Vehicle Nodes Research Articles

An efficient model of enhanced optimization and dilated-GRU based secured multi-access edge computing with blockchain for VANET sector

The establishment of Vehicular Ad Hoc Networks (VANETs) has brought significant advantages to humans, yet it also raises crucial safety considerations. Security is one of the major challenges in VANETs and is receiving significant attention. Hostile Onboard Units (OBUs) can use a variety of tactics including blocking, monitoring and tampering to harm neighboring OBUs to make illicit profits. The widespread use of the decentralized architecture built around blockchain technology has enabled the clear, safe, and dispersed storage and transmission of VANET application-related information without the need for an administrative center of authority. Perhaps the most important parts of the blockchain-driven VANET system involve implementing an efficient and adaptable consensus system, which remains an open academic issue. The objective of the suggested solution is to create a deep learning model for blockchain that will guarantee VANET security. This network topology is comprised of three layers: “perception, edge computing, and services”. The initial layer that comprises the blockchain operation is employed to demonstrate the privacy of VANET information. Additionally, cloud-based services as well as edge computing are used by the perception layer. The data is safeguarded by the service layer through the application of the blockchain system and storage in the cloud. The last layer aids in meeting user criteria for throughput and QoS. The main objective of this model is to evaluate the reliability of vehicle nodes maintained on the blockchain. Here, the node authentication is handled by an Adaptive Dilated Gated Recurrent Unit (AD-GRU), where the hyper-parameters of the recommended AD-GRU model are optimally tuned by an Enhanced Osprey Optimization Algorithm (EOOA). Finally, the proposed system is simulated and its extensive results are carried out. In contrast with other approaches, the novel system delivers the superior results of securing the data over the VANET environment.

IoV-BCFL: An intrusion detection method for IoV based on blockchain and federated learning

In recent years, Internet of Vehicles (IoV) is in a booming stage. But at the same time, the methods of attack against IoV such as Denial of Service (DoS) and deception are great threats to personal and social security. Traditional IoV intrusion detection usually adopts a centralized detection model, which has the disadvantages of untimely detection results and is difficult to protect vehicle privacy in practical applications. Meanwhile, centralized computation requires a large amount of vehicle data transmission, which overloads the wireless bandwidth. Combined the distributed computing resources of Federated Learning (FL) and the decentralized features of blockchain, an IoV intrusion detection framework named IoV-BCFL is proposed, which is capable of distributed intrusion detection and reliable logging with privacy protection. FL is used for distributing training on vehicle nodes and aggregating the training models at Road Side Unit (RSU) to reduce data transmission, protect the privacy of training data, and ensure the security of the model. A blockchain-based intrusion logging mechanism is presented, which enhances vehicle privacy protection through Rivest-Shamir-Adleman (RSA) algorithm encryption and uses Inter Planetary File System (IPFS) to store the intrusion logs. The intrusion behavior can be faithfully recorded by logging smart contract after detecting the intrusion, which can be used to track intruders, analyze security vulnerabilities, and collect evidence. Experiments based on different open source datasets show that FL achieves a high detection rates on intrusion data and effectively reduce the communication overhead, the smart contract performs well on evaluation indicators such as sending rate, latency, and throughput.

Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning

This study presents a comprehensive framework for optimizing intelligent transport systems (ITS) by integrating advanced communication and information technologies into vehicles, roads, and infrastructure. The primary goal is to enhance transportation efficiency, safety, and environmental sustainability while improving overall mobility for people and goods. Leveraging contextual information, the framework offers personalized, proactive services such as real-time traffic updates, route recommendations, and parking availability. Additionally, it enhances safety and security by providing early hazard warnings and adapting to changing road conditions. Our proposed framework utilizes the enhanced coral reef optimization (ECRO) algorithm to efficiently group vehicles for energy-saving data collection, maximizing information gathering efficiency. Collected data is then transmitted to a central data gathering center via a sink node optimized through the modified pelican optimization (MPO) algorithm, considering various vehicle node design constraints. An incident detection module accurately classifies and detects road incidents, enabling timely emergency service requests and alternate route recommendations. To facilitate incident detection, we introduce the deep Rigdelet neural network (DRNN), a novel deep learning technique tailored for decision-making in incident classification. We validate our framework's performance through NS-2 simulations using the SUMO traffic generator, demonstrating its effectiveness in meeting quality of service (QoS) metrics. Through comparative analysis with existing frameworks, our proposed approach stands out for its superior performance and ability to optimize ITS operations.

