centers and all the way to the edge. Thus, a more dynamic and scalable approach toward the deployment of applications in the edge computing paradigm is necessary. We propose DOCMA, a fully distributed and decentralized orchestrator for containerized microservice applications built on peer-to-peer principles to enable vast scalability and resiliency. Secure ownership and control of each application are provided that do not require any designated orchestration nodes in the system as it is automatic and self-healing. Experimental results of DOCMA's performance are presented to highlight its ability to scale.

  • 342.
    Jiménez, Lara Lorna
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Schelén, Olov
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    HYDRA: Decentralized Location-aware Orchestration of Containerized Applications2022Ingår i: IEEE Transactions on Cloud Computing, ISSN 2168-7161, Vol. 10, nr 4, s. 2664-2678Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The edge computing paradigm, spurred by the Internet-of-Things, poses new requirements and challenges for distributed application deployment. There is a need for an orchestrator design that leverages characteristics that enable this new paradigm. We present HYDRA, a decentralized and distributed orchestrator for containerized microservice applications. This orchestrator focuses on scalability and resiliency to enable the global manageability of cloud and edge environments. It can manage heterogeneous resources across geographical locations and provide robust application control. Further, HYDRA enables the location-aware deployment of microservice applications via containerization. Thus, an application's services may be deployed to separate locations according to expected needs. In this paper, the experiments show the orchestrator scaling to 20 000 nodes and simultaneously deploying 30 000 applications. Further, empirical results show that location-aware application deployment does not hinder HYDRA's performance, and the random resource search algorithm currently being employed may be used as a baseline to find resources in this decentralized orchestrator. Therefore, we conclude that HYDRA is a viable orchestrator design for the new computing paradigm.

  • 343.
    Jindal, Anish
    et al.
    CSE Department, Thapar University.
    Dua, Amit
    Department of Computer Science and Information Systems, BITS Pilani.
    Kumar, Neeraj
    CSE Department, Thapar University.
    Vasilakos, Athanasios
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Rodrigues, Joel J.P.C.
    National Institute of Telecommunications (Inatel), Brazil.
    An efficient fuzzy rule-based big data analytics scheme for providing healthcare-as-a-service2017Ingår i: IEEE International Conference on Communications, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, artikel-id 7996965Konferensbidrag (Refereegranskat)
    Abstract [en]

    With advancements in information and communication technology (ICT), there is an increase in the number of users availing remote healthcare applications. The data collected about the patients in these applications varies with respect to volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges that needs a specialized approach. To address this issue, a new fuzzy rule-based classifier for big data handling using cloud-based infrastructure is presented in this paper, with an aim to provide Healthcare-as-a-Service (HaaS) to the users located at remote locations. The proposed scheme is based upon the cluster formation using the modified Expectation-Maximization (EM) algorithm and processing of the big data on the cloud environment. Then, a fuzzy rule-based classifier is designed for an efficient decision making about the data classification in the proposed scheme. The proposed scheme is evaluated with respect to different evaluation metrics such as classification time, response time, accuracy and false positive rate. The results obtained are compared with the standard techniques to confirm the effectiveness of the proposed scheme.

  • 344.
    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 tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks2017Ingår i: Sensors, E-ISSN 1424-8220, Vol. 17, nr 3, artikel-id 642Artikel i tidskrift (Refereegranskat)
    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.

  • 345.
    Jingili, Nuru
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Oyelere, Solomon
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Malmström Berghem, Simon
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Brännström, Robert
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Laine, Teemu H.
    Department of Digital Media, Ajou University, Suwon, South Korea.
    Lindqvist, Anna-Karin
    Luleå tekniska universitet, Institutionen för hälsa, lärande och teknik, Hälsa, medicin och rehabilitering.
    Rutberg, Stina
    Luleå tekniska universitet, Institutionen för hälsa, lärande och teknik, Hälsa, medicin och rehabilitering. Department of Health, Education and Technology, Division of Health Medicine and Rehabilitation, Luleaa University of Technology, Luleå, Sweden.
    A Two-Stage co-Design Process of Battleship-AST Persuasive Game for Active School Transportation in Northern Sweden2024Ingår i: International Journal of Human-Computer Interaction, ISSN 1044-7318, E-ISSN 1532-7590Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This research delves into the dynamics of active school transport (AST) by utilizing a two-stage co-design process and leveraging persuasive technology within a game for promoting AST called Battleship-AST. The primary aim of this research is to thoroughly investigate the two-stage game co-design process employed in creating a Battleship-AST game. Moreover, our research aims to evaluate participants’ perceptions regarding the motivating and engaging potential of the persuasive technology and gamification features embedded within the final iteration of the game. This evaluation aims to understand how these features influence participants’ motivation to increase their usage of AST through gameplay. In pursuit of these objectives, the research builds upon the existing Battleship-AST prototype and actively engages school children in a collaborative two-stage co-design process. Their valuable insights and preferences were harnessed in refining the game, which was subsequently tested during a tech event in Skellefteå, Sweden. The findings shed light on various aspects of the game’s impact, from its reception to the gamification features integrated within. Notably, the research highlights the positive impact of the co-design process, with increased motivation and engagement observed among the participants. Their involvement in shaping the game’s design resulted in a more engaging and enjoyable experience. The persuasive technology features, encompassing competition, collaboration, auditory cues, a virtual reward system, and an emphasis on similarity, played a pivotal role in sustaining engagement and motivating players. Elements such as rewards, leaderboard progression, and badges proved highly effective in encouraging continued participation and fostering a positive feedback loop. However, the study also identifies areas for potential improvement, including the need to measure real-life progress and refine the game’s levelling system. The research indicates that refining feedback mechanisms and tailoring game content to individual preferences could create an even more engaging experience. Additionally, long-term playtesting is proposed to assess the game’s extended impact. The findings offer promising avenues for enhancing motivation and engagement in AST, which can contribute to the promotion of healthier and more sustainable transportation choices among school children.

