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The field of wireless communication has undergone revolutionary changes driven by technological advancements in recent years. Central to this evolution is wireless ad hoc networks, which are characterized by their decentralized nature and have introduced numerous possibilities and challenges for researchers. Moreover, most of the existing Internet of Things (IoT) networks are based on ad hoc networks. This study focuses on the exploration of interference management and Medium Access Control (MAC) schemes. Through statistical derivations and systematic simulations, we evaluate the efficacy of guard zone-based MAC protocols under Rayleigh fading channel conditions. By establishing a link between network parameters, interference patterns, and MAC effectiveness, this work contributes to optimizing network performance. A key aspect of this study is the investigation of optimal guard zone parameters, which are crucial for interference mitigation. The adaptive guard zone scheme demonstrates superior performance compared to the widely recognized Carrier Sense Multiple Access (CSMA) and the system-wide fixed guard zone protocol under fading channel conditions that mimic real-world scenarios. Additionally, simulations reveal the interactions between network variables such as node density, path loss exponent, outage probability, and spreading gain, providing insights into their impact on aggregated interference and guard zone effectiveness. [ABSTRACT FROM AUTHOR]

Abstract One of the significant challenges of the Radio over fibre (RoF) Medium Access Control (MAC) protocol is the propagation delay. This delay can lead to serious issues, such as higher propagation delay resulting in collisions and unnecessary retransmissions. Quantum entanglement is an excellent candidate to overcome the propagation delay of the RoF MAC protocol. A new quantum MAC protocol is proposed, named the Quantum Entanglement‐based MAC protocol (QE‐MAC), in which Quantum Teleportation is utilised to address the propagation delay. Four entanglement states are employed to represent the control packets of the classical MAC protocol, and data is transmitted over the classical channel. Instead of using control packets such as acknowledgement, request to send, and Clear to send, state transitions are employed. This approach avoids the delay and collision issues associated with control packets, resulting in a significant improvement in network performance. The delay, duty cycle (DC), and power consumption of the proposed QE‐MAC protocol are formulated and derived. The protocol is evaluated in terms of delay, DC, and power consumption, demonstrating superior performance compared to the classical RoF MAC protocol. In comparison to published works, our proposed approach has successfully reduced both delay and power consumption by 35%.

Abstract Delay tolerant networks (DTNs) is a network evolved from mobile networks. Differing from the traditional network, which has a stable end‐to‐end transmission path, DTNs are sparse and intermittently connected mobile ad hoc network, which are widely used in harsh environments, such as battlefields, seabed, space communication networks, and so on. In DTNs, intermittent connectivity, partitioned network, long delays and node mobility characteristics make the network fail to communicate frequently, therefore, how to successfully forward the message is of extreme importance. Up to now, almost all the traditional models in DTNs use the store‐carry‐forward method. This paper proposes a novel clustered DTN routing model based on sensing node relationship strength. The routing mechanism takes advantage of the number of other nodes encountered by the nodes in the process of movement and the changes in the number of nodes to calculate the strength of the relationship between nodes, and clusters DTN routing according to the strength of the relationship between nodes. Moreover, the relationship between nodes in a cluster and other clusters is used to transmit messages between clusters, and messages are transmitted within clusters according to the strength of the relationship between nodes. Simulation results show that the routing mechanism not only increases the success rate of message transmission, but also reduces the transmission delay of messages and improves network performance.

Abstract The escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

Summary The cluster networking protocols are the roots that embed intelligent decision‐making and enhance the lifespan of wireless sensor networks (WSNs). Wireless sensors with limited capabilities face several challenges due to the heterogeneous application environments. Especially, the mobility‐incorporated sensors in most situations trouble the cluster network's robustness. Many cluster networking protocols have been presented in the past to enhance the network lifespan and data delivery ratio. However, they lack a dedicated and efficient mechanism for mobility assistance, an adequate cluster management process and cluster head selection criteria. To overcome these issues and for the uniform energy load distribution, we propose a mobility‐compatible cache controlled cluster networking protocol (MC‐CCCNP) in this paper. It is an energy‐efficient cluster networking protocol that supports sensor movement. Network resource management and routing are controlled distributively by an optimal number of cache nodes. It defines a new strategy for cache node deployment based on neighbour density as well as a weight formula for cluster head selection and cluster formation based on the residual energy, the distance to the base station and the node velocity. It also includes techniques for detaching and reconnecting a mobile node to an appropriate cluster cache if it crosses the cluster boundary. We simulate and compare the performance of our protocol with the centralised energy‐efficient clustering routing, energy‐efficient mobility‐based cluster head selection protocol and dual tier cluster‐based routing protocols over different network configurations with varying mobility, scalability and heterogeneity. The MC‐CCCNP showed remarkable improvements in energy utilisation uniformity and energy consumption. With the improved network lifespan, it also maintains a higher data throughput rate of 95% or more in almost all network configurations. [ABSTRACT FROM AUTHOR]

