Search Results (90)

As Flying Ad Hoc Networks (FANETs) are highly vulnerable to security threats such as identity spoofing, session replay and man-in-the-middle attacks in open-air channels, it is crucial to design an authentication key agreement (AKA) scheme to ensure the security of unmanned aerial vehicle (UAV) swarm networking within FANETs. However, existing AKA schemes for FANETs often struggle to balance authentication efficiency and high dynamism within UAV swarms whilst meeting necessary security requirements. To address the issue, this paper proposes CUBAT-AKA (Collaborative UAV Batch Authentication and Tree-based Key Agreement), a lightweight UAV swarm authentication and key agreement scheme based on batch verification and a binary tree structure. The scheme constructs a secure and lightweight three-party authentication mechanism based on aggregated verification and the Chinese Remainder Theorem (CRT). By offloading computational tasks to the authentication center and aggregating authentication responses in batches, it significantly improves the efficiency of UAV access authentication in large-scale FANET scenarios. To address the dynamic nature of UAVs frequently joining and leaving clusters in FANETs, an improved binary tree-based key agreement method has been designed, reducing key update overhead to a logarithmic level and enabling lightweight session key distribution and updates for UAV clusters. Security analysis demonstrates that, under the random oracle model, CUBAT-AKA is resistant to eavesdropping, replay, man-in-the-middle, impersonation and collusion attacks, whilst ensuring forward and backward security during member changes. Performance analysis indicates that this scheme offers significant advantages over comparable solutions in terms of both UAV cluster access authentication efficiency and dynamic key agreement overhead. Full article

Natural and human-made disasters can severely impair terrestrial communication infrastructures and disrupt emergency response coordination in modern smart cities. To address these challenges, this paper introduces the Weighted Average Yo-Yo-based Clustering and Routing (WAY-CR) scheme, an adaptive, secure, and energy-efficient drone-assisted solution for post-disaster network recovery and emergency response. WAY-CR integrates three main components: First, a novel WAY-based metaheuristic optimizer incorporates the concept of Yo-Yo Motion into the conventional Weighted Average Algorithm (WAA), improving the balance between exploration and exploitation during CH selection and clustering. Second, a secure communication model combines the Paillier Homomorphic Cryptosystem (PHC) with a trust evaluation model to provide end-to-end security and authenticity, ensuring that only authenticated and trustworthy drones participate in communication and routing. Third, a Trust-Aware Boltzmann Path Selection method introduces probabilistic decision-making into routing, allowing adaptive selection of secure and energy-efficient routing paths. WAY-CR formulates a multi-objective optimization model that minimizes communication cost and energy consumption while maximizing trust, link stability, and coverage. Stage 1 addresses secure intra-Ground Control Station (GCS) clustering, authentication, and trust management, whereas Stage 2 restores inter-GCS connectivity through a Secure Relay Discovery and Verification procedure based on Boltzmann Path Selection. An adaptive maintenance mechanism further supports dynamic reconfiguration in response to CH failures, mobility, or trust degradation, thereby preserving stable network performance under disaster-induced disruptions. Extensive simulation results show that WAY-CR outperforms state-of-the-art Flying Ad Hoc Network (FANET) baselines in energy efficiency, cluster stability, trust accuracy, and end-to-end packet delivery, highlighting its potential as a resilient, scalable, and secure solution for post-disaster smart-city environments. Full article

