基于jBPM4的临时动态性需求研究

介绍了国内目前的工作流领域特点,尤其对临时动态性需求(会签、撤销、任意回退等)的各种场景进行了分析,提出了基于jBPM4的解决思路及一种动态路由的方法,以解决临时回退的问题.     

双重认证Ad hoc网络安全路由协议设计

由于Ad hoc网络拓扑的动态性和数据传输的多跳性,传统路由协议不能保证Ad hoc网络路由安全。提出一种双重认证Ad hoc网络安全多径路由协议——TASRP（Two-tier Authentication Secure Multi-path Ad hoc Routing Protocol）,在按需路由建立的过程中,引入了双重认证（邻节点之间的身份认证、中间节点处理路由请求包时的相互认证）机制,同时实现了一次性会话密钥的交换,既保证了节点分离路径的安全建立,又实现了端到端的数据安全传输,保证了整个网络运行的安全。最后运用BAN逻辑进行推理分析,分析表明,该路由协议具有高的安全性。     

无线传感器网络LEACH路由协议研究与改进

针对LEACH算法随机选取簇头,未考虑节点剩余能量及单跳与sink节点通信造成能量损耗过快的问题。引入以剩余能量及邻居节点数作为权重因子的时间等候簇头竞争模式选取簇头,并提出LEACH与蚁群算法相结合建立簇间路由机制,采用局部信息素更新,综合簇头节点的剩余能量及节点距离,为簇首与sink节点通信建立多跳路由,达到降低簇头节点能耗过快的目的。仿真实验结果表明,改进算法在降低能耗、延长网络生命周期等方面较LEACH算法有较大提高。     

Proactive multipath routing with a predictive mechanism in software‐defined networks

With the growth of network traffic volume, link congestion cannot be avoided efficiently with conventional routing protocols. By utilizing the single shortest‐path routing algorithm from link state advertisement information, standard routing protocols lack of global awareness and are difficult to be modified in a traditional network environment. Recently, software‐defined network (SDN) provided innovative architecture for researchers to program their own network protocols. With SDN, we can divert heavy traffic to multiple paths in order to resolve link congestion. Furthermore, certain network traffics come in periodic fashion such as peak hours at working days so that we can leverage forecasting for resource management to improve its performance. In this paper, we propose a proactive multipath routing with a predictive mechanism (PMRP) to achieve high‐performance congestion resolution. PMRP has two main concepts: (a) a proactive mechanism where PMRP deploys M/M/1 queue and traffic statistics to simulate weighted delay for possible combinations of multipaths placement of all subnet pairs, and leverage genetic algorithm for accelerating selection of optimized solution, and (b) a predictive mechanism whereby PMRP uses exponential smoothing for demand traffic volumes and variance predictions. Experimental results show a 49% reduction in average delay as compared with single shortest routing, and a 16% reduction in average delay compared with utilization & topology‐aware multipath routing (UTAMP). With the predictive mechanism, PMRP can decrease an additional 20% average delay. Furthermore, PMRP reduces 93% of flow table usage on average as compared with UTAMP.     

Balancing CapEx reduction and network stability with stable routing–virtual topology capacity adjustment (SR–VTCA)

This paper investigates the merits of the SR–VTCA (stable routing–virtual topology capacity adjustment) approach as a mechanism to find a beneficial trade-off between network stability and reduction in capital expenditures (CapEx). These are two main objectives for the entities that own the optical infrastructure, such as network operators (NOs), and those also acting as Internet service providers (ISPs). The SR–VTCA scheme is a novel approach to adapt transparent optical networks to time-varying traffic by adjusting the number of lightpaths between node pairs, while keeping the IP routing unchanged. Lightpath bundling (LB) and anycast (AS) switching are combined in SR–VTCA operation to advertise lightpath additions/removals to the IP layer as mere adjustments (increments or decrements) in the capacity, allowing to keep the IP routing stable, and thus, simplifying control plane operations. On the contrary, a fully-reconfigurable (FR) network design, where IP routing can be also modified, would increase the burden in the control plane, but at a higher CapEx reduction, since the optical infrastructure is used more efficiently. In this work, we investigate the CapEx overprovision introduced by SR–VTCA with respect to a FR scheme. In order to do this, SR–VTCA planning problem is first modeled as a MILP formulation. A heuristic procedure based on traffic domination is then proposed to solve large instances of the problem. Exhaustive experiments are conducted comparing the SR–VTCA solutions obtained by the aforementioned MILP and heuristic proposal with solutions found by other optimization methods presented in the literature to solve the FR planning problem. Finally, the results show that SR–VTCA can achieve similar results to the FR case in terms of CapEx reduction, while a huge number of IP reroutings are saved by maintaining IP stability. Thus, SR–VTCA provides an advantageous balance between CapEx overprovisioning and the control plane overhead associated with IP rerouting.     

