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  • 1.
    Andersson, Karl
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mostafa, Seraj Al Mahmud
    Luleå tekniska universitet.
    Islam, Raihan Ul
    Luleå tekniska universitet.
    Mobile VoIP user experience in LTE2011In: Proceedings of The 36th IEEE Conference on Local Computer Networks (LCN): 5th IEEE Workshop On User MObility and VEhicular Networks, IEEE Computer Society Press , 2011, p. 789-792Conference paper (Refereed)
    Abstract [en]

    3GPP Long-term Evolution (LTE) systems being deployed are fast gaining market shares. High data rates (approaching 100 Mbit/s in the downlink direction and 50 Mbit/s for uplink connections) and small delays are attractive features of LTE. Spectrum flexibility also makes deployment easy on various frequency bands in different parts of the world. However, as LTE offers packet switched services only, mobile broadband connectivity has become the dominant LTE application so far. This paper studies user-perceived quality of service for a mobile Voice over IP (VoIP) application in LTE. Results were achieved using the OPNET Modeler simulation environment.

  • 2.
    Andersson, Karl
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ul Islam, Raihan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Md. Shahadat
    Chittagong University, Chittagong, Bangladesh.
    Project: A belief-rule-based DSS to assess flood risks by using wireless sensor networks2015Other (Other (popular science, discussion, etc.))
  • 3.
    Chowdhury, Rumman Rashid
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Sazzad
    Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
    Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 2019Conference paper (Refereed)
    Abstract [en]

    This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machine learning approaches is presented.

  • 4.
    Hridoy, Md Rafiul Sabbir
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Web Based Belief Rule Based Expert System for Assessing Flood Risk2017In: iiWAS'17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services, New York: ACM Digital Library, 2017, p. 434-440Conference paper (Refereed)
    Abstract [en]

    Natural calamities such as flooding, volcanic eruption, tornado hampers our daily life and causes many sufferings. Flood is one of the most catastrophic among the natural calamities. Assessing flood risk helps us to take necessary steps and save human lives. Several heterogeneous factors are used to assess flood risk on the livelihood of an area. Moreover, several types of uncertainties can be associated with each factor. In this paper, we propose a web based flood risk assessment expert system by combining belief rule base with the capability of reading data and generating web-based output. This paper also introduces a generic RESTful API which can be used without writing the belief rule based expert system from scratch. This expert system will facilitate the monitoring of the various flood risk factors, contributing in increasing the flood risk on livelihood of an area. Eventually, the decision makers should be able to take measures to control those factors and to reduce the risk of flooding in an area. Data for the expert system has been collected from a case study area by conducting interviews.

  • 5.
    Islam, Md. Zahirul
    et al.
    Department of Computer Science and Engineering University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, 2019Conference paper (Refereed)
    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%).

  • 6.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System2017Licentiate thesis, comprehensive summary (Other academic)
    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.

  • 7.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Hossain, Mohammad Shahadat
    University of Chittagong.
    A Web Based Belief Rule Based Expert System to Predict Flood2015In: 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, p. 19-26, article id 3Conference paper (Refereed)
    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.

  • 8.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Centre for Critical Infrastructure and Societal Security.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong.
    Network Intelligence for Enhanced Multi-Access Edge Computing (MEC) in 5G2019Conference paper (Other academic)
    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.

  • 9.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A novel anomaly detection algorithm for sensor data under uncertainty2018In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 22, no 5, p. 1623-1639Article in journal (Refereed)
    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. 

  • 10.
    Islam, Raihan Ul
    et al.
    NEC Europe.
    Schmidt, Mischa
    NEC Europe Ltd..
    Kolbe, Hans-Joerg
    NEC Europe.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Nomadic mobility between smart homes2012In: Proceedings of the 2012 IEEE Globecom Workshops (GC Wkshps), IEEE Communications Society, 2012, p. 1062-1067Conference paper (Refereed)
    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.

  • 11.
    Islam, Raihan Ul
    et al.
    NEC Europe Ltd..
    Schmidt, Mischa
    NEC Europe Ltd..
    Kolbe, Hans-Joerg
    NEC Europe Ltd..
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Secure and scalable multimedia sharing between smart homes2014In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 5, no 3, p. 79-93, article id 6Article in journal (Refereed)
    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.

