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Joint optimization of UAV dual-task co-track and charging station location in large-scale IoT scenarios 大规模物联网场景下无人机双任务协同跟踪与充电站位置联合优化

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-06 DOI: 10.1016/j.comcom.2026.108412

Yi Zhang, Yi Hong, Chuanwen Luo, Xin Fan

Unmanned Aerial Vehicles (UAVs), owing to their high flexibility and mobility, have emerged as efficient tools for data collection in fields such as environmental monitoring and agricultural mapping. However, their limited battery capacity significantly constrains flight range and mission duration. This limitation becomes particularly critical in large-scale Internet of Things (IoT) scenarios involving multiple cooperative UAVs. Existing studies often adopt fixed charging stations or costly mobile charging devices and treat data collection and energy replenishment as separate optimization problems, which hinders task continuity and reduces system energy efficiency. In this paper, we propose a joint optimization framework that integrates charging station placement with collaborative UAV scheduling for dual-task co-track data collection and charging, aiming to maximize data throughput and enhance energy efficiency. A multi-UAV system model is developed that incorporates various constraints, including task allocation, time, and energy. The objective is to jointly optimize the placement of fixed charging stations, the task assignments among UAVs, and the design of flight trajectories that unify data collection and charging operations. To solve this complex joint optimization problem, a path planning collaborative optimization algorithm (PCA) is designed. Simulation results show that, compared with greedy algorithms and fixed charging-station strategies, our method improves energy efficiency by about 31.28% and 15.18%, and reduces task completion time by 31.41% and 14.33%, respectively, clearly demonstrating the effectiveness and advantages of the proposed joint optimization strategy. This study offers a systematic solution for sustainable and efficient UAV-based data collection in complex operational environments.

无人机由于其高度的灵活性和机动性，已成为环境监测和农业制图等领域数据收集的有效工具。然而，它们有限的电池容量极大地限制了飞行距离和任务持续时间。在涉及多个协作无人机的大规模物联网（IoT）场景中，这一限制变得尤为关键。现有研究多采用固定充电站或昂贵的移动充电设备，将数据采集和能量补充作为单独的优化问题处理，阻碍了任务的连续性，降低了系统的能效。本文提出了一种将充电站布局与无人机协同调度相结合的联合优化框架，以实现数据吞吐量最大化和能源效率的提升。开发了一种多无人机系统模型，该模型包含各种约束，包括任务分配、时间和能量。目标是共同优化固定充电站的布局、无人机之间的任务分配以及统一数据收集和充电操作的飞行轨迹设计。针对这一复杂的联合优化问题，设计了一种路径规划协同优化算法（PCA）。仿真结果表明，与贪心算法和固定充电站策略相比，该方法分别提高了约31.28%和15.18%的能源效率，缩短了31.41%和14.33%的任务完成时间，充分体现了所提联合优化策略的有效性和优势。该研究为复杂作战环境下基于无人机的可持续高效数据采集提供了系统解决方案。

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Unmanned aerial vehicle-enabled mobile edge computing for semantic communications 支持无人机的语义通信移动边缘计算

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-05 DOI: 10.1016/j.comcom.2026.108411

Liyuan Xie , Wancheng Xie , Huabing Lu , Helin Yang

With the rapid evolution of wireless networks and the increasing demand for flexible communication and computing services, unmanned aerial vehicles (UAVs) have emerged as a promising solution to enhance the performance of these networks. This paper investigates a UAV-assisted mobile edge computing (MEC) system with semantic communication (SemCom) to improve the efficiency of wireless networks by transmitting only meaningful information, thereby reducing bandwidth and computational resource requirements. We propose a resource scheduling approach to minimize the weighted sum of overall latency for task processing and energy consumption under malicious jamming attacks. The approach jointly optimizes device scheduling, UAV trajectory, task offloading ratio, bandwidth allocation, and the number of transmitted SemCom symbols under different constraints. The optimization problem is complex and non-convex, involving ongoing decision-making due to constantly changing parameters. To address this challenge, we present a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm for real-time resource management. The proposed PPO-based resource scheduling approach effectively schedules both communication and computing resources to minimize the cost of the UAV-enabled wireless network against jamming attacks. Simulation-based performance analysis indicates that the PPO-based SemCom scheme reduces task execution latency and energy consumption compared to baseline approaches across various network scenarios. The proposed framework provides valuable insights into the design and optimization of UAV-assisted MEC systems with SemCom for enhanced wireless network performance in the presence of adversarial jamming.