VSFL: Trajectory prediction framework based on validity-aware semi-asynchronous federated learning in internet of vehicles

Accurate prediction of vehicle trajectories on the Internet of Vehicles is crucial for improving traffic safety. Federated learning has been proposed to address the issue of islands in shared data. Traditional synchronous federated learning requires waiting for each node to complete training before model aggregation can occur, and the aggregation algorithm of the server cannot adjust the aggregation weight in real-time, which leads to increased communication overhead, time cost, and decreased accuracy. To balance these issues, this paper proposes a Validity-aware Semi-asynchronous Federated Learning-based framework for predicting vehicle trajectories. The framework makes the following improvements - firstly, based on the asynchronous cooperation algorithm of generalization research, the models of vehicle nodes within the same RoadSide Unit (RSU) range are aggregated into this RSU to reduce communication overhead and time costs. To further ensure the privacy and security of all participants, we added differential privacy algorithms during the model training process. Secondly, the server uses a weighted aggregation algorithm based on effective perception to aggregate all RSU models into a global model to address potential accuracy issues. This article uses a Social-LSTM like model to validate the effectiveness of this framework in the Next Generation Simulation (NGSIM) Vehicle Trajectories and Support Data dataset. Compared with traditional federated averaging algorithms and trust aware federated learning algorithms, this framework reduces distance errors by 27.80 % and 19.23 %, respectively, and improves accuracy by 22.56 % and 13.59 %.

Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can’t be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively.

Fuzzy-Logic Based Model for VANET Performance Metrics Analysis

Vehicular Ad-hoc Networks (VANETs) allows a spontaneous communication between vehicle to vehicles and road-side units(RSU) using wireless interfaces and road-side infrastructure to form Intelligent Transportation System (ITS). VANET, prime objective is to provide information about accidents or unwanted situations and traffic conditions to the drivers. As VANETs are dynamic in nature, the vehicle nodes to discover the best possible route to broadcast an emergency message is a challenge. To study different highway and urban road scenarios, there is a need to simulate and evaluate the network performance analysis of routing protocols beforehand since real-time implementation is rather challenging or expensive. This paper focuses on the VANET routing protocols evaluation, to find an appropriate protocol which provide suitable Quality of Service (QoS) to end users in both safety and non-safety conditions. To simplify the QoS evaluation problem, Hierarchical Fuzzy Inference System (HFIS) model has been designed and simulated in MATLAB R2018b that analysis the network performance metrics of VANET routing protocols. From the simulations, it is observed that that OLSR routing protocol for both safety and non-safety applications, outperforms other routing protocols as its global QoS reaches to approximately 84% efficiency.

OHRDBN: An optimized hybrid model of RBF and DBN for obstacle‐aware routing with optimal path selection in VANET sector

