信息物理融合系统中最小延迟数据聚集调度算法综述

数据聚集调度问题研究sink节点如何能够无冲突地获取整个网络的汇总信息,是信息物理融合系统中一个非常重要的服务.在数据聚集调度问题中,聚集延迟是指整个网络中最后一个数据包到达sink节点的时间.最小延迟聚集调度问题则寻求一个快速无冲突的调度来最小化聚集延迟.在节点一直醒着的网络中,考虑到整个网络的大小和数据传输中的干扰问题,最小延迟聚集调度问题被证明为NP-难问题.另外,当网络中的节点采用低占空比模式来节省能耗时,由于节点只能在其处于活动状态时接受数据,最小延迟聚集调度问题变得更加复杂.近年来最小延迟聚集调度问题得到了大量的关注,本文将对该问题研究所取得的进展进行综述.     

无线自组网中基于多速率传输调度问题

为了研究无线自组网中多速率调度问题,首先将它模型化为一个优化问题,然后根据是否考虑节点缓冲区因素提出了两个多速率条件下的调度算法--HRFWICB和HRFWCB,它们的基本思想是在保证数据流基本公平性的前提下,优先调度高速率数据流来降低包的传输时间和延迟.实验结果证明,同Luo算法和GPPF算法相比,HRFWICB算法和HRFWCB算法能够明显地降低包的传输时间和延迟.     

MR-MC无线传感器网络最小延迟数据聚集调度研究

单radio单信道无线传感器网络的最小延迟聚集调度是一个NPC问题,已提出许多解决方案。在多radio多信道网络中,节点可以同时接收多个不同节点传输的数据,降低延迟。基于上述特点,考虑树结构约束,时槽、信道和radio分配等约束条件,将多radio多信道无线传感器网络最小延迟聚集调度问题定义为一个优化问题,并分解为建立聚集树和节点调度2个子问题,针对这2个子问题分别提出启发式算法。实验结果表明,提出的算法具有良好的性能。     

单码流场景下P2P流媒体直播数据调度方法

由于具有较高应对节点动态性的能力和较强的扩展性,Mesh-Pull P2P流媒体直播分发方法赢得了学术界和工业界的广泛青睐.提出了传统互联网单码流场景下Mesh-Pull P2P流媒体直播的数据调度算法.该算法采用TOPSIS方法来解决调度算法中数据块优先级的量化这一多属性决策问题,以降低节点的启动延迟.仿真实验表明,本算法可以在保证高视频播放质量的情况下降低用户观看视频的延迟.     

基于数据融合的无线传感器网络路由算法

在分簇协议LEACH和链状协议PEGASIS的基础上,提出一种新的基于数据融合的分簇路由算法.簇首节点采用多跳方式传输数据,并根据周围节点的密集程度构造不同大小的簇;簇内节点计算上行和下行节点构造数据融合树,采用时分复用调度算法进行多跳路由.NS2仿真结果表明该路由算法均衡了各个节点的能量消耗,延长了网络存活时间,并降低了网络延迟.     

最小化延迟的无线传感器网络节点调度算法

如何实现节能和减小延迟一直是无线传感器网络中研究的热点及难点问题。提出一种新的最小化延迟的能量均衡的节点调度（MDS）算法,算法通过对能量以及延迟的分析计算,计算出节点的能量判定阈值,并在选择下一跳的时候,总是选择节点剩余能量在限定范围内且延迟最小的节点,以此实现最小化网络延迟的目的。理论分析及实验结果表明,MDS算法能够较好地把网络的生命周期控制在可接受范围内,同时可以解决在能耗限定条件下的最小化延迟的问题。     

基于数据中心负载分析的自适应延迟调度算法

由于已有的延迟调度算法基于静态的等待时间阈值,因此不能适应云计算数据中心动态的负载变化。针对该问题,提出了等待时间阈值自适应调整模型。基于该模型,设计了自适应延迟调度算法(ADS)。ADS算法通过分析空闲计算节点的到达强度、网络带宽和作业执行状态等参数,自适应调整等待时间阈值,以减少作业响应时间。基于开发的原型系统,验证了自适应调整模型,测试了算法性能。结果表明,ADS算法在作业响应时间等方面优于已有的延迟调度算法。     

