Adaptive Quantum-Inspired Swarm Learning (AQISL) for Energy-Efficient Cluster Head Selection in IoT Infrastructure
Abstract
Abstract The construction of large-scale IoT networks creates several challenges in terms of how efficiently these large-scale networks can maintain communication with one another. One such challenge is providing a means by which large amounts of data can be transmitted reliably while also being energy efficient to ensure the finite energy resources of most IoT nodes, which are primarily powered by batteries, will not be depleted too quickly. The majority of the IoT devices are located in places that do not have easily available sources of electrical power (i.e., rural areas). Therefore, energy must be conserved by careful management. To address those challenges, clustering is commonly employed to organize the nodes in a hierarchical structure. Each node is grouped into clusters and one node in each cluster functions as the cluster head (CH). The CH coordinates all of the communications between the other nodes in the cluster, combines the data being sent from the nodes into a single transmission, and transmits that transmission to a base station, or sink (i.e., former). Therefore, clustering greatly reduces the volume of duplicate transmissions being sent and the total amount of communication overhead associated with sending those duplicate transmissions. Cluster-based communications have a great deal of effectiveness and efficiency based on the method of establishing a CH. The Adaptive Quantum-Inspired Swarm Learning (AQISL) algorithm is developed to optimize cluster head…
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