Optimal Path For Data Transmission Research Articles

A deep reinforcement learning-based intelligent QoS optimization algorithm for efficient routing in vehicular networks

With the rapid development of Telematics, Vehicle Self-Organizing Networks (VANETs) play an increasingly critical role in Intelligent Transportation Systems (ITS). Especially in the environment without roadside assistance units (RSUs), how to effectively manage inter-vehicle communication and improve the stability and communication efficiency of the network has become a hot topic of current research. In this paper, a Deep Reinforcement Learning-based Intelligent QoS-optimized efficient routing algorithm for vehicular networks (DRLIQ) is proposed for VANETs with/without RSU environments, and routing methods are proposed respectively. Among them, in RSU-free environment, the DRLIQ algorithm utilizes the powerful processing capability of deep reinforcement learning to intelligently select the optimal data transmission path by dynamically learning and adapting to the changes in the vehicular network, thus effectively reducing communication interruptions and delays, and improving the accuracy of data transmission. The performance of the DRLIQ algorithm under different vehicle densities is evaluated in simulation experiments and compared with current popular algorithms. The experimental results show that the DRLIQ algorithm outperforms the comparison algorithms in reducing the number of communication interruptions, BER and network delay, especially in vehicle-dense environments. In addition, the DRLIQ algorithm shows higher adaptability and stability in coping with network topology changes and vehicle dynamics.

Optimizing Opportunistic Routing between Communities Using Decision Trees and Apriori Algorithm

This study proposes a novel data transmission framework aimed at optimizing node data transmission between different communities. We have designed an intelligent routing strategy based on node characteristics for situations where the source node and destination node have different communities. Through the decision tree algorithm, we trained a model to identify "messenger nodes" that are specifically responsible for cross community data transmission. In addition, the association rule model constructed using the apriori algorithm can calculate the probability of establishing a connection between the "messenger node" and the destination community node, thereby selecting the optimal path for data transmission. This method effectively reduces transmission delay and improves the efficiency and reliability of data transmission.

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems

The Newton-Raphson-Based Optimizer (NRBO), a new metaheuristic algorithm, is suggested and developed in this paper. The NRBO is inspired by Newton-Raphson's approach, and it explores the entire search process using two rules: the Newton-Raphson Search Rule (NRSR) and the Trap Avoidance Operator (TAO) and a few groups of matrices to explore the best results further. The NRSR uses a Newton-Raphson method to improve the exploration ability of NRBO and increase the convergence rate to reach improved search space positions. The TAO helps the NRBO to avoid the local optima trap. The performance of NRBO was assessed using 64 benchmark numerical functions, including 23 standard benchmarks, 29 CEC2017 constrained benchmarks, and 12 CEC2022 benchmarks. In addition, the NRBO was employed to optimize 12 CEC2020 real-world constrained engineering optimization problems. The proposed NRBO was compared to seven state-of-the-art optimization algorithms, and the findings showed that the NRBO produced promising results due to its features, such as high exploration and exploitation balance, high convergence rate, and effective avoidance of local optima capabilities. In addition, the NRBO also validated on challenging wireless communication problem called the internet of vehicle problem, and the NRBO was able to find the optimal path for data transmission. Also, the performance of NRBO in training the deep reinforcement learning agents is also studied by considering the mountain car problem. The obtained results also proved the NRBO's excellent performance in handling challenging real-world engineering problems.

A comparative analysis: optimal node selection in large data block transmission in VANET using various node relay optimization algorithms

VANETs (Vehicular Ad-hoc Networks) are a kind of Mobile Ad-hoc Networks (MANETs) with increased mobility as well as occasional geographic changes. Clustering is used in VANETs to divide networks into groups of adaptable vehicles to optimize routing, information gathering, as well as routing. As large data block transmission contains several stages and inside those, one of the important stages such as optimal model selection was these selections of quintessential features helps in a further stage called data transmission much more effective. This stage will also bring energy optimization and also improves the effectiveness of the optimal path for data transmission. This paper brings a comparative analysis of this optimal relay node selection. Here, we use Linear Scaling based Red Colobuses Monkey technique and are contrasted with other state-of-the-art models over various measures. Analysis proves that the proposed optimal relay node selection technique boosts the data transmission model.

