Node localisation in wireless sensor network based on self-organising isometric embedding

An accurate distributed localisation method which uses the connectivity measurement to localise nodes in wireless sensor network is proposed in this paper. The proposed method is based on self-organising isometric embedding algorithm that adaptively emphasises the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighbourhood of sensors to update its estimated position by minimising a local cost function and then passes this update to neighbour sensors. Simulation demonstrated that the proposed method is more robust to measurement error than previous proposals and it can achieve comparable results using fewer anchor nodes than previous methods. The applications of the new method in logistics and transportation system monitoring are also illustrated in the paper.     

无线传感器网络中的通信技术

无线传感器网络是由大量传感器节点通过无线通信技术自组织构成的网络,有着巨大的应用前景.针对传统无线通信技术无法适应无线传感器网络低成本、低能量、高容错性等要求,结合新兴无线通信技术ZigBee的特点,阐述了基于ZigBee技术的无线传感器网络的主要优势.     

无线传感器网络中的通信技术

无线传感器网络是由大量传感器节点通过无线通信技术自组织构成的网络,有着巨大的应用前景。针对传统无线通信技术无法适应无线传感器网络低成本、低能量、高容错性等要求,结合新兴无线通信技术ZigBee的特点,阐述了基于ZigBee技术的无线传感器网络的主要优势。     

动态无线传感器应用模拟系统设计

本文从多线程模拟思路出发,通过无线传感器结点的独立通信过程,模拟传感器网络基本业务流程,包括结点通信、信息收集、结点移动和休眠等基本功能。本系统使用Java平台,采用线程池与语义解释器模式提出高效的解决方案。     

A gossip based information fusion protocol for distributed frequent itemset mining

The computational complexity, huge memory space requirement, and time-consuming nature of frequent pattern mining process are the most important motivations for distribution and parallelization of this mining process. On the other hand, the emergence of distributed computational and operational environments, which causes the production and maintenance of data on different distributed data sources, makes the parallelization and distribution of the knowledge discovery process inevitable. In this paper, a gossip based distributed itemset mining (GDIM) algorithm is proposed to extract frequent itemsets, which are special types of frequent patterns, in a wireless sensor network environment. In this algorithm, local frequent itemsets of each sensor are extracted using a bit-wise horizontal approach (LHPM) from the nodes which are clustered using a leach-based protocol. Heads of clusters exploit a gossip based protocol in order to communicate each other to find the patterns which their global support is equal to or more than the specified support threshold. Experimental results show that the proposed algorithm outperforms the best existing gossip based algorithm in term of execution time.     

基于无线传感器网络的AGV定位方法研究

对无线传感器网络及其节点定位技术进行了简单介绍,在分析其特点和优势的基础上,提出了利用无线传感器网络对AGV进行定位的设想;对基于无线传感器网络的AGV定位方法进行了具体研究,构建了新的AGV定位模型。     

无线传感器网络技术在录井中初步应用探讨

目前,录井中参数采集传输主要还是以有线为主,包括总线和分线参数采集传输。而无线传感器网络(WSN)技术在录井数据采集系统中的使用将是录井工程的一个必然发展方向和趋势。无线传感器网络技术在录井数据采集系统中的设计、应用和推广,对于录井工程的发展将有着重要的意义:不仅可以节约录井成本,而且能提高录井效率。     

基于WSN的卧室智能湿度控制系统设计与实现

本文设计并实现了一种基于无线传感器网络技术(WSN)的智能湿度控制系统,系统包含六个模块:WSN子节点模块、人机交互模块、主控模块、加湿干燥模块、GSM通信模块、手机系统终端模块。系统实时采集卧室当前湿度信息,并定时通过WSN子节点模块将某段时间的平均湿度数据传递给主控模块,将该数据与预设湿度值对比,若超过阀阈值则通过主控模块启动加湿干燥模块,并为该系统增加系统终端模块,使用者可以通过手机客户端随时查询当前湿度信息并控制加湿与干燥。     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

Secure smart grid communications and information integration based on digital watermarking in wireless sensor networks

As more and more wireless sensor nodes and networks are employed to acquire and transmit the state information of power equipment in smart grid, we are in urgent need of some viable security solutions to ensure secure smart grid communications. Conventional information security solutions, such as encryption/decryption, digital signature and so forth, are not applicable to wireless sensor networks in smart grid any longer, where bulk messages need to be exchanged continuously. The reason is that these cryptographic solutions will account for a large portion of the extremely limited resources on sensor nodes. In this article, a security solution based on digital watermarking is adopted to achieve the secure communications for wireless sensor networks in smart grid by data and entity authentications at a low cost of operation. Our solution consists of a secure framework of digital watermarking, and two digital watermarking algorithms based on alternating electric current and time window, respectively. Both watermarking algorithms are composed of watermark generation, embedding and detection. The simulation experiments are provided to verify the correctness and practicability of our watermarking algorithms. Additionally, a new cloud-based architecture for the information integration of smart grid is proposed on the basis of our security solutions.     

Cyberattack-resilient load forecasting with adaptive robust regression

Cyberattacks in power systems that alter the input data of a load forecasting model have serious, potentially devastating consequences. Existing cyberattack-resilient work focuses mainly on enhancing attack detection. Although some outliers can be easily identified, more carefully designed attacks can escape detection and impact load forecasting. Here, a cyberattack-resilient load forecasting approach based on an adaptive robust regression method is proposed, where the observations are trimmed based on their residuals and the proportion of the trim is adaptively determined by an estimation of the contaminated data proportion. An extensive comparison study shows that the proposed method outperforms the standard robust regression in various settings.     

