| Abstrakt: |
With the fast evolution of Internet of Things (IoT) applications, Wireless Sensor Networks (WSNs) have become a crucial part of modern infrastructure. The efficient provision of services and wiser use of resources are currently of great importance. WSNs consist of several sensor nodes that collaborate to monitor and send data to a central location known as a sink. The sink, also called a base station, serves as the endpoint for data transmission in each round. However, due to the limited computation, storage, and energy resources of sensor nodes, they often face challenges in changing clusters. Optimal selection of a node, aimed at minimizing network fragmentation and enhancing energy utilization, necessitates a sophisticated evaluation and computational procedure, demanding a substantial energy investment. Subsequently, the task at hand is the development of a system facilitating the connection of remote sensing sources to WSNs with minimal energy consumption. The primary goals of WSNs based on the IoT revolve around extending network longevity and enhancing energy efficiency. In the realm of IoT-based WSNs, where the efficiency of data collection and management is paramount, cluster-based methodologies have demonstrated their effectiveness. This investigation proposes the implementation of a most valuable player algorithm (MVPA) specifically tailored for IoT-based WSNs, taking into account diverse factors influencing node energy and network lifespan. The MVPA is a highly competitive optimization method that converges faster (with fewer function evaluations) and has a greater overall success rate. In this case, the optimum cluster head for an IoT-based WSN was chosen using an MVPA to maximize energy savings. Simulation results demonstrate that the recommended strategy, when compared to other current methods, increases the network lifetime by using the minimum amount of energy needed to function. [ABSTRACT FROM AUTHOR] |
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