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ew 26(1):

Editorial

Predictive Cross-Layer Anti-Interference Optimization for IoT Data Transmission over Unstable Channels in Power Transmission Environments

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  • @ARTICLE{10.4108/ew.13329,
        author={Qingdan Huang and Xihan Xie and Dongyi Xiao and Haian Ye},
        title={Predictive Cross-Layer Anti-Interference Optimization for IoT Data Transmission over Unstable Channels in Power Transmission Environments},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={13},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2026},
        month={9},
        keywords={Internet of Things, Power Transmission Environment, Unstable Wireless Channel, Anti-Interference Communication},
        doi={10.4108/ew.13329}
    }
    
  • Qingdan Huang
    Xihan Xie
    Dongyi Xiao
    Haian Ye
    Year: 2026
    Predictive Cross-Layer Anti-Interference Optimization for IoT Data Transmission over Unstable Channels in Power Transmission Environments
    EW
    EAI
    DOI: 10.4108/ew.13329
Qingdan Huang1,*, Xihan Xie1, Dongyi Xiao1, Haian Ye1
  • 1: Guangdong Power Grid Company (China)
*Contact email: huangqingdan2024@126.com

Abstract

INTRODUCTION: Transmission-line Internet of Things (IoT) networks provide continuous sensing for power-grid monitoring, but their wireless links are vulnerable to channel fluctuation, burst interference, sparse relay deployment, and limited node energy. OBJECTIVES: This paper aims to improve reliable IoT data transmission over unstable transmission-line channels by jointly considering prediction uncertainty, packet urgency, reliability, latency, and energy constraints. METHODS: A Predictive Cross-layer Anti-interference Optimization (PCAO) method is proposed. PCAO predicts near-future channel and interference states through uncertainty-aware temporal modeling, evaluates packet priority from event severity and information freshness, and jointly optimizes channel allocation, transmission power, redundancy, and relay preference through constrained cross-layer optimization. RESULTS: Experiments on DeepMIMO, RadioML, POWDER, and FlockLab show that PCAO consistently outperforms DQN, DDPG, PPO, and SAC, improving average packet delivery ratio by 2.55 percentage points over SAC while reducing delay by about 17.0%. CONCLUSION: The results indicate that predictive, priority-aware, and robust cross-layer control can enhance IoT transmission reliability under unstable channels in power transmission environments.

Keywords
Internet of Things, Power Transmission Environment, Unstable Wireless Channel, Anti-Interference Communication
Received
2026-06-04
Accepted
2026-07-24
Published
2026-09-02
Publisher
EAI
http://dx.doi.org/10.4108/ew.13329

Copyright © 2026 Qingdan Huang et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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