A Neural Based Experimental Fire-Outbreak Detection System for Urban Centres

Agaji, Iorshase; Caleb, Shangbum F.

doi:10.4236/jsea.2016.93006

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The Dempster–Shafer Theory defines that each hypothesis in a frame of discernment receives a mass value. In DST-CEP , we propose to use Algorithm 1 of the mass function adapted from the Min-Max normalization technique [ 62 , 63 ] to calculate the mass value in the range from 0 to 1. Algorithm 1: Mass Function.

…”

Section: Dst-cep Approach For Iot Applicationsmentioning

confidence: 99%

Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

Bezerra

Teles

Coutinho

et al. 2021

Sensors

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The Internet of Things (IoT) has emerged from the proliferation of mobile devices and objects connected, resulting in the acquisition of periodic event flows from different devices and sensors. However, such sensors and devices can be faulty or affected by failures, have poor calibration, and produce inaccurate data and uncertain event flows in IoT applications. A prominent technique for analyzing event flows is Complex Event Processing (CEP). Uncertainty in CEP is usually observed in primitive events (i.e., sensor readings) and rules that derive complex events (i.e., high-level situations). In this paper, we investigate the identification and treatment of uncertainty in CEP-based IoT applications. We propose the DST-CEP, an approach that uses the Dempster–Shafer Theory to treat uncertainties. By using this theory, our solution can combine unreliable sensor data in conflicting situations and detect correct results. DST-CEP has an architectural model for treating uncertainty in events and its propagation to processing rules. We describe a case study using the proposed approach in a multi-sensor fire outbreak detection system. We submit our solution to experiments with a real sensor dataset, and evaluate it using well-known performance metrics. The solution achieves promising results regarding Accuracy, Precision, Recall, F-measure, and ROC Curve, even when combining conflicting sensor readings. DST-CEP demonstrated to be suitable and flexible to deal with uncertainty.

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…”

Section: Dst-cep Approach For Iot Applicationsmentioning

confidence: 99%

Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

Bezerra

Teles

Coutinho

et al. 2021

Sensors

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“…To overcome the drawbacks of conventional fire sensors [1], many visual fire detection methods have been proposed [2][3][4][5][6][7][8]. These methods utilise computer vision algorithms to recognise fire from videos or images.…”

Section: Introductionmentioning

confidence: 99%

Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

Yuan

Shi

Xia

et al. 2018

IET Computer Vision

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It is challenging to recognize smoke from visual scenes due to large variations of smoke colors, textures and shapes. To improve robustness, we propose a novel feature extraction method based on similarity and dissimilarity matching measures of Local Binary Patterns (LBP). Given two bit-sequences of an LBP code pair, the similarity and dissimilarity matching measures are defined as the ratios of the 1-1 bitwise matching number to the 0-0 bitwise matching number and the 1-0 number to the 0-1 number, respectively. To capture local code variations, we calculate the measures between LBP codes of a center pixel and its neighbors. Then we compare each measure with its global mean to propose Similarity Matching based Local Binary Patterns (SMLBP) and Dissimilarity Matching based Local Binary Patterns (DMLBP). Since SMLBP and DMLBP extract spatial variations of the 1st order LBP codes, they actually represent the 2nd order variations of pixel values. Furthermore, we adopt different mapping modes and multi-scale neighborhoods to obtain rotation and scale invariances. Finally, we concatenate the histograms of LBP, SMLBP and DMLBP to generate a feature vector containing 1st and 2nd order information. Experiments show that our method obviously outperforms existing methods.

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“…Sensors have the ability to operate independently, perform intended sensing tasks, process the sensed data, and report to a particular interested remote station [3,14,15]. Besides these advantages, the low cost and reliability of sensors have facilitated the establishment of numerous applications for various monitoring and management purposes including environment, transportation, human activity, detection and target tracking, underground mining and pipeline (such as water, oil, gas), healthcare , precision agriculture, industrial and supply chain [9].…”

Section: Introductionmentioning

confidence: 99%

On the Probability of Detection Ability in Observing Dynamic Environmental Phenomena using Wireless Sensor Networks

Mohammed¹,

Hussin²,

Basari³

2017

ijacsa

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Abstract-Wireless Sensor Network (WSN) is being utilised for several purposes in military and civil domains, including surveillance, monitoring, and management, where networked sensors monitor and detect an event of interest and report to the concerned party through the WSN or the Internet infrastructure. Due to the characteristics of WSN, there are many fundamental technical challenges including the node deployment, event localization, and event tracking, among which, the probability of the event observability has a crucial role. The observability is defined as the capability of observing an evolving event in the monitoring area. The probability of detection ability in observing an event depends on the parameters of the detection function, which in turn rely on the sensor technology and the nature of the surrounding environment. This paper addresses the observation of an event using WSN and how accurately the event is observed in the monitoring area. It presents a practical solution for event observability after formulizing and establishing the complexity of observability issue and tackling its relation and impact on node deployment and event localization. Hence, a feasible event observation model has been proposed and validated in this paper. The numerical results of the experimental evaluation have confirmed that an accurate detection of an occurring event can be achieved by the proposed model.

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A Neural Based Experimental Fire-Outbreak Detection System for Urban Centres

Cited by 6 publications

References 11 publications

Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

On the Probability of Detection Ability in Observing Dynamic Environmental Phenomena using Wireless Sensor Networks

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