A machine learning-based early forest fire detection system utilizing vision and sensors’ fusion technologies

The paper aims at utilizing machine learning (ML) towards designing an early warning forest fire detection system. With the aid of the Internet of Things (IoT) and smart edge computing, an embedded system that utilizes sensors’ fusion technology, machine vision and ML to early detect forest fire has been proposed. Different from most of the proposed fire detection systems in the literature, which either utilize vision or sensors’-based approaches to detect the fire, the proposed system utilizes both approaches jointly, which in turn will make it more accurate for fire detection. Furthermore, this paper focuses on implementing the proposed system utilizing a smart edge node and discusses the incurred technical challenges and how they have been solved.This work has been in part supported by King Abdullah II Fund for development. Grant No.2022/6 and Jordan Design and Development Bureau(JODDB).info:eu-repo/semantics/publishedVersio

PERANCANGAN DAN IMPLEMENTASI SISTEM PENDETEKSI DAN PERINGATAN KEBAKARAN BERBASIS IOT MENGGUNAKAN NODEMCU ESP8266 DAN SENSOR API

Control and monitoring systems that use the Internet-of-Things (IoT) can be found in everyday life, for example, in smart home applications, smart transportation, and smart cities. IoT-based monitoring of environmental conditions both indoors and outdoors is generally carried out to reduce the risk of unwanted events. One example of an unwanted event is a fire. Fires can occur in houses, office buildings, rice fields, or forests. In order to help provide early warning of a flame that can cause a fire, this study aims to design and implement an IoT-based fire detection and warning system using the NodeMCU ESP8266 and a flame sensor. The sensor used is an infrared sensor that will detect infrared light radiation coming from a fire. The test results show that the system successfully detects the presence of fire up to 50 cm from the fire sensor and can be monitored via thinger.io. Additionally, notifications from thinger.io via e-mail and Telegram to users were successfully executed

Design and Construction of Smart House Prototype Based Internet of Things (Iot) Using Esp8266

IoT (Internet of Things) is a concept that aims to expand the benefits of continuously connected internet connectivity continuously. IoT can be used at home as a smart home system for controlling electronic equipment that can be operated by means ofinternet connection (WiFi). This IoT-based smart home system uses ESP 8266 microcontroller as hardware and blynk application as a tool controller and control. This system consists of a lamp controller, knowing humidity and room temperature, detection of human movement at home abandoned, early detection of fire sources to prevent house fires, and LPG gas leak detector for home security. There are four sensors used are PIR sensors to detect human movement, MQ2 sensor to detect LPG gas leaks, flame sensor to detect LPG gas leaks early detection of fire, and the DHT11 sensor to determine humidity and temperature. The design of this system uses a relay which is used as a liaison lamp with system

Development of smart house model to control lighting, temperature, and gas leakage detection system

Smart houses one of Internet of things application. It is difficult to manage the energy loss due to inefficient control of electrical devices running inside the houses. Also fire due to gas leaking could cause a huge damage in the house. This paper is evaluating people awareness about smart houses in Kuala Lumpur and Sydney, and to propose a system to control light, temperature and to detect gas leaking. LabVIEW used to design lighting, temperature and gas leakage detection system. Arduino used to interface between software system and sensors and actuators of hardware system. The result showed that 88.7% and 90.2 % of people in Kuala Lumpur and Sydney respectively heard about smart houses, the offered system is able to control lighting and monitor house environment for humidity, temperature and gas leaking. In conclusion, the smart house model is potential to reduce the losses in the energy, and decrease the danger of fire disasters. Keywords - Smart houses; LabVIEW; Arduino ONU, sensors

Perancangan Sistem Deteksi Dini Pencegah Kebakaran Rumah Berbasis ESP8266 dan Blynk