随着无线网络的快速发展以及对灵活通信和计算服务的需求不断增加，无人机（uav）已经成为提高这些网络性能的一种有前途的解决方案。本文研究了一种具有语义通信（SemCom）的无人机辅助移动边缘计算（MEC）系统，通过仅传输有意义的信息来提高无线网络的效率，从而减少带宽和计算资源需求。我们提出了一种资源调度方法，以最小化在恶意干扰攻击下任务处理的总延迟和能量消耗的加权总和。该方法对不同约束条件下的设备调度、无人机轨迹、任务卸载比、带宽分配和发送SemCom符号数进行了联合优化。优化问题是一个复杂的非凸问题，涉及由于参数不断变化而导致的持续决策。为了解决这一挑战，我们提出了一种基于近端策略优化（PPO）的深度强化学习（DRL）算法，用于实时资源管理。所提出的基于ppo的资源调度方法有效地调度通信和计算资源，使无人机无线网络抵御干扰攻击的成本最小化。基于仿真的性能分析表明，与各种网络场景的基线方法相比，基于ppo的SemCom方案减少了任务执行延迟和能耗。所提出的框架为无人机辅助MEC系统的设计和优化提供了有价值的见解，该系统具有SemCom，可以在对抗性干扰存在的情况下增强无线网络性能。

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Dynamic resource allocation for digital twin-enhanced hierarchical federated learning in sustainable internet of things 可持续物联网中数字孪生增强分层联邦学习的动态资源分配

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-03 DOI: 10.1016/j.comcom.2025.108410

Ze Wei , Rongxi He , Xiaojing Chen , Chengzhi Song

This paper proposes a digital twin (DT)-enhanced hierarchical federated learning framework for sustainable Internet of Things (IoT) networks. In this framework, mobile edge computing servers coordinate collaborative training, while DTs maintain real-time physical-virtual synchronization. Our core contributions are threefold. First, to tackle device heterogeneity, we propose two mechanisms: (1) an elastic time window that dynamically adapts aggregation deadlines based on median training times while incorporating distance-aware resource compensation to mitigate channel degradation, and (2) a DT-enhanced weighting strategy that dynamically balances energy sustainability, channel quality, and model freshness while guaranteeing convergence through closed-loop cross-layer coordination. Second, we derive a convergence bound explicitly linked to the device participation ratio, establishing a direct theoretical connection between resource allocation and learning performance. Then, through theoretical analysis, it can be found that reducing training latency and energy consumption by jointly optimizing computing and communication resources, as well as EH duration, is key to maximizing this ratio without compromising the reliability of the gradients, thereby indirectly enhancing convergence. Third, guided by this insight, we formulate a mixed-integer nonlinear programming problem that aims to maximize the participation ratio while jointly minimizing energy consumption and training latency, by optimizing the energy harvesting time, collaboration ratio, and communication/computation resources. To solve this NP-hard problem, we propose a DT-driven decomposition framework that partitions it into two subproblems, which are then solved by three DT-driven algorithms with provable near-optimality guarantees. Experimental results validate the superiority of our approach, demonstrating significant improvements in convergence performance, latency, energy efficiency, and participant sample rate, while also advancing the sustainability of FL.

