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Energy efficient data handling and coverage for wireless sensor networks

Abstract : In this thesis, we propose energy-efficient data management techniques dedicated to periodic sensor networks based on clustering architecture. First, we propose to adapt sensor sampling rate to the changing dynamics of the monitored condition using one-way ANOVA model and statistical tests (Fisher, Tukey and Bartlett), while taking into account the residual energy of sensor. The second objective is to eliminate redundant data generated in each cluster. At the sensor level, each sensor searches the similarity between readings collected at each period and among successive periods, based on the sets similarity functions. At the CH level, we use distance functions to allow CH to eliminate redundant data sets generated by neighboring nodes. Finally, we propose two sleep/active strategies for scheduling sensors in each cluster, after searching the spatio-temporal correlation between sensor nodes. The first strategy uses the set covering problem while the second one takes advantages from the correlation degree and the sensors residual energies for scheduling nodes in the cluster. To evaluate the performance of the proposed techniques, simulations on real sensor data have been conducted. We have analyzed their performances according to energy consumption, data latency and accuracy, and area coverage, and we show how our techniques can significantly improve the performance of sensor networks.
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Submitted on : Monday, March 27, 2017 - 5:35:11 PM
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  • HAL Id : tel-01496726, version 1


Hassan Moustafa Harb. Energy efficient data handling and coverage for wireless sensor networks. Networking and Internet Architecture [cs.NI]. Université de Franche-Comté; Université libanaise, 2016. English. ⟨NNT : 2016BESA2020⟩. ⟨tel-01496726⟩



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