Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31519
Title: Trajectory-Based Anycast Routing Protocol with MDRUs Assistance in Disaster Response Network
Authors: Fan, Z
Liu, Y
Zhang, M
Cao, Y
He, Y
Wang, K
Keywords: disaster response network;anycasting;MDRU;trajectory-based;vehicular ad-hoc network (VANET)
Issue Date: 24-Mar-2025
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Fan, Z. et al. (2025) 'Trajectory-Based Anycast Routing Protocol with MDRUs Assistance in Disaster Response Network', 2025 IEEE Wireless Communications and Networking Conference (WCNC), Milan, Italy, 24-27 March, pp. 1 - 6. doi: 10.1109/WCNC61545.2025.10978267.
Abstract: Modern rescue operations rely on wireless communications for safety reporting, area monitoring, and rescue coordination. However, natural disasters severely damage ground infrastructure, creating significant challenges for emergency rescue and recovery efforts. This paper establishes a disaster response network using Movable and Deployable Resource Units (MDRUs) in disaster-affected areas, to provide timely and reliable message transmission services. Firstly, to ensure a timely and efficient disaster response, we design a post-disaster emergency vehicle network architecture. Secondly, we propose a three-phase emergency relief model to dynamically deploy MDRUs, aiming to maximize their service coverage. Finally, we propose a Trajectory-Based Anycast Routing (TBAR) protocol, which enhances message transmission efficiency by optimizing route selection. Specifically, by facilitating the flexibility of any cast in delivering messages to anyone of the reachable MDRUs, TBAR utilizes multiple copies of messages to reduce end-to-end latency and increase the delivery ratio. Moreover, TBAR adaptively evaluates the message delivery capability of candidate vehicles using a multi-attribute decision-making algorithm, considering link quality, trajectory similarity, and distance cost. Extensive simulation results show that TBAR significantly outperforms other baseline algorithms in multiple aspects.
URI: https://bura.brunel.ac.uk/handle/2438/31519
DOI: https://doi.org/10.1109/WCNC61545.2025.10978267
ISBN: 979-8-3503-6836-9 (ebk)
979-8-3503-6837-6 (PoD)
ISSN: 1525-3511
Other Identifiers: ORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800
Appears in Collections:Dept of Computer Science Research Papers

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