SummaryVehicular ad hoc networks (VANETs) are increasingly essential for communication architecture utilized in daily human life. The presence of obstacles in the terrain area, as well as the vehicular nodes present in the network, is considered an essential constraint since it reduces the efficacy of the mobile ad hoc network (MANET). To attain the VANET requirements, a novel routing scheme is designed to identify the obstacle. The main aspect of the proposed work is to detect the obstacle and determine the path availability. Initially, the characteristics of vehicle nodes are gathered, which include the packet loss, received signal strength indicator (RSSI), connectivity, energy level, and so on. In the process of data transmission, the proposed model is intended to detect the obstacles and determine the available path by using an optimized hybrid model (OHL) combining radial basis function (RBF) and deep belief network (DBN). The parameter optimization in the hybrid classifier model is done with the help of the adaptive chimp optimization algorithm (ACOA). Once it checks the path availability, then the path optimization is accomplished by using the same ACOA. Finally, the objective function for the proposed work is constructed by considering various constraints like distance, energy, link availability, life span, link duration, and mobility. The performance of the implemented system is analyzed, and its results are validated through different measures. The outcome of the suggested model demonstrates that the implemented system tends to exploit effective routing with obstacle detection when compared to various other existing approaches.

Multi-vehicle trajectory prediction and control at intersections using state and intention information

Traditional deep learning approaches for prediction of future trajectory of multiple road agents rely on knowing information about their past trajectory. In contrast, this work utilizes information of only the current state and intended direction to predict the future trajectory of multiple vehicles at intersections. Incorporating intention information has two distinct advantages: (1) It allows to not just predict the future trajectory but also control the multiple vehicles. (2) By manipulating the intention, the interaction among the vehicles is adapted accordingly to achieve desired behavior. Both these advantages would otherwise not be possible using only past trajectory information Our model utilizes message passing of information between the vehicle nodes for a more holistic overview of the environment, resulting in better trajectory prediction and control of the vehicles. This work also provides a thorough investigation and discussion into the disparity between offline and online metrics for the task of multi-agent control. We particularly show why conducting only offline evaluation would not suffice, thereby necessitating online evaluation. We demonstrate the superiority of utilizing intention information rather than past trajectory in online scenarios. Lastly, we show the capability of our method in adapting to different domains through experiments conducted on two distinct simulation platforms i.e. SUMO and CARLA. The code for this work can be found on the project page here: https://dekai21.github.io/Multi_Agent_Intersection/.

An Anti-Collision Algorithm for Self-Organizing Vehicular Ad-Hoc Network Using Deep Learning

The rapid increase of the number of motor vehicles over the last several decades has driven a corresponding increase in the severity of traffic congestion, which has exhibited a considerable human impact. Intelligent anti-collision control via inter-vehicle communication technology can facilitate vehicles adhering to spacing speed standards and improve road utilization and traffic efficiency. In this paper, we apply a deep learning image estimation model based on joint attention mechanism. The network framework uses a deep estimation network Yolov5 and a location based VANET information fast transmission strategy to work together. This paper mainly considers vehicle distance measurement technology as a point of entry to study multi-sensor information fusion for vehicle collision prevention technology based on the self-organizing vehicular ad-hoc network (VANET). This paper also proposes a solution based on the strategy of one-way transmission of shared information and dynamic valuation of a cluster distance threshold with vehicle density. The proposed vehicle anti-collision control algorithm is designed to realize dynamic vehicle control via inter-vehicle communication. In this paper, the two-way coupling of traffic flow and network simulator is used to randomly generate vehicle nodes on the road, and the behavior of the anti-collision system is simulated. The experimental results show that the predetermined control goal is achieved, which demonstrates the effectiveness of the proposed algorithm.

Machine learning based centralized dynamic clustering algorithm for energy efficient routing in vehicular ad hoc networks