  • 12.
    Jamil, Mohammad Newaj
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Based Expert System for Evaluating Technological Innovation Capability of High-Tech Firms Under Uncertainty2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, 2019Conference paper (Refereed)
    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.

  • 13.
    Kilinc, Caner
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mostafa, Seraj al Mahmud
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Shahzad, Kashif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Indoor taxi-cab: real-time Indoor positioning and location-based services with Ekahau and Android OS2014In: Proceedings of The Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2014) / [ed] Leonard Barolli; Ilsun You, Los Alamos: IEEE Communications Society, 2014, p. 223-228Conference paper (Refereed)
    Abstract [en]

    Positioning and routing in an outdoors environment is still challenging especially in complex buildings, where a number of buildings are combined with tunnels and bridges, and the GPS signal is unreachable. Wasting time for looking for a particular room in an unfamiliar huge indoor environment or a product in an enormous store is a real life problem that everybody faces on daily basis. The paper represents a solution for addressed problem by using Ekahau positioning systems and Android OS through an intermediary server, which acts between these two systems to provide actual room level position on a map by mathematical modeling technique. The system also based on request provides shortest path for a certain destination by computing Dijkstra's search algorithm. The distance between the locations is defined based on Taxi-cab geometry distance definition for the mobile clients. Additionally, the users can also display shortest path for some nearest items such as coffee machine. The implemented system evaluations are carried out in a basement floor on the site.

  • 14.
    Monrat, Ahmed Afif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Belief Rule Based Flood Risk Assessment Expert System using Real Time Sensor Data Streaming2018In: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Piscataway, NJ: IEEE Computer Society, 2018, p. 8-45Conference paper (Refereed)
    Abstract [en]

    Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. The integrated BRBES produces reliable results in comparison to other data-driven approaches. Data for the expert system has been collected by considering different case study areas of Bangladesh to validate the system.

  • 15.
    Monrat, Ahmed Afif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Challenges and Opportunities of Using Big Data for Assessing Flood Risks2018In: Applications of Big Data Analytics: Trends, Issues, and Challenges / [ed] Mohammed M. Alani, Hissam Tawfik, Mohammed Saeed, Obinna Anya, Cham: Springer, 2018, p. 31-42Chapter in book (Refereed)
    Abstract [en]

    Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has disastrous impact on the socioeconomic lifeline of a country. Nowadays, business organizations are using Big Data to improve their strategies and operations for revealing patterns and market trends to increase revenues. Eventually, the crisis response teams of a country have turned their interest to explore the potentialities of Big Data in managing disaster risks such as flooding. The reason for this is that during flooding, crisis response teams need to take decisions based on the huge amount of incomplete and inaccurate information, which are mainly coming from three major sources, including people, machines, and organizations. Hence, Big Data technologies can be used to monitor and to determine the people exposed to the risks of flooding in real time. This could be achieved by analyzing and processing sensor data streams coming from various sources as well as data collected from other sources such as Twitter, Facebook, and satellite and also from disaster organizations of a country by using Big Data technologies. Therefore, this chapter explores the challenges, the opportunities, and the methods, required to leverage the potentiality of Big Data to assess and predict the risk of flooding.

  • 16.
    Thombre, Sumeet
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    IP based Wireless Sensor Networks: performance Analysis using Simulations and Experiments2016In: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 7, no 3, p. 53-76Article in journal (Refereed)
    Abstract [en]

