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Authentication and revocation of users inVehicular Adhoc Networks (VANETS) are two vital security aspects. It is extremely important to perform these actions promptly and efficiently. The past works addressing these issues lack in mitigating the reliance on the centralized trusted authority and therefore do not provide distributed and decentralized security. This paper proposes a blockchain based authentication and revocation framework for vehicular networks, which not only reduces the computation and communication overhead by mitigating dependency on a trusted authority for identity verification, but also speedily updates the status of revocated vehicles in the shared blockchain ledger. In the proposed framework, vehicles obtain their Pseudo IDs from the Certificate Authority (CA), which are stored along with their certificate in the immutable authentication blockchain and the pointer corresponding to the entry in blockchain, enables the Road Side Units (RSUs) to verify the identity of a vehicle on road. The efficiency and performance of the framework has been validated using the Omnet++ simulation environment.

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Vehicular Ad hoc Networks (VANET) is emerging as a promising technology of the Intelligent Transportation systems (ITS) due to its potential benefits for travel planning, notifying road hazards, cautioning of emergency scenarios, alleviating congestion, provisioning parking facilities and environmental predicaments. But, the security threats hinder its wide deployment and acceptability by users. This paper gives an overview of the security threats at the various layers of the VANET communication stack and discuss some of the existing solutions, thus concluding why designing a security framework for VANET needs to consider these threats for overcoming security challenges in VANET.

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Rainfall prediction is the recent research as it set up the farmers to move with the effectual decision-making regarding agriculture both in irrigation and cultivation. The conventional prediction techniques are daunting, the rainfall prediction depends upon three main factors such as rainfall, humidity, and rainfall recorded in the preceding years that ensued in enormous time-consumption and leverages enormous computational efforts related with the evaluation. Hence, this work adopts the rainfall prediction model based on the deep learning network: Back Propagation Neural Network system. The weights of deep learning are tuned optimally by exploiting the Improved Flower Pollination Algorithm to ease the global optimal tuning of the weights and promise improved prediction accuracy. Conversely, the developed deep learning model is modeled in the MapReduce model which set up the effectual handling of the big data.

MANET is the infrastructure-less, self-organized wireless network. Here, the mobile nodes can join the group or leave from the network group dynamically. As the mobile ad hoc network (MANET) is established with the battery power nodes, reducing the power consumption of mobile node poses a major complex in the network system. Efficient and robust secure routing protocols are required in MANET due to the quickly changing network topology such that the overhead incurred in the track is excessive. To achieve the secure routing path mechanism with less delay and minimum energy consumption of the nodes, a multi-objective based optimization algorithm is introduced in this research work. Here, the best optimal route is chosen for routing the data packets from the source to the destination on the basis of defined multiobjectives like: energy, delay, distance and link state stability. The secure path energy efficient path is identified by the Crossover of GA with GSO Algorithm (CGA-GSO). This is the hybridized form of standard Genetic Algorithm (GA) and the Group search Optimization (GSO) algorithm. The performance of the proposed model will be analyzed over the traditional approaches concerning Energy and Delay as well.