一种面向移动边缘计算的多用户细粒度任务卸载调度方法

在移动边缘计算中(Mobile Edge Computing,MEC),任务卸载可以有效地解决移动设备资源受限的问题,但是将全部任务都卸载到边缘服务器并非最优.本文提出一种面向移动边缘计算的多用户细粒度任务卸载调度新方法,把计算任务看作一个有向无环图(Directed Acyclic Graph,DAG),对节点的执行位置和调度顺序进行了优化决策.考虑系统的延迟把计算卸载看作一个约束多目标优化问题(Constrained Multi-object Optimization Problem,CMOP),提出了一个改进的NSGA-Ⅱ算法来解决CMOP.所提出的算法能够实现本地和边缘的并行处理从而减少延迟.实验结果表明,算法能够在实际应用程序中做出最优决策.     

分簇拓扑下backpressure算法的延迟性改进研究

Backpressure算法是一种自适用的路由调度算法,它从理论上解决throughput-optimal问题,但是在实际网络部署中,存在节点维护数据队列的数量繁多和数据路由繁长问题,致使数据传输延迟较长。文中就此问题出发,把backpressure算法应用到分簇拓扑上,使用shadow算法实现backpressure算法下的路由调度,采用LIFO策略调度队列,同时又对路径选择了做了优化。仿真结果表明,数据传输的延迟性大大降低。     

基于能量探测的WSN节点调度算法研究

针对无线传感器网络在节点传输过程中的延迟与链路质量问题,设计一种基于能量探测的节点休眠调度算法EAS,通过对节点插入苏醒时隙来减小端到端之间的延迟,并根据每个节点的剩余能量值进行能量探测,使整个网络中各节点的能量相对均衡地消耗,在保证传输质量的基础上达到延长网络的生命周期的目的。实验结果表明,对比现有算法,EAS算法能够在保证使WSN网络在延迟一定的情况下明显降低能耗,延长整个网络的工作寿命。     

Low-latency algorithm for improving data persistence in mobile low-duty-cycle wireless sensor network

Mobile low-duty-cycle wireless sensor network (MLDC-WSN) are a kind of new ad hoc networks that are appeared in recent years.In MLDC-WSN,the nodes only have limited storage spaces.Moreover,the nodes would move or sleep from time to time.Therefore,these networks have some problems such as connectivity is hard to be maintained and data are hard to be transmitted to their destinations for storage in time.As a result,data persistence (i.e.,the probability that all data can be recovered after some nodes die in the networks) is low.A distributed algorithm named LT-MDS for improving data persistence in MLDC-WSN was proposed.The algorithm used a new infectious data dissemination method to transmit the data,which enabled the data to be received by almost all the mobile nodes in a network with low latency and improved the reliability of the network.When a node receives the data,it would use LT (Luby transform) codes to encode and save them.By this way,the nodes with limited storage spaces can save more data information.Theoretical analyses and simulations show that LT-MDS can complete the process of data dissemination and preservation with low latency,and it can achieve high data persistence.     

Low Latency Wireless Ad Hoc Networking: Power and Bandwidth Challenges and a Solution

This paper is concerned with the scaling of the number of relay nodes (i.e., hops) individual messages have to transit through in a large-scale wireless ad hoc network (WANET); we call this hop-count as network latency (NL). A large network latency affects all aspects of data communication in a WANET, including an increase in delay, packet loss, and the power needed to process and store messages in nodes lying on the relay path. We consider network management and data routing challenges in WANETs with scalable network latency, e.g., when NL increases only polylogarithmically in the network size. On the physical side, reducing network latency imposes a significantly higher power and bandwidth demand on nodes, and are captured in a set of new bounds derived in this paper. On the protocol front, designing distributed routing protocols that can guarantee the delivery of data packets within a scalable number of hops is a challenging task. To solve this, we introduce multi-resolution randomized hierarchy (MRRH), a novel power and bandwidth efficient WANET protocol with scalable network latency. MRRH uses a randomized algorithm for building and maintaining a random hierarchical network topology, which together with the proposed routing algorithm, can guarantee efficient delivery of data packets in the wireless network. For a network of size N, MRRH can provide an average latency of only O(log³ N). The power consumption and bandwidth requirements of MRRH are shown to be nearly optimal for the latency it provides. Therefore, MRRH is a provably efficient candidate for truly large-scale wireless ad hoc networking.     

