Indonesia located in South East Asia countries with tropical region, forest fires in Indonesia is
one of big issue and disaster because it happens in almost of every year, this is because of some of region
consist of peat land that high risk for fire especially in dry season. Riau Province is one of region that
regularly incident of forest fire with affected the length and breadth of Indonesia. Propose development of
Wireless Sensor Networks (WSNs) for detection of land and forest fire hotspot in Indonesia as well as one
of the main consents in this research, case location in Riau province is at one of the regions that high risk
forest fire in dry season. WSNs technology used for ground sensor system to collect environmental data.
Data training for fire hotspot detection is done in data center to determine and conclude of fire hotspot then
potential to become big fire. The deployment of sensors located at several locations that has potential for
fire incident, especially as data shown in previous case and forecast location with potential fire happen.
Mathematical analysis is used in this case for modelling number of sensors required to deploy and the size
of forest area. The design and development of WSNs give high impact and feasibility to overcome current
issues of forest fire and fire hotspot detection in Indonesia. The development of this system used WSNs
highly applicable for early warning and alert system for fire hotspot detection.
Using wireless sensor networks for reliable forest firesLeila_maleke
This document discusses using wireless sensor networks to detect forest fires reliably. It presents two main forest fire detection methods - the Canadian and Korean approaches. The Canadian approach calculates a fire weather index and was found to be more energy efficient and less complex algorithmically. The document also describes the methodology, detection algorithms, experimental results, and concludes that wireless sensor network technology is promising for efficiently detecting forest fires early.
IJRET-V1I1P1 - Forest Fire Detection Based on Wireless Image Processing ISAR Publications
This document proposes an intelligent forest fire detection system using image processing and artificial intelligence techniques. It discusses how traditional fire detection methods are inadequate and outlines the proposed system. The system would use temperature sensors, a GPS module, and small satellites to continuously monitor forests for fires, detect fires rapidly, and transmit location alerts to aid fire station response before fires spread severely. If a temperature threshold is exceeded, the system would transmit an alert along with the fire's GPS location to orbiting small satellites and ultimately to fire stations. The goal is early and accurate fire detection and localization to minimize ecological and economic damage from forest fires.
BITS PILANI IoT PG PROGRAMME DESIGN ASSESSMENT.
The goal of the design assessment is to detect and predict forest fire promptly and accurately to minimize the loss of forests, wild animals, and people in the forest fire.
With the advancements in wireless technology and digital electronics, some tiny devices have started to be used in numerous areas in daily life. These devices are capable of sensing, computation and communicating. They are generally composed of low power radios, several smart sensors and embedded CPUs (Central Processing Units). These devices are used to form wireless sensor network (WSN) which is necessary to provide sensing services and to monitor environmental conditions. In parallel to WSNs, the idea of internet of things (IoT) is developed where IoT can be defined as an interconnection between identifiable devices within the internet connection in sensing and monitoring processes. This paper presents detailed overview of WSNs. It also assesses the technology and characteristics of WSNs. Moreover, it provides a review of WSN applications and IoT applications.
IRJET- An IoT Based Forest Fire Detection and Prevention System using Raspber...IRJET Journal
This document describes an IoT-based forest fire detection and prevention system using Raspberry Pi 3. The system uses various sensors like a flame sensor to detect fires, a PIR sensor to detect intruders via image processing, and a humidity sensor to detect temperature changes. If an event like a fire is detected, the system will immediately send an alert message with the location and a picture of the affected area via GSM and GPS modules. The goal is to detect fires as early as possible before they spread over a large area and help prevent poaching in forests.
1) The document proposes a wireless sensor network framework for real-time forest fire detection and monitoring using sensor nodes to measure temperature, humidity, and flammable gases.
2) The sensor nodes transmit the collected environmental data via Bluetooth modules to a monitoring host computer for analysis and early detection of potential forest fires.
3) The system is intended to address the shortcomings of traditional forest fire monitoring approaches and easily forecast fires before they spread uncontrollably through continuous remote monitoring of factors that influence fire risk.
Intrusion Detection in A Smart Forest-Fire Early Warning Sensory SystemRatul Alahy
Incorporating an Intrusion Detection System(IDS) for a wireless sensor network (WSN) is one of the efficient moves to detect early intrusion to protect the integrity of the system. Machine learning Neural networks makes the intrusion detection task in WSN much easier by analyzing the data collected from sensor nodes(temperature, light, sound, and smoke). However, maintaining security and preventing intrusion in such approach remains a challenge. In this paper, we propose a Smart Intrusion Detection System (SIDS) which will be trained and tested using neural network approaches. The SIDS will be evaluated using parameters such as accuracy, timeliness, and trusted neighbor nodes, and the result is obtained for intrusion scenario using neural networks algorithms.
