Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions

Abstract Wireless Sensor Networks (WSNs) have attracted various academic researchers, engineers, science, and technology communities. This attraction is due to their broad research areas such as energy efficiency, data communication, coverage, connectivity, load balancing, security, reliability, scalability, and network lifetime. Researchers are looking towards cost-effective approaches to improve the existing solutions that reveal novel schemes, methods, concepts, protocols, and algorithms in the desired domain. Generally, review studies provide complete, easy access or solution to these concepts. Considering this as a driving force and the impact of clustering on the deterioration of energy consumption in wireless sensor networks, this review focus on clustering methods based on different aspects. This study’s significant contribution is to provide a brief review in the field of clustering in wireless sensor networks based on three different categories, such as classical, optimization, and machine learning techniques. For each of these categories, various performance metrics and parameters are provided, and a comparative assessment of the corresponding aspects like cluster head selection, routing protocols, reliability, security, and unequal clustering are discussed. Various advantages, limitations, applications of each method, research gaps, challenges, and research directions are considered in this study, motivating the researchers to carry out further research by providing relevant information in cluster-based wireless sensor networks.