In the contemporary environment, mobile ad hoc networks (MANETs) have become necessary. They are absolutely vital in a variety of situations, where setting up a network quickly is required; however, this is infeasible due to low resources. Ad hoc networks have many applications: education, on the front lines of battle, rescue missions, etc. These networks are distinguished by high mobility and constrained computing, storage, and energy capabilities. The main aim of this research is to create a method for identifying blackhole attacks through anomaly detection techniques utilizing Support Vector Machines (SVM). Our detection system looks at node activity to scan network traffic for irregularities. In blackhole scenarios, the attackers exhibit distinct behavioral characteristics that distinguish them from other nodes. This can be efficiently detected by the proposed SVM-based detection system. The proposed detection system is designed to analyze network traffic and identify anomalies by examining node behaviors. Specifically, in the context of blackhole threats, it distinguishes the attackers from normal nodes based on behavioral characteristics. Using this approach, the system effectively detects blackhole attacks. The results demonstrate a very high level of accuracy in detecting blackhole attacks, confirming its efficacy in ensuring the security of mobile ad hoc networks (MANETs) by identifying and isolating malicious nodes. This solution is particularly valuable in scenarios like military operations and disaster management where a reliable communication system is crucial. Furthermore, the proposed detection method, ADS-SVM, was compared to two other methods (J48 classifier and NB classifier) from different researchers. The results indicate that ADS-SVM outperforms the other methods with a detection accuracy of 99.96%, surpassing the 99.2% achieved by J48 classifier and the 96.5% achieved by NB classifier. The results indicate that ADS-SVM is a highly efficient approach for identifying blackhole attacks in MANETs, potentially offering superior accuracy compared to other related methods. [ABSTRACT FROM AUTHOR]

Delay tolerant networks (DTNs) is a network evolved from mobile networks. Differing from the traditional network, which has a stable end‐to‐end transmission path, DTNs are sparse and intermittently connected mobile ad hoc network, which are widely used in harsh environments, such as battlefields, seabed, space communication networks, and so on. In DTNs, intermittent connectivity, partitioned network, long delays and node mobility characteristics make the network fail to communicate frequently, therefore, how to successfully forward the message is of extreme importance. Up to now, almost all the traditional models in DTNs use the store‐carry‐forward method. This paper proposes a novel clustered DTN routing model based on sensing node relationship strength. The routing mechanism takes advantage of the number of other nodes encountered by the nodes in the process of movement and the changes in the number of nodes to calculate the strength of the relationship between nodes, and clusters DTN routing according to the strength of the relationship between nodes. Moreover, the relationship between nodes in a cluster and other clusters is used to transmit messages between clusters, and messages are transmitted within clusters according to the strength of the relationship between nodes. Simulation results show that the routing mechanism not only increases the success rate of message transmission, but also reduces the transmission delay of messages and improves network performance. [ABSTRACT FROM AUTHOR]

One of the significant challenges of the Radio over fibre (RoF) Medium Access Control (MAC) protocol is the propagation delay. This delay can lead to serious issues, such as higher propagation delay resulting in collisions and unnecessary retransmissions. Quantum entanglement is an excellent candidate to overcome the propagation delay of the RoF MAC protocol. A new quantum MAC protocol is proposed, named the Quantum Entanglement‐based MAC protocol (QE‐MAC), in which Quantum Teleportation is utilised to address the propagation delay. Four entanglement states are employed to represent the control packets of the classical MAC protocol, and data is transmitted over the classical channel. Instead of using control packets such as acknowledgement, request to send, and Clear to send, state transitions are employed. This approach avoids the delay and collision issues associated with control packets, resulting in a significant improvement in network performance. The delay, duty cycle (DC), and power consumption of the proposed QE‐MAC protocol are formulated and derived. The protocol is evaluated in terms of delay, DC, and power consumption, demonstrating superior performance compared to the classical RoF MAC protocol. In comparison to published works, our proposed approach has successfully reduced both delay and power consumption by 35%. [ABSTRACT FROM AUTHOR]

