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Abstract <p>Mobile mammography deployments in geographically dispersed and infrastructure-limited regions require reliable and energy-efficient data delivery mechanisms to support timely transmission of AI-assisted diagnostic outputs. This paper proposes a Mammography-Aware Multi-Objective Routing Optimization (MAMRO) framework for LoRa-enabled Flying Ad Hoc Networks (FANETs), which jointly considers LoRa airtime, duty-cycle constraints, UAV residual energy, link reliability, and clinical data priority within a unified distributed routing formulation. Two distributed routing variants are developed: MAMROU, a lightweight utility-driven approach with strict constraint filtering, and MAMROH, a hybrid exploration-based strategy that evaluates multiple candidate paths using priority-aware path-level scoring. Unlike existing routing schemes that consider energy, reliability, or priority in isolation, the proposed framework integrates physical-layer communication constraints with clinical urgency in a unified decision process. Extensive simulations under varying network sizes, UAV (Unmanned Aerial Vehicle) mobility patterns, and LoRa communication parameters demonstrate that MAMROH achieves significant improvements in packet delivery ratio and energy efficiency while maintaining low end-to-end delay compared with representative baseline protocols including MinHop, BBRA, ACORA, and GCCR. Simulation results obtained from extensive Monte Carlo evaluations across networks of up to 20 UAVs demonstrate that MAMROH reduces energy consumption by up to 45%, improves packet delivery ratio by up to 20%, and reduces delay by up to 30% compared to baseline schemes. Moreover, the results highlight the effectiveness of the proposed framework in enabling reliable and responsive medical data delivery in resource-constrained aerial IoT environments.</p>

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Distributed priority-aware multi-objective routing for reliable data delivery in LoRa-enabled UAV networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Tallha Akram,
  • Omer Chughtai,
  • Muhammad Naeem,
  • Youssef Altherwy,
  • Anas Alsuhaibani,
  • Ammar Thabit Zahary

摘要

Abstract

Mobile mammography deployments in geographically dispersed and infrastructure-limited regions require reliable and energy-efficient data delivery mechanisms to support timely transmission of AI-assisted diagnostic outputs. This paper proposes a Mammography-Aware Multi-Objective Routing Optimization (MAMRO) framework for LoRa-enabled Flying Ad Hoc Networks (FANETs), which jointly considers LoRa airtime, duty-cycle constraints, UAV residual energy, link reliability, and clinical data priority within a unified distributed routing formulation. Two distributed routing variants are developed: MAMROU, a lightweight utility-driven approach with strict constraint filtering, and MAMROH, a hybrid exploration-based strategy that evaluates multiple candidate paths using priority-aware path-level scoring. Unlike existing routing schemes that consider energy, reliability, or priority in isolation, the proposed framework integrates physical-layer communication constraints with clinical urgency in a unified decision process. Extensive simulations under varying network sizes, UAV (Unmanned Aerial Vehicle) mobility patterns, and LoRa communication parameters demonstrate that MAMROH achieves significant improvements in packet delivery ratio and energy efficiency while maintaining low end-to-end delay compared with representative baseline protocols including MinHop, BBRA, ACORA, and GCCR. Simulation results obtained from extensive Monte Carlo evaluations across networks of up to 20 UAVs demonstrate that MAMROH reduces energy consumption by up to 45%, improves packet delivery ratio by up to 20%, and reduces delay by up to 30% compared to baseline schemes. Moreover, the results highlight the effectiveness of the proposed framework in enabling reliable and responsive medical data delivery in resource-constrained aerial IoT environments.