Multi-objective joint optimization of task offloading based on MADRL in internet of things assisted by satellite networks

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Houpeng Wang , Suzhi Cao , Huanjing Li , Lei Yan , Zhonglin Guo , Yu’e Gao
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引用次数: 0

Abstract

The Internet of Things (IoT) integrates a large number of heterogeneous terminals and systems, possessing ubiquitous sensing and computing capabilities. Satellite networks are the crucial supplement to terrestrial networks, particularly in remote areas where network infrastructures are sparingly distributed or unavailable. Combining edge computing with satellite networks provides on-orbit computing capabilities for IoT applications, reducing service delay and enhancing service quality. Due to the resource constraints of satellites, achieving collaborative services through task offloading among multiple satellites becomes essential. Both the privacy leakage risk arising from frequent data interactions and the load imbalance resulting from offloading preferences cannot be overlooked. The key challenge of task offloading is to safeguard the privacy of offloaded data and ensure the system’s load balance while minimizing the delay and energy consumption. In this paper, the task offloading problem is formulated as a Partially Observable Markov Decision Process (POMDP), and a task offloading algorithm based on multi-objective joint optimization using multi-agent deep reinforcement learning in a distributed architecture is proposed. The simulation results validate the efficacy of our model and algorithm, demonstrating that our proposed algorithm achieves better performance in minimizing comprehensive offloading costs.
卫星网络辅助物联网中基于 MADRL 的任务卸载多目标联合优化
物联网(IoT)集成了大量异构终端和系统,具有无所不在的传感和计算能力。卫星网络是地面网络的重要补充,尤其是在网络基础设施分布稀少或不可用的偏远地区。将边缘计算与卫星网络相结合,可为物联网应用提供在轨计算能力,减少服务延迟并提高服务质量。由于卫星的资源限制,通过多颗卫星之间的任务卸载实现协作服务变得至关重要。频繁的数据交互带来的隐私泄露风险和卸载偏好导致的负载失衡不容忽视。任务卸载的关键挑战在于保护卸载数据的隐私,确保系统的负载平衡,同时最大限度地减少延迟和能耗。本文将任务卸载问题表述为部分可观测马尔可夫决策过程(POMDP),并提出了一种基于多目标联合优化的任务卸载算法,该算法采用分布式架构中的多代理深度强化学习。仿真结果验证了我们的模型和算法的有效性,表明我们提出的算法在最小化综合卸载成本方面取得了更好的性能。
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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