基于投影矢量的双组播树高效路由数据收集

现有的节点数据收集算法复杂度高,与路由结合效果不理想,且不能满足无线传感器网络高效能量的节点数据收集,而压缩感知理论具有容错性好、编码简单的优点。基于压缩感知的特性,提出了一种传感器网络中基于投影矢量的双组播树高效路由数据收集,该算法将贝叶斯压缩感知理论与传感器路由相结合,解决现有算法不能满足传感器对能耗敏感的问题。算法的基本思想首先根据初始观测矢量来寻求能量高效并得到合适路由的最优投影。然后利用节点系数能耗最小与广义矢量的主分量作为目标节点,采用微分嫡改变量最大的原则进行求解节点最佳投影系数,最后在sink与目标节点路由问题上采用正逆向组播树进行路由构造。理论和仿真结果表明在保证能耗的条件下取得了较好的重建仿真效果,对无线传感器通信具有一定的实用价值。     

无线传感器网络的数据汇聚机制

针方法是首先将传感区域内部的所有传感器节点采集的数据都传送对传感器网络的特点，提出了一种最小传输成本生成树的数据汇聚机制。具体实现给传感区域内离汇聚点最近的节点，经过数据汇聚后，将汇聚的数据通过最短路径传递给汇聚点。仿真结果显示最小传输代价生成树的数据拒聚能够减少数据传输量50％-80％，并具有较小的传输时延。     

无线传感器网络基于数据汇聚的路由

提出了一种针对无线传感器网络的路由协议,该路由采用最小传输成本生成树的数据汇聚机制。具体方法是首先将传感区域内的传感器节点采集的数据传送给传感区域内离汇聚点最近的节点,将这些数据进行汇聚操作后,将汇聚的结果通过最短路径传递给网络汇聚点。仿真结果显示,采用最小传输代价生成树的路由协议能减少数据传输量50%-80%,并具有较小的传输时延。     

Data aggregation in wireless sensor networks using ant colony algorithm

Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs.     

一种能量有效的传感器监测网络成簇协议

提出了一种分布式能量有效的传感器监测网络成簇协议EECTS(energy-efficient clustering protocol for target surveillance).在该协议中,节点根据邻居节点的分布情况以及自己的剩余能量来竞争簇首.为了降低簇首的能量消耗,簇首间形成一个以基站为根的最小生成树,将监测到的数据通过多跳方式直接发送到生成树的上游节点.由于监测网络的首要任务是能够对移动目标进行不间断的监测,此协议还提出了一种簇内调度方法EECTS-1,可监测到网络中的大部分区域,并在此基础上提出了改进方法EECTS-2.这两种方法在目标进入网络时都能取得较高的持续监测度.随着节点密度的提高,EECTS协议在保证监测性能的前提下,使得传感器监测网络的寿命相应于节点数量呈线性增长.仿真结果表明,在监测性能相同的情况下,运行EECTS-1协议的网络寿命与HEED协议相同,比DEEG协议的网络寿命延长了约35%。EECTS-2协议比EECTS-1和HEED协议的网络寿命延长了约70%～80%.EECTS成簇协议可应用于军事目标监测等领域,工作时间长,监测结果具有很高的可靠性.     

稀疏水下传感网中AUV数据移动收集技术研究

由于水下传感器节点的水声通信距离有限、价格昂贵,水下传感器网络中一般采用稀疏方式部署,因此很难保证整体网络的连通性及数据采集效率。自主水下航行器AUV（Autonomous Underwater Vehicle）作为天然的移动数据采集平台,可以弥补固定Sink节点数据采集方式的缺陷。提出了一种基于移动AUV的水下传感网移动数据收集机制。以AUV覆盖区域内的传感器节点作为临时Sink节点,其他传感器节点以临时Sink节点为根节点,采用最小生成树MST（Minimum Spanning Tree）方法将传感数据传输到这些临时Sink节点,然后通过临时Sink节点将汇聚数据传输给AUV。随着AUV的自主移动轨迹,水下传感网的传感数据都能简单高效地被收集起来。仿真结果验证了该方法在保证网络能耗的前提下提高了数据采集效率。     

能量高效的传感器网络数据查询路由*

提出一种能量高效的传感器网络数据查询路由EEDQ(energy-efficient data query),EEDQ以sink节点为根节点,构造最小路由生成树,由sink节点发出查询任务,查询结果由叶子节点向sink节点传输,传输过程中进行数据汇聚.实验表明,EEDQ相比direct transmission,大大提高了传感器网络的生命周期.     