AbstractVehicular ad hoc network (VANET) is a dynamic and self‐configure wireless network that connects vehicles to provide in‐vehicle infotainment services and comfort to both driver and passengers while on the move. Due to the highly dynamic environment, patchy connectivity, roadside barriers, and a scarcity of road side units (RSUs); existing routing protocols of VANET consume high energy during route construction. As a consequence, the VANET lifespan might be shortened. However, saving energy is a critical factor where VANET is projected to play a key role in the building of sustainable green infrastructure. Clustering is an imperative process where vehicle nodes join to make stable clusters based on common mobility features. In VANET, clustering‐based routing algorithms significantly improve network effectiveness and reduce infrastructure costs. This article proposes fuzzy c‐means (FM) machine learning‐based dynamic clustering algorithm (DCA) to identify trustworthy vehicles for energy‐efficient multi‐hop routing by considering different mobility parameters such as position, direction, speed, and remaining energy of each vehicle node. This article also proposes distance and power‐based variation of FM called fuzzy c‐means using distance and residual power (FMDP) to find stable cluster heads (CHs) for performing better data dissemination to the destination. The proposed DCA outperforms the existing k‐means based clustering algorithms as well as topology‐based DSR and AODV routing protocols in terms of packet delivery ratio, application throughput, end‐to‐end delay, network energy consumption, node energy consumption, and RSU energy consumption. Additionally, clustering methods FM and FMDP of suggested DCA improve end‐to‐end delay by 20%, application throughput by 5%, and reduce network energy consumption by 68% compared with the k‐means based clustering model.

DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment.

Simulation of Vehicular Bots-Based DDoS Attacks in Connected Vehicles Networks

Connected vehicles are more vulnerable to attacks than wired networks since they involve rapid mobility, continuous data flow across connected nodes, and dynamic network design in a distributed network environment. Distributed Denial of Service (DDOS) is one of the most common and dangerous security attacks on connected vehicle networks. Attackers can remotely control malicious nodes that are programmed to attack other nodes known. The compromised nodes are known as botnets, which will constantly flood the target nodes with User Datagram Protocol (UDP) packets, disrupting the target nodes data flow and operation. Hence, the goal of this research is to create and simulate a vehicular bot-based Distributed Denial of Service (DDoS) assault in connected vehicle networks. A simulation-based methodology is implemented to observe the impact of the number of bots, DDoS rate, and maximum bulk packet size on network performance. Using the NS-3 network simulator, 73 random mobile vehicle nodes with up to 100 vehicle bots were simulated, and the results are discussed. Regardless of the computational constraints, the findings from this study adds to understanding the risks and problems associated with data transmission by analyzing the impact of vehicular bot-based DDoS attacks on connected vehicle performance. Doi: 10.28991/HIJ-2023-04-04-014 Full Text: PDF

An Efficient and Secure Blockchain Consensus Protocol for Internet of Vehicles

Conventional blockchain consensus protocols tailored for the Internet of Vehicles (IoV) usually face low transaction throughput, high latency, and elevated communication overhead issues. To address these issues, in this paper, we propose ESBCP, an efficient and secure blockchain consensus protocol for the IoV environment. Firstly, considering the significant performance differences among nodes in the IoV, we designed a blockchain consensus model for the IoV. Roadside units execute a trust evaluation mechanism to select high-quality vehicle nodes for the consensus process, thereby reducing the likelihood of malicious nodes in the consensus cluster. Secondly, we designed a node partition strategy to adapt to the dynamic feature of the IoV. Finally, addressing the mobility of nodes in the IoV, we introduced a dynamic unique node list. Vehicle nodes can promptly select nodes with high reliability from the list of communicable nodes to join their unique node list, while also promptly removing nodes with low reliability from their unique node list. Combining these strategies, we propose DK-PBFT, an improved Practical Byzantine Fault Tolerance consensus algorithm. The algorithm meets the efficiency and mobility requirements of vehicular networks. Through theoretical analysis, ESBCP could prevent external and internal security risks while reducing communication overhead. Experimental verification demonstrated that ESBCP effectively reduces consensus latency and improves transaction throughput. Our proposed ESBCP can be used in other application scenarios that require high consensus efficiency.

