Device cooperation and energy efficiency optimization for backscatter-assisted wireless-powered D2D in Industrial Internet of Things

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks Pub Date : 2025-09-10 DOI:10.1016/j.adhoc.2025.104018

Ling Tan, Jing Song, Haifeng Wang, Hai Xu

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引用次数: 0

Abstract

To address the issues of energy lifetime and resource utilization in the Industrial Internet of Things (IIoT), the combination of wireless energy harvesting and Backscatter Communication (BC) technology significantly enhances the large-scale interconnection capability of IIoT. In this paper, we propose a hybrid Device-to-Device (D2D) communication framework that combines BC with Active Transmission (AT), where devices can make intelligent decisions to switch between modes based on distance, channel, and energy conditions. By jointly optimizing device associations, backscatter coefficients, load, and time allocation, the system maximizes energy efficiency while meeting delay, energy, and task requirements. Meanwhile, considering that selfish device behaviors may hinder collaboration, we design a payment mechanism based on resource-sharing benefits, incorporating social relationships to incentivize devices to actively participate in cooperation. Additionally, we propose a Multiagent Deep Deterministic Policy Gradient algorithm in the D2D hybrid communication network to solve the long-term joint optimization problem, enabling devices to dynamically adjust offloading strategies and construct optimal D2D links. Simulation results show that the proposed algorithm achieves higher rewards and faster learning speeds, improving system energy efficiency by approximately 5.3% compared to A3C-HAB and by about 33.3% compared to other benchmark solutions, demonstrating superior energy efficiency performance.

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工业物联网中反向散射辅助无线D2D的设备协作与能效优化

为了解决工业物联网（IIoT）中的能源寿命和资源利用问题，无线能量收集和反向散射通信（BC）技术的结合显著增强了工业物联网的大规模互联能力。在本文中，我们提出了一种混合设备到设备（D2D）通信框架，它结合了BC和主动传输（AT），其中设备可以根据距离、信道和能量条件做出智能决策，在模式之间切换。系统通过共同优化设备关联、反向散射系数、负载和时间分配，在满足时延、能量和任务需求的同时实现能源效率最大化。同时，考虑到设备的自私行为可能会阻碍协作，我们设计了一种基于资源共享利益的支付机制，结合社会关系来激励设备积极参与合作。此外，我们在D2D混合通信网络中提出了一种多智能体深度确定性策略梯度算法，以解决长期联合优化问题，使设备能够动态调整卸载策略并构建最优的D2D链路。仿真结果表明，该算法获得了更高的奖励和更快的学习速度，与A3C-HAB相比，系统能效提高了约5.3%，与其他基准方案相比，系统能效提高了约33.3%，表现出卓越的能效性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ad Hoc Networks 工程技术-电信学

CiteScore

10.20

自引率

4.20%

发文量

131

审稿时长

4.8 months

期刊介绍： The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.