Search Results (419)

This study presents a semi-random mobility model called Semi-Random Elliptical Movement (SREM), developed for relay-oriented Flying Ad Hoc Networks (FANETs). In FANETs, node distribution has a major impact on network performance, making the mobility model a critical design element. While random models offer simplicity and path diversity, they often result in unstable relay paths due to inconsistent node placement. In contrast, planned path models provide alignment but lack the flexibility needed in dynamic environments. SREM addresses these challenges by enabling nodes to move along elliptical trajectories, combining autonomous movement with alignment to the relay path. This approach encourages natural node concentration along the relay path while maintaining distributed mobility. The spatial characteristics of SREM have been analytically defined and validated through the Monte Carlo method, confirming stable node distributions that support effective relaying. Computer simulation results show that SREM performs better than general mobility models that do not account for relaying, offering more suitable performance in relay-focused scenarios. These findings suggest that SREM provides both structural consistency and practical effectiveness, making it a strong candidate for improving the realism and reliability of FANET simulations involving relay-based communication. Full article

Opportunistic Networks (OppNets) lack the capability to maintain consistent end-to-end paths between source and destination nodes, unlike Mobile Ad Hoc Networks (MANETs). This absence of stable routing presents substantial challenges for data transmission in OppNets. Due to node mobility, routing paths are inherently dynamic, requiring the selection of neighboring nodes as intermediate hops to forward data toward the destination. However, frequent node movement can cause considerable delays for senders attempting to identify appropriate next hops, consequently degrading the quality of service (QoS) in OppNets. To mitigate this challenge, this paper proposes an alternative approach for scenarios where senders cannot locate suitable next hops. Specifically, we propose utilizing direct communication via line of sight (LoS) between sender and receiver nodes to satisfy QoS requirements. The proposed scheme is experimented with using the ONE simulator, which is widely used for OppNet experiments and study, and compared against existing schemes such as the history-based routing protocol (HBRP) and AEProphet routing protocol. Full article

Compared to traditional ad hoc networks, self-organizing networks of unmanned aerial vehicle (UAV) are characterized by high node mobility, vulnerability to interference, wide distribution range, and large network scale, which make network management and routing protocol operation more challenging. Cluster structures can be used to optimize network management and mitigate the impact of local topology changes on the entire network during collaborative task execution. To address the issue of cluster structure instability caused by the high mobility and vulnerability to interference in UAV networks, we propose a capacity-constrained weighted clustering algorithm for UAV self-organizing networks under interference. Specifically, a capacity-constrained partitioning algorithm based on K-means++ is developed to establish the initial node partitions. Then, a weighted cluster head (CH) and backup cluster head (BCH) selection algorithm is proposed, incorporating interference factors into the selection process. Additionally, a dynamic maintenance mechanism for the clustering network is introduced to enhance the stability and robustness of the network. Simulation results show that the algorithm achieves efficient node clustering under interference conditions, improving cluster load balancing, average cluster head maintenance time, and cluster head failure reconstruction time. Furthermore, the method demonstrates fast recovery capabilities in the event of node failures, making it more suitable for deployment in complex emergency rescue environments. Full article

Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies. Full article

Routing in flying ad hoc networks (FANETs) is hindered by high mobility, trajectory-induced topology dynamics, and energy constraints. Conventional topology-based or position-based protocols often fail due to stale link information and limited neighbor awareness. This paper proposes a trajectory-informed routing protocol enhanced by Q-learning: Traj-Q-GPSR, tailored for mission-oriented UAV swarm networks. By leveraging mission-planned flight trajectories, the protocol builds time-aware two-hop neighbor tables, enabling routing decisions based on both current connectivity and predicted link availability. This spatiotemporal information is integrated into a reinforcement learning framework that dynamically optimizes next-hop selection based on link stability, queue length, and node mobility patterns. To further enhance adaptability, the learning parameters are adjusted in real time according to network dynamics. Additionally, a delay-aware queuing model is introduced to forecast optimal transmission timing, thereby reducing buffering overhead and mitigating redundant retransmissions. Extensive ns-3 simulations across diverse mobility, density, and CBR connections demonstrate that the proposed protocol consistently outperforms GPSR, achieving up to 23% lower packet loss, over 80% reduction in average end-to-end delay, and improvements of up to 37% and 52% in throughput and routing efficiency, respectively. Full article

