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Multiobjective Gray-Wolf-Optimization-Based Data Routing Scheme for Wireless Sensor Networks
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DOI：10.1109/JIOT.2021.3105425.png)
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Abstract
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In the Internet of Things (IoT)-based smart systems, wireless sensor networks (WSNs) play a vital role in physical object monitoring. It collects data by sensing the environment and sends the data to a central depository. Due to multihop data transmission, sensor nodes stationed near the sink have to relay a huge amount of data packets compared to the sensor nodes stationed far away from the sink. Hence, sensor nodes stationed near the sink consume more energy and die early. It leads to the premature death of the network. This article proposes a multiobjective gray-wolf-optimization-based intelligent data routing mechanism for WSNs that prevents premature death of the network. It significantly improves network lifetime performance. The proposed scheme divides the whole network into different optimal size clusters and selects optimal rendezvous points (RPs). Mobile sink visits each RP through the optimal path and collects data from the sensor nodes. Extensive simulation results show that the proposed scheme effectively prevents premature death of the network and improves the network performance compared to the state-of-the-art algorithms.
Keywords:
Wireless sensor networks
Delays
Clustering algorithms
Energy consumption
Data collection
Routing
Internet of Things
Gray wolf optimization (GWO)
Internet of Things (IoT)
mobile sink (MS)
rendezvous point (RP)
wireless sensor networks (WSNs)
Journal
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8.9
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1.4W
Citations:
7.8W
Organization
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