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.

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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 RajORCID iD for 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

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