Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks

TL;DR

This work tackles reliable disaster-map transmission in networks damaged by catastrophes by introducing a three-layer emergency caching network and a coded caching framework. It models map recovery through coded fragments stored across cooperative UAVs and optimizes deliveries over time with a DRL-based SACRL algorithm that jointly selects cooperative UAVs, allocates bandwidth, and tunes coding parameters. The approach leverages MDS coding $(n,k)$, OFDMA transmissions over Rician channels, and a probability-based recovery metric to maximize the effective map coverage $C_{sum}(t)$ across time slots. Simulations show SACRL outperforms non-coding caching, other DRL baselines, and heuristic methods, demonstrating improved reliability and timeliness of disaster-map updates with practical implications for emergency response.

Abstract

Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation.

Figures (5)

• Figure 1: The hierarchical architecture of emergency caching networks.
• Figure 2: The framework of SACRL algorithm.
• Figure 3: Effects of system parameters.
• Figure 4: Effects of different methods.
• Figure 5: Recovery area over time.