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.     

Periodicity of Kalman-based scheduled filters

In this paper, the sensor scheduling problem for estimation is tackled, in which a set of sensors must share a common network to communicate their measurements. It is shown that, under some mild conditions, a Kalman-based scheduled filter results in a periodic selection of the sensors. In those cases, the computational cost and energy consumption can be reduced, as the sensors can await asleep between two consecutive measurements. A suboptimal one-step ahead strategy is thoroughly studied as a possible example of observation scheme.     

Cost based in-network join strategy in tree routing sensor networks

The tiny and smart sensors enable applications which access a network of hundreds or thousands of sensors. In many applications, joins are used frequently to find relationships of readings of different sensors such as the correlation of sensor readings in distinct regions.In this paper, we present a cost based in-network join strategy called INJECT. Since the optimal join plan is determined according to various conditions such as data distributions and predicates of joins, it wastes the energy of sensors to use a fixed join plan blindly. Based on the analysis on how join queries can be handled in sensor networks, we devise several join plans. In particular, since the data transmission dominates the energy consumption of a sensor, we devise cost models each of which reflects the transmission cost of a join plan. Experimental results confirm that INJECT chooses the optimal or near optimal plan under various conditions.     

基于信息量的分布式协同自组织算法

无线传感器网络中节点协同自组织主要涵盖传感器管理和状态估计,也就是如何选择传感器节点、设置传感器参数并估计被监测系统的状态,因此协同白组织为决策与估计的联合优化．本文提出了一种自适应动态协同自组织算法,以量测所提供的信息量和节点自身的剩余能量做为节点选择的综合指标,根据设定的感知精度,自适应地选择参与感知任务的节点集合,在信息滤波的融合框架下完成状态的分布式估计．相比信息驱动传感器查询（information-driven sensor querying,IDSQ）,算法具有精度可调、强鲁棒,同时尽可能地延长了网络的生命周期．以目标跟踪为应用背景,其仿真结果表明：以跟踪精度、失跟率和网络生命周期作为评价指标,该算法优于IDSQ．     

RS-GA神经网络无人机受风情况估计

针对现有无人机（Unmanned Aerial Vehicle,UAV）风场估计方法中存在的计算复杂、需额外搭载传感器等问题,提出基于粗糙集遗传神经网络的无人机受风状态估计方法。该方法利用粗糙集分析方法对无人机上采集的姿态信息数据集进行约简;利用遗传算法全局搜索能力强的特点优化神经网络的初始权值;用简化的无人机数据集训练神经网络即得到所需神经网络风场估计模型。仿真结果表明,该方法具有较高的识别率以及较短的训练时间,证明了其在无人机风场估计上应用的有效性。     

Occupancy estimation from environmental parameters using wrapper and hybrid feature selection

Occupancy information is essential to facilitate demand-driven operations of air-conditioning and mechanical ventilation (ACMV) systems. Environmental sensors are increasingly being explored as cost effective and non-intrusive means to obtain the occupancy information. This requires the extraction and selection of useful features from the sensor data. In past works, feature selection has generally been implemented using filter-based approaches. In this work, we introduce the use of wrapper and hybrid feature selection for better occupancy estimation. To achieve a fast computation time, we introduce a ranking-based incremental search in our algorithms, which is more efficient than the exhaustive search used in past works. For wrapper feature selection, we propose the WRANK-ELM, which searches an ordered list of features using the extreme learning machine (ELM) classifier. For hybrid feature selection, we propose the RIG-ELM, which is a filter–wrapper hybrid that uses the relative information gain (RIG) criterion for feature ranking and the ELM for the incremental search. We present experimental results in an office space with a multi-sensory network to validate the proposed algorithms.     

Structural environmental informatization using sensor network with heterogeneous sensors

In the present paper, a configuration of a sensor network system for monitoring the usage patterns of a house is proposed. The sensor network system has many active type radio-frequency identifications (RFIDs) as a network node. The RFID has advantages of low cost and low power consumption. Also the RFID can connect various sensors for measuring environment information, since having I/O port as external interface. Environment information is stored on a database through the RFID’s wireless radio communication function. The sensor types in this system are variety from slow changing information like temperature and humidity data to event notification like a human detection data. The paper will be described a acquisition procedure for various characteristic sensor data on the same sensor network. Also, the installation locations of the sensors are critical for highly accurate detection of the environmental conditions. Finally, based on the sensor data, capability of analysis of the usage pattern for a house will be discussed.     

多判据的无线传感器网络分簇路由算法

无线传感器网络由大量能量受限的传感器节点组成,工作在各种复杂物理环境中完成数据采集、协同处理等功能.如何在节点能量受限的情况下延长网络生存期是设计传感器网络需要考虑的首要因素.分簇的路由算法将网络内的节点分成若干个簇,簇内的每个节点只需与簇首进行通信,簇首将簇内的所有数据进行融合后向基站发送.与平面自组织算法相比,分簇算法具有实现简单,显著降低系统能耗,便于网络管理等优点.文中提出了一种基于LEACH协议改进的多判据的分簇路由选择算法.使节点在选择簇首时进一步考虑簇首的剩余能量,实现网络中能量消耗的均衡分布.通过仿真,证明了这种多判据的路由选择算法能显著延长网络的生存期.     

EQOWSN: Evolutionary-based query optimization over self-organized wireless sensor networks

In this paper, a new approach has been introduced that integrates an evolutionary-based mechanism with a distributed query sensor cover algorithm for optimal query execution in self-organized wireless sensor networks (WSN). An algorithm based on an evolutionary technique is proposed, with problem-specific genetic operators to improve computing efficiency. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of spatial queries. Any reduction in communication cost would result in an efficient use of battery energy, which is very limited in sensors. Our objective is to self-organize the network, in response to a query, into a topology that involves an optimal subset of sensors that is sufficient to process the query subject to connectivity, coverage, energy consumption, cover size and communication overhead constraints. Query processing must incorporate energy awareness into the system by reducing the total energy consumption and hence increasing the lifetime of the sensor cover, which is beneficial for large long running queries. Experiments have been carried out on networks with different sensors Transmission radius, different query sizes, and different network configurations. Through extensive simulations, we have shown that our designed technique result in substantial energy savings in a sensor network. Compared with other techniques, the results demonstrated a significant improvement of the proposed technique in terms of energy-efficient query cover with lower communication cost and lower size.     

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.