Enhanced Emergency Communication Services for Post–Disaster Rescue: Multi-IRS Assisted Air-Ground Integrated Data Collection

IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-23 DOI:10.1109/TNSE.2024.3432746
Yi Zhou;Zhanqi Jin;Huaguang Shi;Lei Shi;Ning Lu;Mianxiong Dong
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Abstract

Cellular networks are difficult to meet emergency rescue due to the destruction of base stations and infrastructure caused by natural disasters. Unmanned Ground Vehicles (UGVs) and other mobile communication devices encounter significant challenges when operating in disaster areas due to limited coverage and resources. To tackle this problem, this paper integrates Unmanned Aerial Vehicles (UAVs) into the emergency communication network and constructs an air-ground integration network architecture with UAV-UGV collaboration. Specifically, multi-UGV collaborate to collect disaster information, and multi-aerial intelligent reflecting surfaces with high maneuverability can effectively assist UGVs in transmitting the collected data to the remote control center. However, there is also a serious challenge to optimize the collaboration strategy between UGVs and UAVs. To address the concern, the collaboration between UAVs and UGVs is modeled as bipartite graph, where UAVs and UGVs belong to different sets of nodes, respectively. The problem is transformed into a matching game based on the bipartite graph. A stable Bidirectional Matching Game (BMG) algorithm is proposed, where matching players maximize the utility by adjusting the selection strategy. Extensive experimental results show that the proposed BMG algorithm outperforms other benchmark algorithms in terms of utility for both UAVs and UGVs.
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增强灾后救援应急通信服务:多红外系统辅助空地一体化数据收集
由于自然灾害对基站和基础设施造成破坏,蜂窝网络难以满足紧急救援的需要。由于覆盖范围和资源有限,无人地面飞行器(UGV)和其他移动通信设备在灾区运行时会遇到巨大挑战。为解决这一问题,本文将无人机(UAV)整合到应急通信网络中,并构建了 UAV-UGV 协同工作的空地一体化网络架构。具体来说,多 UGV 协同收集灾害信息,具有高机动性的多空中智能反射面可有效协助 UGV 将收集到的数据传输到远程控制中心。然而,如何优化 UGV 与 UAV 之间的协作策略也是一个严峻的挑战。为了解决这个问题,我们将无人机和无人潜航器之间的协作建模为双向图,其中无人机和无人潜航器分别属于不同的节点集。基于双向图,问题被转化为匹配博弈。提出了一种稳定的双向匹配博弈(BMG)算法,匹配双方通过调整选择策略实现效用最大化。广泛的实验结果表明,所提出的 BMG 算法在 UAV 和 UGV 的效用方面优于其他基准算法。
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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