Access Control Strategy for the Internet of Vehicles Based on Blockchain and Edge Computing

Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called the Blockchain Trust and Weighted Attribute-Based Access Control Strategy (BTWACS). First, we utilize both local and global blockchains to jointly maintain the generation, verification and storage of blocks, achieving distributed data storage and ensuring that data cannot arbitrarily be tampered with. Local blockchain mainly uses Road Side Unit (RSU) technology to calculate trust values, while global blockchain is mainly responsible for data storage and access policy selection. Secondly, we design a blockchain-based trust evaluation scheme called Blockchain-Based Trust Evaluation (BBTE). In this evaluation scheme, the trust value of the vehicle node is based on four factors: initial trust, historical experience trust, recommendation trust and RSU observation trust. CRITIC is used to determine the optimal weights of four factors to obtain the trust value. Then, we use the Network Simulator version 3 (NS3) to verify the security and accuracy of BBTE, improving the recognition accuracy and detection rate of malicious vehicle nodes. Finally, by mining the association relationships between attribute permissions among various roles, we construct a hierarchical access control strategy based on weight and trust, and further optimize the access strategy through pruning techniques. The experiment results indicate that this scheme can effectively respond to gray hole attacks, defamation attacks and collusion attacks from other vehicle nodes. This method can effectively reduce the computing and transmission costs of vehicles and meet the access requirements of multiple entities and roles in the IoV.

Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol for Vehicular Ad Hoc Networks

Vehicle ad-hoc networks (VANETs) have become a prominent research topic in recent years due to rapid dynamic topology, high vehicle mobility, frequent link failures and significant delay constraints. The mobility of the vehicle nodes increases, and the overhead of control traffic due to the high dynamics of the VANET. Efficient routing algorithms are necessary for VANETs to ensure reliable transmission (VANETs). This work proposes a Traffic Density Stable Routing Protocol based on Connection- and Distance (TDSRP-DC) to avoid data packet collisions at intersections and an adaptive routing schedule based on the selection at every instant. Our approach is based on vehicle-to-vehicle communication. Ground vehicles identify the most appropriate next junction and transfer the data packet to the receiver to find the optimal multi-hop route. It relies on transferring the data between all vehicles to estimate real-time traffic fluctuations sporadically. Network formation, neighbor realization, fitness value prediction, and routing methodology are the conceptual process needed in this research. Valid parameters for finding the optimum path in the conceptual model include node distance, node speed, node azimuth, link stability, and link reliability. The suggested solution’s simulation was performed in comparison to traditional algorithms. Compared to existing TFOR algorithms on complex traffic, the proposed TDSRP-DC provides tremendous improvements based on packet delivery rate (10%) and performance (35%). The demonstrated results and comparisons illustrate the proposed routing protocol’s significance with enhanced quality of service performance.

RETRACTED ARTICLE: Intelligent monitoring methodology for large-scale logistics transport vehicles based on parallel Internet of Vehicles

The essence of the Internet of Vehicles is a social and physical information system, including the psychological and organizational factors of human beings. The complexity of the Internet will lead to certain deviations when monitoring vehicles. Therefore, the Parallel Internet of Vehicles is employed to monitor the information on large-scale logistics transport vehicles. This platform is built based on the ACP intelligent approach, which consists of three parts: An artificial system (A), a computational experiment (C), and parallel execution (P). The Adaboost algorithm is used to extract information on large-scale logistics transport vehicles from the ACP parallel Internet of Vehicles, and the Tabu search strategy is applied to optimize the Monte Carlo positioning algorithm. The approximate optimal estimation is obtained by optimizing the filtering to eliminate vehicle positions with fewer possibilities. The weight of important sampling values of the independent vehicle node positions is integrated to complete the posterior probability distribution estimation of the possible positions of vehicles, in order to realize vehicle position monitoring. It is verified that the root-mean square error of the algorithm when positioning a vehicle is less than 0.18, and the monitoring deviation is quite small.

