Reliable and energy-efficient data collection in sparse sensor networks with mobile elements

Sparse wireless sensor networks (WSNs) are emerging as an effective solution for a wide range of applications, especially for environmental monitoring. In many scenarios, a moderate number of sparsely deployed nodes can be sufficient to get the required information about the sensed phenomenon. To this end, special mobile elements, i.e. mobile data collectors (MDCs), can be used to get data sampled by sensor nodes. In this paper we present an analytical evaluation of the data collection performance in sparse WSNs with MDCs. Our main contribution is the definition of a flexible model which can derive the total energy consumption for each message correctly transferred by sensors to the MDC. The obtained energy expenditure for data transfer also accounts for the overhead due to the MDC detection when sensor nodes operate with a low duty cycle. The results show that a low duty cycle is convenient and allows a significant amount of correctly received messages, especially when the MDC moves with a low speed. When the MDC moves fast, depending on its mobility pattern, a low duty cycle may not always be the most energy efficient option.     

一种基于自适应代表节点选择的WSN数据收集方法

采用压缩感知的无线传感网络数据收集方法要求每个节点都参与数据收集,会造成很大的能量浪费.本文提出了一种基于自适应代表节点选择的WSN数据收集方法,在保证压缩感知数据重构精度的同时,减少参与数据收集的节点数.首先,采用主成分分析和混合压缩感知相结合的办法设计稀疏基;然后,通过分析稀疏基的框架势FP(Frame Potential)设计压缩感知的稀疏观测矩阵,从而选择代表节点,以减少参与数据收集的节点数目;最后,根据Sink处数据重构精度,自适应调整稀疏观测矩阵以用作下一时刻数据收集,从而保证数据收集的重构精度.仿真结果表明,该方法有效的降低了网络能耗和数据传输量,同时还保证了每个时刻数据重构的精度.     

基于博弈论的无线传感器网络簇间路由选择算法

针对无线传感器网络(WSN)中,网络覆盖范围大,但传感器节点通信范围有限,长距离传输容易造成数据丢失的问题,提出了一种基于博弈论的无线传感器网络簇间路由算法,通过建立以网络服务质量(QoS)和节点剩余能量为效用函数的博弈模型,并求解其纳什均衡来解决以上问题。仿真结果表明：所提出的博弈模型在优化网络服务质量、降低节点能耗的同时,延长了整个网络的生存时间。     

数据集成技术在树型WSN中的应用与研究

针对无线传感器网络中节点能量有限这一特点,如何提高能量利用率、延长网络寿命是每个WSN研究者所必须面临的问题,同样也是WSN发展过程中必须被解决的难题。为此,本文将一种数据集成算法引入无线传感器网络,通过去除节点间的冗余信息来降低网络中的数据流量,从而降低网络能耗,达到提高网络能量利用率和延长网络寿命的目的。文章以树型无线传感器网络为例,分别对采用数据集成算法前后的网络能量消耗进行分析研究,并给出了相应的能量消耗模型。随后又通过计算机仿真和实验数据监测的方法,验证了此数据集成算法在降低树型WSN能耗中是有效的,而且具有一定的实用价值。     

Energy-aware node placement,topology control and MAC scheduling for wireless sensor networks

In the WSNs, the nodes closer to the sink node have heavier traffic load for packet forwarding because they do not only collect data within their sensing range but also relay data for nodes further away. The unbalanced power consumption among sensor nodes may cause network partition. This paper proposes efficient node placement, topology control, and MAC scheduling protocols to prolong the sensor network lifetime, balance the power consumption of sensor nodes, and avoid collision. Firstly, a virtual tree topology is constructed based on Grid-based WSNs. Then two node-placement techniques, namely Distance-based and Density-based deployment schemes, are proposed to balance the power consumption of sensor nodes. Finally, a collision-free MAC scheduling protocol is proposed to prevent the packet transmissions from collision. In addition, extension of the proposed protocols are made from a Grid-based WSN to a randomly deployed WSN, enabling the developed energy-balanced schemes to be generally applied to randomly deployed WSNs. Simulation results reveal that the developed protocols can efficiently balance each sensor node’s power consumption and prolong the network lifetime in both Grid-based and randomly deployed WSNs.     

