An adaptive in-network aggregation operator for query processing in wireless sensor networks

A wireless sensor network (WSN) is composed of tens or hundreds of spatially distributed autonomous nodes, called sensors. Sensors are devices used to collect data from the environment related to the detection or measurement of physical phenomena. In fact, a WSN consists of groups of sensors where each group is responsible for providing information about one or more physical phenomena (e.g., group for collecting temperature data). Sensors are limited in power, computational capacity, and memory. Therefore, a query engine and query operators for processing queries in WSNs should be able to handle resource limitations such as memory and battery life. Adaptability has been explored as an alternative approach when dealing with these conditions. Adaptive query operators (algorithms) can adjust their behavior in response to specific events that take place during data processing. In this paper, we propose an adaptive in-network aggregation operator for query processing in sensor nodes of a WSN, called ADAGA (ADaptive AGgregation Algorithm for sensor networks). The ADAGA adapts its behavior according to memory and energy usage by dynamically adjusting data-collection and data-sending time intervals. ADAGA can correctly aggregate data in WSNs with packet replication. Moreover, ADAGA is able to predict non-performed detection values by analyzing collected values. Thus, ADAGA is able to produce results as close as possible to real results (obtained when no resource constraint is faced). The results obtained through experiments prove the efficiency of ADAGA.     

无线传感器网络中一种新的分布式定位方案研究

Node localization in wireless sensor networks (WSN) is treated as a functional dual of target tracking from a novel perspective in the paper. Different from the traditional tracking problem in WSN, using the static location-ware node to estimate the moving target, the mobile node is used to help unknown nodes to accurately discover their positions. A new node localization scheme virtual beacons-energy ratios localization (VB-ERL) and its refinements for the WSN are presented. In the scheme, the mobile node moves in the surveillant field based on the Gauss-Markov mobility model and periodically broadcasts the information packets. Each static unknown node receives the virtual beacons and energy in its sensing range, and estimates its location by finding the intersection of a set of hyper-spheres. Simulation results show the proposed scheme is efficient.     

一类不确定Lur'e奇异系统的可靠鲁棒H_∞跟踪控制(英文)

The problem of reliable robust H_∞tracking control for a class of uncertain Lur'e singular systems is studied.A practical and general failure model of actuator and sensor is considered by using convex polytopic uncertainties to describe control surface impairment.Some sufficient conditions are presented for the case of actuator,sensor and control surface failures in terms of linear matrix inequalities (LMIs).The resultant control systems are reliable in that they guarantee closed-loop system robust stability with H_∞performance and the output tracking the reference signal without steady-state error when all control components are operational as well as when some control components experience failures.Finally,a numerical example is given to show the effectiveness of the proposed methods.     

基于LS-SVM的非线性多功能传感器信号重构方法研究

提出了基于最小二乘支持向量机(Least squares support vector machine, LS-SVM)的非线性多功能传感器信号重构方法. 不同于通常采用的经验风险最小化重构方法, 支持向量机(Support vector machine, SVM)是基于结构风险最小化准则的新型机器学习方法, 适用于小样本标定数据情况, 可有效抑制过拟合问题并改善泛化性能. 在SVM基础上, LS-SVM将不等式约束转化为等式约束, 极大地简化了二次规划问题的求解. 研究中通过L-折交叉验证实现调整参数优化, 在两种非线性情况下对多功能传感器的输入信号进行了重构, 实验结果显示重构精度分别达到0.154\%和1.146\%, 表明提出的LS-SVM重构方法具有高可靠性和稳定性, 验证了方法的有效性.     

Fault tolerant multiple event detection in a wireless sensor network

With an increasing acceptance of Wireless Sensor Networks (WSNs), the health of individual sensor is becoming critical in identifying important events in the region of interest. One of the key challenges in detecting event in a WSN is how to detect it accurately transmitting minimum information providing sufficient details about the event. At the same time, it is also important to devise a strategy to handle multiple events occurring simultaneously. In this paper, we propose a Polynomial-based scheme that addresses these problems of Event Region Detection (PERD) by having a aggregation tree of sensor nodes. We employ a data aggregation scheme, TREG (proposed in our earlier work) to perform function approximation of the event using a multivariate polynomial regression. Only coefficients of the polynomial (P$P$) are passed instead of aggregated data. PERD includes two components: event recognition and event report with boundary detection. This can be performed for multiple simultaneously occurring events. We also identify faulty sensor(s) using the aggregation tree. Performing further mathematical operations on the calculated P$P$ can identify the maximum (max) and minimum (min) values of the sensed attribute and their locations. Therefore, if any sensor reports a data value outside the [min, max] range, it can be identified as a faulty sensor. Since PERD is implemented over a polynomial tree on a WSN in a distributed manner, it is easily scalable and computation overhead is marginal. Results reveal that event(s) can be detected by PERD with error in detection remaining almost constant achieving a percentage error within a threshold of 10%$10\%$ with increase in communication range. Results also show that a faulty sensor can be detected with an average accuracy of 94%$94\%$ and it increases with increase in node density.     

