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Peer-to-Peer Networking and Applications

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19-08-2022

A greedy energy efficient clustering scheme based reinforcement learning for WSNs

Authors: Nour El Houda Bourebia, Chunlin Li

Published in: Peer-to-Peer Networking and Applications | Issue 6/2022

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Abstract

Enhancing energy efficiency and extending the network lifetime are major concerns in the field of wireless sensor networks (WSNs). Clustering helps improving the energy efficiency and extends the network lifetime. In each cluster, a cluster head (CH) is chosen to collect data from its cluster members. However, the cluster head selection process is a vital task as it influences the network lifetime and the data delivery in the network. In this paper, the greedy energy efficient clustering scheme (GEECS) is proposed as a routing protocol based clustering. The proposed scheme treats the CH selection process as a multi-armed bandit problem. Cluster heads are selected using an improved \(\epsilon\)-greedy algorithm that balances the CH selection rate and increases the network’s efficiency. The \(\epsilon\)-greedy algorithm is combined with the scalable K-means++ algorithm to perform the clustering process. The performance of GEECS is evaluated in terms of packet delivery ratio, number of dead nodes, network lifetime and average cluster head energy per round with respect to the network densities. The simulation results reveal that the GEECS protocol boosts the data delivery rate to the base station, reduces the CH energy dissipation and prolongs the network lifetime compared to other routing protocols. It is notable that for a network of 100 nodes, GEECS increases the network lifetime to 3155 seconds whereas the LEACH and ICFP protocols exhibit an increase of 2250 and 2738 seconds respectively.

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Data aggregation is the act of gathering and combining relevant data coming from different sources. It helps reduce redundancies and enhance energy efficiency.

l is the oversampling factor. It represents the anticipated number of points picked at each iteration.

Exploration in reinforcement learning, is the process of learning something new (a new knowledge).

Exploitation in reinforcement learning, is the act of selecting the action (knowledge) with the highest reward.

k and l are predefined parameters

An action is selecting a CH for the round i.

The proposed algorithm in [25] is referred to by the abbreviation ICFP in the remaining part of this paper.

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Title: A greedy energy efficient clustering scheme based reinforcement learning for WSNs
Authors: Nour El Houda Bourebia
Chunlin Li
Publication date: 19-08-2022
Publisher: Springer US
Published in: Peer-to-Peer Networking and Applications / Issue 6/2022
Print ISSN: 1936-6442
Electronic ISSN: 1936-6450
DOI: https://doi.org/10.1007/s12083-022-01368-7

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