首页|Applying an Improved Squirrel Search Algorithm (ISSA) for Clustering and Low-Energy Routing in Wireless Sensor Networks (WSNs)
Applying an Improved Squirrel Search Algorithm (ISSA) for Clustering and Low-Energy Routing in Wireless Sensor Networks (WSNs)
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NETL
NSTL
Springer Nature
Wireless sensor networks (WSNs) plays an important role in the advancement of the industrial 4.0/5.0 revolution, as they are integrated into IoT and other cyber-physical systems. Various techniques have been developed to optimize WSNs, but there are still some limitations. Clustering has been a highly effective method for efficient communication with low power and battery usage in high-speed communication systems. In this work, an Improved Squirrel Search Algorithm (I-SSA) is developed for selecting the cluster head (CH) node in WSNs. The improved I-SSA algorithm introduces a sine chaotic mapping strategy to boost population diversity, a backward learning mechanism to constrict the selection of excellent solution sets, and a cross-learning mechanism to enhance the accuracy of the algorithm optimization procedure in order to confront the drawbacks of the SSA algorithm, including such easy having fallen into local optimal solution and inadequate variability. The fitness function, which is employed to assess the performance of the solutions generated by the optimization algorithm, plays a key role in this cluster head selection process. Four parameters including residual energy, average intra-cluster distance, average sink distance, and CH balance factor are used in the fitness function. Network density analysis was performed by changing the number of sensor nodes (SNs) from 100 to 500 and randomly distributing them in the simulation region. For the simulation study, we utilize the most recent and stable release of MATLAB, version 2021a. Results from the simulation indicate that the proposed I-SSA based approach improves network performance and uses more stable energy compared to existing techniques such as SSO-CHST, ACO-based, and GA-based methods.
NETL
NSTL
Springer Nature
