Data In VANETs Research Articles

Improving VANET Data Dissemination Efficiency with Deep Neural Networks

Improving VANET Data Dissemination Efficiency with Deep Neural Networks

SMBF: Secure Data Transmission using Modified Bloom Filter for Vehicular Ad Hoc Networks

Objective: Privacy in VANETs commands short-lived pseudonyms since the nodes are very mobile. It results in big computational overheads during secure communication between vehicles, which could place the safety of people and vehicles at risk. To overcome such a limitation, we are using the validation approach of pseudonyms based on Bloom Filter, which provides less computational overheads than other authentication procedures. A complete end-toend system is developed in three phases: authorization, clustering, encryption, and decryption phase, to establish secure transmission of data in VANETs. Methods: The authorization phase uses Bloom’s Filter authentication based on the pseudonym scheme. Based on the distributed parameters related to vehicles, clusters of vehicles are created to save power and bandwidth in communications. These clusters are chained to the next cluster with the help of the cluster head to share the information. First, only authenticated vehicles that are regular travelers on the road segment are allowed to become part of the cluster. SMBF ensures that any vehicle that is not a frequent traveler on the given road segment is not taking part in the communication process. Clustering is used to ensure the speed of communication. Results: The proposed scheme results are compared with other state-of-art techniques in VANETs. The analysis indicates that the storage and computational processing requirements are reduced by 28%, resulting in decreased communication costs. Conclusion: The proposed authentication process stores the pseudonyms in the certificate authority database. Hence, more memory requirements are required to maintain this technique. Overall, the performance of SMBF needs to be tested under different types of attacks.

Privacy and safety improvement of VANET data via a safety-related privacy scheme

Privacy and safety improvement of VANET data via a safety-related privacy scheme

A Novel Secured Multi-Access Edge Computing based VANET with Neuro fuzzy systems based Blockchain Framework

In vehicle ad-hoc networks, the progression of wireless communication technology to 6G, overcomes storage, processing, privacy, and power limits to create an efficient and intelligent next generation transportation system. Vehicular ad hoc network may now offer remarkable availability, reliability, and throughput using 6G technology. However, the VANET system’s data should be protected. This paper proposes an effective batch authentication and key exchange technique to avoid contact with hostile vehicle users. Moreover, three types of systems are proposed: PKI, ID-based, and MAC-based. The neuro-fuzzy inference technique was used to predict VANET security ratings. The Homogeneous Discrete-Time Markov Chain model is used to secure data transit. Additionally, this research examined the work from a blockchain perspective combined with MEC. There are 3 level to comprise the architecture: perception, edge computing, and services. Throughout the blockchain transmission process, the first layer make certain the security of VANET data. The perception layer makes use of edge computing and cloud services on the edge. The service layer protects data by using both traditional cloud storage and blockchain technology. The lowest layer of the system architecture is dedicated to the throughput and quality of service requirements of MEC users. The primary challenge is achieving consensus across blockchain nodes while maintaining the MEC system’s and blockchain’s performance. To simulate the joint optimization problem, a Markov decision process with reward function is utilized. The simulation results are conferred to illustrate the validity of study assertions.

Improving performance and data transmission security in VANETs

This article proposes a new approach to achieve fast and reliable transfer of data and uses machine learning techniques for data processing to improve the performance and data transmission security of the vehicular network. The proposed approach is the combination of 5G cellular network and alternative data transmission channels. The data collection experiment took place within different areas of the city of Berlin over a 3-month time period and involved the use of 5G technologies. The study carried out the analysis and classification of big data with the help of position-based routing protocols and the Support Vector Machine algorithms. The said techniques were employed to detect non-line-of-sight (NLoS) conditions in real time, which ensure the secure transmission of data without the loss or degradation of network performance. The novelty of the work is that it tackles various traffic scenarios (the extent of road congestion can affect the quality of big data transmission) and offers a way to improve big data transfer using the Support Vector Machine technology. The study results show that the proposed approach is effective enough with big data and can be employed to improve the performance of urban VANET networks and data transmission security. The study results can be useful in developing high-performance 5G-VANET applications to improve traffic safety in urban vehicular environments.

The Delay-Constrained and Network-Situation-Aware V2V2I VANET Data Offloading Based on the Multi-Access Edge Computing (MEC) Architecture

This paper proposes a Multi-access Edge Computing (MEC)-based delay-constrained k-hop-limited VANET data offloading method. In the proposed method, a vehicle X can launch the vehicle-to-vehicle-to-infrastructure (V2V2I) VANET data offload from the cellular network to the IEEE 802.11p/802.11bd Road Side Unit (RSU) when (i) X is not in the corresponding RSU's signal coverage and (ii) there is a multiple-hop vehicle-to-vehicle (V2V) path connecting X with a vehicle Y that is inside the RSU's signal coverage. The MEC paradigm is used to check whether a k-hop-limited V2V2I offloading path exists for vehicle X or not based on the periodically reported contexts from vehicles. In addition to use the V2V2I path's lifetime and quality to derive the suitable V2V2I offloading paths using the snapshot VANET topology on the time point of receiving X's reported context, this work proposed a scheme to derive the potential V2V2I paths that may exist in the future, i.e., in a constrained time interval, to find the better V2V2I offloading path. The performance evaluation shown that the proposed method outperforms the one that (i) only considers the data offloading path's lifetime or (ii) does not consider the candidate V2V2I offloading paths existed in the future.

