research-article

Enabling Computational Intelligence for Green Internet of Things: Data-Driven Adaptation in LPWA Networking

Authors:

Chaofeng Zhang,

Mianxiong Dong,

Kaoru OtaAuthors Info & Claims

IEEE Computational Intelligence Magazine, Volume 15, Issue 1

Pages 32 - 43

https://doi.org/10.1109/MCI.2019.2954642

Published: 01 February 2020 Publication History

Abstract

With the exponential expansion of the number of Internet of Things (IoT) devices, many state-of-the-art communication technologies are being developed to use the lowerpower but extensively deployed devices. Due to the limits of pure channel characteristics, most protocols cannot allow an IoT network to be simultaneously large-scale and energy-efficient, especially in hybrid architectures. However, different from the original intention to pursue faster and broader connectivity, the daily operation of IoT devices only requires stable and low-cost links. Thus, our design goal is to develop a comprehensive solution for intelligent green IoT networking to satisfy the modern requirements through a data-driven mechanism, so that the IoT networks use computational intelligence to realize self-regulation of composition, size minimization, and throughput optimization. To the best of our knowledge, this study is the first to use the green protocols of LoRa and ZigBee to establish an ad hoc network and solve the problem of energy efficiency. First, we propose a unique initialization mechanism that automatically schedules node clustering and throughput optimization. Then, each device executes a procedure to manage its own energy consumption to optimize switching in and out of sleep mode, which relies on AI-controlled service usage habit prediction to learn the future usage trend. Finally, our new theory is corroborated through real-world deployment and numerical comparisons. We believe that our new type of network organization and control system could improve the performance of all green-oriented IoT services and even change human lifestyle habits.

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Cited By

Safitra MLubis MKurniawan MAlhari MNuraliza HAzzahra SPutri D(2023)Green Networking: Challenges, Opportunities, and Future Trends for Sustainable DevelopmentProceedings of the 2023 11th International Conference on Computer and Communications Management10.1145/3617733.3617760(168-173)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1145/3617733.3617760
Chang JZhang FZhu Y(2022)Application of Intelligent Sensor in Mining Electrical Equipment CollectionJournal of Control Science and Engineering10.1155/2022/26330192022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2633019
Li XWang ZLeung VJi HLiu YZhang H(2021)Blockchain-empowered Data-driven NetworksACM Computing Surveys10.1145/344637354:3(1-38)Online publication date: 17-Apr-2021
https://dl.acm.org/doi/10.1145/3446373
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cover image IEEE Computational Intelligence Magazine

IEEE Computational Intelligence Magazine Volume 15, Issue 1

Feb. 2020

95 pages

ISSN:1556-603X

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Copyright © 2019.

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IEEE Press

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Published: 01 February 2020

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Cited By

Safitra MLubis MKurniawan MAlhari MNuraliza HAzzahra SPutri D(2023)Green Networking: Challenges, Opportunities, and Future Trends for Sustainable DevelopmentProceedings of the 2023 11th International Conference on Computer and Communications Management10.1145/3617733.3617760(168-173)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1145/3617733.3617760
Chang JZhang FZhu Y(2022)Application of Intelligent Sensor in Mining Electrical Equipment CollectionJournal of Control Science and Engineering10.1155/2022/26330192022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2633019
Li XWang ZLeung VJi HLiu YZhang H(2021)Blockchain-empowered Data-driven NetworksACM Computing Surveys10.1145/344637354:3(1-38)Online publication date: 17-Apr-2021
https://dl.acm.org/doi/10.1145/3446373
Zhang CDong MOta K(2021)Deploying SDN Control in Internet of UAVs: Q-Learning-Based Edge SchedulingIEEE Transactions on Network and Service Management10.1109/TNSM.2021.305915918:1(526-537)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1109/TNSM.2021.3059159
Zhang CDong MOta K(2020)Vehicular Multi-slice Optimization in 5G: Dynamic Preference Policy using Reinforcement LearningGLOBECOM 2020 - 2020 IEEE Global Communications Conference10.1109/GLOBECOM42002.2020.9348132(1-6)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/GLOBECOM42002.2020.9348132

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