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. Attached Files Name Description MIMEType Size Downloads Title An efficient clustering framework for Massive Sensor Networking in Industrial IoT Author(s) Pokhrel, Shiva Raj orcid.org/0000-0001-5819-765X Verma, Sandeep Garg, Sahil Sharma, Ajay K. Choi, Jinho orcid.org/0000-0002-4895-6680 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 1941-0050 Keyword(s) Industrial IoT industrial sensor networks cluster-based routing energy hole problem clustering 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30148406 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   Connect to Elements publication management system 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, 22:08:11 EST Mon, 01 Mar 2021, 04:01:46 EST Mon, 01 Mar 2021, 07:10:05 EST Wed, 03 Mar 2021, 05:05:32 EST Mon, 08 Mar 2021, 12:26:51 EST Mon, 08 Mar 2021, 12:30:16 EST Sat, 01 May 2021, 02:32:16 EST Sat, 15 May 2021, 02:33:09 EST Filtered Full Citation counts:   Cited 18 times in TR Web of Science Cited 15 times in Scopus Search Google Scholar Access Statistics: 236 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Sun, 28 Feb 2021, 22:08:11 EST