DECCo-A Dynamic Task Scheduling Framework for Heterogeneous Drone Edge Cluster

The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing task requests for the heterogeneous DEC. Benefiting from the latest advances in deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency through a collaborative Advantage Actor–Critic algorithm, which avoids the interference of resource overload and local downtime while ensuring load balancing. To better adapt to the real drone collaborative scheduling scenario, DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance, thus avoiding suboptimal decisions that severely affect Quality of Service (QoS) and Quality of Experience (QoE). With flexible parameter control, DECCo can adapt to various task requests on drone edge clusters. Google Cluster Usage Traces are used to verify the effectiveness of DECCo. Therefore, our work represents a state-of-the-art method for task scheduling in the heterogeneous DEC.

Neural Network-based Dynamic Clustering Model for Energy Efficient Data Uploading and Downloading in Green Vehicular Ad-hoc Networks

Green VANET is an emerging field of research that spurs interest in energy consumption management for the development of smart cities. It also presents a unique variety of research problems for developing trustworthy and scalable routing protocols as vehicles are susceptible to the restrictions of road geometry and the barriers which limit networking capabilities in urban environments. Clustering is a process of assembling vehicle nodes to create a powerful and effective network infrastructure. According to recent studies, clustering-based routing algorithms in green VANET may significantly improve networking effectiveness and lower infrastructure costs. However, sometimes the vehicle nodes are unaware of their OBU energy consumption, which causes network execution issues and topological alterations. In such instances, the energy consumption of onboard sensors becomes a major problem in the clustering-based routing protocol. Hence, this paper proposes a self-organizing map neural network (SOMNN) based dynamic clustering model to identify energy-efficient nodes from each cluster for vehicular data uploading and downloading applications. The simulation results demonstrate that the proposed model solves network lifetime issues and provides superior network effectiveness with enhanced communication stability. The suggested dynamic clustering model reduces network energy consumption by 26% and 18% in comparison to k – means (KM) and fuzzy c – means (FCM) based clustering model.

Computation Offloading and Resource Allocation Based on Game Theory in Symmetric MEC-Enabled Vehicular Networks

Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a multi-tier symmetric Vehicle-to-Everything (V2X) network framework is proposed, which consists of vehicle nodes (VNs), mobile edge computing (MEC) servers and a cloud server to provide computation offloading services for user vehicles. In this symmetric system, besides local computation, tasks can be offloaded to VNs or MEC servers or cloud servers for processing. The computation offloading problem in this network framework is considered as a game based on game theory. Then, in order to achieve the Nash equilibrium (NE) in this game, a joint optimization of computation offloading and resource allocation (JOCORA) algorithm is proposed. The numerical simulations show that the JOCORA algorithm can improve the success probability of offloading and reduce the total latency. The JOCORA algorithm has a better performance compared to other methods.

Optimal Intelligent Vehicle Control System for Emergency Vehicle Using Visible Light Communication VANET

Abstract: Traffic congestion is one of the significant problems in every metropolitan city. Traffic congestion occurs, when large numbers of vehicles are all together and are not able to move or move slowly, it is also known as a traffic jam. The main aim of the proposed OIVC-VLC VANET system is to improve the data transmission rate to control traffic in high density loads for emergency vehicles. Traffic congestion leads to wasting of time, road accidents, delays of trips, and reduces regional economic health and fuel consumption. Moreover, the most critical concern of traffic congestion is a delay of emergency vehicles like ambulance and police vehicles, and firefighting trucks leading to an increase the human death and loss their essential things. In addition, this paper focuses to reduce the traffic congestion and traveling time of emergency vehicles. To overcome traffic congestion issues, an Optimal Intelligent Vehicle Control System for the emergency vehicle by intelligent traffic clearance (OIVC-VLC-VANET) is proposed. Firstly an improved whale optimization (IWO) algorithm is submitted for grouping the vehicle nodes based on their behaviors. Secondly, the differential search algorithm is used to select the next forwarding node using multiple constraints received from vehicles. Thirdly, the dragonfly algorithm is submitted for avoiding extra time due to the control packets exchange process. The results show that the proposed OIVC-VLC-VANET system can perform very efficiently in terms of quality constraints than existing systems.