This research work introduces an Ensemble Adaptive Reinforcement Learning (EARL) approach for efficient load balancing in Mobile Ad Hoc Networks (MANETs). Traditional methods often fail to adapt to the dynamic nature of MANETs, leading to congestion and inefficiency. EARL leverages multiple reinforcement learning agents, trained with Q-learning and Deep Q-Networks (DQN), to optimize routing decisions based on real-time network conditions. The ensemble mechanism combines the strengths of individual agents, enhancing adaptability and performance. Simulation results demonstrate that EARL significantly outperforms traditional methods like AODV and DSR, achieving higher packet delivery ratios, lower end-to-end delays, increased throughput, better energy efficiency, and reduced packet loss, thereby proving its effectiveness in dynamic network environments. [ABSTRACT FROM AUTHOR]

The escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements. [ABSTRACT FROM AUTHOR]

Abstract In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node.

Ultraviolet technology has a wide range of applications in military and civilian fields, such as space detection, ultraviolet light guidance, ultraviolet interference, corona discharge detection and military communications. Starting from the single and multiple scattering of ultraviolet light, this paper first introduces the progress of ultraviolet optical communication at home and abroad. Then it analyzes the different links in ultraviolet optical communication. Next, it discusses the pulse broadening effect and atmospheric turbulence of ultraviolet communication in non-direct link ultraviolet communication. Finally, the paper summarizes the ultraviolet Ad Hoc network, and shows the trend of the development of ultraviolet optical communication.

Abstract Clustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets.

In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node. [ABSTRACT FROM AUTHOR]

Clustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets. [ABSTRACT FROM AUTHOR]

In Flying Ad Hoc Networks (FANETs), the critical resource resides in an Unmanned Aerial Vehicle (UAV) and the user nodes within the UAV's neighborhood defined by its transmission range can request for it. In Local Mutual Exclusion (LME), two nodes in the same neighborhood cannot execute the Critical Section (CS) simultaneously, but two non-neighboring nodes can be in the CS at the same time. This is a variation of traditional Mutual Exclusion (ME). The proposed Priority Aware - Request Collector Local Mutual Exclusion (PA-RCLME) algorithm ensures prioritized LME in such FANET structures. The proposed PA-RCLME algorithm is token-based and takes into account the priority of CS requests. It leverages a slow ageing technique to prevent starvation, to avoid a profusion of priority inversions, and to ensure the bounded waiting property of mutual exclusion algorithms. This algorithm introduces a neighborhood search technique that makes the token holder a secondary request collector, thereby reducing average request latency and increasing efficiency. The rapid movement of UAVs and other user nodes makes FANET topology highly dynamic and fault-prone. PA-RCLME algorithm handles it gracefully. Opportunistic Node Simulator (ONE) is used to simulate the algorithm and appropriate performance metrics have been recorded. A comparative analysis with the existing algorithm in the literature is also presented, and the proposed algorithm performs better. [ABSTRACT FROM AUTHOR]

Quality of Service (QoS) is a crucial aspect of Mobile Ad Hoc Networks (MANET) that needs examination to demonstrate optimal performance. The scientific community is increasingly concerned with the challenge of developing an energy-aware clustering method for MANETs. This is owing to the fact that the battery-operated sensor devices that form the backbone of these wireless networks cannot be recharged. The selection of a cluster's leader is a difficult problem in MANET. Additionally, the research focuses on Optimal Route Selection (ORS) within the MANET context, acknowledging the significance of establishing efficient communication paths between cluster heads and member nodes. Through the integration of a reliability pair factor and node energy considerations, the proposed ORS algorithm generates optimal paths based on maximizing energy efficiency while minimizing the sum of hops between nodes. The study suggests a new algorithm based on the way waterwheel plants move and change their places as they explore and exploit new territory in the quest for food. The suggested method is named Binary Waterwheel Plant Algorithm (BWPA). In this method, a novel model is used to represent both the binary search space and the mapping from continuous to discrete spaces. Particularly, mathematical models of the fitness and cost functions used by the algorithm are constructed. Through the use of a reliability pair factor and node energy, the proposed research paper on Optimal Route Selection (ORS) generates the optimal path between the cluster head and member node and establishes the path based on the maximum energy and sum of hops among the nodes. By fusing the reactive power of differential evolution with the exhaustive search efficiency of the BWPA, the proposed method extends the life of networks. The recommended method increases node lifetime by compounding the dynamic capabilities of discrepancy evolution with the high effectiveness of search. [ABSTRACT FROM AUTHOR]

