Research on a Dual-Trust Selfish Node Detection Algorithm Based on Behavioral and Social Characteristics in VANETs

Data dissemination is a challenging problem in vehicular ad hoc networks (VANETs) due to the characteristics of VANETs such as highly dynamic topology, intermittent connectivity, and the road-constrained mobility. Observed from the fact that on-street parking is a common phenomenon in the city, the parked vehicles at roadside can also contribute their resources to the communications in network. In this paper, an efficient data dissemination protocol (EDP) for VANETS is proposed. Different from the existing works which consider mainly mobile vehicles or some expensive roadside infrastructure, EDP leverages the resources of parked vehicles at roadside to help in forwarding data. EDP groups the parked vehicles at roadside into a cluster, which buffers and relays data from mobile vehicles and manages the duplicates of data. Simulation results based on a real city map show that EDP achieves a higher delivery ratio and lower delivery delay.

Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc networks, preventing nodes from accessing accurate road information. In order to assure the reliability and trustworthiness of information through the networks, assessing the credibility of nodes has become a critical task in vehicular ad hoc networks. A new scheme for malicious node detection is proposed in this work. Multiple factors are fed into a fuzzy logic model for evaluating the trust for each node. Vehicles are divided into clusters in our approach, and a road side unit manages each cluster. The road side unit assesses the credibility of nodes before accessing vehicular ad hoc networks. The road side unit evicts a malicious node based on trust value. Simulations are used to validate our technique. We demonstrate that our scheme can detect and evict all malicious nodes in the vehicular ad hoc network over time, lowering the ratio of malicious nodes. Furthermore, it has a positive impact on selfish node participation. The scheme increases the success rate of delivered data to the same level as the ideal cases when no selfish node is present.

With the proliferation of vehicular technology, modern vehicles are fortified with ever more electronic maneuvers. This advancement ratifies the evolution of Intelligent Transportation Systems (ITS)to provide services like shared travel, smart driving, on-the-go Internet, etc. As a traditional application of ITS, a Vehicular Ad hoc Network (VANET) enables smart communication between vehicle nodes and network infrastructures to provide various expedient services such as road safety, data sharing, traffic management, parking assistance, entertainment, route recommendation, mobile payment, and even cloud applications. The high-speed mobile nodes (vehicles) in VANET perform very differently from other wireless communication networks and have a set of distinct features such as frequent link disconnection, highly dynamic topology, limited coverage area, and heterogeneous system architecture that may affect the performance and service quality of the VANET significantly. With this motivation, this work attempts to develop distributed cooperative cluster-based IDS to identify potential cyberattacks in VANET effectively. Firstly, this work develops a stable and reliable clustering method named Fuzzy Logic-based Clustering (FLC) to create a collaborative and reliable communication environment. A new fuzzy logic-based CH node selection algorithm is also developed based on node degree, average velocity difference, and relative velocity of the vehicle to create a more robust clustering structure with minimum cost in VANET

Vehicular ad hoc networks (VANETs) are a special form of wireless networks made by vehicles communicating among themselves on roads. The conventional routing protocols proposed for mobile ad hoc networks (MANETs) work poorly in VANETs. As communication links break more frequently in VANETs than in MANETs, the routing reliability of such highly dynamic networks needs to be paid special attention. To date, very little research has focused on the routing reliability of VANETs on highways. In this paper, we use the evolving graph theory to model the VANET communication graph on a highway. The extended evolving graph helps capture the evolving characteristics of the vehicular network topology and determines the reliable routes preemptively. This paper is the first to propose an evolving graph-based reliable routing scheme for VANETs to facilitate quality-of-service (QoS) support in the routing process. A new algorithm is developed to find the most reliable route in the VANET evolving graph from the source to the destination. We demonstrate, through the simulation results, that our proposed scheme significantly outperforms the related protocols in the literature.

In Vehicular (Ad-hoc) Network (VANET), besides Vehicle-to-Vehicle communications (V2V), vehicles in VANET also exchange data with Road-Side-Units (RSUs) and Cellular Base Stations (Vehicle-to-Infrastructure communications (V2I)). With the introduction of Intelligent Transport Systems (ITS), VANETs possess a great potential in enabling surveillance services. The rapid development of wireless communication technologies recently results in empowering data exchange among vehicles, RSUs and Cellular Base Stations, improving various types of applications and services such as safety driving, route planning, traffic alert, and context-aware infotainment. However, the benefits offered by VANETs and ITS cannot be fully realized unless there is a mechanism to effectively defend against fake and erroneous information exchange from malicious or dysfunctional nodes to other vehicles and RSUs for their own purposes. In this regards, trust appears as one of the solutions for VANETs to establish secure connectivity and reliable services. The conceptual idea to tackle down this challenges is that only data from trusted vehicles is taken into account. Thus, the aim is to evaluate trust of a vehicle in exchanging high quality of information. This paper presents the trust concept, key characteristics, a trust evaluation model, and a prototype for trusted data exchange activities in VANETs.

Vehicular adhoc networks (VANETs) are made by applying the standards of mobile adhoc networks (MANETs)-the unconstrained making of a remote network of cell phones to the area of vehicles. VANETs were first referenced and presented in 2001 under ”vehicle to-vehicle specially appointed versatile correspondence and systems administration” applications, where networks can be shaped and data can be handed-off among vehicles. It was shown that vehicle-to- vehicle and vehicle-to-road side exchange designs will exist together in VANETs to give street security, route, and other side of the road administrations. VANETs are a vital piece of the intelligent transportation frameworks (ITS) system. At times, VANETs are known as Intelligent Transport Networks. They are perceived as having advanced into a more extensive “Web of vehicles”. Which itself is relied upon to eventually advance into a “Web of self-sufficient vehicles”. The vehicles send some data about street status and traffic. But sometimes the information send from one vehicle to another can be malicious. This malicious information is added by the intruders to cause problem in vehicular network so that accident can occur. In this report, we will analyse the throughput, packet delivery ratio and end to end delay in sending information from vehicle to vehicle when the network is attacked by some malicious nodes.