A heuristic to solve the synchronized log-truck scheduling problem

We present a synchronized routing and scheduling problem that arises in the forest industry, as a variation of the log-truck scheduling problem. It combines routing and scheduling of trucks with specific constraints related to the Canadian forestry context. This problem includes aspects such as pick-up and delivery, multiple products, inventory stock, multiple supply points and multiple demand points. We developed a decomposition approach to solve the weekly problem in two phases. In the first phase we use a MIP solver to solve a tactical model that determines the destinations of full truckloads from forest areas to woodmills. In the second phase, we make use of two different methods to route and schedule the daily transportation of logs: the first one consists in using a constraint-based local search approach while the second one is a hybrid approach involving a constraint programming based model and a constraint-based local search model. These approaches have been implemented using COMET2.0. The method, was tested on two industrial cases from forest companies in Canada.     

A note on the time travel approach for handling time windows in vehicle routing problems

In this paper, we identify two cases in which the proposition for calculating time window penalties presented in Nagata, Y., Bräysy, O. and Dullaert, W. A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows, Computers & Operations Research 2010;37(4): 724–37 yields incorrect results. We derive the corrected proposition and use numerical studies to show that a significant proportion of the evaluations performed by a Tabu Search for VRPTW falls under the two incorrect cases. Moreover, we demonstrate that the incorrect time window handling has a significant negative impact on the solution quality of the Tabu Search.     

Energy‐efficient void avoidance geographic routing protocol for underwater sensor networks

Underwater wireless sensor networks (UWSNs) consist of a group of sensors that send the information to the sonobuoys at the surface level. Void area, however, is one of the challenges faced by UWSNs. When a sensor falls in a void area of communication, it causes problems such as high latency, power consumption, or packet loss. In this paper, an energy‐efficient void avoidance geographic routing protocol (EVAGR) has been proposed to handle the void area with low amount of energy consumption. In this protocol, a suitable set of forwarding nodes is selected using a weight function, and the data packets are forwarded to the nodes inside the set. The weight function includes the consumed energy and the depth of the candidate neighboring nodes, and candidate neighboring node selection is based on the packet advancement of the neighboring nodes toward the sonobuoys. Extensive simulation experiments were performed to evaluate the efficiency of the proposed protocol. Simulation results revealed that the proposed protocol can effectively achieve better performance in terms of energy consumption, packet drop, and routing overhead compared with the similar routing protocol.     

无线传感器网络体系结构和关键技术研究

文章介绍了无线传感器网络的体系结构,提出了一种基于跨层设计的管理控制机制;在对无线传感器网络的关键技术,如媒体接入控制(MAC)技术和无线路由协议,分别进行阐述和比较的基础上,提出了一种把简单的IEEE 802 MAC协议应用于无线传感器网络的思想;为了提高系统容量,提出了多信道策略,可以用来有效减少系统碰撞阻塞率,减少接入时延;认为虽然无线传感器网络的应用前景非常广阔,但是仍存在很多问题,除了要尽量减少功耗外,必须提高系统容量,减少碰撞阻塞率,以加快无线传感器网络实用化的进程.     

Adaptive quality of service‐based routing approaches: development of neuro‐dynamic state‐dependent reinforcement learning algorithms

In this paper, we propose two adaptive routing algorithms based on reinforcement learning. In the first algorithm, we have used a neural network to approximate the reinforcement signal, allowing the learner to take into account various parameters such as local queue size, for distance estimation. Moreover, each router uses an online learning module to optimize the path in terms of average packet delivery time, by taking into account the waiting queue states of neighbouring routers. In the second algorithm, the exploration of paths is limited to N‐best non‐loop paths in terms of hops number (number of routers in a path), leading to a substantial reduction of convergence time. The performances of the proposed algorithms are evaluated experimentally with OPNET simulator for different levels of traffic's load and compared with standard shortest‐path and Q‐routing algorithms. Our approach proves superior to classical algorithms and is able to route efficiently even when the network load varies in an irregular manner. We also tested our approach on a large network topology to proof its scalability and adaptability. Copyright © 2006 John Wiley & Sons, Ltd.