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An improved approach for route selection in MANETs using genetic algorithm for smart grids applications

机译:利用求智能电网应用遗传算法的疯狂轨道中的途径选择方法

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Smart grid (SG) is combination of power resources and communication and information technologies into the electricity grid. Main components of smart grid are sensors, smart meters, monitoring system and data management systems which controlled the flow of information between the different stakeholders. Smart grid represents a huge network of computers, routers, switches, smart meter, house application, wireless sensor, renewable energy resources, and electrical vehicles. Smart grid applications need to communication between heterogeneous environments so they need to use routing protocols to accomplish their work. Ad hoc on demand Distance Vector (AODV) developed initially for Mobile Ad Hoc Networks (MANETs) and with some improvements it can be used for smart grid applications by selecting the best path between smart grid sensors to exchange reliable data. AODV is using single routing metric in the form of the minimum number of hops which not necessary the most reliable path due to the mobility nature of MANETs. Nowadays, researchers have more the tendency to the biological evolution algorithms such as Genetic algorithm (GA) to solve optimization problem. We proposed a multi metric approach using Genetic algorithm (GA) as an optimization algorithm to get reliable data delivery approach in smart grids by using AODV as an underlying routing protocol.
机译:智能电网(SG)是电网的电力资源和通信和信息技术的组合。智能电网的主要组件是传感器,智能电表,监控系统和数据管理系统,可控制不同利益相关者之间的信息流。智能电网代表庞大的计算机,路由器,交换机,智能电表,房屋应用,无线传感器,可再生能源和电动车辆网络。智能电网应用程序需要在异构环境之间进行通信,因此他们需要使用路由协议来完成其工作。 Ad Hoc按需距离向量(AODV)最初开发用于移动临时网络(船只),并且通过选择智能电网传感器之间的最佳路径来交换可靠数据,可用于智能电网应用程序的一些改进。 AODV是使用最小跳数的单个路由度量,这不是由于舰队的移动性质导致的最可靠的路径。如今,研究人员对生物进化算法(如遗传算法(GA))的趋势更为倾向,以解决优化问题。我们提出了一种使用遗传算法(GA)作为优化算法的多度量方法,以通过使用AODV作为底层路由协议获得智能电网中可靠的数据传送方法。

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