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.
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.
Reliable and energy-efficient data dissemination is an important challenge particularly in multi-hop Wireless Sensor Networks (WSNs). Although clustering is considered as one of the promising techniques for energy aware data dissemination, majority of research in this area assume a reliable network, in which no packet is lost due to low link quality. In this paper we propose REC+, a Reliable and Energy-efficient Chain-cluster based routing protocol, which aims to achieve the maximum reliability in a multi-hop network by finding the best place for the Cluster Head (CH) and the proper shape/size of the clusters without the need of using any error controlling approaches that can be quite expensive in terms of computation and communication overhead. Most importantly, REC+ relaxes some strong assumptions that other cluster-based routing algorithms rely on, which make them inapplicable for real WSNs. To the best of our knowledge, REC+ is the first cluster based routing algorithm that considers energy efficiency, transmission reliability and intra-cluster delay all together to construct clusters and select proper CHs in WSNs. In the simulation, we show superiority of our approach over three others in terms of the product of energy consumption and delay.
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.