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Timing determinism in embedded real-time systems requires meeting timing constraints not only for individual tasks but also for chains of tasks that involve multiple messages. End-to-end analysis is a commonly used approach for solving such problems. However, the temporal properties of tasks often have uncertainty, which makes end-to-end analysis challenging and prone to errors. In this paper, we focus on enhancing the precision and safety of end-to-end timing analysis by introducing a novel probabilistic method. Our approach involves establishing a probabilistic model for end-to-end timing analysis and implementing two algorithms: the maximum data age detection algorithm and the end-to-end timing deadline miss probability detection algorithm. The experimental results indicate that our approach surpasses traditional analytical methods in terms of safety and significantly enhances the capability to detect the probability of missing end-to-end deadlines. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a developing technology in the world of communication and embedded systems. The IoT consists of a wireless sensor network with Internet service. The data size of the sensor node is small, but the routing of the data and energy consumption are important issues that need to be advocated. The Mobile Adhoc Network (MANET) plays a very important role in IoT services. In MANET, nodes are moving within the network. So, routes are created dynamically on demand and do not have any centralized units. The route optimization method addresses issues like selecting the best routes in terms of overhead, loop free, traffic control, balancing, throughput, route maintenance, and so on. In this paper, IoT routes are created between sensors to sink through MANET nodes with WSN routing ideology. The Krill Herd and Feed Forward Optimization (KH-FFO)-based method discovers the routes. The Krill herd algorithm clusters the network. This method increases network speed and reduces energy waste. Feed-forward optimization involves learning all the nodes in the network and identifying the shortest and most energy-efficient route from source to sink. The overall performance of the KH-FFO protocol has improved the network's capacity, reduced packet loss, and increased the energy utilization of the nodes in the network. The ns-3 simulation for KH-FFO is tested in different node densities and observed energy utilization is increased by 28%, network life is increased by 7%, Packet delivery ratio improved by 7.5%, the End-to-End delay improved by 31% and the Throughput is 3%. These metrices are better than the existing works in the network. [ABSTRACT FROM AUTHOR]

In software-defined networking (SDN), where software assumes a progressively prominent position in the network administration, it becomes imperative to emphasize the enhancement of data authentication and non-repudiation methodologies. The study presents a novel approach that incorporates Multi-Party Computation (MPC) into symmetric systems to enhance data authentication inside SDN settings. The use of MPC ensures the timely authentication of identities, prevention of repudiation, and maintenance of data accuracy, while simultaneously protecting the confidentiality of sensitive data and parameters. The evaluation of our technique's performance is accomplished through rigorous simulations utilising NS-3. The simulations yielded positive results in terms of packet delivery ratio, end-to-end delay, and throughput. The findings of the research illustrate that the proposed approach demonstrates consistent efficacy and reliability, even under conditions of heightened network node density. This study presents a complete methodology aimed at enhancing data authentication in SDN. This paper examines the increasing security challenges associated with cloud-based SDN solutions. This research demonstrates a significant progress in the quest for a secure and efficient SDN environment, setting new standards for data authentication and non-repudiation. [ABSTRACT FROM AUTHOR]

The fog computing model is a new computing model that has been proposed in recent years by increasing the number of requests sent to the cloud to reduce the delay and workload of the cloud computing model. In addition to its advantages, the fog computing model also has challenges, among which we can mention the issue of service placement in this computing model, which is very effective in the performance of the computing model. So far, many works have been presented to solve the problem of service deployment by considering different goals such as energy consumption, end-to-end delay, load balancing, resource efficiency, etc. Considering the importance of all the mentioned parameters, it is very important to provide a multi-objective method. In multi-objective problems, the method of evaluating the generated solutions is a separate challenge. Therefore, in this paper, a service placement method is presented by considering end-to-end delay criteria and energy consumption based on the modified Harris hawks algorithm to solve multi-objective problems. To increase accuracy, in the proposed method called multi-objective Harris hawks optimization, a multi-objective problem is modeled as several single-objective problems. The simulation results in CloudSim show that the proposed method has achieved better results than other algorithms in terms of reducing energy consumption, end-to-end delay, and network utilization. [ABSTRACT FROM AUTHOR]

Given the resource limitations of wireless sensor networks (WSNs), energy conservation is of utmost importance. Moreover, minimizing data collection delays is crucial to maintaining data freshness. Additionally, it is desirable to increase the number of collected data samples to enhance accuracy and robustness in data collection. For this purpose, this research article proposes a clustering-based routing protocol aimed at maximizing the delivery of data samples while minimizing energy consumption and data collection delays. The protocol employs a scattered search algorithm and fuzzy logic to cluster the sensor nodes. By considering the distance to the sink and the remaining energy level of the battery, the network is dynamically divided into clusters using a lightweight clustering approach. To evaluate the effectiveness of the proposed method, simulations were conducted in OPNET using the AFSRP protocol. The results demonstrate superior performance of the proposed method in terms of end-to-end delay by 13.44%, media access delay by 75.2%, throughput rate by 20.55%, energy consumption by 13.52%, signal-to-noise ratio by 43.40% and delivery rate of successfully sending data to the sink is 0.21% higher than the well-known AFSRP method. [ABSTRACT FROM AUTHOR]

Owing to uncertainties associated with energy and maintenance of large Wireless Sensor Networks (WSN) during transmission, energy-efficient routing strategies are gaining popularity. A Dynamic Threshold Adaptive Routing Algorithm (DTAR) is proposed for determining the most appropriate node to become a Cluster Head (CH) using adaptive participation criteria. For determining the next Forwarder Node (FN), an adaptive ranking scheme depends on distance (d) and Residual Energy ( R egy ). However, additional parameters such as Delivery Ratio (DR), End-to-End delay ( E 2 delay), and Message Success Rate (MSR) should be considered to achieve the most optimal approach to achieve energy efficiency. The proposed DTAR algorithm is validated on variable clustered networks in order to investigate the effect of opportunistic routing with increasing network size and energy resources. The proposed algorithm shows a substantial decrease in energy consumption during transmission. Energy Consumption (EC), Packet Delivery Ratio (PDR), End-to-End delay ( E 2 delay), and Message Success Rate (MSR) are used to illustrate the effectiveness of the proposed algorithm for energy efficiency. [ABSTRACT FROM AUTHOR]

