4.7 Article

Energy-Aware and Density-Based Clustering and Relaying Protocol (EA-DB-CRP) for gathering data in wireless sensor networks

Journal

APPLIED SOFT COMPUTING
Volume 80, Issue -, Pages 154-166

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2019.03.025

Keywords

Wireless sensor networks; Dense clusters; Sensor deactivation; Cluster head weight; Relay node weight

Funding

  1. North Atlantic Treaty Organization (NATO) SPS Project [SPS G4936]

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In this paper, we propose a novel energy-aware and density-based clustering and routing protocol (EA-DB-CRP) for gathering data in wireless sensor networks which basically aims at distributing the load among available sensor nodes which in turn balances the energy consumption in the network and consequently elongates the network lifetime. More precisely, we introduce a network model that ends up of having empirical expressions that describe how to partition the network field efficiently into equal-size layers and sub-layers. In each sub-layer, the role of cluster head is pivoted among all cluster individuals, in a round robin fashion, that are sorted in a list in a descending order based on an extremely effective cluster head weight. Additionally, there are a minimum number of cluster members maintained to guarantee the feasibility of clusters being formed and this is through proposing a cluster merge algorithm. Not only to this extent, but rather, we maintain the consideration of network density over created clusters to balance them and subsequently prolong the network lifetime. Lastly, an effective relaying algorithm is proposed in which cluster heads get aware of those sensor nodes located in a layer ahead toward the base station along with their relay-node weights whereas each cluster head picks the relay node that has the highest weight. Our proposed protocol is evaluated through conducting various MATLAB simulations. Strikingly, results demonstrated that our proposed protocol has a momentous change against other related works regarding network lifetime and energy use. (C) 2019 Elsevier B.V. All rights reserved.

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