基于有限反馈机会波束的无线传感器网络

无线传感器网络中的传感器要把被监测的数据有效地传输到远端的收集器,且其节点的能量有限,为了提高衰落无线信道中无线传感器网络通信的有效性,提出基于有限反馈机会波束的无线传感器网络,基站设置多天线,传感器节点设置单天线,构成MISO系统,在每一时隙基站选择处于峰值状态的传感器节点进行通信。设置反馈门限,当传感器节点的接收信干噪比大于反馈门限时,对信干噪比进行量化,再将量化电平反馈给基站;否则,无需进行量化和反馈。以吞吐量最大化为原则设定最佳反馈门限和量化电平,在瑞利块衰落信道中对系统进行仿真,结果表明:随着节点数的增加,该系统的反馈数可降至传统模拟反馈的1%以下,大大降低了反馈量,提高了系统的效率和节点能量利用率。     

HierTrack: an energy-efficient cluster-based target tracking system forwireless sensor networks

Target tracking is a typical and important application of wireless sensor networks(WSNs).Existing target tracking protocols focus mainly on energy efficiency,and little effort has been put into network management and real-time data routing,which are also very important issues for target tracking.In this paper,we propose a scalable cluster-based target tracking framework,namely the hierarchical prediction strategy(HPS),for energyefficient and real-time target tracking in large-scale WSNs.HPS organizes sensor nodes into clusters by using suitable clustering protocols which are beneficial for network management and data routing.As a target moves in the network,cluster heads predict the target trajectory using Kalman filter and selectively activate the next round of sensors in advance to keep on tracking the target.The estimated locations of the target are routed to the base station via the backbone composed of the cluster heads.A soft handoff algorithm is proposed in HPS to guarantee smooth tracking of the target when the target moves from one cluster to another.Under the framework of HPS,we design and implement an energy-efficient target tracking system,HierTrack,which consists of 36 sensor motes,a sink node,and a base station.Both simulation and experimental results show the efficiency of our system.     

线型无线传感器网络的节点部署策略

尽可能延长无线传感器网络的生命周期是设计和部署网络所面临的最大挑战之一。由于节点配备的能量有限,节点通常采用多跳方式向基站传输数据。分析了节点在多跳通信时的能耗,提出一种非均匀的节点部署策略,得出一个部署传感器节点的密度函数,在靠近基站的区域部署较多的节点。仿真实验表明,非均匀的节点部署策略能有效延长网络的生命周期。     

基于卡尔曼的无线传感器网络时空融合研究

无线传感器网络(WSNs)因其传感节点数目多,且节点易受环境干扰出现故障或失效的特点,对融合技术提出了新的要求。引入中值滤波,利用其良好的抑制脉冲噪声能力,结合卡尔曼滤波开发适用于WSNs的融合算法。采用时空分级融合减少集中计算量,使网络具有实时处理能力。算法具有容错能力,可提高网络鲁棒性。仿真结果表明了算法的有效性。     

Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the existing works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.     

一种基于多播分发树的无线传感器网络代码分发协议

代码分发协议是无线传感器网络（WSNs）在实地部署之后进行软件更新的关键技术。针对现有代码分发协议对特定目标节点分发时需要传输冗余代码镜像的问题,提出了一种基于多播分发树的代码分发（MTCD）协议。MTCD协议通过建立基站节点到目标节点的分发树路径来降低网络中参与代码分发节点的个数,从而降低数据冗余传输和网络能量消耗。TOSSIM仿真结果表明：与TinyOS的标准代码分发协议Deluge相比,MTCD协议在分发时间和数据包传输方面都有更优的性能。     

Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols

The state estimation plays an irreplaceable role in many real applications since it lays the foundation for decision-making and control. This paper studies the multi-sensor estimation problem for a contention-based unreliable wireless network. At each time step, no more than one sensor can communicate with the base station due to the potential contention and collision. In addition, data packets may be lost during transmission since wireless channels are unreliable. A novel packet arrival model is proposed which simultaneously takes into account the above two issues. Two scenarios of wireless sensor networks (WSNs) are considered: the sensors transmit the raw measurements directly and the sensors send the local estimation instead. Based on the obtained packet arrival model, necessary and sufficient stability conditions of the estimation at the base station side are provided for both network scenarios. In particular, all offered stability conditions are expressed by simple inequalities in terms of the packet arrival rates and the spectral radius of the system matrix. Their relationships with existing related results are also discussed. Finally, the proposed results are demonstrated by simulation examples and an environment monitoring prototype system.     

基于时空特性的无线传感器网络节点故障诊断方法

无线传感器网络中故障节点会产生并传输错误数据,这将消耗节点的能量和带宽,同时会形成错误的决策。利用节点感知数据的空间相似性,提出了节点故障诊断的算法,通过对邻节点所感知的传感数据进行比较,从而确定检测节点的状态,并将测试状态向网络中其他相邻节点进行扩散。对于网络中存在的节点瞬时故障,通过时间冗余的检测方法,降低故障诊断的虚警率。该算法对实现故障节点的检测具有较好的性能,实验结果验证了算法的可行性和有效性。     

Distributed fusion Kalman filtering under binary sensors

Binary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.     

