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Optimal Energy-Centric Resource Allocation and Offloading Scheme for Green Internet of Things Using Machine Learning

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Published:14 May 2022Publication History
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Abstract

Resource allocation and offloading in green Internet of Things (IoT) 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.

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        • Published in

          cover image ACM Transactions on Internet Technology
          ACM Transactions on Internet Technology  Volume 22, Issue 2
          May 2022
          582 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/3490674
          • Editor:
          • Ling Liu
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 14 May 2022
          • Revised: 1 October 2020
          • Accepted: 1 October 2020
          • Received: 1 April 2020
          Published in toit Volume 22, Issue 2

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