Adaptive listen for energy-efficient medium access control in wireless sensor networks

Wireless Sensor Networks (WSN) have nodes that are small in size and are powered by small batteries having very limited amount of energy. In most applications of WSN, the nodes in the network remain inactive for long periods of time, and intermittently they become active on sensing any change in the environment. The data sensed by the different nodes are sent to the sink node. In contrast to other infrastructure-based wireless networks, higher throughput, lower latency and per-node fairness in WSN are imperative, but their importance is subdued when compared to energy consumption. In this work, we have regarded the amount of energy consumption in the nodes to be of primary concern, while throughput and latency in the network to be secondary. We have proposed a protocol for energy-efficient adaptive listen for medium access control in WSN. Our protocol adaptively changes the slot-time, which is the time of each slot in the contention window. This correspondingly changes the cycle-time, which is the sum of the listen-time and the sleep-time of the sensors, while keeping the duty-cycle, which is the ratio between the listen-time and the cycle-time, constant. Using simulation experiments, we evaluated the performance of the proposed protocol, compared with the popular Sensor Medium Access Control (SMAC) (Ye et al. IEEE/ACM Trans Netw 12(3):493–506, 39) protocol. The results we obtained show a prominent decrease in the energy consumption at the nodes in the proposed protocol over the existing SMAC protocol, at the cost of decreasing the throughput and increasing the latency in the network. Although such an observation is not perfectly what is ideally desired, given the very limited amount of energy with which the nodes in a WSN operate, we advocate that increasing the energy efficiency of the nodes, thereby increasing the network lifetime in WSN, is a more important concern compared to throughput and latency. Additionally, similar observations relating energy efficiency, network lifetime, throughput and latency exist in many other existing protocols, including the popular SMAC protocol (Ye et al. IEEE/ACM Trans Netw 12(3):493–506, 39).     

基于数据预处理的无线气象传感网数据重构模型

针对无线气象传感网内由于节点数量大、感知数据冗余度高而导致节点通信耗能过高的问题,提出了数据联合稀疏预处理模型,利用监测区域气象要素预报值和各簇头要素值计算出一个全网公共分量并对网内数据进行预处理。将分布式压缩感知应用于簇型传感网中,对各节点感知数据进行压缩观测,在汇聚节点进行数据重构,从根本上降低节点通信量,均衡负载;同时设计了一个基于公共分量异常数据稀疏方法。仿真实验中,相对于单独使用压缩感知,数据联合稀疏预处理模型能够有效利用数据时空相关性提高数据稀疏度,压缩性能提高了25%,重构性能提高46%;同时,异常数据处理方案能够以96%的高概率恢复异常数据。因此,该数据预处理模型能够提高数据重构效率,有效降低网内数据通信量,延长网络寿命。     

无线传感器网络中的目标关联覆盖算法

为提高无线传感器网络的节点覆盖度,提出一种目标关联覆盖算法,利用节点间的关联性和动态分组调整覆盖区域,利用贪心算法对覆盖区域进行优化,以保证所关注的目标节点被传感器节点均匀覆盖,同时提高网络资源的利用率。在每个周期内唤醒部分节点,轮流进行工作,以均衡网络能量消耗。实验结果表明,该算法适应性更强,并且能有效降低网络能耗,提高网络性能。     

基于路由算法的无线传感器网络伪装研究

在研究如何通过无线传感器网络WSN有效获取信息的同时,如何确保敏感地区的信息不被WSN窃取也成为研究者关注的热点问题。基于WSN已有的路由算法,利用其开放的特点在敏感地区设置WSN伪装节点,并使其加入WSN节点的路由构建过程,伪装节点采用路由算法自适应伪装,通过修改数据报中的控制信息进而实现阻止WSN节点构建有效路由,并在最大程度上消耗WSN节点能量的目的。从WSN节点能耗速度和网络生命周期两方面验证了基于路由算法的WSN伪装的有效性。     

负载相似节点分布解决传感器网络能量洞问题

基于多跳路由的传感器网络,靠近sink的节点由于要转发更多的数据,其能量消耗较快.能量消耗的不平衡导致了sink周围产生能量洞(energy hole),造成能量浪费,远离sink的节点还有大量的能量,但产生的数据不能传送到sink.研究了传感器网络的能量洞问题.在网络的连续空间状态下分析节点的负载分布特性,并研究空闲侦听(idle listening)对能量消耗的影响.基于分析结果,提出负载相似节点分布(load-similar node distribution)策略,即根据负载分布特性配置节点,负载重的区域配置较多的节点,以平衡各区域节点的能量消耗,解决能量洞问题.仿真结果显示,在不考虑空闲侦听能量消耗的情况下,与已有的一种非均匀节点分布和均匀节点分布相比,负载相似节点分布策略对网络寿命有显著改善;当考虑空闲侦听时,由于空闲侦听的能量消耗在总能量消耗中占较大比重,影响了网络寿命,但与其他两种节点分布策略相比,负载相似节点分布对网络寿命仍然有所改善.该分析模型和提出的负载相似节点分布策略可适用于其他多跳传感器网络.     

Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply, constrained memory capacity, processing capability, and available bandwidth. The main problem in event gathering in wireless sensor networks is the formation of energy-holes or hot spots near the sink. Due to the restricted communication range and high network density, events forwarding in sensor networks is very challenging, and require multi-hop data forwarding. Improving network lifetime and network reliability are the main factors to consider in the research associated with WSN. In static wireless sensor networks, sensors nodes close to the sink node run out of energy much faster than nodes in other parts of the monitored area. The nodes near the sink are more likely to use up their energy because they have to forward all the traffic generated by the nodes farther away to the sink. The uneven energy consumption results in network partitioning and limit the network lifetime. To this end, we propose an on-demand and multipath routing algorithm that utilizes the behavior of real termites on hill building termed Termite-hill which support sink mobility. The main objective of our proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The performance of our proposed algorithm was tested on static, dynamic and mobile sink scenarios with varying speed, and compared with other state-of-the-art routing algorithms in WSN. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that our proposed routing algorithm was able to balance the network traffic load, and prolong the network lifetime.     

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。     

Heuristic algorithm for finding boundary cycles in location-free low density wireless sensor networks

Wireless sensor networks (WSNs) comprise a large number of sensor nodes, which are spread out within a region to be monitored and communicate using wireless links. In some WSN applications, recognizing boundary nodes is important for topology discovery, geographic routing, tracking and guiding. In this paper, we study the problem of identifying the boundary nodes of a WSN. In a WSN, close-by nodes can establish direct communications with their neighbors and have the ability to estimate distances to nearby nodes, but not necessarily the true distances. Our objective is to find the boundary nodes by using only the connectivity relation and neighbor distance information without any other knowledge of node locations. Moreover, our main aim is to design a distributed algorithm that works even when the average degree is low. We propose a heuristic algorithm to find the boundary nodes which are connected in a boundary cycle of a location-free, low density (average degree 5–6), randomly deployed WSN. We develop the key ideas of our boundary detection algorithm in the centralized scenario and extend these ideas to the distributed scenario. The distributed implementation is more realistic for real WSNs, especially for sparse networks when all local information cannot be collected very well due to sparse connectivity. In addition, the distributed implementation can tolerate faults by recomputing the boundary locally when a boundary node is faulty. Simulations in ns-2 show that the distributed implementation outperforms the centralized one with higher quality of boundaries.     

无线传感网中一种智能数据融合算法的实现及仿真分析

在无线传感网中,传感器节点一般都由自身装配的电池供电,难以进行电量补充,因此节约电量对于无线传感网来说至关重要.为了提高无线传感网能量使用效率,延长网络生存时间,提出了一种结合遗传算法和粒子群算法优化BP神经网络的智能数据融合算法 GAPSOBP(BP Neural Network Data Fusion algorithm optimized by Genetic algorithm and Particle swarm).GAPSOBP算法将无线传感网的节点类比为BP神经网络中的神经元,通过神经网络提取无线传感网采集的感知数据并结合分簇路由对收集的传感数据进行融合处理,从而大幅减少发往汇聚节点的网络数据量.仿真结果表明,与经典LEACH算法和PSOBP算法相比,GAPSOBP算法能有效减少网络通信量,节约节点能量,显著延长网络生存时间.     