无线传感器网络中一种能量有效的数据存储方法

如何有效地对传感器在过去历史时间段内采集的大量感知数据进行存储,以备将来的信息查询和数据分析已经成为无线传感器网络应用面临的一个难题.介绍了一种基于树型路由的分布式数据存储方法,通过采用动态规划方法选择存储节点,使存储节点能量均衡和所有节点能耗之和最小,从而达到整个无线传感器网络能量有效.仿真实验结果表明,这种数据存储方法能够获得较好的能量均衡和总能耗较小,从而有效地延长整个无线传感器网络的生命周期.     

基于能量控制的无线传感网络最优化算法研究

无线传感网络是当前无线网络研究的热点领域.控制节点能量、提高网络生存时间是实现在无线传感网络中传输高效业务的关键.本文首先使用基于泊松点过程的布尔传感模型确定了覆盖率与单位面积内传感器节点密度的函数关系,进而求得达到区域覆盖要求的节点数量;然后利用基于Prim算法的贪心策略,找到具有最大权值的生成树,构造一个最小连通支...     

WSN中一种基于最优投影的数据收集方案

针对目前基于密集投影数据收集方法的能量浪费问题,根据压缩感知理论提出一种基于最优投影的数据收集方法。该方法在获取各个节点的投影值时考虑了节点的传输开销来设计最优投影矩阵,避免不必要的投影操作,并证明其满足RIP性质,设计一棵具有最小传输代价增加值的数据收集树来实现投影值的收集。仿真实验结果表明,该方法在数据重构精度以及能量开销等方面优于传统的MWSF等方法。     

Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system

Coverage and connectivity are the two main functionalities of wireless sensor network. Stochastic node deployment or random deployment almost always cause hole in sensing coverage and cause redundant nodes in area. In the other hand precise deployment of nodes in large area is very time consuming and even impossible in hazardous environment. One of solution for this problem is using mobile robots with concern on exploration algorithm for mobile robot. In this work an autonomous deployment method for wireless sensor nodes is proposed via multi-robot system which robots are considered as node carrier. Developing an exploration algorithm based on spanning tree is the main contribution and this exploration algorithm is performing fast localization of sensor nodes in energy efficient manner. Employing multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor nodes deployment. A novel improvement of this technique in deployment of nodes is having obstacle avoidance mechanism without concern on shape and size of obstacle. The results show using spanning tree exploration along with multi-robot system helps to have fast deployment behind efficiency in energy.     

Wireless sensor network minimum beacon set selection algorithm based on tree model

Wireless sensor networks (WSNs) are highly attractive both in academia and in practice as a wholly new platform for information transmission. Localization technology is a key technology of WSNs. The structure of the beacon node set is very important to the positioning of the nodes. A method for constructing a minimum beacon set is proposed in this thesis based on the tree model, in which unimportant nodes are identified as early as possible and then pruned. Thus, we avoid unnecessary calculations when establishing the minimum beacon set. This method can provide a reliable guarantee for the unknown node localization. According to our experiment, this algorithm is rapid and stable.

     

矩形截面矿井巷道三维WSN覆盖机理

无线传感器节点在矿井巷道内壁上多采用径向直线型和径向V字型部署。针对矩形截面矿井巷道的无线传感器节点如何在三维空间实现单重无缝覆盖的问题,通过分析巷道几何尺寸,推导出其节点在两种部署类型中的最小感知半径;进一步推导出其节点覆盖密度与节点感知半径、巷道高度、巷道宽度之间的函数关系,比较两种部署类型节点覆盖密度,为现实运用选择节点部署方式提供有力依据。仿真实验验证了推导结果的正确性。利用推导的结论结合矿井的几何尺寸,可直接计算出节点必须满足的最小感知半径、部署方法、部署位置和覆盖密度,从而提高节点覆盖的有效性,减少工程成本支出。     

WSN中基于最小延时的数据汇集树构建与传输调度算法

针对现有的无线传感器网络数据汇集算法延时较大的不足,对最小延时数据汇集树和传输调度问题进行了研究。提出一种基于度约束的汇集树构建算法(DCAT)。该算法按照BFS方式遍历图,当遍历到每个节点时,通过确定哪些节点与汇点更近来确定潜在母节点集合。然后,选择图中度数最小的潜在母节点作为当前被遍历节点的母节点。此外,为了在给定的汇集树上进行高效的数据汇集,还提出两种新的基于贪婪的TDMA传输调度算法:WIRES-G和DCAT-Greedy。利用随机生成的不同规模的传感器网络,参照当前最新算法,对所提方法的性能进行了全面评估。结果表明,与当前最优算法相比,将所提调度算法与所提汇集树构建算法结合起来,可显著降低数据汇集的延时。     

A novel gossip-based sensing coverage algorithm for dense wireless sensor networks