Monitoring of Forest Fire Detection System using ZigBeeijtsrd
A large number of valuable forests are destroyed in every second caused by fire around the world. As a result of insufficient information of firefighting cannot be provided in time to the occurrence of fire places. Therefore, the lives of people, animals and valuable forest have been lost for wild fire. This proposed system is designed by using sensors nodes and empowering them to communicate with each other through wireless technologies. The system mainly consists of three parts detecting part, monitoring part and wireless sensor network part. The detecting part was devised using Simulated Sensor Program. Temperature sensor LM35 , humidity sensor DHT 11 and gas sensor MQ2 detect the occurrence of forest fire. The monitoring part is integrated with LCD display. The ZigBee protocol is utilized for the wireless sensor network communication. Sakawah Hlaing | Soe Nay Lynn Aung ""Monitoring of Forest Fire Detection System using ZigBee"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25139.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25139/monitoring-of-forest-fire-detection-system-using-zigbee/sakawah-hlaing
Using wireless sensor networks for reliable forest firesLeila_maleke
This document discusses using wireless sensor networks to detect forest fires reliably. It presents two main forest fire detection methods - the Canadian and Korean approaches. The Canadian approach calculates a fire weather index and was found to be more energy efficient and less complex algorithmically. The document also describes the methodology, detection algorithms, experimental results, and concludes that wireless sensor network technology is promising for efficiently detecting forest fires early.
IJRET-V1I1P1 - Forest Fire Detection Based on Wireless Image Processing ISAR Publications
This document proposes an intelligent forest fire detection system using image processing and artificial intelligence techniques. It discusses how traditional fire detection methods are inadequate and outlines the proposed system. The system would use temperature sensors, a GPS module, and small satellites to continuously monitor forests for fires, detect fires rapidly, and transmit location alerts to aid fire station response before fires spread severely. If a temperature threshold is exceeded, the system would transmit an alert along with the fire's GPS location to orbiting small satellites and ultimately to fire stations. The goal is early and accurate fire detection and localization to minimize ecological and economic damage from forest fires.
BITS PILANI IoT PG PROGRAMME DESIGN ASSESSMENT.
The goal of the design assessment is to detect and predict forest fire promptly and accurately to minimize the loss of forests, wild animals, and people in the forest fire.
With the advancements in wireless technology and digital electronics, some tiny devices have started to be used in numerous areas in daily life. These devices are capable of sensing, computation and communicating. They are generally composed of low power radios, several smart sensors and embedded CPUs (Central Processing Units). These devices are used to form wireless sensor network (WSN) which is necessary to provide sensing services and to monitor environmental conditions. In parallel to WSNs, the idea of internet of things (IoT) is developed where IoT can be defined as an interconnection between identifiable devices within the internet connection in sensing and monitoring processes. This paper presents detailed overview of WSNs. It also assesses the technology and characteristics of WSNs. Moreover, it provides a review of WSN applications and IoT applications.
IRJET- An IoT Based Forest Fire Detection and Prevention System using Raspber...IRJET Journal
This document describes an IoT-based forest fire detection and prevention system using Raspberry Pi 3. The system uses various sensors like a flame sensor to detect fires, a PIR sensor to detect intruders via image processing, and a humidity sensor to detect temperature changes. If an event like a fire is detected, the system will immediately send an alert message with the location and a picture of the affected area via GSM and GPS modules. The goal is to detect fires as early as possible before they spread over a large area and help prevent poaching in forests.
1) The document proposes a wireless sensor network framework for real-time forest fire detection and monitoring using sensor nodes to measure temperature, humidity, and flammable gases.
2) The sensor nodes transmit the collected environmental data via Bluetooth modules to a monitoring host computer for analysis and early detection of potential forest fires.
3) The system is intended to address the shortcomings of traditional forest fire monitoring approaches and easily forecast fires before they spread uncontrollably through continuous remote monitoring of factors that influence fire risk.
Intrusion Detection in A Smart Forest-Fire Early Warning Sensory SystemRatul Alahy
Incorporating an Intrusion Detection System(IDS) for a wireless sensor network (WSN) is one of the efficient moves to detect early intrusion to protect the integrity of the system. Machine learning Neural networks makes the intrusion detection task in WSN much easier by analyzing the data collected from sensor nodes(temperature, light, sound, and smoke). However, maintaining security and preventing intrusion in such approach remains a challenge. In this paper, we propose a Smart Intrusion Detection System (SIDS) which will be trained and tested using neural network approaches. The SIDS will be evaluated using parameters such as accuracy, timeliness, and trusted neighbor nodes, and the result is obtained for intrusion scenario using neural networks algorithms.
