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<p>In Flying Ad Hoc Networks (FANETs), Unmanned Aerial Vehicles (UAVs) frequently exchange location information to ensure secure and coordinated flight, resulting in significant communication overhead. This paper presents a novel approach to compress token messages used in Location Information Sharing (LIS) by formulating the compression task as an two-stage combinatorial optimization problem. The proposed method employs an extended Traveling Salesman Problem (TSP) model to find the optimal arrangement under ideal conditions and reorders the token messages accordingly, reducing the overall computational complexity to polynomial time. Traditional compression methods often require maintaining the order of input and output data, which limits their effectiveness, especially in dynamic network environments like FANETs. By addressing this limitation, the proposed approach achieves a remarkable compression ratio of up to 74.9%, significantly outperforming some well-known algorithms such as Delta encoding, LZW, GZIP, and BZIP.</p>

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Token reordering-based compression for efficient location information sharing in flying ad hoc networks

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Longjiang Li,
  • Tianle Shao,
  • Jianjun Yang,
  • Rong Jia

摘要

In Flying Ad Hoc Networks (FANETs), Unmanned Aerial Vehicles (UAVs) frequently exchange location information to ensure secure and coordinated flight, resulting in significant communication overhead. This paper presents a novel approach to compress token messages used in Location Information Sharing (LIS) by formulating the compression task as an two-stage combinatorial optimization problem. The proposed method employs an extended Traveling Salesman Problem (TSP) model to find the optimal arrangement under ideal conditions and reorders the token messages accordingly, reducing the overall computational complexity to polynomial time. Traditional compression methods often require maintaining the order of input and output data, which limits their effectiveness, especially in dynamic network environments like FANETs. By addressing this limitation, the proposed approach achieves a remarkable compression ratio of up to 74.9%, significantly outperforming some well-known algorithms such as Delta encoding, LZW, GZIP, and BZIP.