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Relay selection, clustering, and data aggregation routing in wireless body area networks
Author(s) -
Ziaei Negin,
Avokh Avid
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4837
Subject(s) - computer science , relay , cluster analysis , computer network , energy consumption , routing (electronic design automation) , wireless sensor network , static routing , dynamic source routing , selection algorithm , node (physics) , geographic routing , selection (genetic algorithm) , routing protocol , distributed computing , artificial intelligence , engineering , power (physics) , physics , structural engineering , quantum mechanics , electrical engineering
Summary This paper addresses the problems of relay selection, clustering, and routing to extend the lifetime of wireless body area networks. We first propose an efficient algorithm called “Energy‐aware Relay Selection and Cluster‐based Routing (ERSCR)” to develop a hybrid data aggregation tree in the network. ERSCR has three phases, including the relay selection, clustering and Cluster Head (CH) selection, and data transmission. It divides the biosensors into several clusters and selects an appropriate CH for each cluster. Each biosensor transmits data to its CH or relay node. The aggregated data are then routed to the sink through an energy‐balanced routing tree. The proposed scheme considers both residual energy and distance for routing data of biosensors. It not only reduces the energy consumption in the network but also balances the energy consumed by different biosensors. Subsequently, we improve the ERSCR algorithm and introduce the “Joint Relay Selection, Clustering, and Routing (JRSCR)” algorithm to achieve a better network performance. JRSCR benefits from the advantages of the ERSCR algorithm. Moreover, it reduces the number of transmissions with the direct use of the relay nodes as CH. As another advantage, both ERSCR and JRSCR algorithms are compatible with the natural physical states of the human body. Simulation results demonstrate the efficiency of the proposed algorithms in terms of energy consumption, network lifetime, and maximum hop count.