Ongoing research is focused on underwater wireless sensor networks (UWSNs), which hold great promise for various applications to improve human lives. Many sensor nodes have been strategically positioned in rivers and oceans to closely monitor and analyze the underwater ecosystem. UWSNs face significant challenges due to energy limitations, unreliable communication channels, hole occurrence, and low packet delivery ratio. This paper introduces effective energy routing, hole detection and mitigation, and reliable delivery schemes to address these issues. In ERR-UWSN, a single-path routing strategy optimizes energy consumption by selecting the best-sending node through a unique weight function. However, the reliability of this approach is compromised in the harsh and unpredictable submarine environment. To overcome this limitation, co-ERR-UWSN combines cooperative routing with relay nodes between source–destination pairs. A carefully selected weight function ensures low energy consumption, reliability, and optimal node selection for data transmission. The simulation results show that ERR-UWSN is better than CEER, Co-EPRR, and FB-DBR in terms of residual energy, end-to-end delay, energy consumption, and total number of alive nodes. Also, Co-ERR-UWSN has a special advantage in the PDR criterion compared to other schemes. [ABSTRACT FROM AUTHOR]

Intelligent transportation system integration is becoming increasingly dependent on robust and energy-efficient protocols for communication between vehicles and infrastructure (V2V and V2I) networks. This study offers an energy-efficient cluster-based routing protocol for interaction between V2V and V2I, with a focus on traffic forecasting. To improve communication efficiency in dynamic vehicle contexts, the protocol makes use of the capabilities of vehicle ad-hoc networks (VANETs). A novel approach is provided to optimize the routing process and meet the issues posed by variable traffic situations. The suggested method combines two advanced hybrid optimization algorithms: The Adaptive Binary Bird Swarm Optimization Algorithm (ABBSOA) and the Adaptive Golden Eagle Optimization Algorithm (AGEOA). The goal of hybridizing these algorithms is to capitalize on their complementary capabilities, resulting in a synergistic impact that improves the protocol's adaptability and efficiency. Through its feeding, guarding, and flying imitation behaviors, ABBSOA, inspired by the collective behaviors of birds, focuses on global optimization, outperforming local optima. The golden eagle hunting patterns served as inspiration for AGEOA, which introduces indirect and direct effects for improved convergence and solution quality. For efficient communication management, the protocol adopts a cluster-based design, with cluster heads (CHs) dynamically determined using the hybrid optimization algorithm. The ABBSOA-AGEOA-based strategy improves the CH selection process by optimizing job distribution among less burdened CHs in changing traffic circumstances. The simulation results indicate that the proposed cluster-based energy-efficient. The routing protocol performs better than current protocols with regard to traffic forecasting accuracy, packet delivery rate (PDR), packet loss rate (PLR), throughput, network lifetime (NLT), cluster lifetime, cluster build time, and energy consumption end-to-end latency. Comparative simulations demonstrate the suggested method's advantage over current VANET routing techniques like SFSR, ICMFO, and FA-OLSR, as shown by metrics using the proposed systems ABBSOA and AGEOA, which work well together to get through the complicated networks of vehicles. This makes the protocol a new way for V2V and V2I to communicate in smart transportation systems that will last for a long time. [ABSTRACT FROM AUTHOR]

In 5G vehicular networks, two key challenges have become apparent, including end-to-end delay minimization and data privacy. Learning-based approaches have been used to alleviate these, either by predicting delay or protecting privacy. Traditional approaches train machine learning models on local devices or cloud servers, each with their own trade-offs. While pure-federated learning protects privacy, it sacrifices delay prediction performance. In contrast, centralized training improves delay prediction but violates privacy. Existing studies in the literature overlook the effect of training location on delay prediction and data privacy. To address both issues, we propose a novel genetic algorithm optimized federated learning (GAoFL) approach in which end-to-end delay prediction and data privacy are jointly considered to obtain an optimal solution. For this purpose, we analytically define a novel end-to-end delay formula and data privacy metrics. Accordingly, a novel fitness function is formulated to optimize both the location of training model and data privacy. In conclusion, according to the evaluation results, it can be advocated that the outcomes of the study highlight that training location significantly affects privacy and performance. Moreover, it can be claimed that the proposed GAoFL improves data privacy compared to centralized learning while achieving better delay prediction than other federated methods, offering a valuable solution for 5G vehicular computing. [ABSTRACT FROM AUTHOR]

Network function virtualization is a key enabling technology for the customization of network services in next-generation networks to support diverse applications. Most enterprise and network services contain specific network functions that are stitched together in a predefined sequence to form a service function chain. The deployment and scheduling of a service function chain onto the substrate network play a vital role in deciding the efficiency of resource utilization and the performance of network management. For a delay-sensitive network service request traversing a service function chain, the end-to-end packet delay is a crucial parameter that indicates the deployment performance. Transmission, propagation, processing, edge queueing, and virtualization delays all impact the order in which virtual network functions execute. Service level agreement violations and incorrect schedules are produced when the controller does not take edge queueing and virtualization delays into account. In this work, we propose a service function chain scheduling problem for the optimization of the end-to-end delay while considering transmission, propagation, queueing, virtualization, and processing delays. Then, we propose a scheduling approach based on the earliest finish times of the physical machines to minimize the end-to-end delay of the service function chain. The performance of the proposed service function chain scheduling approach using the earliest finish time is evaluated in terms of end-to-end delay, service level agreement violation ratio, resource utilization, and acceptance ratio. We compare our proposed algorithm with four existing approaches from the literature. Simulation results show that our proposed approach outperforms existing approaches in terms of end-to-end delay, service level agreement violation ratio, resource utilization, and acceptance ratio. [ABSTRACT FROM AUTHOR]

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project's overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks (VANETs) play a crucial role in the development of Intelligent Transport Systems (ITS), leveraging Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) communication models. These networks, however, face challenges such as node administration, security, and efficient routing, particularly due to the unpredictable nature of vehicle mobility. Traditional routing protocols like Adhoc On-Demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are often inadequate for VANETs. Addressing these challenges, we propose FLQL-VANET, a hybrid mechanism that integrates fuzzy logic and Q-learning for enhanced routing. The clustering mechanism in FLQL-VANET selects cluster heads based on parameters such as connectivity, vehicle direction, distance, velocity, path loss, signal quality, and time to leave. Q-learning optimizes the routing path through a reward mechanism, ensuring efficient data transmission. The comparative analysis demonstrates that FLQL-VANET outperforms existing methods such as RMRPTS, ICA-RBF, and KMRP. Our approach achieves an average packet delivery rate of 96.7%, an end-to-end delay of 0.61 seconds, and an average throughput of 868 kbps. These results indicate significant improvements in routing efficiency and reliability, highlighting the potential of FLQL-VANET to enhance VANET performance in real-world scenarios. [ABSTRACT FROM AUTHOR]

