Search Results (230)

Mobile Ad Hoc Networks (MANETs) or Wireless Sensor Networks (WSNs) have a “decentralized” architecture and are very susceptible to sophisticated attacks, such as identity forgery attacks using deep learning (DL) methods. Secure routing and intrusion detection systems have been developed but they are usually not scalable, consume too much power, introduce too much communication overhead and do not provide enough privacy protection, or are not resilient to changes in adversarial behavior. This paper introduces a Federated Pyramid Swin Vision Transformer (FPSViT) framework that enhances the routing optimization and energy-efficient communication for MANET–WSN networks with the support of Generative AI (GAI), addressing these challenges. The proposed framework incorporates three modules: federated averaging for privacy-preserving distributed learning, Pyramid Swin Vision Transformer (PSViT) for extracting Sybil attack characteristics at multiple scales, and a GAI-based adversarial pattern generation module to boost the robustness of the detection process in the presence of evolving attack patterns. Energy-aware routing optimization: It takes into account the energy level of the nodes, power consumption of the links, link reliability and trust values to optimize the routing for minimum power consumption with secure communication. Results of experimental evaluations on various Sybil attack scenarios show that the proposed FPSViT is able to achieve 98.84%, 98.52%, 98.21%, and 98.36% detection accuracy, precision, recall, and F1-score, respectively, and consume 0.381 J/node on average and increase the lifetime of the network to 2876 rounds. The power consumption analysis demonstrates that FPSViT consumes 12–21% less energy than other methods such as Federated CNN, FL-LSTM, Lightweight Standalone Swin Detector, and RL-based Secure Routing, thanks to optimized routing decisions and avoiding unnecessary transmissions, as well as adaptive trust-based communication. Moreover, the proposed framework achieves an improvement in the packet delivery ratio to 98.24%, decreases communication overhead by 9–17% and increases network lifetime by 10–19%. The results have also validated that FPSViT is a scalable, privacy-preserving, and power-saving security solution for dynamic MANET–WSN environments and is able to successfully resist advanced DL-driven Sybil attacks. Full article

Route discovery in ad hoc on-demand distance vector (AODV)-based mobile ad hoc sensor networks relies on route request (RREQ) dissemination, which improves reachability, but generates redundant rebroadcasts, channel contention, delay, and energy waste. Fixed probabilistic rebroadcasting mitigates broadcast storms, but its performance depends on a manually selected forwarding probability applied uniformly across different local redundancy conditions. This work proposes the Thompson-sampling probabilistic AODV (TSP-AODV), a lightweight adaptive extension, in which each intermediate node selects among a small set of forwarding probability arms using a local duplicate-pressure context and delayed route reply (RREP) feedback. The reward function uses the local observation of a corresponding RREP as delayed feedback while penalising high forwarding probability in locally redundant contexts. TSP-AODV requires no additional control packets, no topology exchange, and only a small number of local Beta belief distribution parameters per node. Evaluated against AODV, fixed probabilistic rebroadcasting, counter-based suppression, and dynamic probabilistic-counter suppression over 1600 simulation runs spanning four node densities and four mobility levels, TSP-AODV achieves the lowest normalised routing overhead and the highest RREQ suppression ratio, while no statistically significant PDR difference relative to the fixed probabilistic baseline was observed under the tested conditions. End-to-end delay is also reduced significantly relative to fixed probabilistic rebroadcasting. The learned belief behaviour confirms context-dependent adaptation, with the dominant forwarding-probability arm decreasing as duplicate pressure increases. An additional 800-run sensitivity analysis characterises the PDR–NRO–delay trade-off across the tested penalty coefficients and feedback-window durations in two representative scenarios. These results are limited to the evaluated parameter grid and do not establish scenario-independent parameter robustness. Full article