In view to reduce accident risks and improve safety on public roads, the vehicular Ad hoc network (VANET) standard was developed to enable inter-vehicular communication and facilitate safety message exchange among the involved nodes. Moreover, a successful message exchange can only be possible if the transmission medium is collision free. Under the Intelligent Transportation System (ITS), the cooperative Awareness messages (CAM) have to be transmitted at the rate of 10 Hz as per standard. To account for congestion management, the Distributed Congestion Control (DCC) mechanism was proposed. However, under higher node density, the DDC becomes inefficient and dramatically contribute to the deterioration of the VANET environment. The present work proposes a Dynamic Broadcast Storm Mitigation Algorithm (DBSMA) which can be used to combat the broadcast storm problem in a Vehicular Network (VN). Results from several simulations confirmed that the DBSMA has a potentiality to conquer the effect of broadcast storm by offering more than 130% improved efficiency against the DCC approach. Other advantage of the DBSMA is that it is very simple to compute and easy to implement.

Vehicular Ad hoc Networks (VANETs) are self-organizing wireless communication networks that allow vehicles to exchange traffic-related messages automatically with neigh-boring vehicles. The goals of VANETs are to aid road users and make traffic management more efficient. However, the open nature of the communication network medium that is used by VANETs exposes the messages in transit to security attacks and the information within to privacy breaches. Researchers proposed many security schemes in their attempt to solve the security and privacy issues in VANETs. However, most existing schemes suffer from high computation and communication overheads. We studied and analyzed the effect of one of the most common security attacks in VANETs, the replay attack. This paper presents the result of the study and analysis, as well as the survey of existing replay attack prevention schemes in VANETs.

Vehicular Networking is of immense importance and continuous research is going on in this field for building the Intelligent Transportation System (ITS). Vehicular ad hoc networks (VANET) are distributed, self-organizing communication networks which progressed from traveling vehicles. Therefore VANET's are characterized by very high speed of vehicles and limited degree of freedom in vehicles movement pattern. VANETs are infrastructure less which allows communication between vehicles without any type of configuration and any worthy cellular networks. Mobility Management for VANET is a challenging task because of the fast mobility of the vehicles. The main goal of VANET is to improve road traffic safety and to efficiently achieve intelligent intervehicle communications. In this paper, the scalability and efficiency of mobility management protocol MMIP6 has been further enhanced to accommodate more number of vehicles with reduction in total cost of the entire network. Numerical analysis has been carried out for comparing the total communication cost of enhanced MMIP6 with Network Mobility protocol (NEMO) of VANET.

An efficient authentication scheme with strong privacy preservation for fog-assisted vehicular ad hoc networks based on blockchain and neuro-fuzzy

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.

Vehicular Ad-hoc Network (VANET) is the road vehicle's communication network without a base station, which is proposed to utilize a link communication between vehicles according to IEEE 802.11p standard. For end-to-end communication between vehicles, a routing protocol is used to find a route based on link properties from one to another vehicle with specific requirements. In VANET, vehicles move at high speed, which makes topology changed all the time, often causing failures in data transfer. Additionally, the failures happen when the signal congestion is high in the VANET; the efficiency of data transfer therefore decreases. Thus, routing in VANET with high speed movement and high signal congestion is challenging. In this article, we present a cross-layer technique that calculates Channel Availability (CAV) at the link layer and applies CAV to AODV routing protocol to improve the communication for vehicle safety applications. We conduct our experiments on vehicular networks using NCTUns simulator with the realistic road-based mobility model. Our experiment results show the improvement on average TCP throughput and average UDP delay on different node density and data traffic density.

Given the resource consumption of transmitting data packets in Vehicular Ad Hoc Network (VANET), selfish nodes might refuse to transmit messages to others just for maximizing their own profits. Therefore, the communication performance within the whole network would be affected. Along with its wide-scale implementation in many road-side applications, selfish behaviors of such nodes are witnessed to be causing serious problems and degradations in VANETs. We address the impacts of the selfish nodes in the performance of VANETs and accordingly, the credit-based method is utilized to re-implement the spirit that can provide incentive for mobile nodes to cooperate with each other and report actions honestly. The simulation results demonstrated that the system earnings would decrease due to the existence of selfish nodes, thereby also proving that the incentive mechanism can facilitate mutual cooperation among nodes.

Integration of IoT and edge cloud computing for smart microgrid energy management in VANET using machine learning

Vehicular Ad hoc Networks (VANETs) are a class of wireless networks built dynamically in an ad hoc network manner. VANET is a key technology for smart and Intelligent Transportation System. Efficient and intelligent algorithms for routing in VANETs are critically required for safety information propagation. Routing in highly dynamic VANET topology is important for the achievement of any vehicular safety applications. The routing mechanism in vehicular networks is critical because of VANETconstraints such as unpredictable network topology, frequent disconnections, varying network densities, etc. Multi agent scheme based routing scheme for VANETs is addressed in the paper. We address the challenge of how to route the data with short communication delay, overhead, and the complexity. Specifically, we consider the scenario for (V2V) vehicle-to-vehicle communication. Multi agent scheme consists of static agent and mobile agents. The scheme operates as follows: (1) establishing a connectivity pattern between the vehicles; (2) creating a set of beliefs; (3) desire development; (4) finalizing the intention. We validate the proposed scheme by simulation. Evaluated simulation results are packet delivery ratio, bandwidth utilization, and route identification delay.