A lifetime enhancement protocol in cluster‐based wireless sensor networks with guaranteed delay

Wireless sensor networks nowadays find application in all the fields of the world. Rare event detection is an important application in which the wireless sensor network is used. In the case of rare event detection, event of interest or the important event occurs very rarely. Battery‐powered sensor nodes are deployed to detect the event and report to the base station. Sensing and communicating the low priority events happen in major portion of the lifetime for the sensor nodes. However, if the event occurs, then it should be detected and communicated at the earliest to the base station or to the sink node. To reduce the network traffic due to low priority data, we propose a cross layered algorithm to improve the lifetime of the sensor nodes in the case of clustered architecture. In spite of the increase in the network lifetime, the time to detect and communicate to the base station is maintained as that of the traditional clustering approach. The proposed algorithm is simulated, and results show significant improvement in the lifetime of the sensor nodes with guaranteed latency. In this paper, we also suggest methods to support the latency to sensor nodes on priority basis for continuous reporting.     

用于VSN的低延时多径路由

视频传感器网络VSN（Video Sensor Network）相对于传统的传感器网络需要更高的带宽和更低的延时。文中提出了一种适合于VSN的多径路由算法MPTT（Multi-path Predicted Transmission Time）。这种路由算法是从MAC层建模,根据链路邻居节点的流量信息来估算出路径的整体延时,再根据每条路径延时的大小分配数据流量使其总体的延时达到最小。经过仿真可以得出,在数据流量不大时,该算法与延时最小的路径延时相当。随着所分配流量的加大,MPTT比DSR延时要小,而且可以提供更高的数据流带宽。     

Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks

A data mule represents a mobile device that collects data in a sensor field by physically visiting the nodes in a sensor network. The data mule collects data when it is in the proximity of a sensor node. This can be an alternative to multihop forwarding of data when we can utilize node mobility in a sensor network. To be useful, a data mule approach needs to minimize data delivery latency. In this paper, we first formulate the problem of minimizing the latency in the data mule approach. The data mule scheduling (DMS) problem is a scheduling problem that has both location and time constraints. Then, for the 1D case of the DMS problem, we design an efficient heuristic algorithm that incorporates constraints on the data mule motion dynamics. We provide lower bounds of solutions to evaluate the quality of heuristic solutions. Through numerical experiments, we show that the heuristic algorithm runs fast and yields good solutions that are within 10 percent of the optimal solutions.     

Low-latency neighbor discovery algorithm based on multi-beacon message in mobile low-duty-cycle sensor network

Neighbor discovery enables nodes in the networks to discover each other through simple information interaction,which was suitable for the new mobile low duty cycle sensor network (MLDC-WSN).However,because the nodes in MLDC-WSN can move randomly and sleep,the network topology was changed frequently,which results in that some nodes need a lot of energy and time to find their neighbors.How to realize fast neighbor discovery for all nodes in the network was a difficult problem in current research.To solve this problem,a new low-latency neighbor discovery algorithm based on multi-beacon messages was proposed.In this algorithm,the nodes were discovered by sending a short beacon message through their neighbor nodes,and by adjusting the time and frequency of beacon message sent,a lower neighbor discovery delay was obtained.Eventually,through quantitative analysis and simulation experiments,it is found that compared with existing algorithms,this algorithm can find all neighbor nodes in MLDC-WSN with less energy consumption,lower latency and greater probability.     

Spanning tree based algorithms for low latency and energy efficient data aggregation enhanced convergecast (DAC) in wireless sensor networks

