One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.
Software-defined networking (SDN) is a programmable architecture for networking domain in which the security is provided by devising the network policies with the help of the network administrator. This is very cumbersome for the administrator to handle different attacks at various planes in SDN architecture. Blockchain can be used to prevent various attacks in SDN by providing a decentralization authentication environment. In this article, a secure Blockchain-based privacy-preserving protocol is proposed to thwart various security vulnerabilities in the SDN architecture. The proposed approach uses Modified-Delegated Proof of Stake as the consensus protocol to ensure safety and reliability in the network. Besides, a security protocol is designed using cryptographic primitives and analyzed using a detailed security analysis. Initially, the consensus protocols are implemented using solidity smart contracts and deployed to the public Blockchain using Ethereum. Consequently, the proposed approach is simulated on OMNet++ using INET framework. The experimental results show that the proposed SDN-Chain is secure, efficient, less incentive to centralize, and practically implementable in resource-limited wireless and mobile environments.
Next-generation wireless network systems and technologies provide a new paradigm for achieving the fastest access to any network. But one of the significant design concerns is the support of handoff, irrespective of the services. The key objective of this work is to enable a node to make appropriate decisions for performing handoff through Reinforcement learning. The work concentrates on the handoff decision phase for choosing the best network with a minimum delay during the handoff process. The reduction in decision delay has been achieved by minimizing the number of handoffs. The environment is modeled as a Markov decision process with the aim of increasing the total anticipated reward per link. The network resources that are used by the link is taken by a reward function and network switching cost that is utilized to model the signaling and processing load incurred on the network during handoff. It has been shown that the total number of unnecessary handoffs can be decreased enhancing the performance of heterogeneous networks. Also, an assessment of the proposed scheme with the existing Vertical handoff decision algorithm like the Simple Additive Weighting method (SAW) has been made and the results show an improved performance over SAW.
Drug delivery in conventional dosage forms often suffers from the drawbacks of repeated drug administration and large fluctuations in blood drug levels. Controlled drug delivery systems are a convenient way of controlling the dosing frequency responsible for rapid absorption and distribution of drug in conventional dosage forms, and are dependent upon two intrinsic properties of the drug, namely, elimination half-life (t1/2) and therapeutic index (TI). The goal is to give a drug at a sufficient rate, frequency and dose so that the ratio Cmax/Cmin in plasma at steady state is always maintained at effective concentrations during the course of therapy, reducing side effects or improving physicochemical and biopharmaceutical properties. The use of polymers provides the potential to control drug delivery both temporally and spatially. The objective of the present investigation was to develop bilayered tablets of orlistat to achieve controlled release and immediate release. The process is predetermined in such a way to release the drug at an IR and CR by using different polymers. Half life of orlistat is 1-2 hrs, as it has been released immediately. This paper mainly focuses on designing the process to release the drug in a controlled manner by using different polymers like sodium alginate, ethyl cellulose, HPMC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.