Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a certain field (e.g., event detection for wireless sensor networks) or the main concern for which techniques have been specifically designed (e.g., fire detection using remote sensing techniques). These different approaches stem from different backgrounds of researchers dealing with fire, such as computer science, geography and earth observation, and fire safety. In this report we survey previous studies from three perspectives: (1) fire detection techniques for residential areas, (2) fire detection techniques for forests, and (3) contributions of sensor networks to early fire detection

Embedded Neural Network for Fire Classification Using an Array of Gas Sensors

Fire is one of the most common hazards in US households. In 2006 alone, 2705 people were killed due to fire in building structures. 74% of the deaths result from fires in homes with no smoke alarms or no working smoke alarms while surveys report that 96% of all homes have at least one smoke alarm. This study discusses the development of a fire sensing system that is not only capable of detecting fire in its early stage but also of classifying the fire based on the smell of the smoke in the environment. By using an array of sensors along with a neural network for sensor pattern recognition, an impressive result is obtained. Instead of confining the ANN to a PC based application, this work extends the implementation of the neural network fire classifier in a general purpose microcontroller. The result is a low cost intelligent embedded fire classifier that can be used in real life situations for fire hazards minimization, for example this intelligent fire classifier can be used for the development of a smart extinguisher that detects the fire, classifies it and then uses appropriate extinguishing material required for extinguishing the particular class of fire

Using wireless sensors and networks program for chemical particle propagation mapping and chemical source localization

Chemical source localization is a challenge for most of researchers. It has extensive applications, such as anti-terrorist military, Gas and oil industry, and environment engineering. This dissertation used wireless sensor and sensor networks to get chemical particle propagation mapping and chemical source localization. First, the chemical particle propagation mapping is built using interpolation and extrapolation methods. The interpolation method get the chemical particle path through the sensors, and the extrapolation method get the chemical particle beyond the sensors. Both of them compose of the mapping in the whole considered area. Second, the algorithm of sensor fusion is proposed. It smooths the chemical particle paths through integration of more sensors\u27 value and updating the parameters. The updated parameters are associated with including sensor fusion among chemical sensors and wind sensors at same positions and sensor fusion among sensors at different positions. This algorithm improves the accuracy and efficiency of chemical particle mapping. Last, the reasoning system is implemented aiming to detect the chemical source in the considered region where the chemical particle propagation mapping has been finished. This control scheme dynamically analyzes the data from the sensors and guide us to find the goal. In this dissertation, the novel algorithm of modelling chemical propagation is programmed using Matlab. Comparing the results from computational fluid dynamics (CFD) software COMSOL, this algorithm have the same level of accuracy. However, it saves more computational times and memories. The simulation and experiment of detecting chemical source in an indoor environment and outdoor environment are finished in this dissertation --Abstract, page iii

Development of the MOOSY4 eNose IoT for Sulphur-Based VOC Water Pollution Detection