Automatic Editing for Business Surveys: An Assessment of Selected Algorithms

Statistical offices are responsible for publishing accurate statistical information about many different aspects of society. This task is complicated considerably by the fact that data collected by statistical offices generally contain errors. These errors have to be corrected before reliable statistical information can be published. This correction process is referred to as statistical data editing. Traditionally, data editing was mainly an interactive activity with the aim to correct all data in every detail. For that reason the data editing process was both expensive and time-consuming. To improve the efficiency of the editing process it can be partly automated. One often divides the statistical data editing process into the error localisation step and the imputation step. In this article we restrict ourselves to discussing the former step, and provide an assessment, based on personal experience, of several selected algorithms for automatically solving the error localisation problem for numerical (continuous) data. Our article can be seen as an extension of the overview article by Liepins, Garfinkel & Kunnathur (1982). All algorithms we discuss are based on the (generalised) Fellegi–Holt paradigm that says that the data of a record should be made to satisfy all edits by changing the fewest possible (weighted) number of fields. The error localisation problem may have several optimal solutions for a record. In contrast to what is common in the literature, most of the algorithms we describe aim to find all optimal solutions rather than just one. As numerical data mostly occur in business surveys, the described algorithms are mainly suitable for business surveys and less so for social surveys. For four algorithms we compare the computing times on six realistic data sets as well as their complexity.     

提高无线传感器网络的运行寿命方法

针对无线传感器网络的节点一般体积小,携带能量低,运算能力弱,能量一旦耗尽不可再生这一特点,本文提出提高网络运行寿命的方法。一方面,在网络节点运行时间上,将基站作为主控中心,根据系统的需求,采用有效的调度算法,来唤醒需要传输的无线传感器节点进行数据采集,然后将传输信息发送给簇头进行网络内传输,直至到达基站,从而获得传感器节点最小运行时间。另一方面,在网络内多跳通信时,靠近基站的簇头节点由于转发大量数据导致节点过早失效问题,本文提出一种不均匀分簇多跳算法,根据节点与基站距离调整簇首概率,能量优先为原则选择簇首,使据基站近的区域簇头多于据基站远的区域。     

基于Echo State Neural Networks的短期交通流预测算法

在城市交通环境,交通流的正确预测是比较困难,因为多个十字路口,这使得预置的交通控制模型之间的相互作用和intertwinement不能保持始终高性能在所有的交通情况。     

Simulation-based estimation of peer effects

The influence of peer behavior on an individual's choices has received renewed interest in recent years. However, accurate measures of this influence are difficult to obtain. Standard reduced-form methods lead to upwardly biased estimates due to simultaneity, common shocks, and nonrandom peer group selection. This paper describes a structural econometric model of peer effects in binary choice, as well as a simulated maximum likelihood estimator for its parameters. The model is nonparametrically identified under plausible restrictions, and can place informative bounds on parameter values under much weaker restrictions. Monte Carlo results indicate that this estimator performs better than a reduced form approach in a wide variety of settings. A brief application to youth smoking demonstrates the method and suggests that previous studies dramatically overstate peer influence.     

Adaptively combined forecasting for discrete response time series

Adaptive combining is generally a desirable approach for forecasting, which, however, has rarely been explored for discrete response time series. In this paper, we propose an adaptively combined forecasting method for such discrete response data. We demonstrate in theory that the proposed forecast is of the desired adaptation with respect to the widely used squared risk and other significant risk functions under mild conditions. Furthermore, we study the issue of adaptation for the proposed forecasting method in the presence of model screening that is often useful in applications. Our simulation study and two real-world data examples show promise for the proposed approach.     

Physics-informed Gaussian process regression for states estimation and forecasting in power grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method.We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power.For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.     

基于多维尺度的静态网络定位算法研究

无线传感器网络中节点的位置信息是至关重要的。节点定位对传感器网络的有效性起着关键的作用。文中提出基于多维尺度的静态网络定位算法。仿真实验表明该算法具有更高的定位精度和更高的定位效率。     

基于改进的PCA方法的车牌汉字识别

针对低分辨率的车牌汉字的识别问题,提出了一种改进的PCA的汉字识别方法。通过实验表明,该方法识别率高,实时性好,具有一定的应用前景。     

Applications of randomized low discrepancy sequences to the valuation of complex securities

This paper deals with a recent modification of the Monte Carlo method known as quasi-random Monte Carlo. Under this approach, one uses specially selected deterministic sequences rather than random sequences as in Monte Carlo. These special sequences are known as low discrepancy sequences and have the property that they tend to be evenly dispersed throughout the unit cube. For many applications in finance, the use of low discrepancy sequences seems to provide more accurate answers than random sequences. One of the main drawbacks of the use of low discrepancy sequences is that there is no obvious method of computing the standard error of the estimate. This means that in performing the calculations, there is no clear termination criterion for the number of points to use. We address this issue here and consider a partial randomization of Owen's technique for overcoming this problem. The proposed method can be applied to much higher dimensions where it would be computationally infeasible for Owen's technique. The efficiency of these procedures is compared using a particular derivative security. The exact price of this security can be calculated very simply and so we have a benchmark against which to test our calculations. We find that our procedures give promising results even for very high dimensions. Statistical tests are also conducted to support the confidence statement drawn from these procedures.