Compression Perceived And Anomaly Identification Algorithms For Event Monitoring In WSN

Wireless Sensor Networks (WSNs) are often deployed for the purpose of detecting significant events or anomalies in the monitored phenomenon, process or structure (henceforth, landslides, air pollution, etc.). Such reactive systems typically collect and process sensor observations to programmatically classify the real world state of the monitored object into one or more classes and take the necessary actions accordingly. For example, in a structural health monitoring system, a building is monitored for structural faults by comparing’a seismic response signal to known stress patterns.Matching or detecting patterns in sensor observations is a common requirement in a number of domains yet the problem of computationally efficient approaches has attracted less attention in comparison with research in network layer protocols. Moreover, solutions are often based on transmitting observations outside the network or to a tier of high capability devices for processing.In this thesis, we assume a homogeneous WSN comprised of resource limited devices and we attempt to solve the problem of pattern matching and detection inside the network. Apart from the ubiquity of the problem, we are motivated by the benefit of an in-network solution, namely prolonged lifetime resulting from reduction of radio communication.We target the extremely resource constrained end of the WSN spectrum that comprises nodes. The constraining factors that differentiate such nodes from other distributed systems are:1) Limited power resources. Typically, nodes are powered by batteries which limit their useful lifetime and specify an energy budget that, in most applications, must be extended as much as possible. Radio communication, sensing and processing share this budget and pose a challenge to developers who must serve the application’s purpose and, at the same time, maximize node lifetime.2) Restricted functionality. Embedded microcontrollers limited RAM and in the vast majority of cases Costly radio communication and limited bandwidth. The fabric that inter connects nodes in a WSN is also the most expensive component with respect to power draw. Minimizing the amount and range of communications, can prolong the lifetime of a WSN. As a rule of thumb, a bit of data transmitted by radio can cost as much as executing1000CPU instructions.Further to the above constraints, WSN application designers are challenged by limited support for software development and a tight coupling between application and system layers.This paper has carried comprehensive summary of event monitoring method includes point exception and mode anomaly detection.1) Point exception if the sensor data exceeds a set threshold; however this method only applies to a single event and combinations of events monitoring tasks. In some long gradient environmental monitoring, sudden complex events is often difficult to be overrun by the specified attribute threshold alarm can not be described using a simple threshold, but can be seen as a pattern may be mode recognition technology to anomaly detection.2) Mode anomaly detection method can be divided into the original data space and the compressed data space anomaly identification. Currently, most of the abnormal pattern detection methods are in the raw data space, i.e. not the sensor nodes collect data for any transformation, although this method has high detection accuracy. However, the computational complexity of the algorithm, fault tolerance and energy-saving effect is limited. Is it possible exception information extraction and recognition after data compression processing space?This paper attempts to three steps to solve this problem:1) explore energy efficient lightweight data compression algorithms;2) seek abnormal event detection algorithms to quickly and accurately in the compressed data space, and be able to quantify the degree of abnormality;3) design of high-precision, quickly signal reconstruction algorithm, to restore the original data of the abnormality information. To this end, the paper from two aspects of in-depth research: space-time data sequence compressed representation model and integration, event monitoring and anomaly identification, event monitoring technology for large-scale long-term deployment of wireless sensor networks to provide a series of new approaches.The work described in this thesis makes contributions that address the WSN application issue of pattern matching and detection, and offers a computationally efficient implementation of reactive functionality. Moreover, it limits radio communication and MCU active time in order to complement the generic goal of prolonged WSN lifetime. Specifically, we make the following contributions:1) Provides the abnormal event detection algorithm based on the ETEO data compression pattern. By the ETEO method compression the scries into patterns set and extraction its features, series will be mapped to the pattern feature space, then using pattern recognition judgment whether the abnormal events occur.2) Propose an algorithm of data compression based on multiple Principal Component Analysis (multiplc-PCA), iteratively using PCA method in multiple layers.3) For the monitoring of the anomaly threshold known single event, in the real-world applications, sensor nodes may be invalid and work abnormally because of the impact of environmental factors or faults. In order to improve the reliability of event detection, a distributed fault-tolerant abnormal event detection scheme, which utilizes temporal and spatial correlation to detect and correct faults, is proposed in this thesis. Confidence level of sensor nodes is used to manage and adjust sensor nodes’ status, resulting in the isolation of the invalid nodes from the network and the decreasing of invalid nodes’ influence on event detection. In addition, the scheme utilizes a sliding window match to detect the trends of sensor data and predict whether the nodes can detect the events to reduce the response time of event detection.4) Spatial correlation of sensor networks has great use in data aggregation, transmission, encoding, data compression and other important applications. This thesis aims to discuss the spatial correlation of the sensor network, which mainly includes giving a general model to describe the spatial correlation, using the model inferring the relationship of parameters, and giving the way of calculation or estimation the parameters, then we give a hypothesis testing the parameters result obtained from the original data set.5) Put forward the secondary hybrid based CS theory and ETEO methods compression anomaly detection algorithm. The algorithm greatly reduces the amount of calculation of the abnormality detecting process; for abnormal precursor signals with a wide dynamic range, low signal-to-noise ratio, the proposed new fast solution based on the L1norm convex optimization method can quickly and accurately reconstruct the original signal.6) Present an improved symbolic aggregate approximation method to build the energy-efficient space-time data compression and fusion mechanism and the chaos representation model of WSN event monitoring to depict the space-time change of the complex event information, by using the statistics and fractal characteristics of the FSAX-MARKOV model.We believe that the above contributions provide a competitive pattern matching and detection family of algorithms that can be used in a variety of reactive WSN applications.