Many wireless sensor networks (WSNs) employ battery-powered sensor nodes. Communication in such networks is very taxing on its scarce energy resources. Convergecast – process of routing data from many sources to a sink – is commonly performed operation in WSNs. Data aggregation is a frequently used energy-conversing technique in WSNs. The rationale is to reduce volume of communicated data by using in-network processing capability at sensor nodes. In this paper, we address the problem of performing the operation of data aggregation enhanced convergecast (DAC) in an energy and latency efficient manner. We assume that all the nodes in the network have a data item and there is an a priori known application dependent data compression factor (or compression factor), γ, that approximates the useful fraction of the total data collected.The paper first presents two DAC tree construction algorithms. One is a variant of the Minimum Spanning Tree (MST) algorithm and the other is a variant of the Single Source Shortest Path Spanning Tree (SPT) algorithm. These two algorithms serve as a motivation for our Combined algorithm (COM) which generalized the SPT and MST based algorithm. The COM algorithm tries to construct an energy optimal DAC tree for any fixed value of α (= 1 − γ), the data growth factor. The nodes of these trees are scheduled for collision-free communication using a channel allocation algorithm. To achieve low latency, these algorithms use the β-constraint, which puts a soft limit on the maximum number of children a node can have in a DAC tree. The DAC tree obtained from energy minimizing phase of tree construction algorithms is re-structured using the β-constraint (in the latency minimizing phase) to reduce latency (at the expense of increasing energy cost). The effectiveness of these algorithms is evaluated by using energy efficiency, latency and network lifetime as metrics. With these metrics, the algorithms’ performance is compared with an existing data aggregation technique. From the experimental results, for a given network density and data compression factor γ at intermediate nodes, one can choose an appropriate algorithm depending upon whether the primary goal is to minimize the latency or the energy consumption.     

Balanced multiple controllers placement with latency and capacity bound in software-defined network

Software-defined network (SDN) used a network architecture which separates the control plane and data plane. The control logic of SDN was implemented by the controller. Because controller's capacity was limited, in large scale SDN networks, single controller can not satisfy the requirement of all switches. Multiple controllers were needed to han-dle all data flows. By the reason that the latency between controller and switch would significantly affect the forwarding of new data flow, the rational placement of controllers would effectively improve the performance of entire network. By partition the network into multiple sub domains, on the base of spectral clustering, a method that added a balanced de-ployment object function into k-means was given and a balanced multiple controllers placement algorithm in SDN net-works which has the latency and capacity limitations was proposed. In this approach, a penalty function was introduced in the algorithm to avoid isolation nodes appearing. The simulations show that this algorithm can balance partition the net-work, keep the latency between controller and switch small and keep loads balancing between controllers.     

一种低时延的短波自组网优化链路状态路由协议

优化链路状态路由(Optimized Link State Routing,OLSR)协议是一种先验式路由协议,网络中的所有节点通过周期性地发送控制消息来计算全网路由信息。在短波自组织网络中,节点周期性地发送控制消息会占据大量的信道资源,大幅增加网络的控制开销,浪费短波有限的带宽资源,导致网络通信性能急剧下降。其次,受到地形地貌、天线方向和接收性能的个体差异等影响,造成无线链路不稳定,导致网络中存在非对称链路,增加了通信端到端时延。为此,提出了一种低时延的短波自组网OLSR协议。该协议在执行MPR(Multipoint Relay)选择算法时综合考虑了节点的连接度和链路可靠性,在优化MPR节点个数的同时选择链路可靠性较大的节点作为MPR节点,在进行路由选择时能够利用网络中的非对称链路。仿真结果表明,该协议能优化数据包投递成功率、吞吐量、端到端时延和网络控制开销等性能指标。     

Benefit-Based Data Caching in Ad Hoc Networks

Data caching can significantly improve the efficiency of information access in a wireless ad hoc network by reducing the access latency and bandwidth usage. However, designing efficient distributed caching algorithms is nontrivial when network nodes have limited memory. In this article, we consider the cache placement problem of minimizing total data access cost in ad hoc networks with multiple data items and nodes with limited memory capacity. The above optimization problem is known to be NP-hard. Defining benefit as the reduction in total access cost, we present a polynomial-time centralized approximation algorithm that provably delivers a solution whose benefit is at least 1/4 (1/2 for uniform-size data items) of the optimal benefit. The approximation algorithm is amenable to localized distributed implementation, which is shown via simulations to perform close to the approximation algorithm. Our distributed algorithm naturally extends to networks with mobile nodes. We simulate our distributed algorithm using a network simulator (ns2) and demonstrate that it significantly outperforms another existing caching technique (by Yin and Cao [33]) in all important performance metrics. The performance differential is particularly large in more challenging scenarios such as higher access frequency and smaller memory.