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Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

机译:网络物理系统中基于竞争群的网关优化部署算法

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Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.
机译:无线传感器网络拓扑优化是一个非常重要的问题,通过选择节点进行拓扑控制可以提高数据转发效率,同时节省能源并延长网络寿命。为了解决在网络物理系统中将无线传感器网络连接到Internet的问题,我们在此提出一种基于竞争性群优化器算法的几何网关部署。粒子群优化(PSO)算法在解决方案空间中具有连续搜索功能,适合查找网关部署的几何中心。但是,其搜索机制仅限于个体最优(最佳)和总体最优(最佳);因此,它很容易陷入局部最优。为了改善粒子搜索机制并提高算法的搜索效率,我们引入了一种新的竞争群优化器(CSO)算法。 CSO搜索算法基于粒子间竞争机制,可以有效避免陷入局部最优状态的种群陷入困境。随着基于对立面的自适应搜索的改进及其动态参数调整的能力,该算法可以维持整个群体的多样性,以解决几何K中心网关部署问题。仿真结果表明,该CSO算法具有良好的全局探索能力和收敛速度,可以通过获得最小的网络覆盖半径来提高电子物理系统的网络服务质量(QoS)水平。我们还发现,与PSO或Kmedoids算法相比,CSO算法在解决几何网关部署问题方面更加稳定,强大和有效。

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