Energy‐efficient data transmissions in heterogeneous ultra‐dense networks using a hybrid metaheuristic optimization

SummaryThe heterogeneous ultra‐dense network (UDN) is a complex network environment where the small cells (SCs) are densely populated to acquire data transmission. The UDN is mainly adopted to deal with the explosive growth of mobile data and its consequential energy consumption issues. The UDN consists of mobile users, restricting the SCs from offering seamless services as the movement may disrupt the transmissions. To provide an effective solution, this paper introduces an energy‐efficient framework that enables effective data transmissions irrespective of the users' mobility. The proposed model considers the clustered SC deployment where the four‐tiered architecture is adopted. The architecture includes a macro base station (BS), microcells, picocells, and femtocells. The SCs are responsible for transferring the data received from the mobile users to the macro BS. The proposed model introduces the hybrid algorithm called the firefly oriented multiverse optimization (FF‐MVO) algorithm to identify the most optimal path for data transmission. This algorithm works iteratively to identify the optimal path to reach the macro BS for each transmission from the user. The proposed model is simulated in the network simulator 3 (NS3) platform, and the results are evaluated with the existing models. The outcomes proved that the proposed algorithm is more optimal than the other models in finding the optimal path to result in energy‐efficient transmissions. The proposed method achieved an average energy consumption of 0.24 J, an average energy efficiency of 10.965 bits/s/J and an average network throughput of 33.907 Gbps.

An implementation of energy efficient fuzzy-optimized routing in wireless sensor networks using Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA)

The clustering approach can improve wireless sensor network parameters such as lifetime enhancement, load balancing, reliable communication, and fault tolerance. The Cluster head in the cluster is responsible for reliable data transmission between node and sink or base station. Selecting suitable cluster heads and establishing an optimal path for data transmission is the main objective of this research work. Fuzzy-based clustering based on cluster head selection, optimized routing using particle swarm optimization (PSO), adaptive whale optimization algorithm (AWOA) are presented in this research work. Fuzzy logic considers the parameters like the distance between base station to node, node centrality, node degree, and residual energy for cluster head selection. The optimization model obtains an optimized node for routing from the selected cluster heads. In terms of network lifetime, delay, energy consumption, packet delivery ratio, and energy efficiency, simulation analysis of the proposed model is compared to conventional routing algorithms such as bacteria foraging optimization (BFO), Tree-based data gathering (TBDG) algorithm, Immune inspired routing (IIR), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy-Efficient Distributed (HEED) protocol. The results demonstrate that the proposed approach outperforms existing approaches in terms of network lifetime and energy efficiency.

A Deep-DrpXML and IAG-GWO based CHST fostered blockchain technology for secured dynamic optimal routing for wireless sensor networks

In wireless sensor network (WSN), routing is one of the substantial maneuvers for distributing data packets to the base station. But malevolent node outbreaks will happen during routing process, which exaggerate the wireless sensor network operations. Therefore, a secure routing protocol is required, which safeguards the routing fortification and the wireless sensor network effectiveness. The existing routing protocol is dynamically volatile during real time instances, and it is very hard to recognize the unsecured routing node performances. In this manuscript, a Deep Dropout extreme Machine learning optimized Improved Alpha-Guided Grey Wolf based Crypto Hash Signature Token fostered Blockchain Technology is proposed for secure dynamic optimal routing in Wireless Sensor Networks (SDOR-DEML-IAgGWO-CHS-BWSN). In this, Crypto Hash signature (CHS) token are generated for flow accesses with a secret key owned by each routing sensor node and it also offers an optimal path for data transmission. Then the secured dynamic optimal routing information is delivered through the proposed Blockchain based wireless sensor network platform with the help of Deep Dropout Extreme Machine learning optimized Improved Alpha-Guided Grey Wolf routing algorithm. Then the proposed method is simulated using the NS-2 (Network Simulator) tool. The simulation performance of the proposed SDOR-DEML-IAgGWO-CHS-BWSN method provide 76.26%, 65.57%, 60.85%, 48.99% and 42.9% lower delay during 30% malicious routing environment, 73.06%, 63.82%, 59.25%, 44.79% and 38.84% lower delay during 60% malicious routing environment is compared with the existing methods.