Monitoring of Forest Fire Detection System using ZigBeeijtsrd
A large number of valuable forests are destroyed in every second caused by fire around the world. As a result of insufficient information of firefighting cannot be provided in time to the occurrence of fire places. Therefore, the lives of people, animals and valuable forest have been lost for wild fire. This proposed system is designed by using sensors nodes and empowering them to communicate with each other through wireless technologies. The system mainly consists of three parts detecting part, monitoring part and wireless sensor network part. The detecting part was devised using Simulated Sensor Program. Temperature sensor LM35 , humidity sensor DHT 11 and gas sensor MQ2 detect the occurrence of forest fire. The monitoring part is integrated with LCD display. The ZigBee protocol is utilized for the wireless sensor network communication. Sakawah Hlaing | Soe Nay Lynn Aung ""Monitoring of Forest Fire Detection System using ZigBee"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25139.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25139/monitoring-of-forest-fire-detection-system-using-zigbee/sakawah-hlaing
Fire Monitoring System for Fire Detection Using ZigBee and GPRS SystemIOSRJECE
Wireless Sensor Networks (WSN) is best suited for applications where continuous and long term data acquisition is required. Forest fire monitoring is one of such application where continuous monitoring of temperature and humidity is essential to detect the wildfire. Monitoring forest for wildfire detection is very much necessary to protect environment and to conserve forest wealth and habitats of biodiversity and livelihood of human. This paper presents an algorithm to detect the wildfire based on the changes occurring in humidity and temperature during fire and presents methodology based on ZigBee and GPRS wireless sensor network which provides low cost solution with long life time, low maintenance and good quality service as compared to the traditional method of wildfire detection. The hardware circuitry of proposed solution is based on Arduino board with ATmega328 microcontroller, temperature sensor and humidity sensor along with ZigBee and GPRS modules.
IRJET- Forest Fire Detection and Alerting SystemIRJET Journal
This document describes a forest fire detection and alerting system that uses sensors and IoT technology. The system is intended to detect forest fires using sensors like a flame detector, gas sensor, sound detection sensor and temperature sensor connected to an Arduino board. When a fire is detected, the system will send alert messages using a GSM or WiFi module to nearby areas like hospitals and fire stations. The system has advantages like low power consumption and providing an effective safety system. It can automatically trigger alerts. However, it also risks false alarms being triggered when no fire is actually present. The overall goal of the system is to help detect forest fires early and solve the problem before it worsens through sending timely alerts.
IRJET- Review on Image Processing based Fire Detetion using Raspberry PiIRJET Journal
The document describes a proposed image processing system using a Raspberry Pi to detect fires. The system would use a camera to capture images and the Raspberry Pi would process the images to detect fire signatures using heat patterns and colors. If a fire is detected, the system would sound an alarm. The proposed system aims to provide early fire detection without the need for additional sensors. It reviews existing fire detection methods and outlines the modules of the proposed system, including image capture, color-based segmentation, fire pattern recognition, and an emergency alarm trigger.
This document surveys automatic fire detection from the perspective of wireless sensor networks. It discusses fire detection techniques for residential areas and forests. For residential fires, the document reviews different sensor combinations and detection techniques, finding that using multiple sensors along with appropriate detection algorithms can reduce false alarms. For forest fires, it discusses indices like the Fire Weather Index system and National Fire Danger Rating System that are used for forest fire monitoring. The document also reviews how wireless sensor networks can contribute to early fire detection in various locations.
IRJET - An Automatic Forest Fire Detection using Lora Wireless Mesh TopologyIRJET Journal
This document describes an automatic forest fire detection system that uses LoRa wireless mesh topology. The proposed system uses a flame sensor integrated with an Arduino, LoRa, and GPS to detect forest fires and send alerts. When a fire is detected, the exact location is determined using GPS and an alert is sent via LoRa transmission to notify officials. The system aims to detect fires early to prevent widespread damage and allow immediate response.
IRJET- A Review of Fire Detection TechniquesIRJET Journal
This document reviews different techniques for fire detection. It discusses monitoring through observation towers, satellite-based monitoring, and systems using digital cameras and wireless sensor networks. Observation towers rely on human monitoring but have limitations. Satellite detection can find fires from space but provides intermittent coverage. Systems using cameras and sensors can automatically detect and verify fires with images. Wireless sensor networks are considered the best solution as sensor nodes can continuously monitor an area for temperature, smoke, and other indicators to detect fires early.