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  • 346.
    Johansson, Dan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A context-aware application mobility approach2012Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Over the last two decades, mobile computing has gone from being a mere vision to becoming a reality, ubiquitously present in our everyday lives. There are different types of mobility, from user and terminal mobility, to mobility of services and sessions. This thesis is mainly about application mobility { the ability for an application to migrate between different host devices during its execution. The aim of this thesis work is to explore and advance the area of application mobility. The thesis approaches this goal through focusing on three research issues: Architectural considerations for application mobility; Context-awareness support and application adaptability; and Concept exploration.The contributions of this thesis include the identification of requirements for application mobility and a proposal for a decentralized, global scale architecture for application mobility, building on the peer-to-peer paradigm. Several prototypes of systems allowing application mobility are deployed, manifesting concepts such as decentralized system layout, context-awareness, context quality and global scope. Evaluations are both quantitative and qualitative. Other contributions of this thesis are the design and evaluation of a framework building on cloud and peer-to-peer technology to enable mobile sessions and an exploration of the concept of application mobility.

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  • 347.
    Johansson, Dan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    A survey of works and an identification of challenges within the field of context-awareness supported application mobility2009Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The number of mobile users grows constantly; an important demand is for IT to be accessible always and everywhere. Users have gone from being stationary to mobile, accessing and using their electronic services everywhere, even while moving. But not only users can be mobile; when migrating an application from one device to another, one has achieved application mobility. As migration is often carried out in heterogeneous environments, applications have to be able to receive and manage input from the user and the surroundings. Taking interfaces and gathered information about the context (defined as any information that can be used to characterize the situation of an entity) with them while migrating, the notion of context-awareness supported application mobility has become a reality, and this would indeed enhance the field of ubiquitous computing, where the vision is often hidden, yet always accessible applications. The purpose of this survey is twofold: to compile previous work on context-awareness supported application mobility and to identify challenges connected to the area. The challenges that are identified concern how to identify and keep track of applications that move between devices; how to best design the model for context-awareness; how to attain and communicate a good enough context quality; how to achieve seamlessness and minimize downtime while executing a migration; and finally how to handle heterogeneity within and between devices, in shape of differences in hardware and software.

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  • 348.
    Johansson, Dan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Session mobility in multimedia services enabled by the cloud and peer-to-peer paradigms2011Ingår i: Proceedings of The 36th IEEE Conference on Local Computer Networks (LCN): 5th IEEE Workshop On User MObility and VEhicular Networks, IEEE Computer Society Press , 2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    Services based on new information technology offering audio and video modality are viewed among the most important today. When users change location or device there is a need to keep these media sessions active. The main purpose of this article is to present a lightweight framework that allows session mobility through making profit of the cloud and peer-to-peer paradigms, while at the same time fulfilling the prevailing requirements for new session mobility multimedia services. We demonstrate the principles of the framework by creating a real prototype, allowing combined video and audio sessions to be migrated between devices, thus showing successful implementation of session mobility meeting requirements such as low degree of service provider dependency, no changes to current common network infrastructure, dealing with privacy issues, providing flexibility and low cost, and letting the user control when and where to migrate the sessions. It is our belief that relatively new paradigms such as peer-to peer computing and cloud services could enhance and support flexible session mobility in a mobile use context.

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  • 349.
    Johansson, Dan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Two Shades of Service Mobility: Application Mobility and Mobile E-services2014Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Over the last two decades, mobile computing has gone from being a mere vision to becoming a reality, ubiquitously present in our everyday lives. There are different types of mobility, from user and terminal mobility, to mobility of services and sessions. This thesis focuses on service mobility, the possibility of accessing and using services regardlessof location or device. In particular, this thesis discusses two specific variants of service mobility, being application mobility (the ability for an application to migrate betweendifferent host devices during its execution), and mobile e-services (internet-based services delivered anytime and anywhere). The aim of this thesis work is to explore and advance the areas of application mobility and mobile e-services respectively. The thesis approaches this goal through focusing on four research issues: Concept exploration andarchitectural considerations for application mobility; Cross-platform support and adaptability for mobile applications and e-services; Design considerations for mobile e-services; and Transforming citizen involvement in e-service processes. The thesis proposes and evaluates a concept for the transformation of citizen involvement in e-government processes through the application of mobile e-services. The contributions of this thesis include the identification of requirements for application mobility and mobile e-services, which inform the design and implementation of prototype systems used as proofs-of-concept. Also, the thesis work specifies the unique characteristics of mobile e-services and presents an identification of the most important challenges to overcome in the area.

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  • 350.
    Johansson, Dan
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Luleå tekniska universitet, Institutionen för system- och rymdteknik, CDT.
    4th Generation e-Services: Requirements for the Development of Mobile e-Services2013Ingår i: eChallenges e-2013 Conference Proceedings / [ed] Paul Cunningham; Miriam Cunningham, Dublin, Ireland: IIMC International Information Management Corporation Ltd , 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we address the predicted fourth generation of e-services, i.e. mobile e-services. As the research area is still new, we examine the fundamentals of mobile e-services, but also more specifically focus on the deployment and usage of these services. We present a prototype e-service, manifesting the characteristics of mobile e-services. User testing is conducted in a real world setting, resulting in qualitative empirical data, which is iteratively analysed, resulting in seven guidelines for the design of mobile e-services. These guidelines are suggestions to design for application and service accessibility, individualization, location utilization, platform independence, service mobility, two-way communication, and usefulness. It is our belief that these guidelines will inform the work on implementing better service mediators in general, and mobile e-services in particular.

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