Hybrid localization scheme using K‐fold optimization with machine learning in WSN

SummaryNode localization technology can identify and track nodes, making observing data more relevant; for example, information received at the sink node would be useless to the client if node localization data from the sensor region were not included. Localization is described as determining the location of unknown sensor nodes named destination nodes applying the recognized location of anchor nodes based on measurements such as time difference of occurrence, time of occurrence, angle of occurrence, triangulation, and maximum probability. The purpose of node localization is to assign coordinates points to all sensor nodes arbitrarily put in the monitoring region and have an unknown location. Localization of nodes is essential to account for the cause of events that help group sensor querying, routing, and network coverage. In this paper, data transmission among the nodes is done by comparing the received signal strength indicator (RSSI) value with the supervised learning value. If the RSSI value is less than the supervised learning value, the data transmission takes place; else, no transmission. This paper proposes a hybrid localization scheme that effectively uses K‐fold optimization with supervised learning and gives good results for distance error and RSSI/energy efficiency. The proposed scheme can effectively detect the optimal path for data transmission, node localization for the destination, and overall performance enhancement using threshold decision making.

A mean field game-theoretic cross-layer optimization for multi-hop swarm UAV communications

Unmanned aerial vehicle (UAV) multi-hop communication networks are foreseen to be widely employed in both military and civilian scenarios. However, in ultra-dense scenarios with swarm UAVs, nodes are highly dynamic mobile, ultra-dense deployment and non-centralized distribution. These characteristics make the centralized resource management policy not apply. Meanwhile, existing routing protocols can't meet the performance challenges of high dynamic, topology and link frequency changes of ultra-dense scenarios with swarm UAVs. To solve the above challenges of resource management and routing protocol, a cross-layer optimization method is presented with a novel mean field game (MFG) in this paper. It is based on the cross-layer design method of the MFG theory and jointly considers the power resources in the physical layer, frequency resources in the medium access control (MAC) layer, and routing resources in the network layer. By dividing into subproblems, the original problem is solved. Meanwhile, the optimal data transmission path can be selected through the management and allocation of frequency resources and power resources. A cross-layer resource management dynamic source routing (CLRM-DSR) protocol is designed based on that which adds link quality measurement. The simulation results show that the presented CLRM-DSR with the proposed resource management scheme can improve the data packet transmission rate, reduce end-to-end delay, and lower routing overhead for the multi-hop swarm UAV communication network.

Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT

Detection of crop diseases is imperative for agriculture to be sustainable. Automated crop disease detection is a major issue in the current agricultural industry due to its cluttered background. Internet of Things (IoT) has gained immense interest in the past decade, as it accumulates a high level of contextual information to identify crop diseases. This study paper presents a novel method based on Taylor-Water Wave Optimization-based Generative Adversarial Network (Taylor-WWO-based GAN) to identify diseases in the agricultural industry. In this method, the IoT nodes sense the plant leaves, and the sensed data are transmitted to the Base Station (BS) using Fractional Gravitational Gray Wolf Optimization. This technique selects the optimal path for data transmission. After performing IoT routing, crop diseases are recognized at the BS. For detecting crop disease, the input image acquired from the IoT routing phase is then forwarded to the next step, that is, preprocessing, to improve the quality of the image for further processing. Then, Segmentation Network (SegNet) is adapted to segment the images, and extraction of significant features is performed using the acquired segments. The extracted features are adapted by the GAN, which is trained by Taylor-WWO. The proposed Taylor-WWO is newly devised by integrating the Taylor series and WWO algorithms. The proposed Taylor-WWO-based GAN showed improved performance with a maximum accuracy of 91.6%, maximum sensitivity of 89.3%, and maximum specificity of 92.3% in comparison with existing methods.

