基于能量和邻居信息的传感器睡眠调度协议

调度冗余节点轮流工作能够有效延长网络寿命。研究传感器网络节点的随机睡眠调度机制,给出了一种基于能量和节点邻居距离的随机调度机制。传感器节点根据自身的能量大小以及邻居的信息确定睡眠概率,使能量较小,并且工作邻居节点数目大的节点获得较大的睡眠概率。最后分析比较了这种机制与另外两种机制的性能。实验结果表明,基于能量和邻居信息的调度机制能够减少工作节点个数,保证较高的网络覆盖率。     

无线传感器网络能量最优的QoS路由发现方法

针对无线传感器网络的动态网络环境和节点能源受限,且通常无法补充的能源特性等不利因素,提出了一种可以实现能量最优的QoS路由发现方法。该方法利用节点选择机制和节点邻居表来建立满足QoS带宽需求的可供选择的节点集合,从而减少路由发现过程中的所要监测的节点数量。所提方法还构建了无线传感器网络的能量评价模型和节点能耗权重模型,使用遗传算法从可供选择的节点集合中构建可实现能量最优的QoS路由,自适应的实现对节点调度,从而延长无线传感器网络的寿命。通过实验仿真与分析,给出了实现无线传感器网络能量最优的遗传算法控制参数的选择区间。实验结果显示:该方法对无线传感器网络具有更好的适应性,且能保证其有更长的寿命。     

基于时间同步的无线传感器网络覆盖控制优化算法*

为了实现网络覆盖控制的优化,减少能耗,提出了一种分布的、高效节能、与节点位置无关的传感器网络覆盖算法。在该算法中,节点与邻居交换信息,并通过能量大小竞选工作节点,节点根据自身与工作节点的距离判断决定进入工作状态或休眠状态,并采用在生成树中广播时间同步算法使工作节点网络达到时间同步。仿真结果比较表明,该算法能够明显减少工作节点数,从而减少能量消耗,延长网络寿命。     

优化无线传感器网络寿命的动态路由算法

针对多跳无线传感器网络能量受限的特点,以提高网络寿命为目标,建立基于最大最小节点寿命的线性规划网络模型。当传感器节点失效时,根据网络的拓扑结构动态更新节点的路由。仿真分析结果表明：基于网络拓扑结构变化动态更新节点路由的方法能够拓展网络寿命,大幅度地增加基站接收信息的数量和提高节点能量的使用效率。     

AntNet的多路径QoS路由算法研究

以AntNet算法为基础,介绍了蚁群网络路由的问题模型和数据结构,通过引入QoS约束机制,提出了一种基于AntNet的多路径QoS路由算法. 该算法采用具有带宽和时延QoS约束的新规则进行节点选择,并利用改进的节点信息更新规则以及根据路由表中概率值随机地选择相邻节点转发数据包. 性能分析和模拟结果显示,基于AntNet的多路径QoS路由算法具有较快的收敛速度和较好的鲁棒性,能够自适应网络状态的动态变化,同时考虑了QoS约束和负载平衡问题     

基于随机网络演算的无线传感器网络延时研究

基于随机网络演算研究了无线传感器网络中延时相关问题,通过随机网络演算建立随机到达曲线和随机服务曲线模型,推导出单个节点和端到端的延时统计界.与节点到节点计算方法的延时界相比,演算方法更为简单.另外,随机延时界比确定性延时界具有更好的包络性,给出了违背界的函数.数值结果表明,运用SNC的卷积公式求得的概率统计型延时边界的结论具有更严格包络性,对无线传感器网络配置和QoS控制具有较强参考意义.     

无线传感器网络信息时空融合模型与算法研究

信息融合是解决无线传感器网络能量和通信带宽受限的有效途径,提出了一种无线传感器网络信息时间和空间多级混合融合的结构模型。首先对距离汇聚节点较远的节点信息进行时间融合和对距离汇聚节点较近的节点信息在汇聚节点进行时空融合,然后在汇聚节点对两种节点的融合结果进行全网络的空间融合。就该模型提出了基于DS证据理论的多级时空融合算法。数据分析结果表明:该模型能够降低节点能耗,延长网络寿命。     

基于功率控制的无线传感器网络定位算法

针对无线传感器网络自身定位问题,提出了一种基于功率控制的定位算法(PCLA),算法通过计算信号覆盖区域的交叉点重心确定节点位置.在算法中,每个信标节点广播不同功率等级的无线信号信息,且节点之间不需要严格的时间同步和相互协作.传感器节点只是简单的接收并保存信标节点传播的有用信息,不需要和邻居节点进行信息交换,复杂的计算由汇聚节点完成,减少了节点的能量消耗,延长了网络寿命.最后仿真结果表明了该算法的有效性.     

