{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:54:04Z","timestamp":1781740444189,"version":"3.54.5"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2022,5,31]]},"abstract":"<jats:p>\n            Resource allocation and offloading in green\n            <jats:bold>Internet of Things (IoT)<\/jats:bold>\n            relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.\n          <\/jats:p>","DOI":"10.1145\/3431500","type":"journal-article","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T12:46:06Z","timestamp":1652532366000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Optimal Energy-Centric Resource Allocation and Offloading Scheme for Green Internet of Things Using Machine Learning"],"prefix":"10.1145","volume":"22","author":[{"given":"Gunasekaran","family":"Manogaran","sequence":"first","affiliation":[{"name":"Computer and Information Science Department, Gannon University, Erie, PA, USA and College of Information and Electrical Engineering, Asia University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bharat S.","family":"Rawal","sequence":"additional","affiliation":[{"name":"Gannon University, Erie, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houbing","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Engineering, Jacksonville University, Jacksonville, FL 32211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chinghsien","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan, and School of Mathematics and Big Data, Foshan University, Foshan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijayalakshmi","family":"Saravanan","sequence":"additional","affiliation":[{"name":"Ryerson University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seifedine Nimer","family":"Kadry","sequence":"additional","affiliation":[{"name":"Beirut Arab University, Lebanon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. Mohamed","family":"Shakeel","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6059343"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11235-019-00597-1"},{"key":"e_1_3_1_4_2","first-page":"1","article-title":"Hybrid seismic-electrical data acquisition station based on cloud technology and green IoT","author":"Qiao S.","year":"2020","unstructured":"S. Qiao, Q. Zhang, Q. Zhang, F. Guo, and W. Li. 2020. Hybrid seismic-electrical data acquisition station based on cloud technology and green IoT. 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