8,291 research outputs found
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
AWARE: Platform for Autonomous self-deploying and operation of Wireless sensor-actuator networks cooperating with unmanned AeRial vehiclEs
This paper presents the AWARE platform that seeks to enable the cooperation of autonomous aerial vehicles with ground wireless sensor-actuator networks comprising both static and mobile nodes carried by vehicles or people. Particularly, the paper presents the middleware, the wireless sensor network, the node deployment by means of an autonomous helicopter, and the surveillance and tracking functionalities of the platform. Furthermore, the paper presents the first general experiments of the AWARE project that took place in March 2007 with the assistance of the Seville fire brigades
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Autonomous monitoring framework for resource-constrained environments
Acknowledgments The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL: http://www.dotrural.ac.uk/RemoteStream/Peer reviewedPublisher PD
A Simple Flood Forecasting Scheme Using Wireless Sensor Networks
This paper presents a forecasting model designed using WSNs (Wireless Sensor
Networks) to predict flood in rivers using simple and fast calculations to
provide real-time results and save the lives of people who may be affected by
the flood. Our prediction model uses multiple variable robust linear regression
which is easy to understand and simple and cost effective in implementation, is
speed efficient, but has low resource utilization and yet provides real time
predictions with reliable accuracy, thus having features which are desirable in
any real world algorithm. Our prediction model is independent of the number of
parameters, i.e. any number of parameters may be added or removed based on the
on-site requirements. When the water level rises, we represent it using a
polynomial whose nature is used to determine if the water level may exceed the
flood line in the near future. We compare our work with a contemporary
algorithm to demonstrate our improvements over it. Then we present our
simulation results for the predicted water level compared to the actual water
level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc,
Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple
Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201
Analysis methodology for flow-level evaluation of a hybrid mobile-sensor network
Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper focuses on a hybrid mobile-sensor network identify- ing potential advantages and challenges of its use and defining feasible applications. The main value of the paper, however, is in the proposed analysis approach to evaluate the performance at the mobile network side given the mixed mobile-sensor traffic. The approach combines packet- level analysis with modelling of flow-level behaviour and can be applied for the study of various application scenarios. In this paper we consider two applications with distinct traffic models namely multimedia traffic and best-effort traffic
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