Time-Constrained Erasure Correction for Data Recovery in UAV-LoRa-WuR Networks

2024-03-14

AI Summary

To address the challenge of reliable sensor data recovery under high packet loss, stringent latency constraints, and limited energy in UAV-assisted LoRa networks integrated with wake-up radio (WuR), this paper proposes a latency-sensitive erasure coding decision framework. Methodologically, we formulate a joint optimization model incorporating sensor energy budgets, UAV hovering time, and ground node density; design two adaptive erasure correction schemes—based on Reed–Solomon and related codes—under latency constraints; and integrate probabilistic packet-loss modeling with joint latency-energy analysis. Our key contribution is the first establishment of coding activation criteria and code-selection guidelines specifically for UAV-LoRa-WuR scenarios. Experimental results demonstrate that, with moderate redundancy, the proposed coding strategy significantly improves data recovery rates over uncoded transmission. Moreover, the analytical framework provides interpretable and quantifiable coding decisions for practical deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We described two erasure-correction schemes for data recovery in UAV-LoRa-WuR networks. Our results show that unless the maximum number for redundant frames a sensor can send per data-collection cycle is very small, erasure coding provides noticeable improvements over an uncoded transmissions. Whether to employ coding -- and if so, which type -- should be determined based on the sensors' energy budget (which dictates the maximum redundancy), the UAV's hovering time, and the node density. The analytical framework presented above aids in this decision making.

Problem

• Erasure-correction schemes for UAV-LoRa-WuR data recovery
• Optimizing redundancy based on energy and hovering constraints
• Framework for choosing coding type in sensor networks

Innovation

• Erasure-correction schemes for UAV-LoRa-WuR networks
• Erasure coding improves uncoded transmissions significantly
• Decision framework based on energy, hovering time, density