A Smart Building Fire and Gas Leakage Alert System with Edge Computing and NG112 Emergency Call Capabilities

Abstract

Nowadays, the transformations of cities into smart cities is a crucial factor in improving the living conditions of the inhabitants as well as addressing emergency situations under the concept of public safety and property loss. In this context, many sensing systems have been designed and developed that provide fire detection and gas leakage alerts. On the other hand, new technologies such edge computing have gained significant attention in recent years. Moreover, the development of recent intelligent applications in IoT aims to integrate several types of systems with automated next-generation emergency calls in case of a serious accident. Currently, there is a lack of studies that combine all the aforementioned technologies. The proposed smart building sensor system, SB112, combines a small-size multisensor-based (temperature, humidity, smoke, flame, CO, LPG, and CNG) scheme with an open-source edge computing framework and automated Next Generation (NG) 112 emergency call functionality. It involves crucial actors such as IoT devices, a Public Safety Answering Point (PSAP), the middleware of a smart city platform, and relevant operators in an end-to-end manner for real-world scenarios. To verify the utility and functionality of the proposed system, a representative end-to-end experiment was performed, publishing raw measurements from sensors as well as a fire alert in real time and with low latency (average latency of 32 ms) to the middleware of a smart city platform. Once the fire was detected, a fully automatic NG112 emergency call to a PSAP was performed. The proposed methodology highlights the potential of the SΒ112 system for exploitation by decision-makers or city authorities.

Adaptive Control of IoT/M2M Devices in Smart Buildings Using Heterogeneous Wireless Networks

“…Also, in some cases, Blynk App's notifications are delayed in sending to the user. In [20] a smart building fire and gas leakage alert system namely SB112, which combines a small-size multisensor-based scheme with an open-source edge computing framework and an automated next-generation (NG) 112 emergency call functionality was proposed using ESP32 as microcontrollers units and Raspberry Pi as Edge gateway. As part of an end-to-end scenario, crucial actors such as IoT devices, public safety answering points (PSAP), middleware for a smart city platform, and relevant operators are involved.…”

“…Among these applications, smart buildings are emerging, which support the flow of information throughout the building, providing advanced functionalities and services, allowing the automatic control, monitoring, management, and maintenance of the various subsystems or applications of the building in an optimal and integrated way, locally and/or remotely [11]. There are many research papers dealing with wireless networkbased IoT/M2M smart building systems, whether MCN or WSN [12]- [20], which will be discussed in detail in the related work part. Comparing the proposed system with all these research works, the drawbacks of existing building management systems include the lack of using IoT and M2M technologies together, reliance on one or two wireless communication technologies at most, a limited wireless transmission range, mostly inconveniently designed user interface, and excessive costs.…”

Research on Multi-Sensor Fusion Indoor Fire Perception Algorithm Based on Improved TCN

“…The vertical axis of ROC curve is true positive rate (TPR). TPR represents the proportion of the predicted positive and actually positive samples to all positive samples, as shown in Equation (15). The horizontal axis of ROC curve is false positive rate (FPR).…”

“…Then the method of multivariate weighted fusion was used to assess the probability of fire occurrence. Avgeris et al [15,16] used edge computing technology to overcome the shortcomings of the fire perception system based on the Internet of Things, such as limited energy resources and lack of real-time processing computing ability. Therefore, the fire perception system with edge computing technology can quickly detect the fire situation and take appropriate measures.…”

An Indoor Fire Detection Method Based on Multi-Sensor Fusion and a Lightweight Convolutional Neural Network

“…Wang et al [ 12 ] designed and performed modified hierarchical analysis to determine the weight of each sensor, subsequently utilizing the multivariate weighted fusion method to assess the probability of fire occurrence. Maltezos et al [ 13 ] used edge computing technology to overcome the shortcomings of the fire perception system based on the Internet of Things, such as limited energy resources and a lack of real-time computer processing ability. These methods improve the real-time accuracy of the fire detection system to a certain extent, but the accuracy is not satisfactory.…”