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS), allowing communication exchanges between vehicles and road infrastructure elements. These networks face challenges from vehicular mobility, including frequent topology changes, link instability, and variable wireless channel quality. This paper presents an extensive evaluation of Software-Defined Networking (SDN) integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols. Nine incremental configurations are evaluated for each protocol, ranging from the default protocol through MPLS-enhanced forwarding, SDN-based centralized optimization, combined SDN-MPLS and SDN-SR integration, to advanced configurations using distance-based IS-IS weighted topology metrics with both Fixed and Adaptive metric computation approaches. Two distinct SDN topology construction methods are compared: a Protocol-based approach that uses routing table entries with equal hop-count metrics, and a distance-based approach using IS-IS weighted metrics. The simulation uses a realistic urban topology with 50 vehicles and 5 RSUs, evaluated across several traffic patterns, representing different application types. Results demonstrate that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay by leveraging RSU infrastructure as reliable forwarding relays. Moreover, the proposed SDN-SR framework reduces routing overhead and control-plane signaling, improving network resource utilization and thereby indicating its potential to enhance the energy efficiency of vehicular communication infrastructures. Full article

Renewable energy system (RES)-based electric vehicle (EV) charging infrastructure enhances energy security. The need for intelligent routing techniques that achieve low latency, high reliability and adaptive path selection under extremely dynamic traffic situations has increased owing to the quick growth of Internet of Things (IoT)-enabled vehicular networks. With an emphasis on recurrent neural networks (RNNs), deep belief networks (DBNs), radial basis function neural networks (RBFNNs), and long short-term memory (LSTM) networks, in addition to convolutional neural networks (CNNs), this analysis looks at cutting-edge AI-based models used for shortest-path routing in IoT-driven vehicular ad hoc networks (VANETs). The paper examines how various designs handle issues such as connection instability, heterogeneous sensor data, quick topological changes, and real-time decision making. A comparative analysis shows that DBN and CNN display strong feature learning for intricate mobility patterns and congestion recognition, while sequence-aware techniques like RNN and LSTM advance spatiotemporal traffic estimation. For low-latency route evaluation, RBFNN compromises rapid nonlinear representation. The examination shows that CNN models greatly improve the scalability, adaptability and optimality of routing, confirming AI-enabled structures as a promising path for next-generation IoT-based vehicular routing methods. 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

Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a one-hop neighbor position table through periodic beacon exchanges, making it highly scalable. Each node forwards packets to the neighbor geographically closest to the destination. However, this distance-only criterion leads to a low packet delivery ratio (PDR). Existing improvements, such as Weight-Based Path-Aware GPSR (W-PAGPSR) combining distance progress, velocity direction, neighbor density, and link duration, incorporate multiple factors but complicate parameter tuning and lack a unified neighbor quality metric. This paper proposes Directional Propagation Capacity Index–GPSR (DPCI-GPSR), integrating neighbor information into a single directional metric capturing propagation capacity. Two enhancements are introduced: (1) an eight-direction DPCI computing a composite propagation capacity index per sector, exchanged via Hello packets, and (2) a trapezoidal link quality function treating 30–200 m as optimal while penalizing edge-zone neighbors. Implemented in NS-3 with SUMO-generated mobility, results across four node densities (30–120 vehicles), five concurrent sender–receiver pairs, and 15 random seeds show DPCI-GPSR achieves 63.08–98.39% PDR, outperforming both W-PAGPSR (52.38–80.14%) and standard GPSR (50.23–66.31%). Full article

Vehicular Ad hoc Networks (VANETs) feature high node mobility and volatile topologies, rendering the conventional Greedy Perimeter Stateless Routing (GPSR) protocol prone to weak link stability and inefficient route discovery due to its lack of direction awareness. Existing direction-aware improvements typically rely on multi-criteria weighting or clustering, introducing heavy parameter fusion and computational overhead that conflict with the resource-constrained nature of onboard units. To overcome these limitations, this paper presents VANET-GPSR+, a lightweight enhanced routing protocol. Its key novelty is that it discards multi-parameter fusion and relies solely on movement direction, supported by a synergistic framework of three lightweight mechanisms: direction-aware neighbor classification to prioritize nodes with consistent trajectories, adaptive greedy forwarding region expansion in sparse and dynamic networks, and path deviation angle-based next-hop selection. This work builds a probabilistic link lifetime model that theoretically quantifies the stability gains of direction awareness—a novel theoretical foundation. Comprehensive urban and highway simulations show that VANET-GPSR+ improves the packet delivery ratio by 16.3% and reduces end-to-end delay by 27.5% compared with standard GPSR, and it outperforms both OP-GPSR and AK-GPSR. It introduces negligible CPU and memory overhead, with CPU usage over 50% lower than the two benchmark protocols at 80 vehicles/km, and demonstrates strong robustness against varying beacon intervals and communication radii. Retaining GPSR’s stateless and distributed traits, VANET-GPSR+ delivers substantial performance gains with minimal overhead, serving as an efficient routing solution for highly dynamic VANETs. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

