An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT

A Wireless Sensor Network (WSN) is of paramount importance in facilitating smart Agricultural Internet Things (Agri-IoT). It connects numerous sensor nodes or devices to develop a robust framework for efficient and seamless communication with improved throughput intelligent networking. Such enhancement has be facilitated by an adequate machine learning-based resource allocation approach. With the ensuing surge volume being deployed from Agri-IoT, applications such as irrigation, crop monitoring fishery would largely benefited. However, existing techniques inefficient anticipated energy-efficient To this end, we distributed artificial intelligence approach that applies multi-agent learning over WSN scenario allocation. The based on dynamic clustering which coupled tightly Back-Propagation Neural empowered Particle Swarm Optimization (BPNN-PSO). We implement overall using Bayesian Network, where outputs BPNN-PSO are supplied weights underlying neuron layer. observe cost function energy consumption demonstrate substantial improvement terms cooperative networking validated simulations under realistic assumptions.