Novel sensor scheduling and energy-efficient quantization for tracking target in wireless sensor networks

This paper focuses on sensor scheduling and information quantization issues for target tracking in wireless sensor networks (WSNs). To reduce the energy consumption of WSNs, it is essential and effective to select the next tasking sensor and quantize the WSNs data. In existing works, sensor scheduling’ goals include maximizing tracking accuracy and minimizing energy cost. In this paper, the integration of sensor scheduling and quantization technology is used to balance the tradeoff between tracking accuracy and energy consumption. The main characteristic of the proposed schemes includes a novel filtering process of scheduling scheme, and a compressed quantized algorithm for extended Kalman filter (EKF). To make the algorithms more efficient, the proposed platform employs a method of decreasing the threshold of sampling intervals to reduce the execution time of all operations. A real tracking system platform for testing the novel sensor scheduling and the quantization scheme is developed. Energy consumption and tracking accuracy of the platform under different schemes are compared finally.     

无线传感器网络中节点故障诊断方法的研究

无线传感器网络中故障节点会产生并传输错误数据,这将消耗节点的能量和带宽,同时会形成错误的决策。利用节点感知数据的空间相似性,提出了节点故障诊断的算法,通过对邻节点所感知的传感数据进行比较,从而确定检测节点的状态,并将测试状态向网络中其他相邻节点进行扩散。该算法对实现故障节点的检测具有较好的性能,实验结果验证了算法的可行性和有效性。     

Random Walk Routing in WSNs with Regular Topologies

Topology is one of the most important characteristics for any type of networks because it represents the network's inherent properties and has great impact on the performance of the network. For wireless sensor networks (WSN), a well-deployed regular topology can help save more energy than what a random topology can do. WSNs with regular topologies can prolong network lifetime as studied in many previous work. However, little work has been done in developing effective routing algorithms for WSNs with regular topologies, except routing along a shortest path with the knowledge of global location information of sensor nodes. In this paper, a new routing protocol based on random walk is proposed. It does not require global location information. It also achieves load balancing property inherently for WSNs which is difficult to achieve by other routing protocols. In the scenarios where the message required to be sent to the base station is in comparatively small size with the inquiry message among neighboring nodes, it is proved that the random walk routing protocol can guarantee high probability of successful transmission from the source to the base station with the same amount of energy consumption as the shortest path routing. Since in many applications of WSNs, sensor nodes often send only beep-like small messages to the base station to report their status, our proposed random walk routing is thus a viable scheme and can work very efficiently especially in these application scenarios. The random walk routing provides load balancing in the WSN as mentioned, however, the nodes near to the base station are inevitably under heavier burden than those far away from the base station. Therefore, a density-aware deployment scheme is further proposed to guarantee that the heavy-load nodes do not affect the network lifetime even if their energy is exhausted. The main idea is deploying sensors with different densities according to their distance to the base station. It will be shown in this paper that incorporating the random walk routing protocol with the density-aware deployment scheme can effectively prolong the network lifetime.     

Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation

A sensor in wireless sensor networks (WSNs) periodically produces data as it monitors its vicinity. The basic operation in such a network is the systematic gathering (with or without in-network aggregation) and transmitting of sensed data to a base station for further processing. A key challenging question in WSNs is to schedule nodes' activities to reduce energy consumption. In this paper, we focus on designing energy-efficient protocols for low-data-rate WSNs, where sensors consume different energy in different radio states (transmitting, receiving, listening, sleeping, and being idle) and also consume energy for state transition. We use TDMA as the MAC layer protocol and schedule the sensor nodes with consecutive time slots at different radio states while reducing the number of state transitions. We prove that the energy consumption by our scheduling for homogeneous network is at most twice of the optimum and the timespan of our scheduling is at most a constant times of the optimum. The energy consumption by our scheduling for heterogeneous network is at most Theta (log {frac{R_{rm max}}{R_{rm min}}}) times of the optimum. We also propose effective algorithms to construct data gathering tree such that the energy consumption and the network throughput is within a constant factor of the optimum. Extensive simulation studies show that our algorithms do considerably reduce energy consumption.     

EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations

In this paper, we present an approximate data gathering technique, called EDGES, for sensor networks that utilizes temporal and spatial correlations. The goal of EDGES is to efficiently obtain the sensor reading within a certain error bound. To do this, EDGES utilizes the multiple model Kalman filter, which is for the non-linear data distribution, as an approximation approach. The use of the Kalman filter allows EDGES to predict the future value using a single previous sensor reading in contrast to the other statistical models such as the linear regression and multivariate Gaussian. In order to extend the lifetime of networks, EDGES utilizes the spatial correlation. In EDGES, we group spatially close sensors as a cluster. Since a cluster header in a network acts as a sensor and router, a cluster header wastes its energy severely to send its own reading and/or data coming from its children. Thus, we devise a redistribution method which distributes the energy consumption of a cluster header using the spatial correlation. In some previous works, the fixed routing topology is used or the roles of nodes are decided at the base station and this information propagates through the whole network. But, in EDGES, the change of a cluster is notified to a small portion of the network. Our experimental results over randomly generated sensor networks with synthetic and real data sets demonstrate the efficiency of EDGES.     

无线传感器网络RCF-MAC协议研究

射频充电（ RFC）技术是一种通过基站发射射频电波为传感器节点补充电量的新兴技术。但在RFC无线传感器网络中,多基站同时发射能量时产生的能量干涉衰减现象会明显降低充电效率,此外,能量传输与数据通信共享信道会降低整个网络的吞吐量。针对上述问题,提出了RFC—MAC协议,为提高充电效率,将请求充电传感器节点周围的基站按距离分为两组,两组基站分时能量传输,以避免能量干涉衰减现象;传感器网络中设双信道,分别用于能量传输与数据通信,以提高网络的吞吐量。仿真结果表明：RFC—MAC协议显著提高了传感器节点的充电效率和平均网络吞吐量。     

Performance analysis of energy efficient modulation and coding schemes for wireless sensor networks

Sensor nodes of a typical wireless sensor network (WSN) are battery driven, so energy conservation is a critical factor for node's life time. Thus optimisation of energy consumption is a major objective in the area of WSNs. One such method is asymmetric communication which uses different channel codes and modulation schemes for downlink (base station (BS) to node link) and uplink (node to BS link). In this paper, a performance analysis of different channel code–modulation pair for energy efficient asymmetric communication is carried out followed by the field programmable gate array implementation of channel codes required at the node. The per information bit node energy for the uplink has been calculated for efficient channel code–modulation pair, for three different channels, viz. additive white gaussian noise, Rayleigh flat-fading and log-normal shadowing channels, resulting in reduction in energy consumption at sensor nodes.     

On Kalman filtering over fading wireless channels with controlled transmission powers

We study stochastic stability of centralized Kalman filtering for linear time-varying systems equipped with wireless sensors. Transmission is over fading channels where variable channel gains are counteracted by power control to alleviate the effects of packet drops. We establish sufficient conditions for the expected value of the Kalman filter covariance matrix to be exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing power control policies which minimize the total sensor power budget. In deriving the optimal power control laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases.     

Distributed optimal dynamic base station positioning in wireless sensor networks

Base station (BS) positioning is an effective method for improving the performance of wireless sensor networks (WSNs). A metric-aware optimal BS positioning and relocation mechanism for WSNs is proposed. This technique locates the BS with respect to the available resources and the amount of traffic traveling through the sensor nodes. The BS calculates its own position over time in response to the dynamic environment in which the sensor nodes operate. In most WSN environments, communication channel experiences nonlinearity that is influenced by path loss, attenuation of signal as it propagated through space, greater than 2. In this work, we solved the problem of BS positioning in nonlinear environments. We propose a weighted linear or nonlinear least squares optimization depending on the value of the path loss exponent. We also propose a distributed algorithm that can effectively handle the required computation by exploiting node cooperation. The goal is to minimize the total energy consumption and to prolong lifetime of the WSNs. The performance of the proposed technique is evaluated for various network setups and conditions. Our simulation results demonstrate that BS positioning and relocation can significantly improve the lifetime and power efficiency in WSNs.     

基于蚁群优化的无线传感器网络路由算法

如何在资源受限的无线传感器网络中进行高效的数据路由是无线传感器网络研究的热点之一。基于群智能优化技术的蚁群优化算法被广泛应用于网络路由算法。提出一种无线传感器网络蚁群优化路由算法,能够保持网络的生存时间最长,同时能找到从源节点到基站节点的最短路径;采用的多路数据传输也可提供高效可靠的数据传输,同时考虑节点的能量水平。仿真结果表明:提出的算法延长了无线传感器网络的寿命,实现无线传感器网络在通信过程中快速、节能的路由。     

无线传感器网络分布式量化卡尔曼滤波

本文针对无线传感器网络中的目标跟踪问题,研究了分布式量化卡尔曼滤波问题.由于网络中存在能量和带宽限制,传感器传输的数据必须经过量化处理.考虑一个线性离散随机动态系统,首先提出了一种动态Lloyd-Max量化器并设计了其在线更新方案,然后基于贝叶斯原理导出了递归形式的最优量化卡尔曼滤波器,同时给出了一种渐近等价的迭代算法,并进一步分析了量化卡尔曼滤波器的稳定性.最后,仿真结果验证了所设计算法的可行性与有效性.