Flying Ad Hoc Networks (FANETs) composed of multiple unmanned aerial vehicles (UAVs) are a promising solution for emergency wireless communications when terrestrial infrastructure is unavailable. However, ensuring reliable Quality of Service (QoS) in these highly dynamic networks remains challenging due to topology changes, varying propagation conditions, and congestion. This work proposes a lightweight machine learning-based QoS optimization framework for multi-UAV emergency communications that combines realistic mobility modeling, empirical channel measurements, and adaptive traffic prioritization. UAV mobility patterns are generated with ArduSim, while LoS/NLoS propagation models are derived from real UAV flight experiments and integrated into ns-3. Multiple supervised machine learning algorithms—including Decision Trees, Random Forest, Support Vector Machines, k-NN, Gradient Boosting, and CatBoost—are trained using four input features derived from the network state: ${\mathrm{CBR}}_{\mathrm{src}}$, ${\mathrm{QP}}_{\mathrm{src}}$, ${\mathrm{CBR}}_{\mathrm{dst}}$, and ${\mathrm{QP}}_{\mathrm{dst}}$. Simulation results show that the proposed AI SMOTE EMERGENCY scheme, based on CatBoost, improves the Packet Delivery Ratio (PDR) by approximately 43% over the No-QoS baseline, achieving 89–93% delivery across all four application ports. Compared with EDCA, the proposed scheme maintains reliable delivery for all services, increases emergency throughput by 34–36%, and reduces end-to-end delay by about 70%. In addition, the higher delivery reliability translates into clear communication energy benefits, reducing energy waste across all evaluated topologies when compared with the No-QoS baseline. The inference time remains below 0.002 s, supporting real-time QoS adaptation in resource-constrained UAV networks. Full article

Mobile Unmanned Aerial Vehicles (UAVs) forming Flying Ad Hoc Networks (FANETs) offer promising applications, but dynamic network structures, limited resources, and potential single points of failure create security challenges. While cluster-based data aggregation, where data is collected and combined at Cluster Heads (CHs) before transmission, improves efficiency, traditional techniques can compromise data privacy. This paper introduces SSDBFAN, a scalable and secure cluster-based data aggregation framework for Flying Ad Hoc Networks (FANETs). The proposed approach integrates the Frilled Lizard Optimization Algorithm (FLOA) for efficient cluster head selection with blockchain technology and post-quantum cryptographic techniques, including lattice-based homomorphic encryption and the Chinese Remainder Theorem, to ensure privacy-preserving data aggregation. Additionally, a hybrid online/offline signature mechanism is employed to achieve secure and efficient authentication with reduced computational overhead. The performance of the proposed framework is evaluated using NS-3 simulations under varying network sizes. Experimental results demonstrate that SSDBFAN significantly improves communication efficiency, reduces computational cost, and enhances network stability compared to existing schemes. Furthermore, scalability analysis with up to 500 UAV nodes confirms that the proposed framework effectively controls blockchain overhead, including bandwidth consumption, consensus latency, and storage requirements. Comparative evaluation with existing optimization algorithms shows that FLOA achieves superior performance in terms of cluster stability, delay, and throughput. These results validate the effectiveness of SSDBFAN as a scalable and security-aware solution for large-scale FANET environments. Full article

By incorporating opportunistic coding, network throughput is enhanced, resulting in improved overall performance. However, applying this paradigm to Flying Ad-hoc Networks (FANETS) faces significant challenges due to the highly dynamic topology caused by the high-velocity mobility of UAVs, alongside the NP-hard complexity of identifying optimal coding opportunities in rapidly evolving aerial network architectures. To address these challenges, this paper proposes a novel coding-aware routing protocol based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). We formulate the routing problem as a multi-agent continuous decision-making process, employing the MADDPG algorithm to optimize routing policies in real-time through decentralized execution and centralized training. To maximize network utility, we design a comprehensive reward function that integrates coding benefits, throughput, energy distribution, and end-to-end delay, ensuring a balance between throughput maximization and the energy sustainability of individual UAV nodes. Simulation results demonstrate that the proposed protocol significantly outperforms state-of-the-art coding-aware routing protocols in terms of throughput, Packet Delivery Ratio (PDR), and transmission delay, exhibiting superior robustness in highly dynamic FANET scenarios. Notably, at a network density of 20 UAVs, MARL-CAR outperforms COPE, DCAR, TSCAR, and RLCAR in terms of coding ratio by 32.23%, 18.93%, 20.35%, and 5.5%, respectively. This research provides a scalable and intelligent networking solution for the next generation of autonomous UAV swarms and collaborative aerial missions. Full article

