Jul 2026· International Journal of Electronics and Communication Engineering· 0 citations· 23 references
TL;DR
A new Enhanced Intelligent-based Energy and Mobility, and Obstacle-aware Clustering (EIEMOC) protocol to control the network congestion while meeting End-to-End Delay (E2D) constraints in delay-constrained FANET applications.
Abstract
Increases in the use of UAVs for communication have led to the widespread emergence of Flying Ad hoc Networks (FANET). Conversely, UAV’s mobility and environmental obstacles affect communication links, resulting in link unreliability and inefficient routing. To combat these challenges, an Energy and Mobility-Aware Stable and Safe clustering (EMASS) protocol has been developed, which prevents obstacles in the routing path and minimizes the influence of high mobility on data transfer. However, it does not address the congestion issue in FANET routing, which degrades the data transfer in delay-constrained applications. Hence, this manuscript proposes a new Enhanced Intelligent-based Energy and Mobility, and Obstacle-aware Clustering (EIEMOC) protocol to control the network congestion while meeting End-to-End Delay (E2D) constraints in delay-constrained FANET applications. The main optimization objectives of this protocol are the cumulative rates over the connections and various factors that influence the E2D for 1-hop communication. First, a dispersed delay-aware congestion control scheme is developed that integrates a 1-hop delay constraint to obtain the best solution. Then, a delay support factor is introduced for every connection, and the 1-hop delay constraint is updated by conjointly merging the cumulative arriving flow and the probability of data being rejected at a specific connection. Thus, this protocol maximizes the system reliability and reduces the E2D in a dispersed manner. Finally, extensive simulations establish that the EIEMOC achieves higher network performance compared to the classical protocols in FANETs.
Simulation results indicate that HOA-MEPFL-CLCT-RP outperforms existing models in terms of Packet Delivery Ratio (PDR), energy efficiency, End-to-End Delay (E2D), and routing overhead.
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Simulation results validate the holistic integration with federated multi-agent learning, with emphasis on contention awareness and energy balancing is essential for scalable and efficient routing in next-generation dense FANETs.
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