    Wireless sensor networks are at the crux of the Internet of Things applications. At the current state, there exist several technologies competing against each other in the IoT space. These proprietary technologies and hardware pose a serious problem of interoperability, which is vital to unleash the vision of the Internet of Things. Moreover, the traditional approach towards wireless sensor networks was to be unlike the internet, primarily because of the power and memory constraints posed by the tiny sensor nodes. The IETF 6LoWPAN technology facilitates the usage of IPv6 communications in sensor networks, which helps solve the problem of interoperability, enabling low power, low cost micro-controllers to be globally connected to the internet. Another IETF technology, CoAP allows interactive communication over the internet for these resource constrained devices. Along with 802.15.4, 6LoWPAN and CoAP, an open, standardized WSN stack for resource constrained devices and environments becomes available. The Contiki OS, touted as the open source OS for IoT, provides low power IPv6 communications and supports the 6LoWPAN and CoAP protocols, along with mesh routing using RPL. Along with these, a CoAP framework, Californium (Cf) provides a scalable and RESTful API to handle IoT devices. These open tools and technologies are employed in this work to form an open, inter-operable, scalable, reliable and low power WSN stack. This stack is then simulated using Contiki's default network simulator Cooja, to conduct performance analysis in varying conditions such as noise, topology, traffic etc. Finally, as a proof of concept and a validation of the simulated stack, physical deployment is carried out, using a Raspberry Pi as a border router, which connects the wireless sensor network to the global internet along with the T-mote sky sensor motes. Therefore, this work develops and demonstrates an open, interoperable, reliable, scalable, low power, low cost WSN stack, both in terms of simulations and physical deployments, and carries out performance evaluation of the stack in terms of throughput, latency and packet loss.

  • 17.
    Thombre, Sumeet
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Hossain, Mohammad Shahadat
    University of Chittagong.
    Performance Analysis of an IP based Protocol Stack for WSNs2016In: Proceedings of the 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Piscataway, NJ: IEEE Communications Society, 2016, p. 691-696, article id 7562102Conference paper (Refereed)
    Abstract [en]

    Wireless sensor networks (WSNs) are the key enablers of the internet of things (IoT) paradigm. Traditionally, sensor network research has been to be unlike the internet, motivated by power and device constraints. The IETF 6LoWPAN draft standard changes this, defining how IPv6 packets can be efficiently transmitted over IEEE 802.15.4 radio links. Due to this 6LoWPAN technology, low power, low cost micro-controllers can be connected to the internet forming what is known as the wireless embedded internet. Another IETF recommendation, CoAP allows these devices to communicate interactively over the internet. The integration of such tiny, ubiquitous electronic devices to the internet enables interesting real-time applications. We evaluate the performance of a stack consisting of CoAP and 6LoWPAN over the IEEE 802.15.4 radio link using the Contiki OS and Cooja simulator, along with the CoAP framework Californium (Cf).

  • 18.
    Uddin Ahmed, Tawsin
    et al.
    Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
    Hossain, Sazzad
    Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Islam, Raihan Ul
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Facial Expression Recognition using Convolutional Neural Network with Data Augmentation2019In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 2019Conference paper (Refereed)
    Abstract [en]

    Detecting emotion from facial expression has become an urgent need because of its immense applications in artificial intelligence such as human-computer collaboration, data-driven animation, human-robot communication etc. Since it is a demanding and interesting problem in computer vision, several works had been conducted regarding this topic. The objective of this research is to develop a facial expression recognition system based on convolutional neural network with data augmentation. This approach enables to classify seven basic emotions consist of angry, disgust, fear, happy, neutral, sad and surprise from image data. Convolutional neural network with data augmentation leads to higher validation accuracy than the other existing models (which is 96.24%) as well as helps to overcome their limitations.

  • 19.
    Ul Islam, Raihan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    University of Chittagong, Bangladesh.
    Heterogeneous Wireless Sensor Networks Using CoAP and SMS to Predict Natural Disasters2017In: Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS): The 8th IEEE INFOCOM International Workshop on Mobility Management in the Networks of the Future World (MobiWorld'17),, Piscataway, NJ: IEEE Communications Society, 2017, p. 30-35Conference paper (Refereed)
    Abstract [en]

    Even in the 21 st century human is still handicapped with natural disaster. Flood is one of the most catastrophic natural disasters. Early warnings help people to take necessary steps to save human lives and properties. Sensors can be used to provide more accurate early warnings due to possibilities of capturing more detail data of surrounding nature. Recent advantages in protocol standardization and cost effectiveness of sensors it is possible to easily deploy and manage sensors in large scale. In this paper, a heterogeneous wireless sensor network is proposed and evaluated to predict natural disaster like flood. In this network CoAP is used as a unified application layer protocol for exchanging sensor data. Therefore, CoAP over SMS protocol is used for exchanging sensor data. Furthermore, the effectiveness of the heterogeneous wireless sensor network for predicting natural disaster is presented in this paper.

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