Implementation of ACO in Vanet with Detection of Faulty Node

Objective: To propose an Ant Colony Optimization (ACO) based routing approach for estimating the reliability parameter of backward and forward ants. Methods: The ACO-based proposed methodology is used to predict path or route discovery in VANET. This study has discovered the best optimal path for data transmission. The forward ant movement has a minimum hop count of four for vehicle number 6. The vehicle 6 is a source vehicle, and it starts data transmission. The pheromone concentration of the entire vehicle defers and, after updating, the performance was analyzed. The best path is selected based on hop count, then a path with maximum pheromone concentration is selected. The pheromone evaporation factor is updated after a certain time interval. Findings: We have checked the reliability of the path, considering pheromone evaporation as a factor, the pheromone table is updated at a regular time interval, and the reliability parameter is also proposed for performing a task without failure in an efficient manner. We have arranged a routing table in descending order of hop count and then select the best of three paths with a minimum hop count. We search for hop count less than or equal to 5 and total pheromone greater than or equal to 17.55. We find that Ant 3 having a hop count of 5 and total pheromone concentration of 19.37. In this work, we have taken the evaporation factor as 0.5. If the transmission of a message is successful, then the reliability assigned to the edge will be 100, and for unsuccessful transmission, reliability is taken down by 20. If the reliability of the path will become 40, then the vehicle has to search for a new path. In this way, we eliminate malicious nodes for efficient transmission of message. Novelty: The performance of VANET is improved using ACO based proposed routing approach. The optimal path is selected with a minimum hop count and reliability parameter to identify the successful and unsuccessful path. Keywords: Network security; VANET; Sybil; ACO; Pheromone Evaporation

An Efficient Data Gathering Technique Using Optimal Minimum Coverage Spanning Tree Algorithm in WSN

A WSN system incorporates a gateway that provides wireless connectivity back to the wired world and distributed nodes. Many existing schemes were utilized for the purpose of increasing the data gathering capacity of sensor nodes from source to sink node. But the existing schemes suffered from maximum distance problem of reaching the sink node. Hence, to overcome those issues, we propose an energy efficient data gathering technique in WSN. Initially, in this paper, the sensor nodes are clustered using LEACH protocol. After the clustering process, cluster heads (CHs) are selected. These selected CHs in all the clusters are used for transferring the messages to the mobile node for data gathering. Then, optimal data transmission path is selected with the help of Optimal Minimum Covering Spanning Tree (OMST) algorithm. The OMST algorithm enhances data gathering and reduces the duration for reaching the sink node via selection of polling points. For reducing time complexity in MST, an efficient optimization algorithm is used. For an optimization purpose, particle gene swarm (PGS) model is used. From the experimental results, the proposed method achieved better results than the existing method in terms of energy, overhead, delay, delivery ratio, NLT and throughput.

A bidirectional congestion control transport protocol for the internet of drones

Wireless sensor networks (WSNs) are composed of energy constrained devices that autonomously form networks through which sensed information is transported from the region of interest to the central control station (sink), integration with unmanned aerial vehicles (UAVs) leads to enlarged monitoring area and to enhance overall network performance. Due to application specific nature of wireless sensor networks, it is challenging to design a congestion control protocol that is suitable for all types of applications in the Internet of Drones (IoD). Congestion avoidance and control in wireless sensor networks mainly aims at reducing packet drop due to congestion and maintaining fair bandwidth allocation to all network flows. In this research work, we propose a reliable and congestion based protocol, which provides both bidirectional reliability and rate adjustment based congestion control. It uses Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to select optimal path for data transmission because TOPSIS selects an alternative such that it has shortest distance from the ideal one and greatest distance from negative ideal solution. Congestion is detected by using proportion of average packet service time over average packet inter-arrival time as congestion degree. Then, congestion is notified using implicit notification to save energy and reduce overhead. To mitigate congestion along with maintaining fairness, an equal priority index is assigned to all data sources and when congestion occurs, rate adjustment to optimal value based on priority value is used for congestion control. This approach helps to diminish packet drops, maintain fairness and get better energy efficiency. Finally, we compare the performance of the proposed protocol with that of existing protocols. Our simulation results show reduced average delivery overhead, drop packet ratio, queue length and delay with increased average delivery ratio. Moreover, our protocol provides better energy efficiency and fairness when compared with the existing competing protocols.

An energy efficient and secure data forwarding scheme for wireless body sensor network

Monitoring human bodies and collecting physiological information are the major healthcare applications for wireless body area networks (WBANs). Due to the limited energy resource of body sensors, their energy depletions will cause severe network performance degradation such as latency and energy efficiency. To solve this problem existing system introduced an energy-efficient data forwarding strategy (EDFS) to balance sensor energy consumption, improve network lifetime and collaborative operations of heterogeneous WBANs. However, the existing system only considered the energy function for optimal relay sensor node selection, does not considered the delay and hop count of node. Hence in this system, the TOPSIS-based artificial bee colony algorithm (TOPSIS-based ABC) is proposed to select the optimal path for data transmission. In order to reduce the network congestion compressed sensing (CS) is performed. To provide high security for patient related data hybrid RSA and Diffie-Hellman algorithm is used. The proposed methodology is implemented by using NS-2 simulator.