Wireless sensor networks (WSNs) have been widely studied and usefully employed in many applications such as monitoring environments and embedded systems. WSNs consist of many nodes spread randomly over a wide area; therefore, the sensing regions of different nodes may overlap partially. This is called the “sensing coverage problem”. In this paper, we define a maximum sensing coverage region (MSCR) problem and present a novel gossip-based sensing-coverage-aware algorithm to solve the problem. In the algorithm, sensor nodes gossip with their neighbors about their sensing coverage region. In this way, nodes decide locally to forward packets (as an active node) or to disregard packets (as a sleeping or redundant node). Being sensing-coverage-aware, the redundant node can cut back on its activities whenever its sensing region is k-covered by enough neighbors. With the distributed and low-overhead traffic benefits of gossip, we spread energy consumption to different sensor nodes, achieve maximum sensing coverage with minimal energy consumption in each individual sensor node, and prolong the whole network lifetime. We apply our algorithm to improve LEACH, a clustering routing protocol for WSNs, and develop a simulation to evaluate the performance of the algorithm.     

基于移动机器人的无线传感器网络数据收集方法

稀疏无线传感器网络中各传感器节点距离较远,而传统的静态数据收集方法要求各传感器节点直接通信,导致网络延迟时间长,能耗高。针对该问题,提出一种基于移动机器人的无线传感器数据收集方法。该方法首先由静态节点选择与路径最短的移动机器人作为簇头,移动机器人比较一定周期内检测到的邻居节点的平均剩余能量与整个网络传感器节点平均剩余能量,根据比较结果决定其是否移动,若移动则采用范围可控的随机移动策略;当移动机器人移动到新位置时,传感器节点更新路由,选择新的移动机器人作为簇头。仿真结果表明,与传统的静态无线传感器网络数据收集方法相比,基于移动机器人的无线传感器网络数据收集方法大大降低了数据传输延迟和节点能量消耗。     

无线传感器网络中基于最小延时的数据汇集树构建与传输调度算法

无线传感器网络的数据通信模式问题是目前的研究热点,针对现有的无线传感器网络数据汇集算法延时较大这一不足,对最小延时数据汇集树和传输调度问题进行了研究。提出一种基于度约束的汇集树构建算法(DCAT)。该算法按照 BFS 方式遍历图,当遍历到每个节点时,通过确定哪些节点与汇点更近来确定潜在母节点集合。然后,选择图中度数最小的潜在母节点作为当前被遍历节点的母节点。此外,为了在给定的汇集树上进行高效地数据汇集,还提出两种新的基于贪婪的TDMA传输调度算法：WIRES-G 和 DCAT-Greedy。利用随机生成的不同规模的传感器网络,参照当前最新算法,对文中方法的性能进行了全面评估。结果表明,与当前最优算法相比,文中调度算法与文中汇集树构建算法结合起来,可显著降低数据汇集的延时。     

Approximation algorithm for constructing data aggregation trees for wireless sensor networks

This paper considers the problem of constructing data aggregation trees in wireless sensor networks (WSNs) for a group of sensor nodes to send collected information to a single sink node. The data aggregation tree contains the sink node, all the source nodes, and some other non-source nodes. Our goal of constructing such a data aggregation tree is to minimize the number of non-source nodes to be included in the tree so as to save energies. We prove that the data aggregation tree problem is NP-hard and then propose an approximation algorithm with a performance ratio of four and a greedy algorithm. We also give a distributed version of the approximation algorithm. Extensive simulations are performed to study the performance of the proposed algorithms. The results show that the proposed algorithms can find a tree of a good approximation to the optimal tree and has a high degree of scalability.     

Approximation algorithm for constructing data aggregation trees for wireless sensor networks

This paper considers the problem of constructing data aggregation trees in wireless sensor networks (WSNs) for a group of sensor nodes to send collected information to a single sink node. The data aggregation tree contains the sink node, all the source nodes, and some other non-source nodes. Our goal of constructing such a data aggregation tree is to minimize the number of non-source nodes to be included in the tree so as to save energies. We prove that the data aggregation tree problem is NP-hard and then propose an approximation algorithm with a performance ratio of four and a greedy algorithm. We also give a distributed version of the approximation algorithm. Extensive simulations are performed to study the performance of the proposed algorithms. The results show that the proposed algorithms can find a tree of a good approximation to the optimal tree and has a high degree of scalability.     

Fault-tolerant design of wireless sensor networks with directional antennas

A tree structure is often used in wireless sensor networks to deliver sensor data to a sink node. Such a tree can be built using directional antennas as they offer considerable advantage over the omni-directional ones. A tree is adequate for data gathering from all sensor nodes if no node fails. We study the problem of enhancing the fault tolerance of a data gathering tree by adding additional links so that failure of a sensor or a pair of adjacent sensors would not disconnect the tree. We prove that the least-cost tree augmentation problem is NP-complete and provide approximation algorithms one for single node failure and the other for a pair of adjacent node failure, with performance bounds of two and four respectively.