This document presents a wild fire monitoring system designed by Mahlalela John S. The system uses wireless sensors to detect fires and transmit information to a host computer. It has four operating modes depending on the received signals: normal, fire detected, power saving, and low battery. The system aims to use electronics to solve real-life problems, design a monitoring GUI and outdoor fire detection system, and transmit warning signals from the field to the host PC. It faces challenges in sensor design, power consumption minimization, and establishing communication between microcontrollers and the PC.
Wireless sensor network is emerging field because of its wide applications. It is a wireless network which subsist a group of small sensor nodes which communicate through radio interface. These sensor nodes are composed of sensing, computation, communication and power as four basic elements. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. But limited energy, communication capability, storage and bandwidth are the main resource constraints. The network should have self-organizing capabilities as the positions of individual nodes are not predetermined. The flexibility, fault tolerance, high sensing fidelity, low cost, and rapid deployment characteristics of sensor networks create many new and exciting application areas for remote sensing. Our survey is based on various aspects of routing protocols in wireless sensor networks.
This document summarizes an article that proposes an IoT-based intelligent modeling system for fire prevention and safety in smart homes. The system uses a wireless sensor network with multiple sensors to detect fires early and reduce false alarms by using Global System for Mobile Communications. It detects fires efficiently by requiring confirmation from two sources - the user responding to a GSM alert or two or more sensors detecting fire. The system was evaluated through simulation and showed it could detect fires early even if a sensor failed while keeping energy consumption low.
Data aggregation in wireless sensor network , 11751 d5811praveen369
The document discusses data aggregation in wireless sensor networks. It explains that sensor networks aim to gather and aggregate data in an energy efficient manner to extend network lifetime. It describes various data aggregation approaches like centralized, LEACH, and TAG. It also discusses cluster-based and tree-based aggregation where nodes aggregate and transmit data to parent nodes or cluster heads. The document outlines types of queries for sensor networks and benefits of data aggregation in reducing traffic and energy consumption while improving data accuracy.
Wireless Sensor networks are dense networks, which consist of small low cost
sensors having severely constrained computational and energy resources, which operate in
an adhoc environment. Sensor network combines the aspects of distributed sensing,
computing and communication. Despite the numerous applications of sensor networks in
various fields there are various issues which need to be explored and resolved such as
resource constraints, routing, coverage, security, information collection and gathering etc.
In this paper we aim to provide the detailed overview of the wireless sensor technologies and
issues related to them, such as advancement of sensor technology, architecture, applications,
issues and the work done in the field of routing, coverage and security.
This document discusses the design and implementation of a fire detection system using computer vision. It proposes using a system with multiple units including a motion detecting unit, color analyzing unit, computing unit, verifying unit, location unit, and alarm unit. Different algorithms are considered for fire detection including color-based approaches, motion-based approaches, and approaches using fuzzy logic and hidden Markov models. The system is intended to automatically identify and locate fires as part of a larger surveillance system with benefits of being cheaper and more accurate than existing sensor-based solutions.
Forest-Fires Surveillance System Based On Wireless Sensor NetworkIJERA Editor
We present the design and evaluation of a wireless sensor network for early detection of forest fires. Wild fires
cause to damage on forest and a mountain which have valuable natural resources during the dry winter season
Where it becomes very paramount to cover the area caused by fire by the forest fighters. Current surveillance
systems utilize a camera, an infrared sensor system and a satellite system. These systems cannot support
authentic time surveillance, monitoring and automatic alarm. Even though it gives information about fire caused
area,but asthe forest looks same in all areas as it is covered with dense trees it is very hard to recognize the exact
area andimage transmition through the transmitter to the officers computer takes too much time .It takes too
much time to load the image. Which in turns waste the time and fire caused area goes on increasing. Taking in
toconsideration all this faults of the prior system in the forest we have designed our modified project.In our
project, we develop a forest fires surveillance system.