基于最优连通功率控制的WSNs跨层路由优化算法

针对非连通区域节点空洞效应和热点区域节点间通信干扰导致的路由服务质量（QoS）下降问题,提出了一种基于最优连通功率控制的无线传感器网络（WSNs）跨层路由优化算法。算法采用自适应最优连通功率控制策略,在避免路由空洞产生和保证网络连通性条件下,降低热点区域节点数据转发竞争干扰;通过位置信息、剩余能量和干扰等级的跨层信息交互,动态选取最优转发节点,提高网络整体性能。仿真实验表明：算法能够提高路由（QoS）、优化网络生命周期和降低热点区域通信干扰。     

基于蛙跳算法的无线传感器网络节点重部署

针对传统无线传感器网络节点重部署覆盖方法没有进行子群节点局部搜索,导致方法存在覆盖率较低、节点连通性较差等问题,提出基于蛙跳算法的无线传感器网络节点重部署方法,初始化无线传感器网络节点,引入蛙跳算法,将全局的信息交换和子群局部搜索结合,确定无线传感器所能够探测的区域范围,利用微积分方法求解不规则区域,获取网络节点最优解,实现无线传感器网络节点的重部署.实验结果表明,研究方法覆盖率较高、节点连通性较好、节点能量较高,具有更好的应用价值.     

Learning automata-based algorithms for finding cover sets in wireless sensor networks

Wireless sensor networks (WSNs) have been widely used in different applications. One of the most significant issues in WSNs is developing an efficient algorithm to monitor all the targets and, at the same time, extend the network lifetime. As sensors are often densely deployed, employing scheduling algorithms can be considered a promising approach that is able ultimately to result in extending total network lifetime. In this paper, we propose three learning automata-based scheduling algorithms for solving target coverage problem in WSNs. The proposed algorithms employ learning automata (LA) to determine the sensors that should be activated at each stage for monitoring all the targets. Additionally, we design a pruning rule and manage critical targets in order to maximize network lifetime. In order to evaluate the performance of the proposed algorithms, extensive simulation experiments were carried out, which demonstrated the effectiveness of the proposed algorithms in terms of extending the network lifetime. Simulation results also revealed that by a proper choice of the learning rate, a proper trade-off could be achieved between the network lifetime and running time.     

HACH: Heuristic Algorithm for Clustering Hierarchy protocol in wireless sensor networks

Wireless sensor networks (WSNs) require energy management protocols to efficiently use the energy supply constraints of battery-powered sensors to prolong its network lifetime. This paper proposes a novel Heuristic Algorithm for Clustering Hierarchy (HACH), which sequentially performs selection of inactive nodes and cluster head nodes at every round. Inactive node selection employs a stochastic sleep scheduling mechanism to determine the selection of nodes that can be put into sleep mode without adversely affecting network coverage. Also, the clustering algorithm uses a novel heuristic crossover operator to combine two different solutions to achieve an improved solution that enhances the distribution of cluster head nodes and coordinates energy consumption in WSNs. The proposed algorithm is evaluated via simulation experiments and compared with some existing algorithms. Our protocol shows improved performance in terms of extended lifetime and maintains favourable performances even under different energy heterogeneity settings.     

无线传感器网络基于模糊信息的QoS路由发现方法

有效使用片上受限资源以保持最长的生命周期是无线传感器网络的核心问题.无线传感器网络不断增长的多媒体应用引发人们研究如何满足具体应用的服务质量约束.能量因素与QoS各参数的相互影响构成了一个NP完全问题.本文引入模糊数学理论中的区间数概念抽象现实网络提供的信息,利用排队论方法将多业务多类型QoS多参数的约束和目标简化,从而得到了一种满足多类型业务多参数QoS要求的路由算法.仿真实验结果表明,在满足多类型业务多参数QoS要求的前提下,本文算法能很好支持多种业务的资源需求分配,可达到全网最优能耗均衡,延长网络寿命.     

