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An intelligent and optimal resource allocation approach in Sensor Networks for smart Agri-IoT

journal contribution
posted on 2021-01-01, 00:00 authored by Sumarga Kumar Sah Tyagi, Amrit Mukherjee, Shiva PokhrelShiva Pokhrel, Kamal Kant Hiran
A Wireless Sensor Network (WSN) is of paramount importance in facilitating smart Agricultural Internet of Things (Agri-IoT). It connects numerous sensor nodes or devices to develop a robust framework for efficient and seamless communication with improved throughput for intelligent networking. Such enhancement has to be facilitated by an adequate and smart machine learning-based resource allocation approach. With the ensuing surge in the volume of devices being deployed from the smart Agri-IoT, applications such as intelligent irrigation, smart crop monitoring and smart fishery would be largely benefited. However, the existing resource allocation techniques would be inefficient for such anticipated energy-efficient networking. To this end, we develop a distributed artificial intelligence approach that applies efficient multi-agent learning over the WSN scenario for intelligent resource allocation. The approach is based on dynamic clustering which coupled tightly with the Back-Propagation Neural Network and empowered by the Particle Swarm Optimization (BPNN-PSO). We implement the overall framework using a Bayesian Neural Network, where the outputs from BPNN-PSO are supplied as weights to the underlying neuron layer. We observe that the cost function and energy consumption demonstrate a substantial improvement in terms of cooperative networking and efficient resource allocation. The approach is validated with simulations under realistic assumptions.



IEEE Sensors Journal


Early Access


Institute of Electrical and Electronics Engineers (IEEE)


Piscataway, N.J.







Publication classification

C1 Refereed article in a scholarly journal

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