The paper introduces the VANET Mobility Regression Forecasting Network (VMRF-Net), a new model for effectively predicting vehicular mobility trends in VANETs (Vehicular adhoc Networks). VMRF-Net uses LSTM (Long Short Term Memory) layers with a 20% dropout rate to avoid overfitting and the 'adam' optimization method to improve learning efficiency. The suggested model produces remarkable results, including a 100% packet delivery ratio at lower node densities (20 and 40 nodes), as well as good performance at higher densities, with a minimum end-to-end delay of 0.29 seconds and a throughput of 1 at a node density of 20. Furthermore, the model shows a strong positive relationship between simulation duration and network sustainability, resulting in constant stability periods and longer operational lifetimes across different simulation lengths. These findings demonstrate the VMRF-Net's efficacy in improving VANET performance and resource utilization. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN), with segregated data and control planes, provides faster data routing, stability, and enhanced quality metrics, such as throughput (Th), maximum available bandwidth (Bd(max)), data transfer (DTransfer), and reduction in end-to-end delay (D(E-E)). This paper explores the critical work of deploying SDN in large-scale Data Center Networks (DCNs) to enhance its Quality of Service (QoS) parameters, using logically distributed control configurations. There is a noticeable increase in Delay(E-E) when adopting SDN with a unified (single) control structure in big DCNs to handle Hypertext Transfer Protocol (HTTP) requests causing a reduction in network quality parameters (Bd(max), Th, DTransfer, D(E-E), etc.). This article examines the network performance in terms of quality matrices (bandwidth, throughput, data transfer, etc.), by establishing a large–scale SDN-based virtual network in the Mininet environment. The SDN network is simulated in three stages: (1) An SDN network with unitary controller-POX to manage the data traffic flow of the network without the server load management algorithm. (2) An SDN network with only one controller to manage the data traffic flow of the network with a server load management algorithm. (3) Deployment of SDN in proposed control arrangement (logically distributed controlled framework) with multiple controllers managing data traffic flow under the proposed Intelligent Sensing Server Load Management (ISSLM) algorithm. As a result of this approach, the network quality parameters in large-scale networks are enhanced. [ABSTRACT FROM AUTHOR]

Implementing an intelligent transportation system that integrates Vehicular Ad-hoc Networks (VANETs) and Flying Ad-hoc Networks (FANETs) can significantly enhance the efficiency and safety of delivery services. There has been lots of research on VANETs and FANETs and how they can improve traffic flow by providing real-time traffic information and enabling efficient communication separately by analyzing various parameters like PDR, Average Throughput, end-to-end delay, etc. In our paper, we attempted to integrate VANETs and FANETs to create an intelligent delivery system. For the integration, we compared protocols AODV and OLSR with performance metrics like PDR, Average Throughput, and end-to-end delay for both VANETs and FANETs, and results show that OLSR has high PDR and less delay with VANETs and AODV performs efficiently than OLSR in FANETs. [ABSTRACT FROM AUTHOR]

Providing seamless connectivity during transit in vehicular ad hoc networks (VANETs) is the most promising and challenging task. In VANET, the vehicles are able to communicate with other vehicles and also with the infrastructure units such as road side unit (RSU). The coverage range of RSU is not sufficient to cover the whole road. The vehicle can drive to an area which is not covered by any of the RSUs, and it is termed as congested zone (CZ). This will extremely lead to traffic congestion and maximize blocking probability. In order to provide connectivity between the road segments and to avoid long delay in message transmission, unmanned aerial vehicles (UAVs) are placed in CZs, which act as relay nodes. To enhance the connectivity of the vehicles, a fuzzy-based UAV-assisted handoff scheme (F-UAV-HO) is proposed in this work. By adopting fuzzy, the unwanted participation of UAV is limited in non-congested traffic, thereby saving the battery resource. To validate the proposed work, the simulations are executed in Network Simulator 2.35. The proposed system outperforms other conventional UAV-assisted schemes in congested traffic in terms of average end-to-end delay, PDR and throughput. [ABSTRACT FROM AUTHOR]

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes' mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes. [ABSTRACT FROM AUTHOR]

The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol's self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications. [ABSTRACT FROM AUTHOR]

With the increasing deployment of FANETs, efficient routing algorithms play a crucial role in ensuring reliable and optimal communication among UAVs. This paper presents a novel Clustered Learning Automata-based Routing Algorithm (called CLARA) for FANETs. The proposed algorithm is designed to enhance the performance of routing by leveraging the capabilities of learning automata. The CLARA consists of five key phases. In the first phase, actions, states, and a Q-Table are initialized to facilitate the learning process. Subsequently, in the second phase, drones within the FANET are clustered based on their geographic location and connection characteristics. This clustering enables efficient management of UAVs and improves network performance. In the third phase, the algorithm selects the next hop for data packet transmission, considering factors such as connectivity and reliability. This selection process ensures robust and efficient data delivery within the network. The fourth phase focuses on Q-Table updating and energy management, which optimizes resource allocation and prolongs network lifetime. Finally, in the fifth phase, the cluster head is selected based on its remaining energy, ensuring effective leadership within each cluster. This dynamic selection process enables the efficient distribution of responsibilities and enhances network stability. Simulation results demonstrate the superiority of the CLARA approach compared to existing methods, including OLSR, AODV, and Q-FANET. CLARA algorithm shows its superiority in the criteria of control overhead, routing overhead, computational overhead of energy consumption, network lifetime, PDR, and end-to-end delay. [ABSTRACT FROM AUTHOR]

Resource Attacks in the Internet of Things (IoT) target to attack resource-related things. It affects the memory, processing, energy, and battery of each node in the IoT environment. Existing protocols use complex mathematical calculations and additional control messages to detect the attacks. That consumes more energy for each node and reduces the lifetime of each node, also bringing the entire network to a halt. To overcome these problems the DE2RA-RPL protocol is proposed. It is a hybrid and lightweight protocol to detect and eliminate Destination Oriented Directed Acyclic Graph (DODAG) Advertisement Object (DAO) Flooding Attacks, Decreased Rank Attacks, and Increased Version Number Attacks at a time. The DE2RA-RPL protocol uses basic RPL control messages, a trickle timer, a minimum rank hysteresis objective function, and small byte flag values to detect and eliminate the attacks. This DE2RA-RPL protocol is suitable for smart communications systems that are used by police and military operations. The Cooja simulator, part of the Contiki operating system, simulates the proposed protocol. The simulation results are compared with existing protocols. It shows that the proposed protocol increases the Packet delivery ratio by 53.23% and Throughput by 59.74%, and decreases the Energy Consumption by 46.5% and, End-to-End Delay by 48.12% than the traditional RPL protocols. [ABSTRACT FROM AUTHOR]