无线传感器网络分簇路由节能研究

在大规模传感和环境监测中,节约能源延长传感器节点生命已成为无线传感器网络最重要的研究课题之一。提供合理的能源消耗和改善无线网络生命周期的传感器网络系统,必须设计一种新的有效的节能方案和节能路由体系。方案采用一种聚类算法减少无线传感器网络的能量消耗,创建一种cluster-tree分簇路由结构的传感器网络。该方案主要目标是做一个理想的分簇分配,减少传感器节点之间的数据传输距离,降低传感器节点能源消耗,延长寿命。实验结果表明,该方案有效地降低了能源消耗从而延长无线传感器网络生命。     

一种基于AHP和FIS的WSN路由算法

无线传感器网络（WSN）由许多传感器节点组成,这些传感器节点为了降低能量消耗会周期性地在醒与睡2种模式下进行切换。在异步WSN中,发送节点往往要等接收节点醒来才能进行数据转发,为了缩短该等待时延,发送节点选择多个节点作为候选转发节点,由于任何候选转发节点都有可能进行数据路由,使得邻居节点评估和候选转发节点选择对网络性能产生较大影响。为了更好地进行节点评估与选择,提出一种基于层次分析法（AHP）和模糊推理系统（FIS）的WSN路由算法DAF。将剩余能量、距离和角度作为评估准则,利用AHP确定评估准则的权重,通过FIS动态构建AHP中的成对比较矩阵,并根据该矩阵动态计算出邻居节点的评分,按评分高低选择候选转发节点。实验结果表明,在改变节点数量、睡眠时长和通信半径的对比测试中,DAF在生命周期、能量消耗和平均冗余传输性能方面均优于ORW和ORR算法。     

无线传感器网络自适应MAC协议

提出了一种根据无线传感网络流量自动调节节点睡眠-活动时间比例的MAC协议-ATMAC,在无线传感器网络TMAC协议的基础上,以低能耗、低延迟为目标,主要采用自适应、多级别的占空比及自适应竞争窗口,数据优先级队列使节点在流量较小时能更多地处于睡眠状态以节省能量,而在流量较大时,传输所涉及的节点可相对长时间地进入活动状态,且大流量和小流量节点所采取的占空比可以不同,从而节省低流量节点用于空闲侦听的能耗,降低数据传输的延迟,增大网络的吞吐量。仿真结果显示新协议在能量消耗、数据延迟等方面要超过TMAC。     

一种无线传感器网络信道接入自适应占空比算法

无线传感器网络中MAC协议最重要的设计目标是减少能量消耗.*基于已有的低占空比工作方式,融入了"七折线"法和加权统计的思想,提出一种新的自适应占空比算法.分析与仿真结果表明,该算法在网络流量较小时,可有效降低空闲节点的占空比、减少能量消耗;并能预测节点下一周期的数据流量,调整其占空比,减少碰撞和阻塞发生的概率;尤其在任务突发模式下,能有效提高节点的数据吞吐量.     

基于队列长度的自适应占空比MAC协议

由于无线传感器网络中设备电池能量有限,实现能量高效性是其主要考虑的问题。众所周知优秀的数据传输协议对降低能耗,延长网络生命周期有重要意义。而在基于竞争的MAC协议中,空闲监听是主要能量消耗来源,所以通常设计较低占空比的MAC协议来减少空闲监听时间。但是如果占空比过小,使得网络中所有数据传输集中在较短时间内,会导致节点无法完成所需要的通信。所以选择合适的节点占空比,对于资源受限的网络来说是一种很重要的节能方法。文中提出了一种新型的异步MAC协议QL-MAC,节点利用CSMA争用信道,发送一系列短的信标包唤醒目标节点,采用虚拟载波监听防止串听。不同于其他改变接收节点的方法,该协议从发送节点来考虑,可以根据缓冲区队列长度判断该网络负载,从而自适应调整节点的占空比,使节点及时发送数据,减少时延,并告知接收节点做出相应改变,进一步节省能量。在OMNET++上实现了QL-MAC协议,仿真结果表明QL-MAC协议展现了良好的时延和能量有效性。     

速率自适应的树状采集型MHWSN跨层MAC协议

中高速传感器节点能量严重受限,节能是中高速传感器网络（Medium and High Rate Sensor Networks,MHWSN）中MAC协议设计的首要问题。针对SMAC协议固定占空比不能适应中高速网络中多种速率混合业务传输的特点,提出了一种适应于数据采集型应用的速率自适应的MAC协议（AMAC）。AMAC协议在SMAC基础上,采用了基于跨层的速率自适应机制和交错唤醒机制,根据节点速率动态调整占空比,上层节点较下层节点延迟一段时间后激活。仿真结果表明,在数据采集树中该算法节点速率较小时,可有效降低空闲节点的占空比、减少能量消耗,节点速率较大时,调整占空比大小,减少碰撞和阻塞发生的概率,尤其在多种速率任务突发的中高速传感器网络中,能有效提高节点的数据吞吐量并降低时延。