This paper addresses the challenges of multicasting in Mobile Ad Hoc Networks (MANETs), where communication relies exclusively on direct interactions between mobile nodes without the support of fixed infrastructure. In such networks, efficient information dissemination is critical, particularly in scenarios where an event detected by one node must be reliably communicated to a designated subset of nodes. The highly dynamic nature of MANET, characterized by frequent topology changes and unpredictable connectivity, poses significant challenges to stable and efficient multicasting. To address these issues, we adopt a Publish/Subscribe (Pub/Sub) model that utilizes brokers as intermediaries for information dissemination. However, ensuring the robustness of broker-based multicasting in a highly mobile environment requires novel strategies to mitigate the effects of frequent disconnections and mobility-induced disruptions. To this end, we propose a framework based on three key principles: (1) leveraging the Disruption-Tolerant Networking Implementations of the Bundle Protocol 7 (DTN7) at the network layer to sustain message delivery even in the presence of intermittent connectivity and high node mobility; (2) dynamically generating broker replicas to ensure that broker functionality persists despite sudden node failures or disconnections; and (3) enabling brokers and their replicas to periodically broadcast advertisement packets to maintain communication paths and facilitate efficient data forwarding, drawing inspiration from Named Data Networking (NDN) techniques. To evaluate the effectiveness of our approach, we conduct extensive simulations using ns-3, examining its impact on message delivery reliability, latency, and overall network throughput. The results demonstrate that our method significantly reduces message delivery delays while improving delivery rates, particularly in high-mobility scenarios. Additionally, the integration of DTN7 at the bundle layer proves effective in mitigating performance degradation in environments where nodes frequently change their positions. Our findings highlight the potential of our approach in enhancing the resilience and efficiency of broker-assisted multicasting in MANET, making it a promising solution for real-world applications such as disaster response, military operations, and decentralized IoT networks. Full article

A maritime mobile ad hoc network (M-MANET) is an essential part of the maritime communication network and plays a key role in many maritime scenarios. However, the topology of M-MANET dynamically changes with the movement of vessels, which leads to unstable link states and poses the risk of data transmission interruption. In this paper, a mobility model for small unmanned surface vessels based on smooth Gaussian semi-Markovian and a trajectory prediction method for large vessels based on a bi-directional long short-term memory network are proposed to better simulate the nodes’ movement in the M-MANET. Then, a link available based routing metric is proposed for M-MANET scenarios, which incorporates factors of mobility model and vessel trajectory. Experiments demonstrate that compared with the benchmark methods, the proposed mobility model depicts the movement characteristics of vessels more accurately, the proposed trajectory prediction method achieves higher prediction accuracy and stability, the proposed routing metric scheme has a reduction of 14.59% in end-to-end delay, a 1.54% increase in packet delivery fraction, and a 4.43% increase in network throughput on average. Full article

The Social Opportunistic Internet of Things (SO-IoT) is a rapidly emerging paradigm that enables mobile, ad-hoc device communication based on both physical and social interactions. In such networks, routing decisions heavily depend on the selection of intermediate nodes to ensure secure and efficient data dissemination. Traditional approaches relying solely on reliability or social interest fail to capture the multifaceted trustworthiness of nodes in dynamic SO-IoT environments. This paper proposes a trust-based route optimization framework that integrates social interest and behavioral reliability using Bayesian inference and Jeffrey’s conditioning. A composite trust level is computed for each intermediate node to determine its suitability for data forwarding. To validate the framework, we conduct a two-phase simulation-based analysis: a scenario-driven evaluation that demonstrates the model’s behavior in controlled settings, and a large-scale NS-3-based simulation comparing our method with benchmark routing schemes, including random, greedy, and AI-based protocols. Results confirm that our proposed model achieves up to an $88.9\%$ delivery ratio with minimal energy consumption and the highest trust accuracy ($86.5\%$), demonstrating its robustness and scalability in real-world-inspired IoT environments. Full article

Wireless sensor networks (WSNs) play a pivotal role in monitoring and acting applications. However, suboptimal deployments and traffic imbalances lead to rapid network exhaustions. To address this, topology changes could be carried out by mobile robots. In this work, a software package to study different strategies and algorithms for the deployment, operation, and retrieval of mobile WSN is introduced. This package employs the globally known software ecosystem for robotics, ROS (Robot Operating System) 2, allowing to study the above-mentioned strategies and algorithms in simulation or in actual deployments. Two strategies concerning robot control are compared, the Social Potential Fields-only approach and an intelligent Agent layer. Each strategy is tested and optimized with different parameters. Results show that the Agents approach yields more consistent results and globally better metrics in terms of network lifetime and coverage. Full article