This research introduces an Energy oriented Multi-objective Donkey Smuggler Optimization Algorithm (EM-DSOA) for cluster-based routing in Wireless Sensor Networks (WSNs). The proposed method aims to enhance energy efficiency and network lifespan by optimizing cluster head selection and routing. EM-DSOA utilizes a two-phase approach: a non-adaptive Smuggler algorithm to find potential solutions and an adaptive Donkey algorithm for optimal path selection. The method employs multi-objective fitness functions considering energy, communication cost, cluster head balancing factor, and node degree. K-Means clustering is used for initial node grouping. The performance of EMDSOA is evaluated using metrics such as energy efficiency, end-to-end delay, packet loss, and network throughput. Simulation results demonstrate that EM-DSOA outperforms existing techniques like Multi Weight Chicken Swarm Based Genetic Algorithm (MWCSGA) and Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization (AHCSGWO). The proposed method achieves higher energy efficiency (99.13%), lower end-toend delay (17.04 ms for 20 nodes), reduced packet loss, and improved network throughput (1370.34 Kbps) compared to existing approaches. EM-DSOA's effectiveness in optimizing cluster-based routing contributes to prolonging the overall lifespan of WSNs. [ABSTRACT FROM AUTHOR]

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Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion. [ABSTRACT FROM AUTHOR]

Mobility has ultimate applications in Wireless Sensor Networks (WSNs) including several areas such as wildlife intensive care, health monitoring, flood and fire detection, and much more. Mobile WSNs (MWSNs) have captivated customer attention in recent centuries because of their applications in numerous areas. Mobile WSNs are source constraints and request performance studies by many nodes' movement outlines. Usually, routing algorithms in MWSN have been examined for specified movement. However, for instantaneous network scenarios, designing an efficient routing protocol/algorithm and analysing changes in numerous movement outlines on routing approaches are important to provide efficient results. Hence, keeping in the assessment of the aforesaid problem, an Energy Proficient Mobile Routing (EPMR) protocol is proposed. All the simulations have been completed in MATLAB on varied constraints to assess the effectiveness of EPMR and state-of-the-art routing protocols. Simulation outcomes demonstrate that EPMR provides improved performance than Distributed Efficient Multi-hop Clustering (DEMC), Geographic Robust Clustering (GRC), Mobility Aware Routing (MAR), Distributed Efficient Clustering Approach (DECA), and Improved Energy Mobile Routing (ECMR). EPMR enhances packet delivery ratio by 13–15%, reduces packet loss percentage by 12–14%, extends throughput by 14–16%, decreases overhead by 13–14%, minimizes average end-to-end delay by 13–15%, minimizes energy consumption by 16–18%, and extends network lifetime 17–19% on sensors' mobility. EPMR achieves better outcomes with the Random Waypoint Mobility (RWPM) model than the Random Walk Mobility (RWM) model and Pathway Mobility (PM) model. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are increasingly being utilized in environments where human presence is limited or dangerous. The main goal is to enhance the data processing capabilities of these components to extend the overall lifespan of the design. Researchers have explored conventional energy-saving methods to address the energy constraints of sensor nodes. However, it became clear that traditional routing methods, specifically those based on packet grouping, were inadequate. The proposed system, known as Optimized Group-Centric Data Routing (OGC-DR), introduces an efficient method of data routing by utilizing the concept of grouping nodal points. This approach enhances data routing management by differentiating between routing within a nodal group and routing between adjacent nodal groups. Group Heading Nodes (GHN) are assigned to each group of sensory nodes according to fitness criteria. The implementation of a tree-based routing structure improves data routing by creating a "meeting-zone" and strategically selecting intermediary nodes between the source and destination node. To improve data privacy, a sender and receiver engage in an asymmetric secret-key exchange at nodal points. Data is then directed to its ultimate destination via predetermined intermediary nodes and Group Heading Nodes. Simulations of the proposed method indicate several advantages, such as lower end-to-end delays, reduced energy consumption, higher active node count, and enhanced packet delivery rates. Furthermore, it improves data privacy for all communication within the sensory architecture. [ABSTRACT FROM AUTHOR]

Research on wireless body area networks (WBAN), also known as wireless body sensor networks (WBSN), has been increasingly important in medical applications recently and is now crucial for patient monitoring. To create a dependable body area network (BAN) system, several factors need to be considered at both the software and hardware levels. One such factor is the designing and implementation of routing protocols in the network layers. Protocols for routing can detect and manage the routing paths in a network to facilitate efficient data transmission between nodes. Therefore, the routing protocol is crucial in wireless sensor networks (WSN) to provide dependable communication among the sensor nodes. Different clustering methods can be used in WBAN systems. However, these techniques often produce many cluster heads (CHs), which leads to higher energy consumption. Increased consumption of energy reduces the lifespan of WBANs and raises costs of monitoring. This research proposes a recent metaheuristic algorithm to select the optimal clusters to provide an energy-effective protocol for healthcare monitoring. This research aims to minimize the energy utilization of WBANs by choosing the most suitable CHs based on the BWO. The proposed BWO-based routing protocol demonstrates superior performance in WBANs based on energy consumption, packet loss, packet delivery ratio, network lifetime, end-to-end delay, and throughput. It optimizes energy consumption by effectively selecting CHs and routing paths, leading to balanced energy usage and prolonged network operation. The BWO model significantly reduces end-to-end delay by ensuring data packets follow the shortest and least congested routes, which is significant for real-time health monitoring. It achieves a high packet delivery ratio, typically between 95% and 98%, indicating reliable data transmission, while maintaining a low packet loss rate, generally between 1% and 5%. Additionally, the BWO-based routing protocol extends network lifetime by preventing early node depletion and enhances network throughput by reducing congestion and packet collisions, thereby supporting continuous and robust health data monitoring. [ABSTRACT FROM AUTHOR]

The routing protocol for Vehicular Ad Hoc Networks (VANETs) uses hop-count as a routing metric to determine the shortest route from source to destination. The presence of unstable links in the selected route may cause packet failures, resulting in an increase in packet retransmissions, causing network congestion, increasing total end-to-end delay, and reducing throughput. Thus, route selection based on hop count may not always be optimal in terms of route link quality. In Software-Defined Vehicular Networks (SDVN), the global view of link-state information for the vehicular networks is created at the controller; thus, a better route can be computed. Our study proposes an SDVN routing technique that considers link quality while determining the route. Our proposed framework examines the link quality using two metrics: Expected Transmission Count (ETX) and Expected Transmission Time (ETT), and this information is used by the controller to compute the flow rule. The routing performance of the proposed scheme is compared to traditional VANETs and other existing works with the help of network simulations using the NS-3 simulator. The obtained simulation results for our proposed scheme outperform in terms of performance metrics such as packet delivery ratio, average throughput, and end-to-end delay. [ABSTRACT FROM AUTHOR]

