Wireless Sensor Network Application for Fire Hazard Detection and Monitoring

Μανωλάκος, Ηλίας Σ.; Logaras, Evangelos; Paschos, Fotis

doi:10.1007/978-3-642-11870-8_1

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Once each individual sensor makes its decision, each sends its decision to the local fusion centre through a communication channel whose noise model is given by (2). Due to channel noise the fusion centre may receive an altered decision.…”

Section: Optimal Degree Of Coveragementioning

confidence: 99%

“…Features such as random deployment over large inaccessible terrain and decentralized collaborative nature make wireless sensor networks (WSNs) apt for a wide range of applications, ranging from smart home system to space station surveillance [1,2]. Although earlier WSNs concentrated mostly on data gathering for monitoring purposes, recently however, more attention is shifted towards event detection which is crucial for many applications, such as disaster management, pollution detection, industrial monitoring, fault detection [1] etc.…”

Section: Introductionmentioning

confidence: 99%

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QoS Support in Event Detection in WSN through Optimal k-Coverage

Alam

Karmakar

Murshed

et al. 2011

Procedia Computer Science

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Wireless sensor networks promise to guarantee accurate, fault tolerant and timely detection of events in large scale sensor fields. To achieve this the notion of k-coverage is widely employed in WSNs where significant redundancy is introduced in deployment as an event is expected to be sensed by at least k sensors in the neighborhood. As sensor density increases significantly with k, it is imperative to find the optimal k for the underlying event detection system. In this work, we consider the detection probability, fault tolerance and latency as the Quality of Service (QoS) metrics of an event detection system employing k-coverage and present a probabilistic model to guarantee given QoS support with the minimum degree of coverage taking into account the noise related measurement error, communication interference and sensor fault probability. This work eventually resolves the problem of over or under deployment of sensors, increases scalability and provides a well defined mechanism to tune the degree of coverage according to performance needs.

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Section: Optimal Degree Of Coveragementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QoS Support in Event Detection in WSN through Optimal k-Coverage

Alam

Karmakar

Murshed

et al. 2011

Procedia Computer Science

View full text Add to dashboard Cite

show abstract

“…Smoke particle detection can be combined with temperature sensing [ 53 ] (either as absolute value or variation speed). The detection of temperature patterns can be used alone (and this is arguably one of the most cost- and energy-effective techniques) [ 61 ], or correlated with humidity readings [ 44 ], or with humidity, wind and rainfall data that are periodically retrieved from the application server [ 48 ]. In general, techniques based on data fusion from multiple transducer types can increase detection reliability [ 35 , 62 , 63 ].…”

Section: System Definitionmentioning

confidence: 99%

Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring

Lazarescu

2015

Sensors

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Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery.

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“…The efficient processing of sensor data is a central topic in a wide variety of research areas, which is underlined by advances in sensor technology and capabilities, e.g., for odor [ 1 ] and taste recognition [ 2 ] or by advances in visual information processing [ 3 ] as well as by applications in robotics [ 4 ] and sensor networks [ 5 , 6 ]. In particular to process data of multiple sensors, the well-known Kalman filter [ 7 ] has evolved into a key component of data fusion algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimally Distributed Kalman Filtering with Data-Driven Communication

Dormann

Noack

Hanebeck

2018

Sensors

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For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. A significant disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested. In this article, different extensions of the optimally distributed Kalman filter are proposed that employ data-driven transmission schemes in order to reduce communication expenses. As a first relaxation of the full-rate communication scheme, it can be shown that each node only has to transmit every second time step without endangering consistency of the fusion result. Also, two data-driven algorithms are introduced that even allow for lower transmission rates, and bounds are derived to guarantee consistent fusion results. Simulations demonstrate that the data-driven distributed filtering schemes can outperform a centralized Kalman filter that requires each measurement to be sent to the center node.

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Wireless Sensor Network Application for Fire Hazard Detection and Monitoring

Cited by 8 publications

References 7 publications

QoS Support in Event Detection in WSN through Optimal k-Coverage

QoS Support in Event Detection in WSN through Optimal k-Coverage

Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring

Optimally Distributed Kalman Filtering with Data-Driven Communication

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