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Author: search.filters.author.Boulkaboul, SaharSubject: search.filters.subject.Wireless sensor networks

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    DFIOT: Data Fusion for Internet of Things
    (Springer Science, 2020) Boulkaboul, Sahar; Djenouri, Djamel
    In Internet of Things (IoT) ubiquitous environments, a high volume of heterogeneous data is produced from different devices in a quick span of time. In all IoT applications, the quality of information plays an important role in decision making. Data fusion is one of the current research trends in this arena that is considered in this paper. We particularly consider typical IoT scenarios where the sources measurements highly conflict, which makes intuitive fusions prone to wrong and misleading results. This paper proposes a taxonomy of decision fusion methods that rely on the theory of belief. It proposes a data fusion method for the Internet of Things (DFIOT) based on Dempster–Shafer (D–S) theory and an adaptive weighted fusion algorithm. It considers the reliability of each device in the network and the conflicts between devices when fusing data. This is while considering the information lifetime, the distance separating sensors and entities, and reducing computation. The proposed method uses a combination of rules based on the Basic Probability Assignment (BPA) to represent uncertain information or to quantify the similarity between two bodies of evidence. To investigate the effectiveness of the proposed method in comparison with D–S, Murphy, Deng and Yuan, a comprehensive analysis is provided using both benchmark data simulation and real dataset from a smart building testbed. Results show that DFIOT outperforms all the above mentioned methods in terms of reliability, accuracy and conflict management. The accuracy of the system reached up to 99.18% on benchmark artificial datasets and 98.87% on real datasets with a conflict of 0.58%. We also examine the impact of this improvement from the application perspective (energy saving), and the results show a gain of up to 90% when using DFIOT.
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    FDAP: Fast Data Aggregation Protocol in Wireless Sensor Networks
    (IEEE/Springer, 2012-08) Boulkaboul, Sahar; Djenouri, Djamel; Badache, Nadjib
    This paper focuses on data aggregation latency in wireless sensors networks. A distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks is proposed. The proposed algorithm is based on maximal independent sets. It modifies DAS scheme and proposes criteria for node selection amongst available competitors. The selection objective function captures the node degree (number of neighbors) and the level (number of hops) contrary to DAS that simply uses node ID. The proposed solution augments parallel transmissions, which reduces the latency. The time latency of the aggregation schedule generated by the proposed algorithm is also minimized. The latency upper-bound of the schedule is 17R+6Δ+8 time-slots, where R is the network radius and Δ is the maximum node degree. This clearly outperforms the state-of-the-art distributed data aggregation algorithms, whose latency upper-bound is not less than 48R+6Δ+16 time-slots. The proposed protocol is analyzed through a comparative simulation study, where the obtained results confirm the improvement over the existing solutions.