Addressing latency concerns and ensuring high-quality video services in Heterogeneous Mobile Adhoc Networks (H-MANETs) are paramount challenges. This paper presents a pioneering solution: the Pre-emptive Priority Queueing based Multipath Routing algorithm (PPQM). Our approach prioritizes video traffic within OpenFlow switches, directing it across multiple paths in H-MANETs. Integrating the PPQ module within Cluster Heads operating in the software-defined networking (SDN) architecture is central to our design. We rigorously evaluate delay for each path by employing an M/M/1 queueing policy based on a Poisson arrival process and an exponential service time distribution. Utilizing Burke's theorem, our calculation spans the entire route from the cluster head to a sink node. By meticulously assessing the delay characteristics of individual paths, our model facilitates the selection of the most optimal path to minimize overall delay and enhance network performance. Our proposed model amalgamates clustering, FIFO with M/M/1 queueing, and SDN techniques. In a comprehensive evaluation against existing technologies, the implementation of PPQM demonstrates superior performance in crucial Quality of Service (QoS) metrics, including end-to-end delay, queue size, waiting time, throughput, and response time. Furthermore, our research achieves a significant 4.2% improvement in QoS metrics compared to contemporary approaches, highlighting the effectiveness of the PPQM algorithm in enhancing network performance. This research contributes a robust solution for advancing QoS in H-MANETs, demonstrating the efficacy of the PPQM algorithm compared to contemporary approaches. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) has been exploited in numerous regions which can be hardly accessed by humans. However, it is essential to convey the information accumulated by the sensing devices or nodes to the Base Station (BS) for further processing. Multipath routing protocols are found to address these challenges and provide reliable communication. This paper aims to find an optimal path to the gateway with minimum energy consumption and reduced error rate while meeting the end-to-end delay requirements. In this research, an effective multipath routing based on energy prediction and hybrid optimization is developed. Here, a Deep Q-Network (DQN) is applied to predict the energy, and the process is augmented by the usage of a proposed Tangent Search Remora Optimization (TSRO) algorithm. Further, the multipath routing is executed using the TSRO algorithm, considering a fitness function formulated using various factors, like residual energy, distance, throughput, reliability, trust factors, predicted energy, Link Life Time (LLT), delay, and traffic intensity. The devised TSRO-routing is scrutinized for its competence based on trust, throughput, energy, distance, and delay and has achieved superior values of energy of 0.402 J, throughput at 25.056Mbps, trust at 84.975, and minimal distance of 29.964 m, and delay of 0.750 ms. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has been the subject of recent studies and is expected to be extremely important to the future growth of the Internet. The essential component of the IoT is the low-power and lossy networks (LLNs), which are necessary to connect hundreds or even thousands of devices. The IPv6 Routing Protocol for LLNs (RPL), the standard routing protocol for IoT, uses a variety of routing metrics to construct a destination-oriented directed acyclic graph (DODAG), which helps in selecting the Objective Function (OF). To designate the most efficient route to the destination, it must choose the best parent of the nodes. The two OFs used by the standard RPL have drawbacks as they only employ a single metric. As a result, several studies reveal RPL limitations, and just a few IoT applications gain from it. Therefore, in this paper, we will go over the drawbacks of using a single metric and the downsides of utilizing a composite metric by proposing an equilibrium optimizer-RPL (EO-RPL) protocol that selects the preferred parent using a novel optimization method known as an EO algorithm, which relies on a composite metric. The simulation results demonstrate that our novel OF outperforms its counterparts in terms of Packet Delivery Ratio (PDR), control overhead, end-to-end delay, and energy consumption. The results indicate that the average improvement ratios of the PDR of EO-RPL regarding Energy-based Path Cost-RPL (EPC-RPL), Energy Efficient RPL (E-RPL), and Energy Efficient Optimal Parent Selection-RPL (EEOPS-RPL) are 66.9%, 10.3%, and 8.3%, respectively. For control overhead, the improvement ratios of EO-RPL in comparison with EPC-RPL, E-RPL, and EEOPS-RPL are 37.7%, 22%, and 12.4%, respectively. Not only that but, the improvement ratios with regard to the end-to-end delay of EO-RPL are, in relation to EPC-RPL, E-RPL, and EEOPS-RPL, 77.06%, 45.6%, and 32.46%, correspondingly. Finally, for EPC-RPL, E-RPL, and EEOPS-RPL, the improvement ratios of EO-RPL are 16.09%, 14.1%, and 12.12% for energy consumption, respectively. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) clusters offer significant potential in civil, military, and commercial fields due to their flexibility and cooperative capabilities. However, characteristics such as dynamic topology and limited energy storage bring challenges to the design of routing protocols for UAV networks. This study leverages the Deep Double Q-Learning Network (DDQN) algorithm to optimize the traditional Greedy Perimeter Stateless Routing (GPSR) protocol, resulting in a multi-objective optimized GPSR routing protocol (DDQN-MTGPSR). By constructing a multi-objective routing optimization model through cross-layer data fusion, the proposed approach aims to enhance UAV network communication performance comprehensively. In addition, this study develops the above DDQN-MTGPSR intelligent routing algorithm based on the NS-3 platform and uses an artificial intelligence framework. In order to verify the effectiveness of the DDQN-MTGPSR algorithm, it is simulated and compared with the traditional ad hoc routing protocols, and the experimental results show that compared with the GPSR protocol, the DDQN-MTGPSR has achieved significant optimization in the key metrics such as the average end-to-end delay, packet delivery rate, node average residual energy variance and percentage of node average residual energy. In high dynamic scenarios, the above indicators were optimized by 20.05%, 12.72%, 0.47%, and 50.15%, respectively, while optimizing 36.31%, 26.26%, 8.709%, and 69.3% in large-scale scenarios, respectively. [ABSTRACT FROM AUTHOR]

