A Comprehensive Review on Optimal Cluster Head Selection in WSN-IoT

• Published in: Advances in engineering software (1992) 2022-09, Vol.171, p.103170, Article 103170
• Main Authors: Ramya, R., Brindha, Dr. T.
• Format: Article
• Language: English
• Subjects: energy, network lifetime; optimization; WSN-IoT, Cluster Head Selection
• ISSN: 0965-9978
• DOI: 10.1016/j.advengsoft.2022.103170

Nowadays, Wireless Sensor Network (WSN) based Internet of Things (IoT) is a highly developed field that shows a promising area for research because they are economical and simple to manage and maintain. The network comprises a group of sensor nodes that can sense, compute and transmit. In WSN-IoT, energy conservation becomes a significant challenge to extend the network lifetime. So far, numerous efforts have been taken to enhance the routing protocols in the network through the clustering approach, since clustering is considered an effective and appropriate technique transmits the data without any issue. Nevertheless, in the clustering concept, Cluster Head Selection (CHS) is considered a complex process as it has to persuade specific metrics for effective performance. If appropriate clustering is not performed, the network will undergo energy depletion which fails in the network. As a solution to this, suitable techniques for the selection of Cluster Head (CH) play an important role in WSN-IoT. Therefore, this review presents a comprehensive analysis of 85 research papers based on conventional CHS approaches in the WSN-IoT. Additionally, this work analysis numerous approaches based on their categories, and utilized software tools, published years, and performance metrics and are discussed. Here, the categorization of approaches is performed based on optimization algorithms, clustering techniques, Artificial Intelligence techniques, Low-Energy Adaptive Clustering Hierarchy (LEACH) approach, and other approaches. From the analysis, it is clear that conventional approaches prolong the network lifetime as well as reduce energy depletion, but they fail to provide enhanced security, and Quality of Service (QoS), and also they fail to balance the temperature and load of WSN-IoT devices. Subsequently, numerous problems and challenges have been laid down, and finally, concluded that when an optimization algorithm is integrated with the machine learning approach, QoS, security, temperature, and load balance can be attained besides energy efficiency as well as network longevity.