The rise of the Internet of Things (IoT) has made it possible to explore different types of communication, such as underwater IoT (UIoT). This new paradigm allows the interconnection of ships, boats, coasts, objects in the sea, cameras, and animals that require constant monitoring. The use of sensors for environmental monitoring, tracking marine fauna and flora, and monitoring the health of aquifers requires the integration of heterogeneous technologies as well as wireless communication technologies. Aquatic mobile sensor nodes face various limitations, such as bandwidth, propagation distance, and data transmission delay issues. Owing to their versatility, wireless sensor networks support remote monitoring and surveillance. In this work, an architecture for a general packet radio service (GPRS) wireless sensor network is presented. The network is used to monitor the geographic position over the coastal area of the Gulf of Mexico. The proposed architecture integrates cellular technology and some ad hoc network configurations in a single device such that coverage is improved without significantly affecting the energy consumption, as shown in the results. The network coverage and energy consumption are evaluated by analyzing the attenuation in a proposed channel model and the autonomy of the electronic system, respectively. Full article

Flying ad hoc networks (FANETs) have highly dynamic and energy-limited characteristics. Compared with traditional mobile ad hoc networks, their nodes move faster and their topology changes more frequently. Therefore, the design of routing protocols faces greater challenges. The existing routing schemes rely on frequent and fixed-interval Hello transmissions, which exacerbates network load and leads to high communication energy consumption and outdated location information. MP-QGRD combined with the extended Kalman filter (EKF) is used for node position prediction, and the Hello packet transmission interval is dynamically adjusted to optimize neighbor discovery. At the same time, reinforcement learning methods are used to comprehensively consider link stability, energy consumption, and communication distance for routing decisions. The simulation results show that compared to QMR, QGeo, and GPSR, MP-QGRD has an increased packet delivery rate, end-to-end latency, and communication energy consumption by 10%, 30%, and 15%, respectively. Full article

For the optimization and performance evaluation of mobile ad hoc networks, a beneficial but challenging act is to derive from nodal movement behavior the steady-state spatial density function of nodal locations over a given finite area. Such derivation, however, is often intractable when any assumption of the mobility model is not basic, e.g., when the movement area is irregular in shape. As the first endeavor, we address this density derivation problem for the classic random waypoint mobility model over irregular convex polygons including triangles (i.e., 3-gons) and quadrilaterals (i.e., 4-gons). By mixing multiple Dirichlet distributions, we first devise a mixture density neural network tailored for density approximation over triangles and then extend this model to accommodate convex quadrilaterals. Experimental results show that our Dirichlet mixture model (DMM) can accurately capture the irregularity of ground-truth density distributions at low training cost, markedly outperforming the classic Gaussian mixture model (GMM). Full article

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems. Full article

Mobile ad hoc networks (MANETs) are becoming a popular networking technology as they can easily be set up and provide communication support on the go. These networks can be used in application areas, such as battlefields and disaster relief operations, where infrastructure networks are not available. Like media access control protocols, MANET routing protocols can also play an important role in determining network capacity and system performance. Research on the impact of heterogeneous nodes in terms of MANET performance is required for proper deployment of such systems. While MANET routing protocols have been studied and reported extensively in the networking literature, the performance of heterogeneous nodes/devices in terms of system performance has not been fully explored yet. The main objective of this paper is to review and compare the performance of four selected MANET routing protocols (AODV, OLSR, BATMAN and DYMO) in a heterogeneous MANET setting. We consider three different types of nodes in the MANET routing performance study, namely PDAs (fixed nodes with no mobility), laptops (low-mobility nodes) and mobile phones (high-mobility nodes). We measure the QoS metrics, such as the end-to-end delays, throughput, and packet delivery ratios, using the OMNeT++-network simulator. The findings reported in this paper provide some insights into MANET routing performance issues and challenges that can help network researchers and engineers to contribute further toward developing next-generation wireless networks capable of operating under heterogeneous networking constraints. Full article

The exponential growth of wireless traffic has imposed new technical challenges on the Internet and defined new approaches to dealing with its intensive use. Caching, especially cooperative caching, has become a revolutionary paradigm shift to advance environments based on wireless technologies to enable efficient data distribution and support the mobility, scalability, and manageability of wireless networks. Mobile ad hoc networks (MANETs), wireless mesh networks (WMNs), Wireless Sensor Networks (WSNs), and Vehicular ad hoc Networks (VANETs) have adopted caching practices to overcome these hurdles progressively. In this paper, we discuss the problems and issues in the current wireless ad hoc paradigms as well as spotlight versatile cooperative caching as the potential solution to the increasing complications in ad hoc networks. We classify and discuss multiple cooperative caching schemes in distinct wireless communication contexts and highlight the advantages of applicability. Moreover, we identify research directions to further study and enhance caching mechanisms concerning new challenges in wireless networks. This extensive review offers useful findings on the design of sound caching strategies in the pursuit of enhancing next-generation wireless networks. Full article