With technological advancements in networking and wireless communication, the flying ad hoc network has emerged as a cost-effective relay network. Networks of unmanned aerial vehicles (UAVs) can be cooperatively organized as an ad hoc network for effectively accomplishing complex tasks. Such a UAV network is deployed as a relay network that forwards and exchanges accurate and reliable end-to-end in-formation. However, the design of routing protocols is rendered difficult by flying constraints and extreme topology changes over a short time period. The performance of the routing protocols in such networks is considerably affected by high-speed nodes and extreme topology changes. Evaluating the routing protocol in a UAV network gives insight regarding the performance of the entire network. This paper evaluates, analyzes, and compares the performances of three routing protocols: Ad hoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination-Sequenced Distance-Vector (DSDV). In most of the simulated cases, the AODV outperformed the OLSR and DSDV. The number of nodes, i.e., the network size, largely affected the throughput, packet delivery ratio (PDR), and end-to-end delay (E2E) of the network. However, the OLSR improved the performance of the average E2E; therefore, it is suitable for delay-sensitive applications. However, AODV outperformed the other protocols in terms of throughput and PDR because it reacts to topology changes. DSDV delivered the poorest PDR and throughput. [ABSTRACT FROM AUTHOR]

Machine failures during the manufacturing process can have severe consequences, causing extensive downtime and financial losses. Hence, predictive maintenance (PdM) plays a crucial role within the Industrial Internet of Things (IIoT) by estimating the remaining useful life (RUL) of machines so that proactive maintenance measures can be taken to mitigate potential failures and minimize disruptions. RUL estimation is enabled by gathering and processing the data sensed by sensors mounted on and around the machines at the central server (base station or cloud) after analyzing the failure patterns. However, this approach imposes a significant load on network bandwidth and leads to poor response time for the monitoring system because a large volume of sensed data has to be transmitted to the central server for processing. Moreover, due to the singularity of the computing resource, many problems, such as inefficient resource utilization, frequent offloading, single-point failure, etc., have become major challenges. To address these issues, this article proposes an edge computing-enabled predictive maintenance framework called "Collaborative Predictive Maintenance Framework" (CollabRULe), which first identifies the optimal locations for edge server placement in the deployment region by considering the QoS parameters, such as energy, delay and connectivity. Then, it uses federated learning for predictive maintenance by estimating the RUL of the machines. Simulation results show that the proposed mechanism effectively minimizes the overall network energy consumption and end-to-end delay by ≈ 60 % and ≈ 35 % , respectively, as compared to state-of-the-art approaches. Furthermore, it shows significant improvement in the accuracy of RUL prediction as compared to its counterparts. [ABSTRACT FROM AUTHOR]

Wireless communication offers significant advantages in terms of flexibility, coverage and maintenance compared to wired solutions and is being actively deployed in the industry. IEEE 802.15.4 standardizes the Physical and the Medium Access Control (MAC) layer for Low Power and Lossy Networks (LLNs) and features Timeslotted Channel Hopping (TSCH) for reliable, low-latency communication with scheduling capabilities. Multiple scheduling schemes were proposed to address Quality of Service (QoS) in challenging scenarios. However, most of them are evaluated through simulations and experiments, which are often time-consuming and may be difficult to reproduce. Analytical modeling of TSCH performance is lacking, as only one-hop communication with simplified traffic patterns is considered in state-of-the-art. This work proposes a new framework based on queuing theory and combinatorics to evaluate end-to-end delays in multihop TSCH networks of arbitrary topology, traffic and link conditions. The framework is validated in simulations using OMNeT++ and shows below 6% root-mean-square error (RMSE), providing quick and reliable latency estimation tool to support decision-making and enable formalized comparison of existing scheduling solutions. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSN), the multifunctional low-power sensor nodes are linked to base stations and are highly responsible for sensing various information. However, maintaining the network's lifespan is a major issue because of limited battery capacity and node failures. Thus, the proposed study introduced a new Scalable Trust-based Energy Efficient Routing Algorithm (STEERA) with Adaptive Grasshopper Algorithm (AGA) to enhance the network lifespan. At first, the trust values (Tv) are generated for each node to eliminate the malicious node with the help of penalty factor and volatilization. To make efficient routing process, the nodes are formed into cluster groups. A suitable cluster head is selected for each cluster to mitigate the energy consumption problem through the AGA approach by optimizing the parameters like Tv, distance and residual energy. Finally, the data packets are effectively transmitted through a multi-hop routing process by selecting an appropriate path and satisfying certain parameters through Fire Hawks Optimization (FHO). To simulate the proposed techniques, NS2 software is employed, and the performance is measured over various metrics like energy consumption, residual energy, the lifespan of a network, packet loss ratio and average end-to-end delay. The performance analysis shows that the proposed protocol design has obtained higher results than conventional routing protocols. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet-connected devices has led to concerns regarding privacy and security. In particular, related to the routing protocol for low-power and lossy networks (RPL), which lacks robust security functions, many IoT devices in RPL networks are resource-constrained, with limited computing power, bandwidth, memory, and battery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifically targets neighboring nodes through DIO messages and poses a significant security threat to RPL-based IoT networks. Recent studies have proposed methods for detecting and mitigating this attack; however, they produce high false-positive and false-negative rates in detection tasks and cannot fully protect RPL networks against this attack type. In this paper, we propose a novel fuzzy logic-based intrusion detection scheme to secure the RPL protocol (FLSec-RPL) to protect against this attack. Our method is built of three key phases consecutively: (1) it tracks attack activity variables to determine potential malicious behaviors; (2) it performs fuzzy logic-based intrusion detection to identify malicious neighbor nodes; and (3) it provides a detection validation and blocking mechanism to ensure that both malicious and suspected malicious nodes are accurately detected and blocked. To evaluate the effectiveness of our method, we conduct comprehensive experiments across diverse scenarios, including Static-RPL and Mobile-RPL networks. We compare the performance of our proposed method with that of the state-of-the-art methods. The results demonstrate that our method outperforms existing methods in terms of the detection accuracy, F1 score, power consumption, end-to-end delay, and packet delivery ratio metrics. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the implementation of routing protocols is crucial owing to their limited computational capacities. Tree routing is a suitable method in wireless sensors owing to its minimal routing overhead. However, single-hop metric schemes, such as hop count, cause congestion at specific nodes, whereas multiple metric schemes cannot control dynamically changing network environments. To address these issues, we propose a scheme to implement enhanced tree routing with adaptive metrics based on hop count. This approach assigns different weights to metrics to select suitable parent nodes based on hop count. The parent-selection algorithm utilizes hop count, buffer occupancy, and received signal strength indicator (RSSI) as metrics. This study evaluates the performance through simulation scenarios to analyze whether improvements can be made to address problems encountered in traditional tree routing. The performance metrics include packet delivery speed, throughput, and end-to-end delay, which vary depending on the volume of network traffic. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) is an advanced standard that utilizes one or more cooperative end-users or neighboring edge devices for communication, storage, configuration, control, management, and measurement operations. This paper studies the reliability of VFC which is explored as a function of its reliable hardware. The architecture of VFC is a multi-layered architecture, therefore composite modeling technique is adopted for the reliability study. Different models are constructed for each layer using reliability block diagrams, and then the models are integrated to obtain the reliability of the entire VFC network. Further, this paper analyses the performance of VFC and evaluates various measures such as end-to-end delay, throughput and utilization. A queueing network model depicting the message dissemination scheme in the network is proposed to evaluate the performance measures. Numerical results are also obtained to show the efficacy of the proposed models. [ABSTRACT FROM AUTHOR]