Ad Hoc network is widely used because of its distributed flexibility, in which nodes may generate different types of traffic, and some high-priority traffic requires lower delay and other Quality of service (QoS) requirements. However, as the load of traffic increases, the probability of conflict increases significantly, which makes it difficult to guarantee the requirement of these traffic. It is significant to design the efficient multiple access protocol to ensure the quality of different kinds of traffic. Channel reservation mechanism is an important method to ensure QoS. However, the existing channel reservation mechanism does not fine-adjust reservation parameters according to the QoS when dealing with various traffic differently, and the differentiation mechanism is rough. In addition, it is difficult to ensure the performance of common services when low-latency services are met. Therefore, a differentiated service guarantee adaptive reservation mechanism (DSGARM) to solve the above problems is proposed. The core idea of the protocol is to provide differentiated services in detail according to the delay tolerance requirements of each business. Absolute reservation mechanism is used to guarantee delay-sensitive services, and relative reservation mechanism is used to service relatively delay-insensitive services. In the protocol, a model is established to design an adaptive algorithm to quantitatively calculate reservation parameters that meet the delay requirements. On the premise of prioritized reservation for various services, appropriate reservation parameters can be adjusted according to network conditions to ensure the delay tolerance requirements of each type of service. The present work can be widely applied to scenarios with differentiated business requirements. The simulation results show the advantages of the present protocol and have great practical significance.

Abstract Poisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely.

The cooperative QoS Aware Based Data Congestion Control (QoS-ADCC) algorithm is proposed to achieve this goal. It leverages QoS indicators such as connection probability, packet arrival rate, and delay to determine the best route from the source to the destination. These indicators are evaluated for each road segment and combined with estimated relay quality. The QoS-ADCC algorithm combines passive and preventive elements to establish and maintain the routing of data packets. If the source route to the target path is unavailable, the algorithm employs forward and backward reactions to search for candidate routes. The selected route is implemented in the wireless network, and pheromone levels are updated as packets pass through intersections. This approach reduces overhead and transmission delay, utilizing dedicated lanes on urban buses to establish a mobile backbone network and enhance vehicle connectivity. The proposed QoS-ADCC algorithm selects relay data packets between intersections based on QoS dynamic road segments, aiming for a straightforward and efficient routing mechanism. The research involves evaluating different routing protocols and congestion control mechanisms, considering factors such as route stability, end-to-end delay, throughput, and packet loss rate. Simulation experiments using network simulators like NS-3 and OMNET++ as well as real-world testbed experiments will be conducted to assess the performance of the proposed system. The findings of this research will contribute to the development of efficient and reliable data routing mechanisms in MANETs for intelligent transportation systems, ensuring high QoS and improved network performance. Overall, the proposed QoS-ADCC algorithm demonstrates promising results with a throughput value of 31000 Mbps, a Link Stability Speed of 40 meters, a Packet Delivery Ratio of 1.2%, and a routing overhead of 1. These outcomes highlight the potential of the algorithm to enhance data delivery in MANETs within ITS.

Self-organization is a key strategy for improving the performance of an aerial swarm ad hoc network. The proliferation of low-cost VTOL drones has broadened the application domain of aerial swarms, and the need for synchronized communication among network entities has become crucial. However, existing ad hoc approaches struggle to maintain multi-hop connections in contested environments characterized by frequent topology changes and intermittent links. To overcome these limitations, we introduced STDMA protocol, which enables the self-configuration of drones without reliance on a ground controller. In continuation of the earlier work, we conduct extensive experiments to evaluate the performance of the proposed protocol. Comparative simulation experiments cover various scenarios with different network sizes, frame lengths, and traffic loads. The STDMA protocol achieves optimal access delay in highly contested environments and reduces delay by approximately 19.181%. Moreover, it exhibits improved channel utilization compared to the E-ASAP/SM protocol, with a 4.5 times increase.

