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Thèse de Toldov Viktor

Soutenue le 24-01-2017 13:30 , Amphithéâtre IRCICA

Adaptive MAC layer for interference limited WSN

In the era of the Internet of Things, the number of connected devices is growing dramatically. Often, connected objects use Industrial, Scientific and Medical radio bands for communication. These kinds of bands are available without license, which facilitates development and implementation of new connected objects. However, it also leads to an increased level of interference in these bands. Interference not only negatively affects the Quality of Service, but also causes energy losses, which is especially unfavorable for the energy constrained Wireless Sensor Networks (WSN). In the present thesis the impact of the interference on the energy consumption of the WSN nodes is studied experimentally. The experimental results were used to estimate the lifetime of WSN nodes under conditions of different levels of interference. Then, a Thompson sampling based Cognitive Radio (CR) adaptive solution is proposed and evaluated via both, simulation and hardware implementation. Results show that this approach finds the best channel quicker than other state of the art solutions. An extension for multihop WSN was proposed for this CR solution and evaluated by hardware implementation in the framework of EWSN Dependability Competition. Finally, an adaptive WildMAC MAC layer protocol is proposed for the usecase of the LIRIMA PREDNET wildlife animal tracking project. Obtained field range test data were used to theoretically estimate cell densities and deployment zone coverage in this Low Power Widea Area Network (LPWAN). Then performance of the protocol was evaluated in WSNet simulation. The results show performance that allows to respect PREDNET project requirements with the given coverage.

Jury de these:

RapporteursProf. University of Poitiers, France
CR, Inria, France
ExaminateursResearch Associate, University of Bologna, Italy
MCF University of Strasbourg, France
Prof. University of Lille 1, France
Prof. University of Lille 1, France
DirecteursProf. IMT, IMT Lille Douai, France
DR, Inria, France

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