[EN] In this paper, we describe a new low-cost and portable electronic nose instrument, the Multisensory Odor Olfactory System MOOSY4. This prototype is based on only four metal oxide semiconductor (MOS) gas sensors suitable for IoT technology. The system architecture consists of four stages: data acquisition, data storage, data processing, and user interfacing. The designed eNose was tested with experiment for detection of volatile components in water pollution, as a dimethyl disulphide or dimethyl diselenide or sulphur. Therefore, the results provide evidence that odor information can be recognized with around 86% efficiency, detecting smells unwanted in the water and improving the quality control in bottled water factories.This work was supported by the I+D+i Program of the Generalitat Valenciana, Spain [AICO/2016/046], and the II Program UPV-La Fe [2013/0504].Climent-Martí, E.; Pelegrí Sebastiá, J.; Sogorb Devesa, T.; Talens-Felis, J.; Chilo, J. (2017). 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Performance definition and standardization of electronic noses. Sensors and Actuators B: Chemical, 33(1-3), 60-67. doi:10.1016/0925-4005(96)01819-9Wilson, A., & Baietto, M. (2009). Applications and Advances in Electronic-Nose Technologies. Sensors, 9(7), 5099-5148. doi:10.3390/s90705099Jia, X.-M., Meng, Q.-H., Jing, Y.-Q., Qi, P.-F., Zeng, M., & Ma, S.-G. (2016). A New Method Combining KECA-LDA With ELM for Classification of Chinese Liquors Using Electronic Nose. IEEE Sensors Journal, 16(22), 8010-8017. doi:10.1109/jsen.2016.2606163Jing, Y.-Q., Meng, Q.-H., Qi, P.-F., Cao, M.-L., Zeng, M., & Ma, S.-G. (2016). A Bioinspired Neural Network for Data Processing in an Electronic Nose. IEEE Transactions on Instrumentation and Measurement, 65(10), 2369-2380. doi:10.1109/tim.2016.2578618Fine, G. F., Cavanagh, L. M., Afonja, A., & Binions, R. (2010). Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring. Sensors, 10(6), 5469-5502. doi:10.3390/s100605469Santra, S., Guha, P. 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An electronic nose system for monitoring the quality of potable water. Sensors and Actuators B: Chemical, 69(3), 336-341. doi:10.1016/s0925-4005(00)00482-2Goschnick, J., Koronczi, I., Frietsch, M., & Kiselev, I. (2005). Water pollution recognition with the electronic nose KAMINA. Sensors and Actuators B: Chemical, 106(1), 182-186. doi:10.1016/j.snb.2004.05.055Guadayol, M., Cortina, M., Guadayol, J. M., & Caixach, J. (2016). Determination of dimethyl selenide and dimethyl sulphide compounds causing off-flavours in bottled mineral waters. Water Research, 92, 149-155. doi:10.1016/j.watres.2016.01.016Wilson, A. D. (2012). Review of Electronic-nose Technologies and Algorithms to Detect Hazardous Chemicals in the Environment. Procedia Technology, 1, 453-463. doi:10.1016/j.protcy.2012.02.101Becher, C., Kaul, P., Mitrovics, J., & Warmer, J. (2010). The detection of evaporating hazardous material released from moving sources using a gas sensor network. 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Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems. Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems. This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool. The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction

Detection algorithms for the Nano nose

The Nano nose is an instrument with an array of nano sized optical sensors that produces digital patterns when exposed to radiation passing through a gaseous mixture. The digital patterns correspond to the amount of photocurrent registered on each of the sensors. The problem is to find the gas constituents in the gaseous mixture and estimate their concentrations. This thesis outlines an algorithm using a combination of a mixed gas detector and a gas concentration predictor. The mixed gas detector is an array of neural networks corresponding to the number of gases. There are two techniques outlined for the implementation of the gas concentration predictor which are the partial least squares regression (PLS) and the Kalman filter. The output of the developed algorithm would not only show the detection of the individual constituents in the gaseous mixture but also provide the prediction of their concentrations. The algorithm designed is entirely re-configurable providing greater amount of flexibility and has detected the constituents along with the prediction of their concentrations of a mixture of three gases

Real-time classification of multivariate olfaction data using spiking neural networks

Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an SNN-based solution for the classification of rank-order spiking patterns to provide continuous recognition results in real-time. The SNN classifier is deployed on a neuromorphic hardware system that enables massively parallel and low-power processing on incoming rank-order patterns. Offline learning is used to store the reference rank-order patterns, and an inbuilt nearest neighbor classification logic is applied by the neurons to provide recognition results. The proposed system was evaluated using two different datasets including rank-order spiking data from previously established olfactory systems. The continuous classification that was achieved required a maximum of 12.82% of the total pattern frame to provide 96.5% accuracy in identifying corresponding target gases. Recognition results were obtained at a nominal processing latency of 16ms for each incoming spike. In addition to the clear advantages in terms of real-time operation and robustness to inconsistent rank-orders, the SNN classifier can also detect anomalies in rank-order patterns arising due to drift in sensing arrays

Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.Comment: 14 Pages, 9 Figure