Mobile ad hoc networks (MANETs) are highly vulnerable to denial-of-service (DoS) attacks because their decentralized operation, rapidly changing topology, and constrained node resources limit the use of heavyweight security mechanisms. This paper presents an Adaptive Clustering and Random-Forest-based Intrusion Detection System (ACRF-IDS), a lightweight intrusion detection framework that combines mobility-aware adaptive clustering with supervised learning to detect network-layer DoS behaviors. Cluster heads are elected using a multi-metric utility (residual energy, link stability, and mobility) to stabilize observations under node movement. Within fixed monitoring windows, cluster heads aggregate routing-, forwarding-, and energy-related features and classify nodes using a Random Forest model; a temporal voting scheme further suppresses transient mobility-induced alarms. Using ns-2.35 simulations with Ad hoc On-Demand Distance Vector (AODV) and both flooding and blackhole DoS scenarios, ACRF-IDS is compared with (i) a static clustering-based threshold IDS, (ii) a non-clustered Support Vector Machine (SVM)-based IDS, and (iii) AIFAODV, which specializes in flooding. Across the evaluated network sizes (4–50 nodes), the proposed method achieves a higher detection rate and F1-score while maintaining a lower false positive rate than the baseline techniques. We additionally quantify network-level impact using PDR, throughput, and routing overhead, showing that ACRF-IDS improves availability under DoS while adding bounded overhead. Future work will extend the evaluation to more diverse attack behaviors and validate the approach in real-world MANET testbeds. 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

UAV-Assisted Mobile Ad Hoc Networks (UAMANETs) provide flexible communication support in dynamic and infrastructure-limited environments. This paper studies a representative UAMANET architecture in which a subset of UAVs forms stable task clusters with ground nodes while simultaneously acting as relays in an airborne backbone network. To characterize the network capacity under contention-based medium access and multi-hop routing, we introduce Aggregate Multi-hop Information Efficiency (AMIE), a capacity-oriented metric that jointly accounts for MAC-layer contention, multi-hop routing, and end-to-end transmission reliability. Based on an IEEE 802.11p access model, we extend Bianchi’s CSMA/CA analytical framework to UAMANETs, enabling a quantitative characterization of how spectrum resource allocation affects AMIE through link activation probability, transmission interruption, and end-to-end hop count. Building on the derived analytical insights, we further develop a soft centralized resource management framework, in which an existing MSF-PSO algorithm is employed as a numerical solver to optimize resource allocation under implicit MAC-layer coupling constraints. Numerical results demonstrate that, compared with conventional IEEE 802.11p spectrum resource settings, the proposed framework can achieve substantial AMIE improvements under representative network configurations. 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

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

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

Mobile Ad Hoc Networks (MANETs) exhibit diverse deployment forms, such as unmanned swarms, mobile wireless sensor networks (MWSNs), and Vehicular Ad Hoc Networks (VANETs). While providing significant social application value, MANETs also face the challenge of accurately and efficiently evaluating connectivity reliability. Building on existing studies—which mostly rely on the assumptions of imperfect nodes and perfect links—this paper comprehensively considers link communication quality and channel capacity, and extends the imperfect link assumption to analyze and evaluate the connectivity reliability of MANETs. The Couzin-leader model is used to characterize the ordered swarm movement of MANETs, while various probability models are employed to depict the multiple actual failure modes of network nodes. Additionally, the Free-Space-Two-Ray Ground (FS-TRG) model is introduced to quantify link quality and reliability, and the probability of successful routing path information transmission is derived under the condition that channel capacity follows a truncated normal distribution. Finally, a simulation-based algorithm for solving the connectivity reliability of MANETs is proposed, which comprehensively considers node characteristics and link states. Simulation experiments are conducted using MATLAB R2023b to verify the effectiveness and validity of the proposed algorithm. Furthermore, the distinct impacts of link communication quality and channel capacity on the connectivity reliability of MANETs are identified, particularly in terms of transmission quality and network lifetime. Full article