MANET is widely utilized for interactions among devices without relying on a central governing body. The frequent changes in network topologies expose it to various security vulnerabilities. Enhancing security within MANET is of paramount concern and demands resolution. Addressing these security-related issues stands as the sole approach to ensuring proper data sharing in MANET. Numerous security-oriented techniques have been introduced, but they have encountered several challenges, leading to increased costs and time complexity. These difficulties have necessitated additional resources to be allocated towards mitigating issues and grappling with the growing complexity, thereby introducing a layer of complexity that can impede seamless implementation and efficacy. In this context, a Dynamic Algorithm Switching (DAS) algorithm is proposed to strengthen security in MANET. The process begins with Node clustering, followed by cluster formation through the implementation of the Modified Density-Based Spatial Clustering of Applications (DBSCAN). The proposed method incorporates Cluster Head selection (CHS), which in turn enhances energy efficiency within MANET. The effectiveness of the DAS method is evaluated through experimentation, measuring key aspects such as Packet Delivery Ratio (PDR), end-to-end delay, throughput97.9, network lifetime, and energy consumption with the attained values 98.7%, 15.81mas, 97.9% 952 s as well as 11 J/s respectively. However the existing approaches such as ANFIS-EESC, QoSDSBS, EBR and IBADConBE achieved rate of PDR as 98.3%, 97.6%, 97.1% and 96.9% that diminished the performance. Comparative analysis demonstrates that the proposed method outperforms other techniques across all metrics, establishing its superior efficiency. [ABSTRACT FROM AUTHOR]

In the emerging world the Vehicular Ad-hoc Network (VANET) plays a vital role in the Intelligent Transportation Sector (ITS). Basically VANET are exposed rapid topology changes, dynamic network environment, intermittent connectivity and fluctuating communication range which leads to security and privacy concerns. To address these issues, in this work, an enhanced pairing free certificate less aggregate authentication scheme is proposed. The proposed method employs Elliptic Curve Cryptography (ECC) instead of bilinear pairing algorithm to reduce the overhead and to provide efficient security in the network. Further, during the communication and authentication phases the message transmission takes place in an aggregate manner which in turn reduces the time and storage in the network. The novelty of the proposed work is that the use of aggregation scheme which reduces the delay during the communication in the network. The formal security analysis is provided to the proposed protocol by using the Random Oracle Model (ROM) to prove the level of security and privacy in the proposed system. The proposed system is compared with various existing system and has improved its performance metrics in terms of computational, communication cost, signing delay, verification delay and message end to end delay. The implementation of the proposed protocol is carried out in NS3 simulator. The simulator results also proves that the proposed protocol improves the transmission overhead by 42.28%, computational cost by 39.45%, communication cost by 49.75%, signing delay by 45.23% and verification delay by 44.35% when it is compared with other existing systems. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes, each sensor nodes are equipped with limited resources. WSNs are strategically distributed throughout an environment to continuously monitor, and measure physical and natural parameters such as temperature, humidity, and motion. In the deployed environment, gathered data from the sensor nodes are transmitted to a remote server or Base Station (BS) using multi-hop communication. WSNs are applied in diverse domains such as agriculture, healthcare monitoring, environmental monitoring, and target tracking. However, the sensor nodes, being tiny and deployed in harsh environments, face energy constraints due to limited battery life and the difficulty of recharging or replacing batteries. Therefore, enhancing the energy efficiency and providing security in WSNs is a critical challenge. In the proposed work, Honey Badger Optimization Algorithm (HBO) is employed to provide to elect cluster head (CH) to optimize the energy consumption of network. Remora optimization Algorithm (RO) is employed to discover optimal path during routing in the network. Moreover, the proposed protocol employs Identity based Digital Signature with Conditional Privacy Preservation Algorithm (IDS-CPPA) to provide efficient authentication among the nodes in the networks. The effectiveness of the proposed protocol is implemented by using the NS3 simulator. The simulation results shows that proposed protocol improves average energy efficiency by 13%, packet delivery ratio by 10%, end to end delay by 15%, network lifetime by 17% and network throughput by 13% when it is compared with other existing protocols. Moreover, the proposed protocol improves the Quality of Service (QoS) parameters such as energy efficiency, throughput, packet delivery ratio, network lifetime, and latency and provides better security to the nodes which can withstand various types of attacks in the network. [ABSTRACT FROM AUTHOR]

In Vehicular Ad Hoc Networks, reliable information transmission relies on an effective routing strategy. Most existing reinforcement learning-based routing methods are ineffective in dynamic network environments and cannot prevent inefficient network routing. Efficient network routing can be controlled by network traffic management, so this paper proposes an intelligent routing strategy based on Deep Reinforcement Learning to enhance routing performance. By integrating intersection forwarding and traffic awareness capabilities, this paper addresses the problem of local optimality and utilizes the Deep Q Network to make intersection forwarding decisions. The state space of this strategy consists of intersection nodes, road information between intersections, and forwarding packet information. When a vehicle node carrying a packet approaches an intersection based on the state space, the intersection node uses a neural network to select the optimal next-hop relay intersection from past learning experiences. It generates appropriate vehicle routing decisions based on information from the current and candidate relay intersections. Finally, we use the real taxi trajectory data of Beijing City to conduct extensive simulation experiments. Simulation results and analysis demonstrate that the proposed strategy outperforms related research regarding higher average packet delivery ratio, shorter average end-to-end delay, and lower average overhead ratio in dense and sparse traffic periods under real road environments. Consequently, this strategy provides efficient and reliable message transmission services for Vehicular Ad Hoc Networks. [ABSTRACT FROM AUTHOR]

