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Localised Energy Based Clustering with Incentives for Efficient M2M Communications

Raymond W. Juma, Anish M. Kurien, and Thomas O. Olwal
Department of Electrical and Electronic Engineering, Tshwane University of Technology, Pretoria, South Africa
Abstract — In the existing literature, multi-hop communication-based clustering techniques for Machine-type Devices (MTDs) have been extensively studied to ensure energy-efficient Machine to Machine (M2M) communications. The techniques presented demonstrated advantages such as improved scalability and reliability performance in large scale M2M communication networks. However, significant waste in energy has been noted with some of the techniques during cluster formation and due to the inherent selfish behaviours of some of the MTDs when routing traffic from the edge to the sink regions. To mitigate selfish behaviours, encourage cooperation, and improve efficient energy performance amongst MTDs, this paper proposes a new method of clustering MTDs using local energy parameters augmented incentive, referred to as Local Energy based Clustering with Incentive Algorithm (LECIA). In this work, probing signals from the MTDs are considered to partition the network into regions. Local energy parameters are identified and then applied to cluster the MTDs in the partitioned regions. Centralised relay selection and incentive management system (CRSIMS) are invoked for relay device selection and stimulation of multihop transmissions respectively. Simulation results have indicated that the proposed approach has on average 5% and 37% more number of surviving devices, and 6% and 55 % more amount of remaining energy than the closely related conventional approaches, namely, the Hybrid Energy Efficient Distributed (HEED) and the Low Energy Adaptive Clustering Hierarchy-Centralised (LEACH), respectively.
 
Index Terms—Clustering, energy efficient, local energy, M2M, Partition, incentive

Cite: Raymond W. Juma, Anish M. Kurien, and Thomas O. Olwal, “Localised Energy Based Clustering with Incentives for Efficient M2M Communications,”Journal of Communications vol. 14, no. 12, pp. 1168-1179, 2019. Doi: 10.12720/jcm.14.12.1168-1179
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