An efficient clustering framework for Massive Sensor Networking in Industrial IoT

Pokhrel, Shiva Raj, Verma, Sandeep, Garg, Sahil, Sharma, Ajay K. and Choi, Jinho 2021, An efficient clustering framework for Massive Sensor Networking in Industrial IoT, IEEE Transactions on Industrial Informatics, vol. Early Access, doi: 10.1109/tii.2020.3006276.

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Title An efficient clustering framework for Massive Sensor Networking in Industrial IoT
Author(s) Pokhrel, Shiva RajORCID iD for Pokhrel, Shiva Raj
Verma, Sandeep
Garg, Sahil
Sharma, Ajay K.
Choi, JinhoORCID iD for Choi, Jinho
Journal name IEEE Transactions on Industrial Informatics
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 1551-3203
Keyword(s) Industrial IoT
industrial sensor networks
cluster-based routing
energy hole problem
Summary Massive Machine Type IoT Communication (mMTIC) has the potential for high impact in anticipated future industry 4.0 sensor networking applications. However, the energy limitation and battery life of the IoT nodes have always been one of the long-standing problems. Clustering Routing Protocol (CRP) being the most efficient existing approach often suffers when nodes closer to the sink depletes their energy, thereby producing an unwanted energy hole, where packets in flight towards the sink often get interrupted. Considering mMTIC covering a large geographical area, such as monitoring bush fires, the multi-hop communication among the nodes often causes such an energy hole problem. In this paper, we develop an AI-based CRP (CIRP) framework for incorporating a small periphery of a fixed shaped area to ameliorate such energy holes. Our proposed framework is not only energy-optimized but also acts as a robust approach for massive communication and informed data collection.
Language eng
DOI 10.1109/tii.2020.3006276
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©1969, IEEE
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