Method for Effectively Utilizing Node Energy of WSN for Coal Mine Robot

As a special application scenario, the data collected by wireless sensor networks of coal mine robot is from vital and dangerous environment. Therefore, the nodes need to work as long as possible. In order to efficiently utilize the node energy of wireless sensor network, this paper proposes a self-organizing routing method for wireless sensor networks based on Q-learning. The method takes many factors into account, such as the hop number, distance, residual energy, and node communication loss and energy. Each node of the wireless sensor networks is mapped into an Agent. Periodic training is carried out to optimize the route choice. Each Agent chooses the optimal path for data transmission according to the calculated Q evaluation value. Simulation results show that the self-organizing sensor networks using Q-learning can balance the energy consumption of the nodes and prolong the lifetime of the networks.

An Efficient Mechanism Protocol for Wireless Sensor Networks by using Grids

Multilevel short-distance clustering communication is an important scheme to reduce lost data packets over the path to the sink, particularly when nodes are deployed in a dense WSN (wireless sensor network). Our proposed protocol solves the problems of single hop paths in the TDTCGE (two-dimensional technique based on center of gravity and energy) method, which addresses only single-hop problems and does not minimize distances between nodes by using multi-hop nodes with multilevel clustering grids to avoid dropped packets and to guarantee reliable paths without failures. In multilevel clustering grids, transmitted data are aggregated from lower-level grids to upper-level grids. In this paper, the proposed protocol obtains the optimal path for data transmission between cluster heads and the sink for heterogeneous WSNs. The cluster head nodes play an important role in forwarding data originating from other normal nodes that aggregate data to upper clusterheads. This routing approach is more efficient than other routing approaches, and it provides a reliable protocol for avoidance of data loss. In addition, the proposed protocol produces sleep and wakeup signals to the nodes and cluster heads via an MD (mediation device), thereby reducing energy consumption. Simulation results demonstrate the efficiency of the proposed method in terms of fewer dropped packets and high energy efficiency. The network environment overcomes the drawbacks of failure paths and provides reliable transmission to the sink.

Energy Efficient Event Detection Using Probabilistic Inference in Wireless Sensor Networks

ABSTRACTA dense deployment in a wireless sensor network results in multiple nodes reporting the same event. Although data correlation is a much sought parameter for accurate event detection, such uncontrolled transmissions result in high in-network traffic, network congestion, and packet loss. Existing event detection schemes usually consider an acceptable trade-off between the detection accuracy and energy conservation. Therefore, a novel delay-based event detection scheme is proposed in this work to minimize the in-network traffic at the leaf level and optimize the overall energy consumption of the deployed network. The optimal data transmission path to the sink is chosen via a fitness function calculated for all the potential cliques in a Markov random field-based sensor network. Extensive simulation analysis of the proposed scheme proves its efficacy in terms of detection accuracy, communication cost, delivery delay, network energy, and packet delivery ratio.

A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks.

With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service.

An Intelligent Energy Efficient Routing Protocol for Mobile Ad-Hoc Network

Objectives: To develop an energy-efficient routing protocol for Mobile Ad Hoc Network (MANET). Methods: The research work includes the Bee Colony Optimization (BCO) technique for evolving energy efficient routing algorithm. The Intelligent Cross-Layer approach based Bee Routing (ICLBR) is inspired by BCO in order to discover the optimal path for data transmission. The ICLBR Intelligent Cross Layer Approach based Bee Routing algorithm is used to overcome the congestion problem, link failure and energy loss using cross layer approach. The effectiveness of the route has been evolved with the parameter of node degree and connectivity ratio. Findings: This paper proposes an ICLBR algorithm to discover the energy efficient route in order to increase the network lifetime for MANET. The ICLBR is experimented in NS2.34, to systematically validate the routing algorithm. The experimental results show that the ICLBR improves the packet delivery ratio by 9.08% and minimizes the average end-to-end delay is by 5.16% over the Ad hoc On-Demand Distance Vector (AODV) routing protocol. Improvement: The proposed ICLBR mechanism able to enhance the network resource and also improves the network lifetime as compared to the state-of-art works.