Delay characterization and performance evaluation of cluster-based WSN with different deployment distributions

Provisioning of quality of service (QoS) is the ultimate goal for any wireless sensor network (WSN). Several factors can influence this requirement such as the adopted cluster formation algorithm. Almost all WSNs are structured based on grouping the sensors nodes into clusters. Not all contemporary cluster formation and routing algorithms (e.g. LEACH) were designed to provide/sustain certain QoS requirement such as delay constraint. Another fundamental design issue is that, these algorithms were built and tested under the assumption of uniformly distributed sensor nodes. However, this assumption is not always true. In some industrial applications and due to the scope of the ongoing monitoring process, sensors are installed and condensed in certain areas, while they are widely separated in other areas. Also unlike the random deployment distributions, there are many applications that need deterministic deployment of sensors like grid distribution. In this work, we investigated and characterized the impact of sensor node deployment distributions on the performance of different flavors of LEACH routing algorithm. In particular, we studied via extensive simulation experiments how LEACH cluster formation approach affects the delay (inter and intra-cluster delay) and energy efficiency expressed in terms of packet/joule for different base station locations and data loads. In this study, we consider four deployment distributions: grid, normal, exponential and uniform. The results showed the significant impact of nodes distribution on the network energy efficiency, throughput and delay performance measures. These findings would help the architects of real time application wireless sensor networks such as secure border sensor networks to design such networks to meet its specifications effectively and fulfill their critical mission.     

Sleep–wake up scheduling with probabilistic coverage model in sensor networks1

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work.     

无线传感器/执行器网络中能量有效的实时分簇路由协议

无线传感器/执行器网络(WSANs)主要应用于自动控制领域,实时性问题是其面临的首要挑战.根据实际环境中的节点部署情况,建立了系统模型;研究了分簇策略与功率控制技术对于自组织网络实时性的影响,提出了一种可适用于WSANs的能量有效的实时分簇路由协议——RECRP协议.该协议采用二级成簇策略使网络中的各类节点稳定分簇.分簇后的各类节点具有不同发射功率,利用执行器节点的强大通信能力有效降低网络延时.采用能量有效性算法使网络中的传感器节点轮换担任簇首,从而使网络能量均匀消耗,延长网络的生存时间.实验结果证明,在WSANs中RECRP协议可使网络稳定分簇,并且在网络的实时性与能量有效性方面与现有典型路由协议相比具有更优越的性能.     

Range-Free Localization Using Expected Hop Progress in Wireless Sensor Networks

Localization algorithm continues to be an important and challenging topic in today's wireless sensor networks (WSNs). In this paper, a novel range-free localization algorithm using expected hop progress (LAEP) to predict the location of any sensor in a WSN is proposed. This algorithm is based on an accurate analysis of hop progress in a WSN with randomly deployed sensors and arbitrary node density. By deriving the expected hop progress from a network model for WSNs in terms of network parameters, the distance between any pair of sensors can be accurately computed. Since the distance estimation is a key issue in localization systems for WSNs, the proposed range-free LAEP achieves better performance and less communication overhead as compared to some existent schemes like DV-Hop and RAW. In addition, we study the effect of anchor placement on the algorithm performance by deriving the corresponding mean position error range. Extensive simulations are performed and the results are observed to be in good agreement with the theoretical analysis.     

QoS guided Min-Min heuristic for grid task scheduling

Task scheduling is an integrated component of computing.With the emergence of Grid and ubiquitous computing,new challenges appear in task scheduling based on properties such as security,quality of service,and lack of central control within distributed administrative domains.A Grid task scheduling framework must be able to deal with these issues.One of the goals of Grid task scheduling is to achivev high system throughput while matching applications with the available computing resources.This matching of resources in a non-deterministically shared heterogeneous environment leads to concerns over Quality of Service (QoS).In this paper a novel QoS guided task scheduling algorithm for Grid computing is introduced.The proposed novel algorithm is based on a general adaptive scheduling heuristics that includes QoS guidance.The algorithm is evaluated within a simulated Grid environment.The experimental results show that the nwe QoS guided Min-Min heuristic can lead to significant performance gain for a variety of applications.The approach is compared with others based on the quality of the prediction formulated by inaccurate information.     

Markov-based vertical handoff decision algorithms in heterogeneous wireless networks

In this paper, we propose a received signal strength (RSS)-based single-attribute handoff decision algorithm at first, and investigate handoff decision model based on connection lifetime, which can keep mobile terminals (MTs) staying long enough in the preferred network. Since the preferred quality of service (QoS) parameters may be distinct among different MTs, we then formulate the vertical handoff decision problem as a Markov decision process, with the objectives of maximizing the expected total reward and minimizing average number of handoffs. A reward function is constructed to assess the QoS during each connection, and the G1 and entropy methods are applied in an iterative way, by which we can work out a stationary deterministic handoff decision policy. Numerical results demonstrate the superiority of our proposed schemes compared with other existing algorithms.