With the increasing complexity of the space electromagnetic environment, traditional omnidirectional antenna-aided communication and networking techniques can no longer meet the collaboration requirements of aircraft clusters. To achieve goals such as anti-jamming, anti-interception, and enhanced spatial multiplexing, an increasing number of aircraft are being equipped with high-gain directional antennas. However, modeling of directional antenna-constrained Flying Ad Hoc Networks (FANETs) is far more complex than modeling of omnidirectional antenna-aided networks. The former task is highly dependent on the real-time flight state and the spatial topology of the network. In response to the communication challenges posed by directional networking of highly-dynamic aircraft clusters, this study proposes an antenna selection and beam pointing control algorithm, which is deeply integrated with the aircraft’s Guidance, Navigation, and Control (GNC) system. By introducing parameters that characterize dynamic flight state, such as position and attitude information, and combining them with high-precision multi-coordinate system transformations and spatial geometric analysis methods, the proposed algorithm enables the real-time optimization of antenna selection and beam pointing under the relative motion trends of aircraft. It effectively maintains high-quality connections between flying nodes. Digital simulation and physical experiment results demonstrate that the proposed method can accurately calculate the appropriate antenna selection and determine precise beam pointing directions based on the position data of flying nodes. This provides an important reference for the design of optimized communication strategies used in directional networking of highly-dynamic aircraft clusters. Full article

With the advancement of UAV (Unmanned Aerial Vehicle) technology, flying ad-hoc networks (FANETs), composed of multiple coordinating UAVs, demonstrate tremendous application potential in disaster relief, environmental monitoring and intelligent logistics. However, inherent resource constraints and unpredictable operating environments make UAV failures a frequent and critical challenge. Particularly in mission-critical applications, simultaneous or consecutive failures of multiple UAVs can severely disrupt network topology, leading to catastrophic consequences such as network fragmentation and service interruptions. Furthermore, traditional topology reconstruction algorithms suffer from high computational overhead and significant communication delays. Primarily designed for single-node failure recovery, they are ill-equipped to address the challenge of concurrent multi-node failures. To address these challenges, this paper proposes a topology reconstruction algorithm tailored for multi-node failure scenarios in FANETs. The core objective of this algorithm is to minimize communication overhead and secondary damage to the network during the reconstruction process while ensuring basic reconstruction results, thereby improving the system’s energy efficiency and robustness. The proposed framework integrates three key phases: First, overlapping communication coverage areas among neighbors of failed nodes are leveraged to define first and second regions, enabling rapid identification of connection restoration candidate positions and avoiding computationally intensive global calculations. Second, a comprehensive importance evaluation mechanism is constructed based on the topological and functional attributes of node, categorizing nodes into different importance types. For failed nodes of varying importance, differentiated search ranges and retry strategies are employed to ensure the most suitable nodes are selected for reconstruction tasks. Third, the inflexibility of repulsion ranges in traditional artificial potential field (APF) method is addressed by introducing dynamic repulsion influence zones and a composite repulsion model. The improved APF algorithm enhances safety in high-speed scenarios and reduces the probability of UAVs becoming trapped in local minima. Finally, extensive simulations validate that the proposed algorithm accurately identifies critical network nodes and promptly implements effective reconstruction measures to minimize network damage. Full article

Achieving autonomous and reliable unmanned aerial vehicle (UAV) swarm applications requires a flexible and efficient communication network structure. Unfortunately, the high-speed movement of UAVs leads to drastic changes in wireless links and topology structures, posing significant challenges to reliable data transmissions. Geographic routing protocols exhibit better adaptability to highly dynamic network topologies and have garnered extensive attention in UAV networks. However, existing works did not effectively address the impact of factors such as link state fluctuations and routing holes on the performance of these protocols. To this end, by considering future evolution of link states, this paper proposes a reinforcement-learning-based geographic routing protocol (Evo-QGeo) and introduces a new routing hole bypass method. Thanks to the evaluation of future evolution of link states and the multihop optimization capability of reinforcement learning, the end-to-end packet reception rate of Evo-QGeo is improved by up to 11.81~44.61% compared to existing ones. Meanwhile, the energy consumption is reduced by up to 36.94~74.47%, the latency is reduced by up to 21.63~38.68%, and the end-to-end expected transmission count is reduced by up to19.60~26.10%. This makes Evo-QGeo more suitable for highly dynamic UAV networks. Full article

