Extensive research attention has been devoted to the Vehicular Sensor Network (VSN) owing to its great potential in environment monitoring. Still, it is difficult to diminish the broadcast storm and data collisions in vehicular senor environment. Due to improper broadcasting of safety message and transmission of packet at same time from multiple vehicles leads to collision. Our key intention is to overwhelm these shortcomings in VSN. Hence, we propose Novel Segment based Safety message broadcasting in Cluster (NSSC) based VSN. Our NSSC is mainly concentrated in three successive processes that are Cluster Formation, Collision Avoidance and Safety Message Broadcasting. Cluster formation is performed originally to sustain stable vehicular environment. Here, Variant based Clustering (VbC) Scheme is proposed to elect Cluster Head (CH) and to form clusters. CH is selected using Chaotic Crow Search (CCS) algorithm. In accord to mitigate the data collision during transmission between CH and Cluster Member, we propose Adaptive Carrier Sense Multiple Access/Collision Avoidance (Ada-CSMA/CA) protocol. Safety message broadcasting adopts Segment based Forwarder Selection (SFS) scheme which selects optimal forwarder using Fuzzy-Vikor method. In this, optimal forwarder is selected concerning to broadcast safety message which reduces the broadcast storm. In regard to validate the proposed NSSC, we have conducted simulations on Omnet++ and SUMO simulator based Veins framework. The acquired results are auspicious in terms of succeeding metrics reachability, average number of collision, duplicate data packets, latency, packet delivery ration and throughput.

The current cellular technology and vehicular networks cannot satisfy the mighty strides of vehicular network demands. Resource management has become a complex and challenging objective to gain expected outcomes in a vehicular environment. The 5G cellular network promises to provide ultra-high-speed, reduced delay, and reliable communications. The development of new technologies such as the network function virtualization (NFV) and software defined networking (SDN) are critical enabling technologies leveraging 5G. The SDN-based 5G network can provide an excellent platform for autonomous vehicles because SDN offers open programmability and flexibility for new services incorporation. This separation of control and data planes enables centralized and efficient management of resources in a very optimized and secure manner by having a global overview of the whole network. The SDN also provides flexibility in communication administration and resource management, which are of critical importance when considering the ad-hoc nature of vehicular network infrastructures, in terms of safety, privacy, and security, in vehicular network environments. In addition, it promises the overall improved performance. In this paper, we propose a flow-based policy framework on the basis of two tiers virtualization for vehicular networks using SDNs. The vehicle to vehicle (V2V) communication is quite possible with wireless virtualization where different radio resources are allocated to V2V communications based on the flow classification, i.e., safety-related flow or non-safety flows, and the controller is responsible for managing the overall vehicular environment and V2X communications. The motivation behind this study is to implement a machine learning-enabled architecture to cater the sophisticated demands of modern vehicular Internet infrastructures. The inclination towards robust communications in 5G-enabled networks has made it somewhat tricky to manage network slicing efficiently. This paper also presents a proof of concept for leveraging machine learning-enabled resource classification and management through experimental evaluation of special-purpose testbed established in custom mininet setup. Furthermore, the results have been evaluated using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). While concluding the paper, it is shown that the LSTM has outperformed the rest of classification techniques with promising results.

The detection of secure vehicles for content placement in vehicle to vehicle (V2V) communications makes a challenging situation for a well-organized dynamic nature of vehicular ad hoc networks (VANET). With the increase in the demand of efficient and adoptable content delivery, information-centric networking (ICN) can be a promising solution for the future needs of the network. ICN provides a direct retrieval of content through its unique name, which is independent of locations. It also performs better in content retrieval with its in-network caching and named-based routing capabilities. Since vehicles are mobile devices, it is very crucial to select a caching node, which is secure and reliable. The security of data is quite important in the vehicular named data networking (VNDN) environment due to its vital importance in saving the life of drivers and pedestrians. To overcome the issue of security and reduce network load in addition to detect a malicious activity, we define a blockchain-based distributive trust model to achieve security, trust, and privacy of the communicating entities in VNDN, named secure vehicle communication caching (SVC-caching) mechanism for the placement of on-demand data. The proposed trust management mechanism is decentralized in nature, which is used to select a trustworthy node for cluster-based V2V communications in the VNDN environment. The SVC-caching strategy is simulated in the NS-2 simulator. The results are evaluated based on one-hop count, delivery ratio, cache hit ratio, and malicious node detection. The results demonstrate that the proposed technique improves the performance based on the selected parameters.