Power‐efficient optimized clustering method with intelligent fog computing for wireless sensor networks

• Published in: Concurrency and computation Vol. 34; no. 15
• Main Authors: Jain, Abhishek, Bhardwaj, Amit Kumar
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 10.07.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; ant lion optimization; Cloud computing; Clustering; Data collection; Energy consumption; fog computing; k‐means clustering; Optimization; Packet transmission; routing protocol; Sensors; Wireless networks; Wireless sensor networks
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6983

One of the most essential characteristics that is taken into consideration while dealing with wireless sensor networks (WSNs) is to optimize energy consumption during the transmission of data packets. Routing algorithms must provide optimal solutions to reduce the amount of energy consumed during the process. In wireless sensor networks, the end devices layer comprises of clustering of sensor nodes using ant lion optimization (ALO) which is followed by fog layer consisting of k‐means clustering technique. When nature inspired optimization solution is implemented, the accuracy majorly depends upon complexity and number of parameters. The algorithm proposed majorly focuses on the bottom layer and not on the fog layer. The results of the proposed algorithm were proved to significantly better than the conventional algorithms used for the very same purpose. The proposed algorithm focuses on ALO only on the bottom layer and not on the fog layer. To evaluate the performance of the proposed algorithm, the results are compared with the findings of several traditional algorithms, such as energy‐efficient cross‐layer‐sensing clustering method, Distributed and Morphological Operation‐based Data Collection Algorithm, and trust‐based secure routing. Comparative results showed that the proposed algorithm presented significant improvement.