Performance of flying ad hoc networks (FANETs) largely depends on the routing protocol used. Applying conventional traditional mobile ad hoc networks (MANETs) routing frameworks to FANETs requires a careful assessment of their compatibility. Crucially, these protocols must be robust enough to handle the volatile link states and rapid topological shifts inherent in high-mobility UAV clusters. Although there have been many works that evaluated and compared the performance of different MANET routing protocols in FANET scenarios through simulation, they ignored the comparative evaluation of various pathfinding schemes across diverse movement patterns. This research addresses this limitation by examining the efficiency of three representative protocols under distinct mobility scenarios using extensive simulations. The findings demonstrate that the selected mobility model influences not only the protocol’s efficiency but also the comparative ranking of different routing protocols. The conclusion of which routing protocol is better or worse obtained under a specific mobility model is usually not universal and only holds for the specific mobility model used. These conclusions will be more helpful for selecting appropriate routing protocols to adapt to the complex and ever-changing UAV network application scenarios. Full article

Flying Ad Hoc Networks (FANETs) formed by cooperative Unmanned Aerial Vehicles (UAVs) require formally proven secure and resource-efficient authentication because open wireless channels allow active adversaries to inject commands, replay traffic, and impersonate nodes. Conventional certificate-based mechanisms impose key management overhead and remain vulnerable under device capture, while existing lightweight and Physical Unclonable Function (PUF)-assisted proposals commonly assume stable connectivity, lack formal adversarial verification, or are evaluated only through simulation. This paper presents a lightweight PUF-assisted authentication protocol designed for dynamic multi-hop FANET operation. The scheme provides mutual UAV–Ground Station (GS) authentication and session key establishment and further enables secure UAV–UAV communication using an off-path ticket mechanism that eliminates continuous infrastructure dependence. The protocol is constructed through verification-driven refinement and formally analysed under the Dolev–Yao model, establishing authentication and session key secrecy and resistance to replay and impersonation attacks. Implementation-oriented latency measurements on Raspberry-Pi-class embedded platforms demonstrate that cryptographic processing time can be further reduced with hardware improvements, while the overall end-to-end delay is still largely determined by channel conditions and connection behaviour. Comparative evaluation shows reduced communication cost and broader security coverage relative to existing UAV authentication schemes, indicating practical deployability in large-scale FANET environments. Full article

Unmanned Aerial Vehicles (UAVs) are emerging as a fundamental part of Flying Ad Hoc Networks (FANETs). However, owing to the limited energy capacity of UAV batteries, wireless power transfer (WPT) technologies have recently gained interest from researchers, offering recharging possibilities for FANETs. Based on this background, this study highlights the need for wireless charging to enhance the operational endurance of FANETs in Internet-of-Things (IoT) environments. This review investigates WPT power replenishment to explore the dynamic usage of UAVs in two ways. The former is for using a UAV as a mobile charger to recharge the ground nodes, whereas the latter is for WPT applications in in-flight (UAV-to-UAV) charging. For the two research domains, we describe the different methods of WPT and its latest advancements through the academic and industrial research literature. We categorized the results based on the power transfer range, efficiency, wireless charger topology (ground or in-flight), coordination among multiple UAVs, and trajectory optimization formulation. A crucial finding is that in-flight UAV charging can extend the endurance by three times compared to using standalone batteries. Furthermore, the integration of IoT for the deployment of a clan of UAVs as a FANET is rigorously emphasized. Our data findings also indicate the present and future forecasting graphs of UAVs and IoT-integrating UAVs in the global market. Existing systems have scalability issues beyond 20 UAVs; therefore, future research requires edge computing for WPT scheduling and blockchains for energy trading. Full article