Abstract In a mobile ad hoc network (MANET), all nodes are communicated with one another across wireless networks to create a temporary network without the support of centralised management. Due to dynamic topology in MANET, secure routing is a crucial issue. The existing secure routing protocol and their security concerns are analysed in this work. The suggested Swarm Intelligence‐based Secure Ad‐hoc On‐demand Distance Vector (SIS‐AODV) algorithm offers security by applying a secret key and hash mechanism to prevent the involvement of malicious nodes in routing operations. A secure routing system of MANET guards against internal and external network attacks. The proposed SIS‐AODV algorithm consists of two sections: the secret key value generated by applying Elliptical Curve Cryptography (ECC)‐based algorithm and the PRESENT algorithm to encrypt the data packets. Besides, authentication and non‐repudiation are applied using the H‐PRESENT 128 algorithm. The PRESENT algorithm and H‐PRESENT 128 hash function require less computational power. Centralised management is optional in this scheme, so overhead decreases. The second section of SIS‐AODV consists of Ant Colony Grey Wolf Optimization over the AODV algorithm to improve network performance while implementing a security algorithm over MANET. Analysis results show maximum performance with a packet delivery ratio of 98% and throughput of 85%. In addition, end‐to‐end delay is reduced by up to 25%, and routing overhead decreases by up to 20%. Keywords: AODV, Elliptical Curve, PRESENT, H‐PRESENT, Euclidean Algorithm, ACO, GWO, Blackhole attack.

The field of wireless communication has undergone revolutionary changes driven by technological advancements in recent years. Central to this evolution is wireless ad hoc networks, which are characterized by their decentralized nature and have introduced numerous possibilities and challenges for researchers. Moreover, most of the existing Internet of Things (IoT) networks are based on ad hoc networks. This study focuses on the exploration of interference management and Medium Access Control (MAC) schemes. Through statistical derivations and systematic simulations, we evaluate the efficacy of guard zone-based MAC protocols under Rayleigh fading channel conditions. By establishing a link between network parameters, interference patterns, and MAC effectiveness, this work contributes to optimizing network performance. A key aspect of this study is the investigation of optimal guard zone parameters, which are crucial for interference mitigation. The adaptive guard zone scheme demonstrates superior performance compared to the widely recognized Carrier Sense Multiple Access (CSMA) and the system-wide fixed guard zone protocol under fading channel conditions that mimic real-world scenarios. Additionally, simulations reveal the interactions between network variables such as node density, path loss exponent, outage probability, and spreading gain, providing insights into their impact on aggregated interference and guard zone effectiveness.

Poisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely. [ABSTRACT FROM AUTHOR]

We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios. [ABSTRACT FROM AUTHOR]

The OSI model used to be a common network model for years. In the case of ad hoc networks with dynamic topology and difficult radio communications conditions, gradual departure is happening from the classical kind of OSI network model with a clear delineation of layers (physical, channel, network, transport, application) to the cross-layer approach. The layers of the network model in ad hoc networks strongly influence each other. Thus, the cross-layer approach can improve the performance of an ad hoc network by jointly developing protocols using interaction and collaborative optimization of multiple layers. The existing cross-layer methods classification is too complicated because it is based on the whole manifold of network model layer combinations, regardless of their importance. In this work, we review ad hoc network cross-layer methods, propose a new useful classification of cross-layer methods, and show future research directions in the development of ad hoc network cross-layer methods. The proposed classification can help to simplify the goal-oriented cross-layer protocol development. [ABSTRACT FROM AUTHOR]

