Event Detection In Sensor Networks Research Articles

An In-Depth Study and Improvement of Isolation Forest

Historically, anomalies detection was an important issue for industrial applications such as the detection of a manufacturing failure or defect. It is still a current topic that tries to meet the ever increasing demand in different fields such as intrusion detection, fraud detection, ecosystem change detection or event detection in sensor networks. That’s why anomalies detection remains a research topic of great interest for various research communities. In this paper, we focused on Isolation Forest (IForest), a well known, efficient anomalies detection algorithm. We provided a deep and complete view on IForest. We evaluated the impact of its input parameters (number of trees, sample size and decision threshold) on the efficiency of the detection and on the execution time. We discussed the benefit of including some anomalies into the training phase. To address the limits of IForest, we performed different experiments on commonly used real datasets and also on synthetic datasets with non trivial distributions. We designed multidimensional datasets where anomalies are carried by several dimensions simultaneously. Moreover, we used a varying density and distance between anomalies and normal data, for a variable similarity between these two data classes. We compared the performance of IForest against its improved version called Extended IForest. Finally, we designed and validated a new extension of IForest, based on the different individual trees decisions instead of a global forest decision that we call Majority Voting IForest (MVIForest). The experiments show that MVIForest has a shorter execution time than IForest, with almost the same accuracy.

On the Exponential Approximation of Type II Error Probability of Distributed Test of Independence

This paper studies distributed binary test of statistical independence under communication (information bits) constraints. While testing independence is very relevant in various applications, distributed independence test is particularly useful for event detection in sensor networks where data correlation often occurs among observations of devices in the presence of a signal of interest. By focusing on the case of two devices because of their tractability, we begin by investigating conditions on Type I error probability restrictions under which the minimum Type II error admits an exponential behavior with the sample size. Then, we study the finite sample-size regime of this problem. We derive new upper and lower bounds for the gap between the minimum Type II error and its exponential approximation under different setups, including restrictions imposed on the vanishing Type I error probability. Our theoretical results shed light on the sample-size regimes at which approximations of the Type II error probability via error exponents became informative enough in the sense of predicting well the actual error probability. We finally discuss an application of our results where the gap is evaluated numerically, and we show that exponential approximations are not only tractable but also a valuable proxy for the Type II probability of error in the finite-length regime.

An Adaptive Framework for Event Detection in Wireless Sensor Network

A wireless sensor network is the new generation technology that holds the capability and it has transformed to interact with the physical environment according to the user’s demands. The features of a sensor network play the major role in various application fields that fit in the broad category for tracking, monitoring, automation and detection and in that category WSN plays the predominant role in event monitoring. Event detection in sensor networks is the process of observing; evaluating an event based on multiple attributes and generates an alarm at the appropriate time. A framework was developed to detect the event in in-door environment. The proposed event detection model comprises of three phases. In the first phase the network id grouped and cluster head is determined and subsequently composite model was developed to determine the event and the final phase involves in the fuzzy rule optimization. A simulation setup has created and the experimental evaluation validates the event detection mechanism through various metrics such as cluster communication, event accuracy are measured and evaluated. The results are compared with the energy efficient HEED algorithm and connected dominating set based cluster MCDS-CCH algorithm further the composite event detection mechanism with optimization rule outperforms than the well-established J48 decision tree classification algorithm.

Mining of high utility-probability sequential patterns from uncertain databases.

High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPSPs in precise data. But in real-life, uncertainty is an important factor as data is collected using various types of sensors that are more or less accurate. Hence, data collected in a real-life database can be annotated with existing probabilities. This paper presents a novel pattern mining framework called high utility-probability sequential pattern mining (HUPSPM) for mining high utility-probability sequential patterns (HUPSPs) in uncertain sequence databases. A baseline algorithm with three optional pruning strategies is presented to mine HUPSPs. Moroever, to speed up the mining process, a projection mechanism is designed to create a database projection for each processed sequence, which is smaller than the original database. Thus, the number of unpromising candidates can be greatly reduced, as well as the execution time for mining HUPSPs. Substantial experiments both on real-life and synthetic datasets show that the designed algorithm performs well in terms of runtime, number of candidates, memory usage, and scalability for different minimum utility and minimum probability thresholds.

Event Detection in Sensor Networks With Non-Linear Amplifiers via Mixture Series Expansion

We develop a new algorithm for binary event detection in wireless sensor networks where the sensing process exhibits non-linear behavior causing clipping of the signals at random. We formulate the optimal event detection as a likelihood ratio test under a mixture model, which results in intractable marginal likelihood functions. To evaluate the intractable marginal likelihood functions, we develop a novel approximation via a non-parametric probability density estimator that is based on a series expansion of the Beta-Jacobi family of basis functions. The advantage of our algorithm is that it provides both high detection rate as well as very low computational complexity. In fact, our algorithm only requires the calculation of the first few moments of the model, which can be evaluated offline and is simple to implement in practice. Another feature of our model is that it is general enough to incorporate the probabilistic sensor non-linearities as well as transmission over random wireless channels. Simulation results demonstrate the benefits of using our framework, and that accounting for such practical features significantly improves the detection performance compared with the widely used Gaussian approximation method.

