Multiplier is the most commonly used circuit in digital devices. Multiplication is one of the basic functions used in digital signal processing. Gate Diffusion Input (GDI) logic reduces the power dissipation and area of digital circuits while maintaining low complexity of logic design. In this paper, GDI technique is used for low-power design of 8-bit multiplier. Reduction in power and area can be achieved using Booth encoding and Wallace tree technique since they generate partial products efficiently and are most suited for multiplication of signed numbers. Multiplier designed in GDI logic requires lesser number of devices as compared to CMOS logic [3]. Hence, GDI multiplier substantially dissipates lesser power as compared to CMOS design.

Machine learning (ML) will improve the outcomes through the use of methods that categorize the information into the predetermined set. This work is to present an estimation and assessment of machine learning techniques for achieving privacy preservation in vehicular ad hoc networks (VANETs). This method generates two distinct group keys for prime and secondary users. Road side units (RSUs) are deployed to broadcast one group key from the trusted authority (TA) to the primary users, and secondary users are utilized to transmit the other group key. The main aim of this network is developed to avoid vulnerable attacks and to enhance the privacy of this network, Naïve Bayesian classifier (BC), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural networks (ANN), Bayesian network (BN) methods are utilized in correlation with the proposed deep neural networks (DNN) with the black widow optimization (BWO) for protection preserving. These learning characterization procedures are assessed concerning delay, network lifetime, throughput, delivery ratio, and drop and this proposed calculation (DNN-BWO) shows improved results than the current methodologies.

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Abstract:In this work, the decision probability of the handoff are modeled and simulated for smaller bandwidths. The smaller bandwidth is chosen just for simulation purposes and to demonstrate the applicability of the algorithm. The probability of handover and probability of incorrect decision in the handover is modeled. Two nodes of the network are modeled and the probabilities of four different states of the mobile node are also modeled. The results are presented for two cases with and without the probabilities of four different states of the mobile nodes.

VANET has exposed improvements in solving traffic congestion; the fundamental idea is to use RSU (Road Side Unit) or another vehicle to send traffic related parameters. The broadcast of traffic information used by safety road applications in wireless channel makes secure data hazardous and testing problem in vanet. Mislead of these messages causes accidents and breakdown of human lives at poorer level and thereby, Privacy in vehicular adhoc network has become a great issue, concerning to drivers convenience. Vehicles have to be barred from these attacks and mishandling of privacy information. For this reason, privacy preserving scheme is major requirement for vehicular adhoc network. The identity batch verification privacy scheme is implemented using ECC algorithm which is considered as more secure and practically efficient.