Analyzing the techniques that improve fault tolerance of aggregation trees in sensor networks

Sensor networks are finding significant applications in large scale distributed systems. One of the basic operations in sensor networks is in-network aggregation. Among the various approaches to in-network aggregation, such as gossip and tree, including the hash-based techniques, the tree-based approaches have better performance and energy-saving characteristics. However, sensor networks are highly prone to failures. Numerous techniques suggested in the literature to counteract the effect of failures have not been carefully analyzed. In this paper, we focus on the performance of these tree-based aggregation techniques in the presence of failures. First, we identify a fault model that captures the important failure traits of the system. Then, we analyze the correctness of simple tree aggregation with our fault model. We then use the same fault model to analyze the techniques that utilize redundant trees to improve the variance. The impact of techniques for maintaining the correctness under faults, such as rebuilding or locally fixing the tree, is then studied under the same fault model. We also do the cost-benefit analysis of using the hash-based schemes which are based on FM sketches. We conclude that these fault tolerance techniques for tree aggregation do not necessarily result in substantial improvement in fault tolerance.     

轮胎圆度检测控制系统设计

该文介绍了一种轮胎外形轮廓检测系统的检测原理、系统组成及控制系统设计。采用两级计算机控制系统,上位机完成数据的采集、处理、显示以及计算结果和检测参数的管理,下位机由三菱Q系列PLC的高性能CPUQ02H、运动控制器172H和J3系列伺服控制系统组成,驱动三支激光传感器运动,完成精确定位并通过它们来宾现对轮胎外形轮廓的精确测量。上、下位机之间采用以太网通信,更换规格时上位机自动下传传感器位置参数到PLC。该系统通过长时间在青岛高校软控股份有限公司生产的动平衡及不圆度试验机上的应用,表明其运行稳定,测试效率高。     

Multi-sensor calibration through iterative registration and fusion

In this paper, a new multi-sensor calibration approach, called iterative registration and fusion (IRF), is presented. The key idea of this approach is to use surfaces reconstructed from multiple point clouds to enhance the registration accuracy and robustness. It calibrates the relative position and orientation of the spatial coordinate systems among multiple sensors by iteratively registering the discrete 3D sensor data against an evolving reconstructed B-spline surface, which results from the Kalman filter-based multi-sensor data fusion. Upon each registration, the sensor data gets closer to the surface. Upon fusing the newly registered sensor data with the surface, the updated surface represents the sensor data more accurately. We prove that such an iterative registration and fusion process is guaranteed to converge. We further demonstrate in experiments that the IRF can result in more accurate and more stable calibration than many classical point cloud registration methods.     

Design for diagnosability of multistation manufacturing systems based on sensor allocation optimization

System monitoring and diagnosing can be achieved through measuring sensing data. Meanwhile, diagnosability is the capability of a system in terms of diagnosing dimensional variation and root cause identification. In order to obtain enough sensing data, special attention should be taken to the sensor allocation optimization within the framework of ensuring system diagnosability, which can lower sensing cost and reduce time to diagnosis. This paper investigates both theoretically and experimentally the effect of sensor allocation optimization on the diagnosability of a multistation manufacturing system (MMS). The design for diagnosability (DFD) is formulated and implemented in the early design phase. Three indices, namely detectability, locatability, and isolability, are proposed to measure the system diagnosability. A two-step process of diagnosing variation sources is presented to specify the variation transmission between stations and variation diagnosis within a station. An optimal methodology of sensor allocation is then proposed. A typical machining process is demonstrated as an example of the analytical procedure and reference for sensor allocation in practice. Four sensing strategies are conducted for performance comparison. Results indicate that the optimal sensor allocation yields less sensing cost and shorter time to diagnosis without loss of diagnosability.     

Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.     

A sensitive and selective sensor for dopamine determination based on a molecularly imprinted electropolymer of o-aminophenol

A simple and reliable method was proposed for preparing a selective dopamine (DA) sensor based on a molecularly imprinted electropolymer of o-aminophenol. The sensor is selective for the determination of DA in the presence of high concentrations of ascorbic acid (AA), with a maximum molar ratio of 1/1000. The molecular imprinted (MIP) sensor was tested by cyclic voltammetry (CV) as well as differential pulse voltammetry (DPV) to verify the changes in oxidative currents of ferricyanide. In optimized conditions, DA at concentrations of 2 × 10⁻⁸ to 0.25 × 10⁻⁶ mol/L could be determined with a detection limit of 1.98 × 10⁻⁹ mol/L (S/N = 3). The MIP sensor showed high selectivity, sensitivity, and reproducibility. Determination of DA in simulated samples of dopamine hydrochloride showed good recovery.     

Palladium nanoclusters-coated polyfuran as a novel sensor for catecholamine neurotransmitters and paracetamol

A promising electrochemical biosensor was developed by electrodeposition of palladium nanoclusters on polyfuran film modified platinum electrode. This biosensor electrode was used to determine some catecholamines, namely dopamine, epinephrine and norepinephrine, ascorbic acid and paracetamol. The method of formation of the polymer film and deposition of Pd particles plays a key role in the electroactivity of the resulting hybrid material. This sensor effectively resolved the overlapping anodic peaks of ascorbic acid (AA), dopamine (DA) and paracetamol (ACOP) into three well-defined voltammetric peaks in differential pulse voltammetry analysis. The detection limit of DA in the absence and presence of AA and ACOP are eventually the same which indicates that the oxidation processes of DA, AA and ACOP are independent and that the simultaneous measurements of the three analytes are possible without interference. The electrodeposition of Pd on polyfuran improved exceptionally the detection limit about four decades. Moreover, diffusion coefficient measurements confirmed the fast electron transfer kinetics of the electrochemical oxidation of the analyte molecules at the sensor/solution interface. It is very interesting to note that the electrocatalytic effect of PF/Pd composite has been increased to be sometimes 21 times that of the pristine PF which has been considered for a long time to be of low conductivity and attracted low attention as a result of the difficulty of its formation and poor conductivity.     

无线传感器网络中能量均衡可靠路由度量方法

针对无线传感器网络的资源限制以及对传输可靠性的特殊要求,提出一种能量均衡路由度量方法EBETX。以ETX值作为路由度量的同时,兼顾网络中节点能量消耗情况,保证网络各节点能量均衡消耗,延长网络的生存时间,实现高效路由与能量均衡的有效权衡。通过NS2仿真实验验证了该方法的可行性。     

椭圆曲线密码体制在WSN上的应用

分析椭圆曲线等公钥技术在无线传感器网络中的应用。介绍无线传感器网络操作系统TinyOS的体系结构、NesC程序开发基本流程和基于TinyOS的椭圆曲线密码库TinyECC。利用TinyECC设计并实现基于椭圆曲线密码体制的轻量级节点验证协议。分析结果证明该协议在无线传感器网络上可行。     

多通信方式的WSN汇聚节点的设计与实现

针对无线传感器网络汇聚节点通信方式单一的问题,设计实现基于PXA270处理器的、具有以太网、CF无线局域网及GPRS网络等多种通信方式的无线传感器网络汇聚节点,完成汇聚节点硬件平台引导加载程序的移植,构建无线传感器网络实验与研究平台。实际应用证明,多通信方式的汇聚节点具有较高的实用性。