Sutharshan Rajasegarar

Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

Anomaly detection in wireless sensor networks

Anomaly detection in wireless sensor networks is an important challenge for tasks such as fault diagnosis, intrusion detection, and monitoring applications. The algorithms developed for anomaly detection have to consider the inherent limitations of sensor networks in their design so that the energy consumption in sensor nodes is minimized and the lifetime of the network is maximized. In this survey article we analyze the state of the art in anomaly detection techniques for wireless sensor networks and discuss some open issues for research.

Distributed Anomaly Detection in Wireless Sensor Networks

Identifying misbehaviors is an important challenge for monitoring, fault diagnosis and intrusion detection in wireless sensor networks. A key problem is how to minimize the communication overhead and energy consumption in the network when identifying misbehaviors. Our approach to this problem is based on a distributed, cluster-based anomaly detection algorithm. We minimize the communication overhead by clustering the sensor measurements and merging clusters before sending a description of the clusters to the other nodes. In order to evaluate our distributed scheme, we implemented our algorithm in a simulation based on the sensor data gathered from the Great Duck Island project. We demonstrate that our scheme achieves comparable accuracy compared to a centralized scheme with a significant reduction in communication overhead

Centered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor Networks

Anomaly detection in wireless sensor networks is an important challenge for tasks such as intrusion detection and monitoring applications. This paper proposes two approaches to detecting anomalies from measurements from sensor networks. The first approach is a linear programming-based hyperellipsoidal formulation, which is called a centered hyperellipsoidal support vector machine (CESVM). While this CESVM approach has advantages in terms of its flexibility in the selection of parameters and the computational complexity, it has limited scope for distributed implementation in sensor networks. In our second approach, we propose a distributed anomaly detection algorithm for sensor networks using a one-class quarter-sphere support vector machine (QSSVM). Here a hypersphere is found that captures normal data vectors in a higher dimensional space for each sensor node. Then summary information about the hyperspheres is communicated among the nodes to arrive at a global hypersphere, which is used by the sensors to identify any anomalies in their measurements. We show that the CESVM and QSSVM formulations can both achieve high detection accuracies on a variety of real and synthetic data sets. Our evaluation of the distributed algorithm using QSSVM reveals that it detects anomalies with comparable accuracy and less communication overhead than a centralized approach.

Clustering ellipses for anomaly detection

Comparing, clustering and merging ellipsoids are problems that arise in various applications, e.g., anomaly detection in wireless sensor networks and motif-based patterned fabrics. We develop a theory underlying three measures of similarity that can be used to find groups of similar ellipsoids in p-space. Clusters of ellipsoids are suggested by dark blocks along the diagonal of a reordered dissimilarity image (RDI). The RDI is built with the recursive iVAT algorithm using any of the three (dis) similarity measures as input and performs two functions: (i) it is used to visually assess and estimate the number of possible clusters in the data; and (ii) it offers a means for comparing the three similarity measures. Finally, we apply the single linkage and CLODD clustering algorithms to three two-dimensional data sets using each of the three dissimilarity matrices as input. Two data sets are synthetic, and the third is a set of real WSN data that has one known second order node anomaly. We conclude that focal distance is the best measure of elliptical similarity, iVAT images are a reliable basis for estimating cluster structures in sets of ellipsoids, and single linkage can successfully extract the indicated clusters.

Elliptical anomalies in wireless sensor networks

Anomalies in wireless sensor networks can occur due to malicious attacks, faulty sensors, changes in the observed external phenomena, or errors in communication. Defining and detecting these interesting events in energy-constrained situations is an important task in managing these types of networks. A key challenge is how to detect anomalies with few false alarms while preserving the limited energy in the network. In this article, we define different types of anomalies that occur in wireless sensor networks and provide formal models for them. We illustrate the model using statistical parameters on a dataset gathered from a real wireless sensor network deployment at the Intel Berkeley Research Laboratory. Our experiments with a novel distributed anomaly detection algorithm show that it can detect elliptical anomalies with exactly the same accuracy as that of a centralized scheme, while achieving a significant reduction in energy consumption in the network. Finally, we demonstrate that our model compares favorably to four other well-known schemes on four datasets.

Hyperspherical cluster based distributed anomaly detection in wireless sensor networks

This article describes a distributed hyperspherical cluster based algorithm for identifying anomalies in measurements from a wireless sensor network, and an implementation on a real wireless sensor network testbed. The communication overhead incurred in the network is minimised by clustering sensor measurements and merging clusters before sending a compact description of the clusters to other nodes. An evaluation on several real and synthetic datasets demonstrates that the distributed hyperspherical cluster-based scheme achieves comparable detection accuracy with a significant reduction in communication overhead compared to a centralised scheme, where all the sensor node measurements are communicated to a central node for processing. Detecting anomalies in data is challenging on resource constrained networks.We propose a distributed algorithm using hyperspherical cluster based data models.The scheme is capable of identifying global anomalies at an individual node level.Comparable detection accuracy with significant reduction in communication overhead.Implemented and demonstrated on a real wireless sensor network test-bed.

Anomaly detection by clustering ellipsoids in wireless sensor networks

A major challenge for the management of low-cost sensor networks is how to ensure the integrity of the data collected, and how to detect unusual events. In this paper, we present a distributed algorithm for anomaly detection in wireless sensor networks, which reduces the amount of data that needs to be communicated through the network. Our approach learns an ellipsoidal boundary for normal data at each sensor, and introduces a method to cluster these ellipsoids at a global level in order to model normal behaviour in the network. We demonstrate that our approach can achieve greater accuracy in non-homogeneous sensing environments than existing methods, while achieving low communication and computational overhead in the network.

Anomaly Detection in Wireless Sensor Networks in a Non-Stationary Environment

Anomaly detection in a WSN is an important aspect of data analysis in order to identify data items that significantly differ from normal data. A characteristic of the data generated by a WSN is that the data distribution may alter over the lifetime of the network due to the changing nature of the phenomenon being observed. Anomaly detection techniques must be able to adapt to a non-stationary data distribution in order to perform optimally. In this survey, we provide a comprehensive overview of approaches to anomaly detection in a WSN and their operation in a non-stationary environment.

Anomaly Detection in Environmental Monitoring Networks [Application Notes]

We apply a recently developed model for anomaly detection to sensor data collected from a single node in the Heron Island wireless sensor network, which in turn is part of the Great Barrier Reef Ocean Observation System. The collection period spanned six hours each day from February 21 to March 22, 2009. Cyclone Hamish occurred on March 9, 2009, roughly in the middle of the collection period. Our system converts sensor measurements to elliptical summaries. Then a dissimilarity image of the data is built from a measure of focal distance between pairs of ellipses. Dark blocks along the diagonal of the image suggest clusters of ellipses. Finally, the single linkage algorithm extracts clusters from the dissimilarity data. We illustrate the model with three two-dimensional subsets of the three dimensional measurements of (air) pressure, temperature and humidity. Our examples show that iVAT images of focal distance are a reliable basis for estimating cluster structures in sets of ellipses, and that single linkage can successfully extract the indicated clusters. In particular, we are able to clearly isolate the cyclone Hamish event with this method, which demonstrates the ability of our model to detect anomalies in environmental monitoring networks.