In a mobile ad hoc network (MANET), all nodes are communicated with one another across wireless networks to create a temporary network without the support of centralised management. Due to dynamic topology in MANET, secure routing is a crucial issue. The existing secure routing protocol and their security concerns are analysed in this work. The suggested Swarm Intelligence‐based Secure Ad‐hoc On‐demand Distance Vector (SIS‐AODV) algorithm offers security by applying a secret key and hash mechanism to prevent the involvement of malicious nodes in routing operations. A secure routing system of MANET guards against internal and external network attacks. The proposed SIS‐AODV algorithm consists of two sections: the secret key value generated by applying Elliptical Curve Cryptography (ECC)‐based algorithm and the PRESENT algorithm to encrypt the data packets. Besides, authentication and non‐repudiation are applied using the H‐PRESENT 128 algorithm. The PRESENT algorithm and H‐PRESENT 128 hash function require less computational power. Centralised management is optional in this scheme, so overhead decreases. The second section of SIS‐AODV consists of Ant Colony Grey Wolf Optimization over the AODV algorithm to improve network performance while implementing a security algorithm over MANET. Analysis results show maximum performance with a packet delivery ratio of 98% and throughput of 85%. In addition, end‐to‐end delay is reduced by up to 25%, and routing overhead decreases by up to 20%. Keywords: AODV, Elliptical Curve, PRESENT, H‐PRESENT, Euclidean Algorithm, ACO, GWO, Blackhole attack. [ABSTRACT FROM AUTHOR]

Ad hoc networks are a new perspective of wireless communication for versatile hosts. Security is a colossal worry for ad hoc networks, especially for those security-touchy applications. The huge highlights of ad hoc networks cause both difficulties and openings in accomplishing security objectives. One such aim is to consider the assaults from within the system by compromised nodes correspondingly as to consider harmful assaults propelled from outside the system. Designing an Intrusion Detection System (IDS) that suits the security needs and characteristics of ad hoc networks for viable and proficient performance against intrusions is one potential solution to vanquish vulnerabilities. This paper examines a genuine and hurtful attack called, "Malicious Packet Dropping Attack" in the network layer. To secure against this attack, a novel methodology utilizing game theory is proposed. The proposed system monitors the conduct of the neighbor nodes and conquers the demerits such as false positives present in traditional IDS, thereby providing secure correspondence between nodes that communicate with one another to course the traffic from source to destination. With the existence of malicious nodes, the proposed system has accomplished a 42% increase in the packet delivery ratio. [ABSTRACT FROM AUTHOR]

Abstract Given the observed developments of novel communication modes and the establishment of next‐generation cellular networks, mobility modelling and ad hoc routing requirements have emerged. Flying ad hoc networks are key pivots in enabling technological leaps in the domain of on‐demand communications, especially in emergency scenarios; as such, resorting to application‐ and mobility‐aware routing is a promising enabler of this emerging set of use cases. This article investigates swarm mobility modelling, and applicable routing protocols, conducting comparative analysis that leads to the introduction of the new Anchored Self‐Similar 3D Gauss‐Markov Mobility Model (ASSGM‐3D), which incorporates a novel set of spatio‐temporal statistical metrics.

The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises. [ABSTRACT FROM AUTHOR]

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Vehicular ad hoc networks consist of self-organizing nodes using multi-hop wireless links for communication without any infrastructure support. Traditionally, ad hoc routing protocols use the minimum hop count for their routing metric since a smaller number of transmissions is typically equivalent to a higher throughput, lower delay, and minimal power consumption. However, with the muti-rate capability of emerging radio interfaces, e.g., 802.11ax/be standards, the min-hop metric no longer results in high throughput. For instance, if the higher data rate links are selected for the route, it could result in a higher throughput even if the route takes more hop counts. In this paper, we propose a high throughput routing scheme, called MARV, which makes two key contributions. MARV searches for high throughput paths using an on-demand route searching algorithm so that the routing overhead is smaller compared to other multi-rate-aware routing schemes. MARV also searches for multiple paths to maintain both min-hop and high-throughput paths to select the adequate path depending on the data packet size. We conduct simulations to demonstrate that MARV outperforms not only min-hop path metrics but also previously proposed high-throughput metrics. [ABSTRACT FROM AUTHOR]

Given the observed developments of novel communication modes and the establishment of next‐generation cellular networks, mobility modelling and ad hoc routing requirements have emerged. Flying ad hoc networks are key pivots in enabling technological leaps in the domain of on‐demand communications, especially in emergency scenarios; as such, resorting to application‐ and mobility‐aware routing is a promising enabler of this emerging set of use cases. This article investigates swarm mobility modelling, and applicable routing protocols, conducting comparative analysis that leads to the introduction of the new Anchored Self‐Similar 3D Gauss‐Markov Mobility Model (ASSGM‐3D), which incorporates a novel set of spatio‐temporal statistical metrics. [ABSTRACT FROM AUTHOR]

Abstract The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises.