Some challenges when implementing the optimized link state routing (OLSR) protocol on real-life devices and simulators are unmanageable: link quality, rapid energy depletion, and high processor loads. The causes of these challenges are link state processing, unsuitable multipoint relay (MPR) nodes, and information base maintenance. This paper proposes a structured, energy-efficient link sensing and database maintenance technique. The improved OLSR in the paper replaces the OLSRv2's HELLO, HELLO, and Topology Control (TC) message sequence with a new sequence. MPR nodes are not mandated to broadcast TC messages if the number of nodes and their OLSRv2 addresses remain unchanged after subsequent broadcasts or if no node reported 2-hop symmetric connections. The paper further proposes an MPR selection technique that considers four parameters: node battery level, mobility speed, node degree, and connection to the base station for optimum relay selection. It combines the four parameters into one metric to reduce energy dissipation and control routing overhead. The modifications were implemented in NS-3, and the simulation results show that our improved OLSR outperforms the existing OLSR, OLSRv2 and other improved routing protocols in energy consumption, routing overhead, the packet delivery ratio and end-to-end delay, as compared to the related literature. [ABSTRACT FROM AUTHOR]

Routing in vehicular ad hoc networks (VANETs) enables vehicles to communicate for safety and non-safety applications. However, there are limitations in wireless communication that can degrade VANET performance, so it is crucial to optimize the operation of routing protocols to address this. Various routing protocols employed the expected transmission count (ETX) in their operation as one way to achieve the required efficiency and robustness. ETX is used to estimate link quality for improved route selection. While some studies have evaluated the utilization of ETX in specific protocols, they lack a comprehensive analysis across protocols under varied network conditions. This research provides a comprehensive comparative evaluation of ETX-based routing protocols for VANETs using the nomadic community mobility model. It covers a foundational routing protocol, ad hoc on-demand distance vector (AODV), as well as newer variants that utilize ETX, lightweight ETX (LETX), and power-based light reverse ETX (PLR-ETX), which are referred to herein as AODV-ETX, AODV-LETX, and AODV-PLR, respectively. The protocols are thoroughly analyzed via ns-3 simulations under different traffic and mobility scenarios. Our evaluation model considers five performance parameters including throughput, routing overhead, end-to-end delay, packet loss, and underutilization ratio. The analysis provides insight into designing robust and adaptive ETX routing for VANET to better serve emerging intelligent transportation system applications through a better understanding of protocol performance under different network conditions. The key findings show that ETX-optimized routing can provide significant performance enhancements in terms of end-to-end delay, throughput, routing overhead, packet loss and underutilization ratio. The extensive simulations demonstrated that AODV-PLR outperforms its counterparts AODV-ETX and AODV-LETX and the foundational AODV routing protocol across the performance metrics. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) is an innovative computing paradigm with an exceptional ability to improve the vehicles' capacity to manage computation-intensive applications with both low latency and energy consumption. Moreover, more and more Artificial Intelligence (AI) technologies are applied in task offloading on the Internet of Vehicles (IoV). Focusing on the problems of computing latency and energy consumption, in this paper, we propose an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for an IoV network, which ensures reliable low-latency communication, efficient task offloading in the IoV network by using a Software-Defined Vehicular-based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in the IoV layer to implement the task allocation of each vehicle. By applying AI technologies such as reinforcement learning (RL), Markov decision process, and deep learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) offer a decentralized communication solution ideal for infrastructure-less environments like disaster relief zones. However, their inherent lack of central control and dynamic topology make them vulnerable to attacks. This paper examines the impact of various attacks on mobile nodes within two network types: randomly and uniformly distributed stationary networks. Four types of attacks are investigated: delay, dropping, sinkhole (alone), and a combined black hole attack (dropping + sinkhole). The effects of these attacks are compared using the packet delivery ratio, throughput, and end-to-end delay. The evaluation results show that all single attacks negatively impacted network performance, with the random network experiencing the most significant degradation. Interestingly, the combined black hole attack, while more disruptive than any single attack, affected the uniformly distributed network more severely than the random network. [ABSTRACT FROM AUTHOR]

Target tracking is a crucial application in wireless sensor networks. Current algorithms for target tracking primarily involve node scheduling based on trajectory prediction. However, when the target is lost due to prediction errors, a target recovery mechanism initiates a search operation, potentially activating numerous nodes and leading to increased energy consumption. Furthermore, the recovery process may result in data loss. To address these challenges, we propose a fault-tolerant clustering approach using the Cat Optimization Algorithm to minimize the probability of target loss. To assess the effectiveness of our approach, simulations were conducted in OPNET using the NODIC, DCRRP, BFOABMS, and AFSRP protocols. The results illustrate that our method excels over existing approaches across various metrics. Specifically, compared to the well-known NODIC method, our approach reduces end-to-end delay by 84.93%, media access delay by 15.08%, increases throughput rate by 3.84%, lowers energy consumption by 4.49%, improves signal-to-noise ratio by 9.99%, and enhances delivery rate of data to the sink by 1.02%. Additionally, compared to the widely recognized DCRRP method, our method improves media access delay by 2.90%, throughput rate by 2.02%, reduces energy consumption by 0.30%, enhances signal-to-noise ratio by 7.36%, and improves the delivery rate of data to the sink by 0.41%. Moreover, our proposed method decreases the end-to-end delay by 10.28% compared to DCRRP. Also, the superior performance of the proposed method in terms of end-to-end delay is 1.52%, media access delay by 8.73%, throughput rate by 1.97%, energy consumption by 0.33%, signal-to-noise ratio by 9.25%, and delivery rate of successfully sending data to the sink is 0.76% higher than the well-known AFSRP method.Additionally, compared to the widely recognized BFOABMS method, our method improves media access delay by 9.56% and enhances the delivery rate of data to the sink by 0.70%. However, in our proposed method, the energy consumption criterion has increased by 13.63%, the end-to-end delay criterion by 50.78%, the signal-to-noise ratio decreased by 15.66%, and the throughput ratio decreased by 26.88% compared to BFOABMS. [ABSTRACT FROM AUTHOR]

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols. [ABSTRACT FROM AUTHOR]

In the framework of the space-air-ground-ocean integrated network, the underwater acoustic sensor network (UASN) plays a pivotal role. The design of media access control (MAC) protocols is essential for the UASN to ensure efficient and reliable data transmission. From the perspective of differentiated services in the UASN, a service-aware and scheduling-based hybrid MAC protocol, named the SSH-MAC protocol, is proposed in this paper. In the SSH-MAC protocol, the centralized scheduling strategy is adopted by sensor nodes with environmental monitoring service, and the distributed scheduling strategy is adopted by sensor nodes with target detection service. Considering the time-varying data generation rate of sensor nodes, the sink node will switch the scheduling mode of sensor nodes based on the specific control packet and adjust the resource allocation ratio between centralized scheduling and distributed scheduling. Simulation results show that the performance of the SSH-MAC protocol, in terms of utilization, end-to-end delay, packet delivery ratio, energy consumption, and payload efficiency, is good. [ABSTRACT FROM AUTHOR]