A probabilistic approach to statistical QoS provision of event detection in sensor networks

It is of significant importance to provide network-wide Quality of Service (QoS) for a wide range of event detection applications in wireless sensor networks. This paper investigates the important problem of QoS provision to abnormal event detection. For event detection applications, there are two key performance metrics, i.e., detection probability and detection latency. This paper considers both metrics, aiming to provide statistical QoS for abnormal event detection. It is, however, a challenging issue because of stringent resource constraints of sensor nodes and unpredictable randomness of physical events. We propose a probabilistic approach to statistical QoS provision for event detection in sensor networks. We propose a distributed algorithm that iteratively determines the active probability of each sensor node. The probability is kept small for energy efficiency but sufficiently large to provide the required detection QoS. Our approach is flexible and can detection QoS customized by applications. Comprehensive simulation experiments have been conducted, which demonstrate that our approach is able to provide the required detection QoS for event detection and achieves considerably longer the system lifetime compared with other competing schemes.

Optimal Index Policies for Anomaly Localization in Resource-Constrained Cyber Systems

The problem of anomaly localization in a resource-constrained cyber system is considered. Each anomalous component of the system incurs a cost per unit time until its anomaly is identified and fixed. Different anomalous components may incur different costs depending on their criticality to the system. Due to resource constraints, only one component can be probed at each given time. The observations from a probed component are realizations drawn from two different distributions depending on whether the component is normal or anomalous. The objective is a probing strategy that minimizes the total expected cost, incurred by all the components during the detection process, under reliability constraints. We consider both independent and exclusive models. In the former, each component can be abnormal with a certain probability independent of other components. In the latter, one and only one component is abnormal. We develop optimal index policies under both models. The proposed index policies apply to a more general case where a subset (more than one) of the components can be probed simultaneously. The problem under study also finds applications in spectrum scanning in cognitive radio networks and event detection in sensor networks.

Statistically Bounding Detection Latency in Low-Duty-Cycled Sensor Networks

Detecting abnormal events represents an important family of applications for wireless sensor networks. To achieve high performance of event detection, a sensor network should stay active most of the time, which is energy inefficient for battery driven sensor networks. This paper studies the fundamental problem of bounding detection delays when the sensor network is low duty cycled. We propose a novel approach for statistically bounding detection latency for event detection in sensor networks. The key issue is the wakeup scheduling of sensor nodes and minimization of wakeup activity. We propose a lightweight distributed algorithm for coordinating the wakeup scheduling of the sensor nodes. A distinctive feature of this algorithm is that it ensures that the detection delay of any event occurring anywhere in the sensing field is statistically bounded. In addition, the algorithm exposes a convenient interface for users to define the requirement on detection latency, thereby tuning the intrinsic tradeoff between energy efficiency and event detection performance. Extensive simulations have been conducted and results demonstrate that this algorithm can successfully meet delay bound and significantly reduce energy consumption.

Detection of variable length anomalous subsequences in data streams

We consider the problem of anomaly detection in data streams, which is the problem of extracting subsequences that do not match an expected behaviour. The main challenge for detecting anomalous subsequences from data streams in the existing techniques is to determine the lengths of the normal and anomalous subsequences. Therefore, creating a robust model for detecting the anomalous subsequences is of critical importance. In this paper, we propose an incremental algorithm based on the dynamic time warping technique to detect anomalous subsequences in data streams. The proposed algorithm is able to detect anomalous subsequences under relaxed length constrains of the normal and/or the anomalous subsequences. That is the proposed algorithm is able to detect variable length anomalous subsequences from among variable length normal sequences. The proposed robust model can be applied in areas such as system health monitoring, event detection in sensor networks, and detecting eco-system disturbances, etc. The cost of the proposed algorithm is linear with time and memory.

Pattern-based event detection in sensor networks

Many applications of wireless sensor networks monitor the physical world and report events of interest. To facilitate event detection in these applications, in this paper we propose a pattern-based event detection approach and integrate the approach into an in-network sensor query processing framework. Different from existing threshold-based event detection, we abstract events into patterns in sensory data and convert the problem of event detection into a pattern matching problem. We focus on applying single-node temporal patterns, and define the general patterns as well as five types of basic patterns for event specification. Considering the limited storage on sensor nodes, we design an on-node cache manager to maintain the historical data required for pattern matching and develop event-driven processing techniques for queries in our framework. We have conducted experiments using patterns for events that are extracted from real-world datasets. The results demonstrate the effectiveness and efficiency of our approach.