A secure IoT and edge computing based EV selection model in V2G systems using ant colony optimization algorithm

IF 0.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Pervasive Computing and Communications Pub Date : 2022-09-21 DOI:10.1108/ijpcc-06-2022-0245
Gopinath Anjinappa, Divakar Bangalore Prabhakar
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引用次数: 1

Abstract

Purpose The fluctuations that occurred between the power requirements have shown a higher range of voltage regulations and frequency. The fluctuations are caused because of substantial changes in the energy dissipation. The operational efficiency has been reduced when the power grid is enabled with the help of electric vehicles (EVs) that were created by the power resources. The model showed an active load matching for regulating the power and there occurred a harmonic motion in energy. The main purpose of the proposed research is to handle the energy sources for stabilization which has increased the reliability and improved the power efficiency. This study or paper aims to elaborate the security and privacy challenges present in the vehicle 2 grid (V2G) network and their impact with grid resilience. Design/methodology/approach The smart framework is proposed which works based on Internet of Things and edge computations that managed to perform an effective V2G operation. Thus, an optimum model for scheduling the charge is designed on each EV to maximize the number of users and selecting the best EV using the proposed ant colony optimization (ACO). At the first, the constructive phase of ACO where the ants in the colony generate the feasible solutions. The constructive phase with local search generates an ACO algorithm that uses the heterogeneous colony of ants and finds effectively the best-known solutions widely to overcome the problem. Findings The results obtained by the existing in-circuit serial programming-plug-in electric vehicles model in terms of power usage ranged from 0.94 to 0.96 kWh which was lower when compared to the proposed ACO that showed power usage of 0.995 to 0.939 kWh, respectively, with time. The results showed that the energy aware routed with ACO provided feasible routing solutions for the source node that provided the sensor network at its lifetime and security at the time of authentication. Originality/value The proposed ACO is aware of energy routing protocol that has been analyzed and compared with the energy utilization with respect to the sensor area network which uses power resources effectively.
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基于蚁群优化算法的安全物联网和边缘计算的V2G系统电动汽车选择模型
目的:在功率要求之间发生的波动显示出更高的电压调节范围和频率。波动是由于能量耗散的实质性变化引起的。在电力资源创造的电动汽车(ev)的帮助下,电网的运行效率降低了。该模型表现为有源负荷匹配调节功率,且存在能量谐波运动。本文研究的主要目的是对能源进行稳定处理,从而提高了系统的可靠性和效率。本研究或论文旨在阐述车辆2网(V2G)网络中存在的安全和隐私挑战及其对电网弹性的影响。设计/方法/方法提出了基于物联网和边缘计算的智能框架,该框架设法执行有效的V2G操作。在此基础上,设计了每辆电动汽车的最优充电调度模型,以实现用户数量最大化,并利用蚁群算法选择最优电动汽车。首先是蚁群算法的构建阶段,蚁群中的蚂蚁生成可行解。构造阶段与局部搜索生成一种蚁群算法,该算法利用异质蚁群,有效地寻找最知名的解决方案来克服问题。结果现有的在线串行编程插电式电动汽车模型的电量使用结果在0.94 ~ 0.96 kWh之间,与提出的蚁群算法的电量使用结果相比,前者随时间的使用结果分别为0.995 ~ 0.939 kWh。结果表明,基于蚁群算法的能量感知路由为源节点提供了可行的路由方案,保证了传感器网络的生命周期和认证时的安全性。独创性/价值提出的蚁群算法对能量路由协议进行了分析,并与有效利用电力资源的传感器区域网络的能量利用进行了比较。
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来源期刊
International Journal of Pervasive Computing and Communications
International Journal of Pervasive Computing and Communications COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.60
自引率
0.00%
发文量
54
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