ID 原文 译文
17635 该算法能够结合紫外光非直视、低窃听等优点,克服传统无线电易被监听的缺点,在均衡能耗的同时为长机收集僚机信息提供可靠保证。 The proposed algorithm combines theadvantages of ultraviolet in non-line-of-sight and low eavesdropping, overcomes the disadvantage of thetraditional radio, which can easily be monitored. It can provide reliable assurance for the leader to collectinformation of wingmen while balancing the energy consumption.
17636 通过引入考虑距离和剩余能量的优先级函数,提出基于分簇机制的改进算法BEAD-LEACH,并采用改进算法对无人机随机部署和呈圆形编队部署时进行仿真。 The improved algorithm is proposal based on cluster mechanism via introducing the priority function, which considers distance and residual energy.Adopting the improved algorithm to simulate under two scenarios in which UAVs are deployed randomly or UAVs are deployed in circle formation respectively,
17637 仿真结果表明,在两种部署方式下,网络中50%节点出现死亡经历的时间分别延长了12%, 16%,改进算法能够有效地均衡网络的通信能耗,延长无人机网络的生存时间。 the simulation results show that the time of 50% death nodes occurring in UAV network is prolongal by 12% and 16% respectively under two types of deployment, and the improved algorithm can effectively balance the communication energy consumption of the network and prolong the survival time of UAV network.
17638 针对无线自组织网络在窃听环境中的安全传输问题,该文提出了一种无线多跳自组织网络的联合安全路由和功率优化算法。 A joint security routing and power optimization algorithm for wireless multi-hop Ad hoc network isproposed in an eavesdropping environment.
17639 首先,在窃听者服从泊松簇过程(PCP)这一假设下推导得到了系统安全中断概率(SOP)和连接中断概率(COP)的表达式; Firstly, the Secrecy Outage Probability (SOP) and expressions ofConnection Outage Probability (COP) are derived under the assumption that the distribution of theeavesdroppers follows the Poisson Cluster Process (PCP).
17640 然后以安全中断概率约束下的连接中断概率最小为准则,针对给定路径推导得到了源与各跳中继的最优传输功率,并进一步获得了源与目的节点间的最优路由。 Then, in view of minimizing COP with the constraintof SOP, the optimal transmission power of each hop is derived for any given path. Based on that, the optimal route from the source to the destination is obtained.
17641 仿真结果表明,该文所提系统安全中断概率和连接中断概率的表达式与蒙特卡洛仿真结果相符,所提算法可获得与穷举搜索方法接近的安全性能,显著优于传统方法。 The simulations on COP and SOP show that the derivedtheoretical results agree well with the Monte-Carlo simulations. It is also shown that the security performanceof the proposed algorithm is close to that of exhaustive searching, and also outperforms the traditionalmethod.
17642 随着物联网(IoT)迅速发展,移动边缘计算(MEC)在提供高性能、低延迟计算服务方面的作用日益明显。 With the rapid development of the Internet of Things (IoT), Mobile Edge Computing (MEC)becomes increasingly effective in improving processing capacity and providing low-latency computing services.
17643 然而,在面向IoT业务的MEC(MEC-IoT)时变环境中,不同边缘设备和应用业务在时延和能耗等方面具有显著的异构性,对高效的任务卸载及资源分配构成严峻挑战。 However, in the time-varying MEC-IoT environment, heterogeneous devices and applications cause serious challenges on efficient task offloading and resource allocation.
17644 针对上述问题,该文提出一种动态的分布式异构任务卸载算法(D2HM),该算法利用分布式博弈机制并结合李雅普诺夫优化理论,设计了一种资源的动态报价机制,并实现了对不同业务类型差异化控制和计算资源的弹性按需分配, A Distributed Dynamic Heterogeneous task offloading Methodology (D2HM) algorithm is proposed in this paper by exploiting game theory and Lyapunov optimization, which can achieves heterogeneous control and allocation of computation resources by dynamic quote price mechanism.