Summary: A routing protocol for cognitive radio ad hoc networks (CRAHNs), namely, primary user supported routing (PSR) is demonstrated in this research. Unlike existing routing protocols for CRAHN, where routing of cognitive users (CUs) is accomplished within CUs, in this proposed protocol, some of the primary users (PUs) support CUs to communicate, by forwarding CU packets. This service provided by PU is of voluntary nature. However, such assistance shall be provided by the PUs, only when they are idle, thereby, preserving the principles of cognitive radio networks (CRNs), which indicates that the operation of PU should not be disturbed by the CU activities. The proposed work is compared with cognitive ad hoc on‐demand distance vector (CAODV) protocol. The performance parameters considered are routing overhead, rate of loss of packets, and e2e packet delay. PSR outperforms CAODV in all these performance parameters. There has been on average 26.25% improvement in routing overhead, 34.12% decrease in loss, and 27.01% improvement in e2e delivery in the proposed PSR. [ABSTRACT FROM AUTHOR]

The increasing demand for Internet of Things (IoT) applications has resulted in vast amounts of data, requiring the utilization of big data analytics. The integration of big data analytics in IoT-based smart cities can greatly benefit from the development of wireless communication protocols, among which the Thread protocol has emerged as a promising option. Thread is IEEE 802.15.4 based and has advanced capabilities like mesh networking, IPv6 support, and multiple gateways providing no single point of failure. This paper presents the design and evaluation of a low-cost mesh network using Raspberry Pi, nRF52840 dongle, and OpenThread 1.2 (i.e., an open-source software implementation of the Thread protocol stack). The research elaborates on the hardware and software solutions used, as well as the network topologies adopted. To evaluate the performance of the developed system, extensive real-time tests are performed, considering parameters, such as jitter, packet loss, and round trip time. These tests effectively demonstrate the effectiveness of the Thread network. Furthermore, the impact of varying payload size and bitrate on the network is analyzed to understand its influence. The behavior of the multi-hop network is also examined under link failure scenarios, providing insights into the network's robustness. Our findings provide valuable insights for researchers interested in designing low-cost and efficient mesh networks for various IoT applications, including home automation, building/campus monitoring systems, distributed industrial IoT applications, and smart city infrastructure. [ABSTRACT FROM AUTHOR]

In Mobile ad hoc Networks (MANETs), the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss. In this paper, a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling (MFPA-FSPS) Mechanism is proposed for sustaining Quality of Service (QoS) in the network. This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets' priority for guaranteeing optimal network performance. It further used the modified Firefly Optimization Algorithm to optimize the rules utilized by the fuzzy inference engine to achieve the potential packet scheduling process. This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time. The statistical validation of the proposed MFPAFSPS conducted using a one-way Analysis of Variance (ANOVA) test confirmed its predominance over the benchmarked schemes used for investigation. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study of capacity characterization in mobile wireless networks employing directive communications and full-duplex communication technology. We address the impact of nodes’ mobility, directive beams, and full-duplex capabilities on the network capacity. Firstly, we derive the signal-to-interference-plus-noise ratio (SINR) for a reference receiver, considering the combined effects of mobility, directive beams, and full-duplex communications. The derived SINR is then utilized to calculate the network’s capacity. To validate the proposed model, extensive simulations are performed, incorporating various scenarios of mobility levels, self-interference suppression levels in the full-duplex wireless system, and different receiving thresholds. The simulation results confirm the accuracy and effectiveness of the derived SINR-based capacity characterization model under real-world conditions. Furthermore, we study the probability of a receiver simultaneously decoding multiple packets as a function of the receiver’s threshold and full-duplex self-interference suppression. The probability is crucial for assessing the network performance and facilitating future optimization schemes aimed at regulating the number of competing nodes in a given spatial region. The validated model and probability characterization offer valuable insights into network performance evaluation and can be instrumental in devising future efficient optimization strategies for managing network contention at the medium access control and enhancing the network performance.