Apr 2026· SEAMS@ICSE· pp. 13-23· 0 citations· 28 references
Computer Science
TL;DR
RAMNA is introduced, a resource-aware optimizing algorithm designed to maximize network availability by autonomously repositioning UAVs at runtime, and contributes to the self-healing and self-optimization properties required for resilient, long-lived flying ad-hoc networks operating under energy uncertainty.
Abstract
Flying ad-hoc networks (FANETs) are decentralized systems of autonomous unmanned aerial vehicles (UAVs) that must self-organize and sustain connectivity in dynamic and uncertain environments. These networks are invaluable where infrastructure is absent or damaged, yet they face strict energy limitations: battery-powered UAVs acting as communication relays near gateways deplete faster, leading to the hot-spot problem and progressive network fragmentation. This paper introduces RAMNA, a resource-aware optimizing algorithm designed to maximize network availability by autonomously repositioning UAVs at runtime. RAMNA continuously monitors network energy balance and link quality, and triggers adaptive role-swaps among UAVs to prevent premature depletion of critical nodes. We evaluate RAMNA across 66 heterogeneous topology scenarios, comparing it with the state-of-the-art Swap Level algorithm and a passive baseline, using real UAV energy consumption profiles that include both mobility and communication costs. Results show that RAMNA increases network availability by 85.5%–175.9% over passive operation and achieves up to 23.8 percentage-point improvement over Swap Level. We discuss how RAMNA contributes to the self-healing and self-optimization properties required for resilient, long-lived flying ad-hoc networks operating under energy uncertainty.
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.
Shaleena H, Sumangala K· International journal of com...· 0 citations
Unmanned Aerial Vehicles (UAVs) are increasingly deployed as embodied aerial agents in low-altitude economies, forming mobile aerial edge networks that enable flexible computation offloading for vehicles. However, their limited endurance and frequent join/leave behaviours result in highly dynamic topologies, undermining long-term resource availability. Moreover, existing vehicle-centric task scheduling strategies cause resource contention and decision complexity in dense environments. To address these challenges, this paper proposes a hierarchical and scalable reinforcement learning-based scheduling framework (SkySched). In SkySched, UAVs collaboratively make deployment and task scheduling decisions. The framework consists of two tightly coupled modules. First, an adaptive UAV deployment module introduces a capability encoding mechanism that compresses heterogeneous UAV attributes into a unified one-dimensional capability index. This compact representation enables a Scalable Proximal Policy Optimization (SPPO) algorithm to efficiently coordinate UAV positioning, maximizing task coverage and sustaining network-wide computing availability under dynamic topology variations. Second, a hierarchical task scheduling module is designed, where K-means-based Roadside Unit (RSU) clustering enables vertical task offloading, while a SPPO-driven horizontal UAV-to-UAV task redistribution mechanism achieves fine-grained load balancing across the UAV swarm. Simulations demonstrate that SkySched consistently outperforms state-of-the-art methods in terms of task coverage and load fairness, validating its effectiveness as an agentic AI-driven embodied networking solution for UAV-assisted vehicular edge computing.
Meng Yi, V. Lee, Miao Du et al.· IEEE Transactions on Cogniti...· 0 citations
A comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives, and offers insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.
Shafkat Khan Siam, Muhammad Yeasir Arafat, Muhammad Morshed Alam et al.· Artificial Intelligence Revi...· 0 citations
An in-depth review of energy-efficient routing protocols that have been developed for FANETs and highlights the main research challenges, such as high mobility, dynamic topology, routing overhead, scalability, and security, and discusses future research directions to design more intelligent and energy-aware routing protocols.
Ragvinder Kaur, Amit Sharma· Journal of Intelligent Decis...· 0 citations
Abstract The rapid proliferation of smart devices and multimedia-intensive applications poses unprecedented demands on 5G mobile infrastructure, especially when the terrestrial base stations (BS) are unavailable or overwhelmed. When used as an aerial BS, Unmanned Aerial Vehicles (UAVs) offer a compelling and flexible way to restore and improve wireless coverage. In this work, we examine the allocation of social-aware resources for multicast device-to-device (D2D) communications under UAV assisted dense 5G networks, where reducing delays and traffic offloading are the main objectives. The challenge of delays and transmission delays, particularly in emergencies such as natural disasters, that span vast geographical areas, enabling the use of UAVs as scalable on-demand BSs, the density of which can be adjusted proportionally to the network load. A three-dimensional social tie strength model, which jointly captures social overlap (Jaccard similarity of friend sets), similarity of interests (inverse-popularity-weighted content preferences) and contact quality (Gamma-distributed contact duration probability), governs D2D cluster head (CH) selection and resource block (RB) allocation. A scheme of heuristic resource allocation, HSARA (Heuristic Social-Aware Resource Allocation) is proposed to solve the interference management problems intrinsic in the coexistence of D2D clusters and cellular users sharing a common spectrum; the algorithm proceeds through a deferred-acceptance initialization phase and a swap-matching refinement phase and is proven to converge to a stable bilateral exchange matching in a finite number of steps. Simulations are conducted in MATLAB R2020a for a downlink single-cell UAV-assisted dense network in which users are uniformly distributed within a 500 m radius. The proposed HSARA scheme achieves significant throughput gains and nearly quadruples the performance of the social-aware, social-unaware, and MSARA baseline schemes as network density increases, while a non-trivial optimal UAV altitude of 300 m is identified that balances line-of-sight gain against induced interference.
Zain ul Abidin Jaffri, Asif Kabir, Sameer Ahmad et al.· Journal of Electrical Engine...· 0 citations
The Enhanced RDAP (e-RDAP), a multi-criteria association policy that combines RSSI, Packet Delivery Ratio (PDR), and communication delay with an adaptive deployment strategy is introduced, indicating that QoS-aware multi-criteria association provides additional gains beyond load-aware association alone.
Lucas Baptista de Moraes, N. Fernandes, Fernanda G. O. Passos et al.· Annals of Telecommunications· 0 citations