In recent years, flying ad hoc networks (FANETs) have attracted attention as aerial communication platforms for large-scale disasters. In wide, city-scale disaster zones, survivors’ devices often form multiple isolated clusters, while battery-powered unmanned aerial vehicles (UAVs) must periodically return to a ground station (GS). Under such conditions, conventional delay/disruption-tolerant networking (DTN) routing (e.g., PRoPHET) often traps bundles in clusters or UAVs, degrading the bundle delivery ratio (BDR) to the GS. This study proposes a DTN-based FANET architecture that integrates (i) a mobility model assigning UAVs to information–exploration UAVs that randomly patrol the disaster area and GS–relay UAVs that follow spoke-like routes to periodically visit the GS, and (ii) an extended PRoPHET-based routing protocol that exploits exogenous information on GS visits to bias delivery predictabilities toward GS–relay UAVs and UAVs returning for recharging. Simulations with The ONE in a 10 km × 10 km scenario with multiple clusters show that the proposed method suppresses BDR degradation by up to 41% relative to PRoPHET, raising the BDR from 0.27 to 0.39 in the five-cluster case and increasing the proportion of bundles delivered with lower delay. These results indicate that the proposed method is well-suited for relaying critical disaster-related information. Full article

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS comprising M passive elements, enabling dynamic manipulation of the wireless propagation environment. We address the joint power allocation and RIS configuration problem to maximize the sum spectral efficiency, subject to constraints on maximum transmit power and unit-modulus phase shifts. The formulated optimization problem is non-convex due to coupled variables and interference. We develop an alternating optimization-based joint power and phase shift (AO-JPPS) algorithm that decomposes the problem into two subproblems: power allocation via successive convex approximation and phase optimization via Riemannian manifold optimization. A key contribution is addressing the RIS coupling effect, where the configuration of each RIS simultaneously influences multiple communication links. Complexity analysis reveals polynomial-time scalability, while derived performance bounds provide theoretical insights. Numerical simulations demonstrate that our approach achieves significant spectral efficiency gains over conventional FANETs, establishing the effectiveness of RIS-assisted drone networks for future wireless applications. Full article

In recent years, with the improvement of unmanned aerial vehicle (UAV) performance, various applications have been explored. In environments such as disaster areas, where existing infrastructure may be damaged, alternative uplink communication for transmitting observation data from UAVs to the ground station (GS) is critical. However, conventional mobile ad hoc network (MANET) routing protocols do not sufficiently account for GS-oriented traffic or the highly mobile UAV topology. This study proposed a flying ad hoc network (FANET) routing protocol that introduces a control option called GS flood, where the GS periodically disseminates routing information, enabling each UAV to efficiently acquire fresh source routes to the GS. Evaluation using NS-3 in a disaster scenario confirmed that the proposed method achieves a higher packet delivery ratio and practical latency compared to the representative MANET routing protocols, namely DSR, AODV, and OLSR, while operating with fewer control IP packets than existing methods. Furthermore, although the multihop throughput between UAVs and the GS in the proposed method plateaued at approximately 40% of the physical-layer maximum, it demonstrated performance exceeding realistic satellite uplink capacities ranging from several hundred kbps to several Mbps. Full article

With the growing deployment of Flying Ad hoc Networks (FANETs) in military and civilian applications, constructing a stable and efficient communication backbone has become a critical challenge. This paper tackles the Cluster Head (CH) optimization problem in large-scale and highly dynamic FANETs by formulating it as a Minimum Connected Dominating Set (MCDS) problem. However, since MCDS is NP-complete on general graphs, existing heuristic and exact algorithms suffer from limited coverage, poor connectivity, and high computational cost. To address these issues, we propose TGN-MCDS, a novel algorithm built upon the Temporal Graph Network (TGN) architecture, which leverages graph neural networks for cluster head selection and efficiently learns time-varying network topologies. The algorithm adopts a multi-objective loss function incorporating coverage, connectivity, size control, centrality, edge penalty, temporal smoothness, and information entropy to guide model training. Simulation results demonstrate that TGN-MCDS rapidly achieves near-optimal CH sets with full node coverage and strong connectivity. Compared with Greedy, Integer Linear Programming (ILP), and Branch-and-Bound (BnB) methods, TGN-MCDS produces fewer and more stable CHs, significantly improving cluster stability while maintaining high computational efficiency for real-time operations in large-scale FANETs. Full article