Abstract
Wireless sensor networks (WSNs) usually consist of a small number of sensors that track their surroundings, gather data, and send it to remote servers for analysis. Despite the fact that WSNs are advised highly adaptable adhoc networks, network management has been a critical concern in these systems due to the scale of implementation and accompanying quality problems such as resources planning, sustainability, and dependability. Hierarchy control is thought to be a possible solution to these issues. In WSNs, grouping is the most often used topology management technique. Nodes are grouped together to make them easier to control and to conduct different functions in a decentralized manner. Where-as clustering methods are most commonly used to reduce energy usage, they can also be used to fulfil a variety of efficiency objectives. This paper investigates the network proper-ties endorsed by available bio-inspired optimization WSN clustering approaches, such as particle swarm optimization (PSO), Genetic Algorithm (GA), Ant colony Optimization (ACO), and Grey Wolf Optimization (GWO), in depth.
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Maheshwar, K., Veenadhari, S., Almelu, S. (2022). Performance Analysis of Energy Efficient Optimization Algorithms for Cluster Based Routing Protocol for Heterogeneous WSN. In: Unhelker, B., Pandey, H.M., Raj, G. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 925. Springer, Singapore. https://doi.org/10.1007/978-981-19-4831-2_51
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