Event Detection In Wireless Sensor Networks Research Articles

Improving Rare Events Detection in WSN through Cluster-Based Power Control Mechanism

Rare events detection is one of the main applications in Wireless Sensor Networks (WSN) and is currently a central concern of a vast literature. Compressed Sensing (CS) theory has been proved to be quite adapted to this objective. Although this is not the first work on applying CS to sparse events detection in WSN, it is the first to highly justify the validity of the targets detection and counting problem formulation. In order to enhance the CS recovery capacity in WSN, this work considers an approach based on a coherence reduction of the sensing matrix premised on the transmitted power control (PC). Simulation results prove that, under the constraint of equal power consumption, the detection and counting performance is improved when the proposed power control scheme is employed compared to the case without PC.

Fault Tolerant Complex Event Detection in WSNs: A Case Study in Structural Health Monitoring

Fault-tolerant event detection (FTED), whose objective is to correctly detect events of interest in the presence of faulty nodes, remains to be one of the hot research areas in wireless sensor networks (WSNs). However, many recently emerged ‘domain-specific’ applications of WSNs, such as structural health monitoring (SHM) and volcano monitoring, have shown some distinct features from traditional WSN applications. For example, data collected each time from sensor nodes is not a scalar but a long dynamic data sequence. In addition, detecting an event in these applications generally requires low-level collaboration of multiple sensors. As a consequence, existing FTED schemes usually cannot work well in these applications. In this paper, we realize FTED in a typical domain-specific application of WSNs: SHM. The main contribution of this work is I-FUND, a faulty node detection algorithm that takes feature vectors as input and can even handle the ‘element mismatch problem’ where comparable elements in vectors are located at unknown different positions. In addition, I-FUND adopts adaptive stop criterion identified from data and turns out to be reliable even when a large percentage of the sensor nodes report erroneous observations. The effectiveness of the proposed scheme is demonstrated through both simulations and real experiments.

Dynamic collaborative in-network event detection in wireless sensor networks

Many applications of wireless sensor networks (WSNs) require events be detected around moving targets and delivered to a user, whose location may also change over time. Consequently, it is not desirable to have a single sensor node serving as the sink node for centralized event processing. Furthermore, an event usually occurs across different locations and lasts for a period of time that are unknown in advance. Hence it cannot be detected by a static set of sensor nodes. In this paper, we propose a general framework for such dynamic in-network event detection in WSNs. This framework enables a flexible number of sensor nodes to dynamically collaborate in detecting and delivering any specified event. So far there has been no such work reported in the literature. We have designed a protocol for the sensor nodes to autonomously determine whether they should participate in and which tasks they should perform to collaboratively process an event. We have implemented a prototype of the proposed framework using MicaZ motes and evaluated it through both simulations and experiments. The results demonstrate that our framework is effective and efficient in terms of energy cost and event processing delay.

Use of wireless sensor networks for distributed event detection in disaster management applications

Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity.

A Distributed Method for Two Simultaneous Events Detection in WSNs

It is important to detect multi simultaneous events in Wireless Sensor Networks (WSNs). WSNs are usually consist of tiny sensor nodes which are often deployed in harsh environment and facing the fault conditions. In this paper, a fault tolerant distributed two-event detection method based on the Bayesian approach is proposed for WSNs. In addition, a data fusion algorithm is employed to involve the statuses of the neighborhoods in the decision of each sensing point. The proposed distributed method is used to detect two events simultaneously in a WSN with 200 sensors that distributed randomly. Also some random faults occur in all sensing nodes. Results show the accuracy and significant performance of the proposed two events detection approach.

Cellular Microscopic Pattern Recogniser – A Distributed Computational Approach for Macroscopic Event Detection in WSN

A light weight one-shot learning pattern recognition scheme, known as Cellular Microscopic Pattern Recogniser (CMPR) is proposed that will lead to macroscopic event detection with wireless sensor networks (WSN). The scheme simplifies computations for energy conservation and speeds up recognition by leveraging the parallel distributed processing capabilities of WSN. Experimental results show that the scheme is able to detect noisy patterns with comparable accuracy to the conventional techniques.

Event-to-sink reliable transport in wireless sensor networks

Wireless sensor networks (WSNs) are event-based systems that rely on the collective effort of several microsensor nodes. Reliable event detection at the sink is based on collective information provided by source nodes and not on any individual report. However, conventional end-to-end reliability definitions and solutions are inapplicable in the WSN regime and would only lead to a waste of scarce sensor resources. Hence, the WSN paradigm necessitates a collective event-to-sink reliability notion rather than the traditional end-to-end notion. To the best of our knowledge, reliable transport in WSN has not been studied from this perspective before. In order to address this need, a new reliable transport scheme for WSN, the event-to-sink reliable transport (ESRT) protocol, is presented in this paper. ESRT is a novel transport solution developed to achieve reliable event detection in WSN with minimum energy expenditure. It includes a congestion control component that serves the dual purpose of achieving reliability and conserving energy. Importantly, the algorithms of ESRT mainly run on the sink, with minimal functionality required at resource constrained sensor nodes. ESRT protocol operation is determined by the current network state based on the reliability achieved and congestion condition in the network. This self-configuring nature of ESRT makes it robust to random, dynamic topology in WSN. Furthermore, ESRT can also accommodate multiple concurrent event occurrences in a wireless sensor field. Analytical performance evaluation and simulation results show that ESRT converges to the desired reliability with minimum energy expenditure, starting from any initial network state.