# PDSR: Efficient UAV Deployment for Swift and Accurate Post-Disaster Search and Rescue

arXiv: https://arxiv.org/abs/2410.22982 · HTML: https://mlsanity.com/paper/2410.22982/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences](https://mlsanity.com/paper/2305.00135/) (2023)
- [Can Balloons Produce Li-Fi? A Disaster Management Perspective](https://mlsanity.com/paper/1712.04687/) (2017)
- [On the Topological Aspects of UAV-Assisted Post-Disaster Wireless Communication Networks](https://mlsanity.com/paper/2109.13671/) (2021)
- [Two-Tier UAV-based Low Power Wide Area Networks: A Testbed and Experimentation Study](https://mlsanity.com/paper/2305.11267/) (2023)

## Built on by (most cited first, 1 of 1)

- [SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search](https://mlsanity.com/paper/2606.01325/) (2026)

## Abstract (arXiv)

This paper introduces a comprehensive framework for Post-Disaster Search and Rescue (PDSR), aiming to optimize search and rescue operations leveraging Unmanned Aerial Vehicles (UAVs). The primary goal is to improve the precision and availability of sensing capabilities, particularly in various catastrophic scenarios. Central to this concept is the rapid deployment of UAV swarms equipped with diverse sensing, communication, and intelligence capabilities, functioning as an integrated system that incorporates multiple technologies and approaches for efficient detection of individuals buried beneath rubble or debris following a disaster. Within this framework, we propose architectural solution and address associated challenges to ensure optimal performance in real-world disaster scenarios. The proposed framework aims to achieve complete coverage of damaged areas significantly faster than traditional methods using a multi-tier swarm architecture. Furthermore, integrating multi-modal sensing data with machine learning for data fusion could enhance detection accuracy, ensuring precise identification of survivors.