Consistency-driven data quality management of networked sensor systems

With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this paper, we propose a consistency-driven data quality management framework called Orchis that integrates the quality of data into an energy efficient sensor system design. Orchis consists of four components, data consistency models, adaptive data sampling and process protocols, consistency-driven cross-layer protocols and flexible APIs to manage the data quality, to support the goals of high data quality and energy efficiency. We first formally define a consistency model, which not only includes temporal consistency and numerical consistency, but also considers the application-specific requirements of data and data dynamics in the sensing field. Next, we propose an adaptive lazy energy efficient data collection protocol, which adapts the data sampling rate to the data dynamics in the sensing field and keeps lazy when the data consistency is maintained. Finally, we conduct a comprehensive evaluation to the proposed protocol based on both a TOSSIM-based simulation and a real prototype implementation using MICA2 motes. The results from both simulation and prototype show that our protocol reduces the number of delivered messages, improves the quality of collected data, and in turn extends the lifetime of the whole network. Our analysis also implies that a tradeoff should be carefully set between data consistency requirements and energy saving based on the specific requirements of different applications.     

Scanning Depth of Route Panorama Based on Stationary Blur

This work achieves an efficient acquisition of scenes and their depths along long streets. A camera is mounted on a vehicle moving along a straight or a mildly curved path and a sampling line properly set in the camera frame scans the 1D images over scenes continuously to form a 2D route panorama. This paper proposes a method to estimate the depth from the camera path by analyzing a phenomenon called stationary blur in the route panorama. This temporal blur is a perspective effect in parallel projection yielded from the sampling slit with a physical width. We analyze the behavior of the stationary blur with respect to the scene depth, vehicle path, and camera properties. Based on that, we develop an adaptive filter to evaluate the degree of the blur for depth estimation, which avoids error-prone feature matching or tracking in capturing complex street scenes and facilitates real time sensing. The method also uses much less data than the structure from motion approach so that it can extend the sensing area significantly. The resulting route panorama with depth information is useful for urban visualization, monitoring, navigation, and modeling.     

Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.     

The effect of γ-sterilization on the pH-ChemFET behaviour

One of the key requirements biomedical and pharmaceutical sensors have to satisfy is their ability to withstand sterilization cycle. In this paper, the influence of γ-sterilization on the electrical and electrochemical characterizations of pH-sensitive chemical field effect transistors (pH-ChemFET) is examined before and after γ-irradiation. It is shown that γ-irradiation is suitable for pH-ChemFETs sterilization in the field of in-vitro analysis and more precisely for single-use bioreactors.     

Remote flood monitoring system based on plastic optical fibres and wireless motes

This paper summarises an effort in the development of a remote flood monitoring system based on plastic optical fibre (POF) sensors and a wireless mote network. The wireless mote, comprising of a network of MICA2DOT™ units, was used as a platform to monitor and record the signal from the POF sensors and transmit this information to a base station wirelessly. A prototype of the integrated wireless POF sensor unit has been constructed, rendering it possible to deploy the autonomous unit remotely at multiple monitoring points as required. A flood monitoring simulation was carried out in a 24 m × 10 m × 0.9 m wave basin where four of these wireless optical fibre mote sensors were used to detect the rising water level in the basin. The novelty of the work lies in the successful integration of the wireless platform to a POF-based liquid level sensor and the subsequent demonstration of the prototype of the system for the purposes of flood monitoring applications.The sensing principle of the POF sensor developed here is well-known and is based on the loss of total internal reflection of the optical signal as the sensor probe comes in contact with the liquid. Compared to optical fibre-based sensors reported previously in the literature, the probe profile used in this study differs in terms of its simplicity in design, while exhibiting an excellent signal intensity loss ratio without the need for additional attachments to the probe such as optical prisms. The tests carried out showed that the POF sensor is capable of detecting a variety of fluids. Exhibiting good signal stability, the sensor also detects the liquid level reliably when the liquid rises or falls to the predetermined level. The responsiveness of the optical fibre sensor was evaluated by simulating different rates at which the liquid rises by immersing the sensor tip into the liquid and vice-versa at various speeds ranging from 1 mm/min to 500 mm/min.