Research and Design of Distributed Fire Alarm System of Indoor Internet of Things Based on LoRa

Chen, Wei; He, ChenYu; Lu, JianRong; Yan, Kui; Liu, Jin; Zhou, Feng; Xu, Xiaohu; Hao, Xiao

doi:10.1155/2021/7462331

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…In practical applications, the sensing end and the collection end will occupy the serial port of the UART0 single chip microcomputer. The information data of the collection end will be transmitted to the application end, so UART1 needs to be used as the data serial port [7] . This means that the application end does not need to send data and commands to the collection end and can complete data transmission tasks by itself.…”

Section: Collecting Endmentioning

confidence: 99%

Design and implementation of a distributed fire monitoring and alarm system based on LoRa

Wang,

Tan

2023

J. Phys.: Conf. Ser.

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Fire has strong destructive power and wide influence, which is a serious hidden danger to regional economic development. This paper takes the distributed fire monitoring and alarm system based on LoRa as the research object. Firstly, the basic frame structure is described, and then the system is formed from three aspects: perception, collection and application. Finally, the system is tested and analyzed to ensure the orderly development of social economy for the development direction of fire monitoring and alarm system.

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Section: Collecting Endmentioning

confidence: 99%

Design and implementation of a distributed fire monitoring and alarm system based on LoRa

Wang,

Tan

2023

J. Phys.: Conf. Ser.

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“…In general, fire detection systems consist of multiple sensor nodes, IoT gateways, management servers, and end-users (e.g., firefighters, public residents, etc.) [10,18]. Many sensor nodes are distributed on certain building locations and are connected to the gateways for transmitting observed multisensor data to management servers or end users.…”

Section: A Framework Overviewmentioning

confidence: 99%

Uncertainty Assessment-Based Active Learning for Reliable Fire Detection Systems

Kim

2022

IEEE Access

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Deep learning technologies, due to their advanced pattern extraction and recognition of highdimensional data, have been widely adopted into multisensor-based fire detection systems. Since deep learning approaches can generate erroneous predictions due to incomplete training datasets, a retraining process over unseen observations is needed. However, storing a large amount of data from continuous multisensor streams and labeling them to create a retraining dataset are costly and time-consuming. In this paper, we propose an active learning framework based on an informative experience memory that is populated with meaningful retraining data by assessing the uncertainty of the data. In the proposed framework, the deep learning model predicts fire occurrence and estimates model uncertainty by taking advantage of a Bayesian neural network using Monte Carlo dropout. By storing only higher uncertain data points into the fixed-size informative experience memory and querying them to the system managers, the storage and labeling costs are minimized while improving performance. To evaluate our active learning framework with different neural network structures, we develop three Bayesian neural networks based on conventional classification networks, including the feedforward neural network, fully convolutional network, and long short-term memory. We further investigate various uncertainty assessment scoring methods for classification tasks such as entropy, BALD, variation ratios, and mean STD. Experiments on a real dataset show that the Bayesian FCN using the BALD assessment method has the highest performance gain with an F1 score of 0.95, with an improvement of 24% using only 700 data points.

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“…On the other hand, fre can seriously harm people and damage their property. Systems for detecting fres are necessary to reduce the loss of personal property due to both induced and artifcial fres [1,2]. Te International Association of Fire and Rescue Services reported that from 1993 to 2020, there were 1123.6 thousand fre-related fatalities [3].…”

Section: Introductionmentioning

confidence: 99%

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Vaegae,

Annepu,

Bagadi

et al. 2024

Journal of Optimization

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As smart cities expand rapidly, the demand for strong fire protection has become even more essential. A significant challenge lies in ensuring that fire detection systems integrate seamlessly into our modern infrastructures. Leveraging multisensor systems can yield reliable data on potential fires. Particularly in smart buildings, the effectiveness of multisensor fire detection algorithms becomes paramount. This research introduces a fuzzy logic-driven method that harnesses the power of smoke, flame, and temperature sensors. While smoke, flames, and elevated temperatures are primary fire indicators, they can manifest concurrently or sequentially. We have amalgamated various fuzzy logic rules (IF-THEN structures) to gauge the intensity of fires. When a fire ignites, the sensors spring into action, identifying its source and promptly notifying users via the Internet and GSM modems. Moreover, they relay the fire’s precise geographic coordinates to fire departments. The fire’s status will be consistently updated on a dedicated online portal. It was observed from our results that the system proficiently sends fire alerts to residents, and the fire status undergoes regular updates at 45-second intervals. This refreshing is initiated by the identification of a designated percentage of smoke and flame. These outcomes validate the system’s effectiveness in enhancing the precision and responsiveness of fire detection capabilities.

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Research and Design of Distributed Fire Alarm System of Indoor Internet of Things Based on LoRa

Cited by 7 publications

References 29 publications

Design and implementation of a distributed fire monitoring and alarm system based on LoRa

Design and implementation of a distributed fire monitoring and alarm system based on LoRa

Uncertainty Assessment-Based Active Learning for Reliable Fire Detection Systems

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

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