An Enhanced Adaptive Intelligent Clustering (AI-C) in Networks of Wireless Sensors
TL;DR
The research proposed an Enhanced Adaptive Intelligent Clustering (EAI-C) based on the concept of machine learning techniques and dynamically modifies clustering parameters of node density, energy levels, and communication overhead of the current state of the network in cluster head (CH) formation to achieve a better network performance, reduced energy consumption.
Abstract
For real-time applications, wireless sensor network technology driven by artificial intelligence (AI) is the way of the future. This technology makes it possible to collect data from almost any type of environment, analyze it instantly, and use its outcomes to improve operations and procedures. To optimize the clustering process in Wireless Sensor Networks (WSNs), an Adaptive Intelligent Clustering (AI-C) algorithm has been proposed previously where cluster heads are chosen probabilistically based on node distances and network density, though the scheme has shortcomings with respect to power usage and short network lifespan. The research proposed an Enhanced Adaptive Intelligent Clustering (EAI-C) based on the concept of machine learning techniques and dynamically modifies clustering parameters of node density, energy levels, and communication overhead of the current state of the network in cluster head (CH) formation. The cluster heads are selected intelligently and minimize energy depletion during data transfer. The proposed algorithm enhances the lifespan of sensor nodes while maintaining efficient network coverage. However, the scheme outperforms previous approaches of LEACH and AI-C in terms of extending network lifetime. Furthermore, the simulation result shows that the proposed approach achieved a better network performance, reduced energy consumption.