本文提出了一种用于可持续物联网（IoT）网络的数字孪生（DT）增强分层联邦学习框架。在这个框架中，移动边缘计算服务器协调协同训练，而dt保持实时物理-虚拟同步。我们的核心贡献有三个方面。首先，为了解决设备异构问题，我们提出了两种机制：(1)弹性时间窗，该弹性时间窗基于中值训练时间动态适应聚合截止日期，同时结合距离感知资源补偿以减轻信道退化；(2)dt增强加权策略，该策略动态平衡能量可持续性、信道质量和模型新鲜度，同时通过闭环跨层协调保证收敛。其次，我们推导了一个明确与设备参与率相关的收敛界，建立了资源分配与学习绩效之间的直接理论联系。然后，通过理论分析可以发现，在不影响梯度可靠性的前提下，通过联合优化计算资源和通信资源来减少训练延迟和能量消耗，以及EH持续时间，是最大化该比值的关键，从而间接增强收敛性。第三，在此基础上，通过优化能量收集时间、协作比例和通信/计算资源，提出了一个混合整数非线性规划问题，以最大限度地提高参与率，同时最大限度地降低能耗和训练延迟。为了解决这个np困难问题，我们提出了一个dt驱动的分解框架，该框架将其划分为两个子问题，然后通过三个具有可证明的近最优性保证的dt驱动算法来解决这两个子问题。实验结果验证了我们的方法的优越性，证明了在收敛性能、延迟、能源效率和参与者采样率方面的显著改进，同时也提高了FL的可持续性。

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CISF: Consensus-based Information Sharing Framework for robust consistency in UAVs swarm disaster response 基于共识的无人机群灾响应鲁棒一致性信息共享框架

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-02 DOI: 10.1016/j.comcom.2025.108408

Xuefeng Du , Yanqi Cheng , Li Yin , Ning Tong , Fengqiang Xu , Fengqi Li

In disaster response scenarios, distributed unmanned aerial vehicle (UAV) swarms face substantial challenges in maintaining real-time information consistency due to network instability, communication delays, and potential Byzantine faults. Traditional approaches often fail to balance fault tolerance, communication latency, and task execution efficiency under such dynamic and adversarial conditions. This paper proposes the Consensus-based Information Sharing Framework (CISF), a novel solution specifically designed to ensure information consistency in dynamic disaster environments. CISF integrates a Stratified Parallel Byzantine Fault Tolerance (SPBFT) mechanism — optimized via a dynamic capability-reputation evaluation model — with a Multi-Round Search and Patrol Model (MSPM) based on an improved Cuckoo Search algorithm. MSPM employs a multi-objective fitness function to jointly optimize temporal efficiency, spatial coverage, and task priority, enabling comprehensive area exploration and continuous information validation. Theoretical analysis derives the optimal hierarchical ratio and the maximum fault tolerance threshold for CISF. Simulation results show that CISF maintains 93.8% consistency under Byzantine interference and reduces consensus latency by up to 56.2%, while remaining effective in highly dynamic environments. Overall, this study establishes a robust and efficient framework for achieving real-time, fault-tolerant information consistency in interference-prone UAV networks, offering broad applicability for future swarm-based disaster response systems.

在灾难响应场景中，由于网络不稳定、通信延迟和潜在的拜占庭故障，分布式无人机（UAV）群在保持实时信息一致性方面面临着巨大的挑战。在这种动态和对抗的条件下，传统的方法往往无法平衡容错性、通信延迟和任务执行效率。本文提出了基于共识的信息共享框架（CISF），这是一种专门用于确保动态灾难环境下信息一致性的新解决方案。CISF集成了分层并行拜占庭容错（SPBFT）机制（通过动态能力-声誉评估模型优化）和基于改进布谷鸟搜索算法的多轮搜索和巡逻模型（MSPM）。MSPM采用多目标适应度函数，共同优化时间效率、空间覆盖和任务优先级，实现区域的综合勘探和信息的持续验证。理论分析得出了CISF的最优层次比和最大容错阈值。仿真结果表明，CISF算法在拜占庭干扰下保持了93.8%的一致性，减少了56.2%的共识延迟，同时在高动态环境下仍然有效。总体而言，本研究建立了一个鲁棒且高效的框架，用于在易受干扰的无人机网络中实现实时、容错的信息一致性，为未来基于群体的灾害响应系统提供了广泛的适用性。

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