随机时变路网下的城市应急服务车辆路径分析

针对城市应急服务车辆（110、119、120等）的最优路径选择问题及路网的随机性与时变性,利用鲁棒优化理论,考虑路网在最坏情况下的行程时间,建立了随机时变条件下的应急车辆路径选择模型,并对 Dijkstra算法进行改进,将算法的路阻矩阵进行实时更新,以在时变条件下实现对路径选择模型的求解。经算例分析表明,该模型能有效解决随机时变路网环境下的应急车辆最优路径问题,与基于概率分布的期望行程时间最短的方法相比,该模型拥有更高的鲁棒性和易操作性。     

一种线性时间复杂度的高效路由保护方法

如何高效快速地应对网络中的故障是设计路由协议的基本要求和主要任务。由于动态路由协议在应对网络中的故障时,在协议动态收敛的过程中将会有大量的报文被丢弃。因此,目前路由器厂商普遍采用路由保护方法来克服网络故障,在众多的路由保护方法中,DC（downstream criterion）规则是一种被普遍认可的方法。然而,已有的实现 DC规则算法的时间复杂度普遍较高,并且复杂度随着网络节点平均度的增加而迅速增加。为了应对上述问题,提出一种线性时间复杂度的高效路由保护方案ERPLR（an efficient routing protection method with linear time complexity）,该方法首先提出了备份下一跳计算规则,然后在已有最短路径树的基础上,根据备份下一跳计算规则为所有的源目的节点对计算备份下一跳。在计算备份下一跳的过程中,每个节点和其邻居最多被访问一次,因此ERPLR的时间复杂度为O（V+E）。实验结果表明,与已有的实现DC规则相比较,ERPLR在故障保护率和路径拉伸度两个度量指标结果相似的情况下,在真实网络拓扑和模拟拓扑中,ERPLR分别降低了大约74.93%和78.91%的计算开销,该方法可以极大地降低DC规则的计算开销。     

基于活动区域的移动无线传感器网络路由协议

为了解决移动无线传感器网络中能量效率问题,提出了一种基于活动区域的移动无线传感器网络（WSN）路由协议。本方法使用源和sink节点相对位置来形成路由的活动区域,网络中的移动节点使用睡眠唤醒模式来节约能源。移动向量信息（如当前位置、方向和速度）以及节点的剩余能量,用于选择能够提供最大连接保留时间的邻居,移动向量信息也被用来唤醒活动区域中的移动节点。实验表明,与其他路由协议进行比较,该方法在分组传输过程中具有更高的可靠性。     

国外柴油车减少排放污染的发展趋势

叙述了美国和欧共体柴油车排放法规和最新发展及世界主要固定柴油质量和规格的最新发展趋势，报道了柴油组成和性质对排放物影响的主要研究结果；分析了减少柴油车排放污染的主要措施，例如改进发动机、改进炼油工艺、采用尾气处理、采用颗粒物过滤－捕集器及采用减少尾气排污的三类柴油添加剂，最后提出了几点建议。     

An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.     

Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles

An obstacle avoidance problem of rear-steered wheeled vehicles in consideration of the presence of uncertainties is addressed. Modelling errors and additional uncertainties are taken into consideration. Controller designs for driving and steering motors are designed. A proportional-derivative-type driving motor controller and a sliding-mode steering controller combined with radial basis function neural network (RBFNN) based estimators are proposed. The convergence properties of the RBFNN-based estimators are proven by the Stone–Weierstrass theorem. The stability of the proposed control law is proven using Lyapunov stability analysis. The obstacle avoidance strategy utilising the sliding surface adjustment to an existing navigation method is presented. It is concluded that the driving velocity and steering-angle performances of the proposed control system are satisfactory.     

LDBR: Low-deflection bufferless router for cost-sensitive network-on-chip design

In network-on-chip (NoC) designs, the bufferless router is more energy-efficient than the conventional router with buffers. However, in the bufferless network, deflections cause great performance loss. In this paper, three deflection models are firstly constructed for analyzing the causes of deflections. Then, we propose a low-deflection bufferless router (LDBR), in which a multi-channel network interface and a novel deflection routing based on turn model are designed for reducing the deflections during packet transmissions. Finally, LDBR is evaluated against the latest bufferless routers using synthetic and real-world traffic patterns. The experimental results exhibit that the deflection rate of LDBR network is reduced by 41% compared to other bufferless networks, and LDBR also shows superiority in cost and power consumption across all workloads.     

Non-derivative algorithm design for efficient routing over unreliable stochastic networks

We study the question of routing for minimum average drop rate over unreliable servers that are susceptible to random buffer failures, which arises in unreliable data or manufacturing networks. Interestingly, we first reveal that the traditional Join-the-Shortest-Queue (JSQ) or optimal Randomized Splitting (RS) strategies are consistently outperformed by the Constant Splitting Rule (CSR) where the incoming traffic is split with a constant fraction towards the available servers.This finding motivates us to obtain the optimal splitting fraction under CSR. However, the objective function to be minimized depends on the mean queue length of the servers, whose closed-form expression is not available and often intractable for general arrival and service processes. Thus, we use non-derivative methods to solve this optimization problem by approximately evaluating the objective value at each iteration. To that end, we explicitly characterize the approximation error by utilizing the regenerating nature of unreliable buffers. By adaptively controlling the precision of this approximation, we show that our proposed algorithm converges to an optimal splitting decision in the almost sure sense. Yet, previous works on non-derivative methods assume continuous differentiability of the objective function, which is not the case in our setup. We relax this strong assumption to the case when the objective function is locally Lipschitz continuous, which is another contribution of this paper.     

Caching and optimized request routing in cloud-based content delivery systems

Geographically distributed cloud platforms enable an attractive approach to large-scale content delivery. Storage at various sites can be dynamically acquired from (and released back to) the cloud provider so as to support content caching, according to the current demands for the content from the different geographic regions. When storage is sufficiently expensive that not all content should be cached at all sites, two issues must be addressed: how should requests for content be routed to the cloud provider sites, and what policy should be used for caching content using the elastic storage resources obtained from the cloud provider. Existing approaches are typically designed for non-elastic storage and little is known about the optimal policies when minimizing the delivery costs for distributed elastic storage.In this paper, we propose an approach in which elastic storage resources are exploited using a simple dynamic caching policy, while request routing is updated periodically according to the solution of an optimization model. Use of pull-based dynamic caching, rather than push-based placement, provides robustness to unpredicted changes in request rates. We show that this robustness is provided at low cost. Even with fixed request rates, use of the dynamic caching policy typically yields content delivery cost within 10% of that with the optimal static placement. We compare request routing according to our optimization model to simpler baseline routing policies, and find that the baseline policies can yield greatly increased delivery cost relative to optimized routing. Finally, we present a lower-cost approximate solution algorithm for our routing optimization problem that yields content delivery cost within 2.5% of the optimal solution.