Home fire accidents that start from LPG leakage in the kitchen space often occur around us. One of the solutions to secure the kitchen from potential fires is to apply smart home technology. The purpose of this study was to design an fire prevention early detection system based on Arduino, gas sensor, fire sensor, ESP8266 and Blynk application-based notification system on smartphones. The system design consits of a series of hardware that work in accordance with the command software. The hardware circuit consists of the Arduino Mega2560 microcontroller, the MQ6 Gas sensor, the flame sensor and the ESP8266 board as an embedded chip that communicate WiFi-based. The ESP8266 module is used as a client from a WiFi router. The function of this module is to send and receive information data between the microcontroller and smartphone. The communication is supported by the Blynk Llibraries and the Blynk Application as a graphical user interface on an android smartphone. The results of the system design were first tested as a fire detection device. The response of the system to the presence of fire is changing the color of virtual LED in the Blynk application. Further testing of the fire detection system is in the form of data on variations in the distance of fire sensors and sources of fire on system response time. Another separate test is to detect the presence of LPG leakage. The response of the system to the presence of LPG leakage is in the form of changing Blynk's virtual LEVEL on smartphone. The changing of the virtual level represents the gas concentration value declared by the gas sensor voltage values. The results of the design of the smarthome are expected to be one of the references for IoT-based fire potensial prevention systems.Key words:Blynk, ESP8266, flame sensor , gas sensor , IoT, smart hom

Smart remote nodes fed by power over fiber in Internet of Things applications

Smart IoT solutions integrated into power grid stations are important due to their high economic and social value. Power over fiber technology to remotely feeding sensors and control electronics is a good choice in these environments of high electromagnetic interference. A sensing system design for magnetic field monitoring, fire and temperature/presence detection, and remotely fed by optical means is discussed. This design includes two types of nodes, smart and passive. Smart remote nodes have an energy manager to provide power on demand. Asymmetric splitting is proposed to optimize power distribution. Some tests on remote node power consumption, feeding, sensing, and centralized monitoring in one type of those nodes are successfully performed and reported.This work was supported in part by the Spanish Ministerio de Ciencia, Innovación y Universidades, Comunidad de Madrid and H2020 European Union Programme under Grants TEC2015-63826-C3-2-R and RTI2018-094669-B-C32, and Grant Y2018/EMT-4892 (TEFLON-CM), in part by FSE, and in part by 5G PPP Bluespace project under Grant nº 762055, respectively.Publicad

Early forest fire detection by vision-enabled wireless sensor networks

Wireless sensor networks constitute a powerful technology particularly suitable for environmental monitoring. With regard to wildfires, they enable low-cost fine-grained surveillance of hazardous locations like wildland-urban interfaces. This paper presents work developed during the last 4 years targeting a vision-enabled wireless sensor network node for the reliable, early on-site detection of forest fires. The tasks carried out ranged from devising a robust vision algorithm for smoke detection to the design and physical implementation of a power-efficient smart imager tailored to the characteristics of such an algorithm. By integrating this smart imager with a commercial wireless platform, we endowed the resulting system with vision capabilities and radio communication. Numerous tests were arranged in different natural scenarios in order to progressively tune all the parameters involved in the autonomous operation of this prototype node. The last test carried out, involving the prescribed burning of a 95 x 20-m shrub plot, confirmed the high degree of reliability of our approach in terms of both successful early detection and a very low false-alarm rate. Journal compilationMinisterio de Ciencia e Innovación TEC2009-11812, IPT-2011-1625-430000Office of Naval Research (USA) N000141110312Centro para el Desarrollo Tecnológico e Industrial IPC-2011100

On-site forest fire smoke detection by low-power autonomous vision sensor

Early detection plays a crucial role to prevent forest fires from spreading. Wireless vision sensor networks deployed throughout high-risk areas can perform fine-grained surveillance and thereby very early detection and precise location of forest fires. One of the fundamental requirements that need to be met at the network nodes is reliable low-power on-site image processing. It greatly simplifies the communication infrastructure of the network as only alarm signals instead of complete images are transmitted, anticipating thus a very competitive cost. As a first approximation to fulfill such a requirement, this paper reports the results achieved from field tests carried out in collaboration with the Andalusian Fire-Fighting Service (INFOCA). Two controlled burns of forest debris were realized (www.youtube.com/user/vmoteProject). Smoke was successfully detected on-site by the EyeRISTM v1.2, a general-purpose autonomous vision system, built by AnaFocus Ltd., in which a vision algorithm was programmed. No false alarm was triggered despite the significant motion other than smoke present in the scene. Finally, as a further step, we describe the preliminary laboratory results obtained from a prototype vision chip which implements, at very low energy cost, some image processing primitives oriented to environmental monitoring.Ministerio de Ciencia e Innovación 2006-TIC-2352, TEC2009-1181

Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding