Grey wolf optimization with fuzzy logic for energy‐efficient communication in wireless sensor network‐based Internet of Things scenario

• Published in: International journal of communication systems 2021-11, Vol.34 (17), p.n/a
• Main Authors: Verma, Kritika, Baliyan, Niyati
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; fuzzy C‐means clustering; fuzzy inference system; Fuzzy logic; grey wolf optimization; Internet of Things; Nodes; Optimization; Sensors; Wireless communications; Wireless networks; wireless sensor network; Wireless sensor networks
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.4981

Summary Wireless sensor network is an essential building block for Internet of Things (IoT) due to its usage for collecting and sensing data by the sensor nodes in a nearby environment. Energy efficiency is an important requirement for these networks as the sensors used are short on power and memory. In this paper, an enhanced energy‐efficient clustering protocol is proposed that works on three major phases under network setup: grid formation and grid head selection, clustering, and cluster head (CH) selection. Clustering is achieved using nature‐inspired grey wolf optimization (GWO) algorithm, and CH selection is accomplished using fuzzy inference‐based system. Simulation is performed in MATLAB software, and the protocol is evaluated in terms of network lifetime, the first node dead (FND), half node dead (HND), and last node dead (LND). Additionally, a prototype modeling of IoT using the Thingspeak IoT Platform provided by Mathworks is also integrated with the proposed protocol to demonstrate its usage in IoT applications. The simulation results indicated that the proposed scheme attained better performance in preserving energy and extending network lifetime. Highlights: 1. Improved network lifetime and reduced energy consumption of WSN by introducing GWO fuzzy algorithm planned for effective clustering and CH selection. 2. The conventional clustering approach is enhanced by using GWO optimization algorithm. Optimal CH selection is achieved with fuzzy logic preferring four distinct constraints to grade the cluster nodes and the highest graded node becomes the CH for current communication round. 3. Demonstrated IoT‐based network prototype integrating with data generation at sensor‐level and represented it on ThingSpeak IoT server by MathWorks.