Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and become one of the enabling technologies for disaster early-warning 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. Keywords -Disaster early warning systems, event detection, wireless sensor networksI.
Prompt and accurate residential fire detection is important for on-time fire extinguishing and consequently reducing damages and life losses. To detect fire sensors are needed to measure the environmental parameters and algorithms are required to decide about occurrence of fire. Recently, wireless sensor networks (WSNs) have been used for environmental monitoring and real-time event detection because of their low implementation costs and their capability of distributed sensing and processing. Although there are several works on fire detection using WSNs, they have rarely paid sufficient attention to investigate the optimal sensor sets and usage of suitable artificial intelligence (AI) methods. Therefore, by aiming at residential fire detection, this paper investigates proper sensor sets and proposes AI-based techniques for fire detection in WSNs. The proposed methods are evaluated in terms of detection accuracy rate and computational complexity.
Recently, Wireless Sensor Networks (WSN) community has witnessed an application focus shift. Although, monitoring was the initial application of wireless sensor networks, in-network data processing and (near) real-time actuation capability have made wireless sensor networks suitable candidate for event detection and alarming applications as well. Unreliability and dynamic (e.g. in terms of deployment area, network resources, and topology) are normal practices in the field of WSN. Therefore, effective and trustworthy event detection techniques for the WSN require robust and intelligent methods of mining hidden patterns in the sensor data, while supporting various kinds of dynamicity. Due to the fact that events are often functions of more than one attribute, data fusion and use of more features can help increasing event detection rate and reducing false alarm rate. In addition, sensor fusion can lead to more accurate and robust event detection by eliminating outliers and erroneous readings of individual sensor nodes and combining individual event detection decisions. In this paper, we propose a two-level sensor fusion-based event detection technique for the WSN. The first level of event detection in our proposed approach is conducted locally inside the sensor nodes, while the second level is carried out in a level higher (e.g., in a cluster head or gateway) and incorporates a fusion algorithm to reach a consensus among individual detection decisions made by sensor nodes. By considering fire as an event, we evaluate our approach through several experiments and illustrate impact of sensor fusion on achieving better results.
Abstract-Outliers or anomalies are generally considered to be those observations that are considerably diverged from normal pattern of data. Due to their special characteristics, e.g. constrained available resources, frequent physical failure, and often harsh deployment area, wireless sensor networks (WSNs) are more likely to generate outliers compared to their other wireless counterparts. Potential sources of deviated data in a series of measurements are errors, events, and/or malicious attacks on the network. Current studies tend to handle events and errors separately and propose different techniques for event detection as for outlier detection. By bringing the concept of outlier and event close together and assuming that events are some sorts of outliers, in this paper, we investigate applicability of pattern matching-based event detection techniques for outlier detection. Through extensive experiments, we evaluate performance of various event detection techniques to detect outliers and compare them with a recent outlier detection study. I.INTRODUCTION Compared to wired networks, wireless sensor networks (WSNs), comprised of a large number of tiny, low-cost sensor nodes, equipped with sensing, computational power and shortrange wireless communication capabilities, have strong resource constraints in terms of energy, memory, computational capacity and communication bandwidth. The large-scale and high density vision of the WSN suggests that the network usually has to operate in a harsh and unattended environment. Moreover WSNs are vulnerable to faults and malicious attacks (e.g., denial of service attacks or black hole attacks), and cause unreliable and inaccurate sensor readings. Generally speaking, the potential sources of outliers include noise and errors, events, and malicious attacks on the network. To ensure a reasonable data quality, secure monitoring and reliable detection of interesting and critical events, and to facilitate effective and correct decision-making using data collected by WSNs, identifying anomalous measurements in point of action is a must.In WSNs, outliers, are those measurements that do not conform to the normal behavioral pattern of the sensed data [1]. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to specific requirements, dynamic nature, and limitations of the wireless sensor networks [28]. An appropriate outlier detection technique for the WSN should pay attention to computing, communication and storage limitations of the network and deal with the distributed data analysis. The key objective of outlier detection in WSNs is to identify outliers in the distributed streaming data in an online manner with a high
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.