An intelligent and optimal resource allocation approach in Sensor Networks for smart Agri-IoT Tyagi, Sumarga Kumar Sah, Mukherjee, Amrit, Pokhrel, Shiva Raj and Hiran, Kamal Kant 2021, An intelligent and optimal resource allocation approach in Sensor Networks for smart Agri-IoT, IEEE Sensors Journal, vol. Early Access, doi: 10.1109/jsen.2020.3020889. Attached Files Name Description MIMEType Size Downloads Title An intelligent and optimal resource allocation approach in Sensor Networks for smart Agri-IoT Author(s) Tyagi, Sumarga Kumar Sah Mukherjee, Amrit Pokhrel, Shiva Raj orcid.org/0000-0001-5819-765X Hiran, Kamal Kant Journal name IEEE Sensors Journal Volume number Early Access Total pages 8 Publisher Institute of Electrical and Electronics Engineers (IEEE) Place of publication Piscataway, N.J. Publication date 2021 ISSN 1530-437X 2379-9153 Keyword(s) Agriculture-IoT Bayesian Neural Networks Wireless Sensor Networks (WSN) Summary 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. Language eng DOI 10.1109/jsen.2020.3020889 Indigenous content off Field of Research 0205 Optical Physics 0906 Electrical and Electronic Engineering 0913 Mechanical Engineering HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2020, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30148405 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Sun, 28 Feb 2021, 22:06:41 EST Mon, 01 Mar 2021, 04:01:44 EST Mon, 01 Mar 2021, 07:10:03 EST Wed, 03 Mar 2021, 05:05:30 EST Mon, 08 Mar 2021, 10:14:27 EST Mon, 08 Mar 2021, 10:24:33 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 12 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Sun, 28 Feb 2021, 22:06:41 EST

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