A novel dynamic clustering approach for energy hole mitigation in Internet of Things‐based wireless sensor network

Citations Over TimeTop 1% of 2021 papers

Abstract

Summary With the advancements in the technology, Internet of Things (IoT)‐based wireless sensor networks (WSNs) have shown a tremendous growth in rendering a huge number of applications across the globe. However, it is observed that IoT‐based sensor nodes suffer from energy limitations. To resolve this, cluster‐based topology is adapted by the various researchers for rendering green energy‐efficient solution for communication of IoT devices. In this paper, the dynamic and energy‐efficient clustering for energy hole mitigation (DECEM) is proposed. The proposed framework is composed of the following proposed attributes; network is divided in two halves of regions, in each half, a gateway node (GN) is selected that collects data from their corresponding half region. Further, in each cluster, two cluster heads (CHs) are selected among whom one is made active at a moment (remains active until 60% of its energy is consumed) and other stays in sleep mode. First, the GN is selected in each side of the network (divided into two halves), and later, clustering is done, and selection of two CHs in each cluster is performed. The parameters for the selection of GN and CHs include residual energy, separation between the node and the sink, the number of neighbour nodes and network's residual energy. The simulation experiments reveal that DECEM has enhanced stability period by 5% and 31% as compared to the MEEC and IDHR protocols, respectively. The network lifetime is gigantically improved by 56% as compared to MEEC protocol.

Related Papers

• → Energy-Aware Sensor Node Design With Its Application in Wireless Sensor Networks(2013)138 cited
• → Tuning in-sensor data filtering to reduce energy consumption in wireless sensor networks(2004)24 cited
• → Saving Energy with Cooperative Group-Based Wireless Sensor Networks(2010)13 cited
• → Energy Optimization of a Base Station using Q-learning Algorithm(2023)6 cited
• → Modelling the Energy Efficient Sensor Nodes for Wireless Sensor Networks(2014)3 cited