Multisensor data fusion for fire detection

Zervas, E.; Mpimpoudis, A.; Anagnostopoulos, Christos; Sekkas, Odysseas; Hadjiefthymiades, Stathes

doi:10.1016/j.inffus.2009.12.006

Cited by 96 publications

(48 citation statements)

References 14 publications

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“…Also, we put Y ∞ = −∞ as naturally the cost of an infinite number of observation is infinite. Consider now the V * the expected quality reward for the System based on an optimal stopping rule in (3). Suppose that the mechanism induces cost c and observe the M 1 .…”

Section: Time-optimized Quality-driven Mechanismmentioning

confidence: 99%

“…In the state of the art, it is possible to find quite a few IoT monitoring and predictive analytics solutions such as forest monitoring [2], fire-event prediction and classification [3], agriculture monitoring [4], marine environment states prediction [5], watershed prediction systems [20], health states prediction in rivers [21], or energy management solutions to reduce both the amount of resources needed and the atmospheric emissions [22]. The reader could also refer to the survey [23] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

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Quality-optimized predictive analytics

Anagnostopoulos

2016

Appl Intell

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On-line statistical and machine learning analytic tasks over largescale contextual data streams coming from e.g., wireless sensor networks, Internet of Things environments, have gained high popularity nowadays due to their significance in knowledge extraction, regression and classification tasks, and, more generally, in making sense from large-scale streaming data. The quality of the received contextual information, however, impacts predictive analytics tasks especially when dealing with uncertain data, outliers data, and data containing missing values. Low quality of received contextual data significantly spoils the progressive inference and on-line statistical reasoning tasks, thus, bias is introduced in the induced knowledge, e.g., classification and decision making. To alleviate such situation, which is not so rare in real time contextual information processing systems, we propose a progressive time-optimized data quality-aware mechanism, which attempts to deliver contextual information of high quality to predictive analytics engines by progressively introducing a certain controlled delay. Such a mechanism progressively delivers high quality data as much as possible, thus eliminating possible biases in knowledge extraction and predictive analysis tasks. We propose an analytical model for this mechanism and show the benefits stem from this approach through comprehensive experimental evaluation and comparative assessment with qualityunaware methods over real sensory multivariate contextual data.

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Section: Time-optimized Quality-driven Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality-optimized predictive analytics

Anagnostopoulos

2016

Appl Intell

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“…However, several fusion models (as in [4] [5]) treat the alert issue based on the acquisition of data provided from the nodes deployed in the surveillance field, in the case of a fire event and at the very beginning, minorities of the nodes immediately detect the fire, while, the other nodes that are still far from the event, have not detected the fire yet. The system, then, starts the calculation based on all the collected data.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, many of these proposed approaches of researchers (as in [4] and [5]), have not discussed the information filtering processing, which is an interesting operation that allows to avoid the impact of erroneous data on the processing of information fusion.…”

Section: Introductionmentioning

confidence: 99%

Hybridized Model for Early Detection and Smart Monitoring of Forest Fire

Abbassi

Jilbab

Bourouhou

2017

TMLAI

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The demand for wireless sensor network technology has been increasingly needed in recent years for several major applications, including environmental monitoring, where nodes deployed in nature detect, process and transfer the environmental data in an autonomous way. However, the performance of early detection of the fire event after data fusion processed by this WSN's system will be less reliable since the majority of the other nodes do not detect the fire yet (at the beginning of event). That is why the present work is conducted. It proposes an intelligent strategy of data fusion of temperature and humidity sensors hybridized with an intelligent scanning technique. This model will allow the early detection of alarm from the beginning of fire event and guarantee a good monitoring of area state with an ongoing localization of fire zone. The result proves a very good performance in terms of reliability of the early detection and tracking fire propagation.

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“…Information fusion is the merging of information from disparate sources with differing conceptual, contextual, and typographical representations. It has been successfully applied in data mining and the consolidation of data from unstructured or semi-structured resources, and it has also led to many achievements in various fields [1,4,8]. Fusion methods include product fusion (such as the Bayes posterior probability model), linear fusion (SVM classifiers), and nonlinear fusion (super-kernel integration) [23].…”

Section: Introductionmentioning

confidence: 99%

Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function

Cao

et al. 2015

IFS

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Abstract. An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems.

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Multisensor data fusion for fire detection

Cited by 96 publications

References 14 publications

Quality-optimized predictive analytics

Quality-optimized predictive analytics

Hybridized Model for Early Detection and Smart Monitoring of Forest Fire

Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function

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