RETRACTED ARTICLE: Optimal Hop Selection Based Novel Trust Based DDoS Attack Removal Framework for Reliable and Secured Transmission of Data in VANETs

This paper proposes a new Trust based DDoS Attack Removal Framework (T-DARF) for effective trust management in VANETs. Based on a newly developed data centric validation unit and intrusion detection unit, all the misbehaving nodes are removed and allows the framework to prevent DDoS attacks in a distributed and collaborative manner. In other words, our proposed framework ensures a trusted, reliable communication between vehicles and delivering reduced network overhead by handling the DDoS attacks. T-DARF is built of three elements: (1) for the detection of dishonest nodes it includes a collaborative and distributed elements, (2) to filter the malicious data it includes a data-centric validation element, and (3) a delay checking element for the detection and prevention against DoS and DDoS attacks. Also, a new trust-based routing protocol is developed that uses the idea of companions (i.e. integrating the notions of link quality and trust value of neighbors) to select the trusted vehicles and to select the optimal path we imposed a hybrid Dragonfly based particle swarm optimization (DPSO) algorithm. The simulation results proved the efficiency of our proposed trust based framework on detecting dishonest nodes, the malicious data transmitted by the honest/dishonest nodes and also detecting the colluding attacks that destroys the network resources in a short period. Also, T-DARF in a worst case scenario can sustain its performance degrees and outperforms the effectiveness of existing schemes such as TRIP, AECFV and T-CLAIDS.

Adaptive Load Balanced Approach for Multi-path Routing in VANETS

The VANETs carry several security considerations. One in every of the popular and dangerous attacks is launched within the variety of Sybil, connectivity holes or cut-up attack, wherever associated in Nursing assaulter inserts a faux position inside within the cluster. The inserted faux node data is used by the hackers within the case of inconsiderate driver, traffic jams, selective collisions and different similar dangerous things. To avoid such things the VANETs should be protected against such attacks. During this paper, a completely unique answer has been projected to beat the Sybil and cut-up attacks on the VANETs. The game theoretic approach has been designed to calculate per user resource requirement and the total load of the cell in order to calculate the utility, which denotes the resource consumption on the WSN cluster or cell. In this research, the load measurement is the primary concern in order to perform the load balancing using the game theoretic approach. The proposed model has removed the attack by the means of load balancing mechanism based upon the pushback algorithm. The proposed model shows the improved results than the existing models for the VANET data propagation.

MCNC: Data Aggregation and Dissemination in Vehicular Ad hoc Networks Using Multicast Network Coding

Nowadays, more and more APs are deployed for the wireless access. The roadside APs are able to improve the bandwidth of VANETs. In this paper, we design a Multicast Network Coding (MCNC) method to aggregate and disseminate data in VANETs. It is appropriate for the areas where APs have been deployed but cannot cover the all range of vehicles movement. Data transmission is divided into 3 stages in MCNC. Network coding, multicast aggregating, and decoding are implemented in the 3 stages, respectively. Simulation results show that MCNC is able to cut down 2/3 overhead messages of Epidemic when the cover area of the APs is only 1/5. Its loss rate and delay are lower than the other methods. We also discuss its benefit for the deployment of VANETs.

Adaptive Forwarding Delay Control for VANET Data Aggregation

In-network data aggregation is a useful technique to reduce redundant data and to improve communication efficiency. Traditional data aggregation schemes for wireless sensor networks usually rely on a fixed routing structure to ensure data can be aggregated at certain sensor nodes. However, they cannot be applied in highly mobile vehicular environments. In this paper, we propose an adaptive forwarding delay control scheme, namely Catch-Up, which dynamically changes the forwarding speed of nearby reports so that they have a better chance to meet each other and be aggregated together. The Catch-Up scheme is designed based on a distributed learning algorithm. Each vehicle learns from local observations and chooses a delay based on learning results. The simulation results demonstrate that our scheme can efficiently reduce the number of redundant reports and achieve a good trade-off between delay and communication overhead.

A probabilistic method for cooperative hierarchical aggregation of data in VANETs

We propose an algorithm for the hierarchical aggregation of observations in dissemination-based, distributed traffic information systems. Instead of transmitting observed parameters directly, we propose soft-state sketches—an extension of Flajolet–Martin sketches—as a probabilistic approximation. This data representation is duplicate insensitive, a trait that overcomes two central problems of existing aggregation schemes for VANET applications. First, when multiple aggregates of observations for the same area are available, it is possible to combine them into an aggregate containing all information from the original aggregates. This is fundamentally different from existing approaches where typically one of the aggregates is selected for further use while the rest is discarded. Second, any observation or aggregate can be included into higher-level aggregates, regardless if it has already been previously—directly or indirectly—added. Those characteristics result in a very flexible aggregate construction and a high quality of the aggregates. We demonstrate these traits of our approach by a simulation study.