Cloud Based Forest Fire Alert System using IoTijtsrd
The most common hazard in forest fire Accident are themselves destroy the forest and great threat for wildlife and peoples. The number of trees has reduced drastically so the forest are creates an unhealthy environment for animals to survive in the forest. Forest covers 31 of the world and 21 of the India. It has found in a survey that 80 of losses are caused due to fire. This project of a system fire was detected early stages. It was tracking and alarming for protection of trees against forest fire. Now IOT Internet of things devices and cloud based to forest fire alerting system. If cloud can used to storing the information of data to monitoring the different environmental variables such as temperature, smoke, moisture of the forest. So finally to prevent the fire to spread over a huge area and precaution the forest. Ajith. S | Chandru. S | Boopalan. E | Periyathambi. P ""Cloud Based Forest Fire Alert System using IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30070.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30070/cloud-based-forest-fire-alert-system-using-iot/ajith-s
The document discusses wireless sensor networks and their applications. It provides information on key aspects of wireless sensor networks including smart sensors, ad hoc network topology, wireless communication modalities, and applications such as environmental monitoring, civil structure monitoring, and health care. Distributed relaying is shown to decrease the power consumption per sensor node. Hierarchical data processing and communication architectures can improve energy efficiency in large sensor networks.
The document describes a cloud-based active health monitoring system with an optimal communication scheme between sensor, edge, and cloud layers called Sensor-Cloud Integration Platform as a Service (SC-iPaaS). SC-iPaaS uses a push-pull communication between layers to maximize available sensor data for cloud applications. It formulates an optimization problem to determine optimal data transmission rates for sensors and edge nodes across objectives of bandwidth consumption, energy consumption, and data yield. A simulation evaluates the system monitoring multiple patients using sensors like ECG and shows the optimization algorithm finds pareto-optimal communication configurations.
IRJET- Use of the Embedded System for Forest Fire Monitoring and DetectionIRJET Journal
1) The document proposes a forest fire monitoring and detection system using an embedded system with sensors to detect fires early.
2) The system uses temperature, humidity, and air quality sensors connected to nodes placed in the forest. If sensor readings exceed thresholds, the node passes information to other nodes until it reaches the main terminal.
3) The main terminal then uses a GSM modem to alert a forest fire monitoring center via cell phone if a fire is detected. The goal is to detect fires early to reduce damage and casualties from forest fires.
Forest fire detection & alarm thermal imaging camera Elsa Wang
The document describes a forest fire detection intelligent monitoring system developed by Jinan Hope Wish Photoelectronic Technology Co.,Ltd. The system uses thermal imaging cameras to detect fires and sends alarm signals to the monitoring center. It integrates technologies like image recognition, network monitoring and GIS to automatically detect and locate fires, then trigger alarms and lock onto the fire point. The system provides all-weather, 24/7 monitoring to prevent large forest fires and reduce fire damage.
A wireless sensor network consists of spatially distributed sensor nodes that monitor physical conditions and communicate wirelessly. Nodes sense data, process it, and transmit it to other nodes or a central gateway. The gateway provides a connection to the wired world to collect, process, analyze and present measurement data. Routers can extend the communication range between nodes and the gateway. Sensor nodes are small, require little power, are programmable and cost-effective to purchase and maintain.
Estimating Fire Weather Indices Via Semantic Reasoning Over Wireless Sensor N...IJwest
Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region. Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.
SUPPORT VECTOR MACHINE-BASED FIRE OUTBREAK DETECTION SYSTEMijscai
This study employed Support Vector Machine (SVM) in the classification and prediction of fire outbreak based on fire outbreak dataset captured from the Fire Outbreak Data Capture Device (FODCD). The fire outbreak data capture device (FODCD) used was developed to capture environmental parameters values used in this work. The FODCD device comprised DHT11 temperature sensor, MQ-2 smoke sensor, LM393 Flame sensor, and ESP8266 Wi-Fi module, connected to Arduino nano v3.0.board. 700 data point were captured using the FODCD device, with 60% of the dataset used for training while 20% was used for testing and validation respectively. The SVM model was evaluated using the True Positive Rate (TPR), False Positive Rate (FPR), Accuracy, Error Rate (ER), Precision, and Recall performance metrics. The performance results show that the SVM algorithm can predict cases of fire outbreak with an accuracy of 80% and a minimal error rate of 0.2%. This system was able to predict cases of fire outbreak with a higher degree of accuracy. It is indicated that the use of sensors to capture real world dataset, and machine learning algorithm such as support vector machine gives a better result to the problem of fire management.
Fire Monitoring System for Fire Detection Using ZigBee and GPRS SystemIOSRJECE
Wireless Sensor Networks (WSN) is best suited for applications where continuous and long term data acquisition is required. Forest fire monitoring is one of such application where continuous monitoring of temperature and humidity is essential to detect the wildfire. Monitoring forest for wildfire detection is very much necessary to protect environment and to conserve forest wealth and habitats of biodiversity and livelihood of human. This paper presents an algorithm to detect the wildfire based on the changes occurring in humidity and temperature during fire and presents methodology based on ZigBee and GPRS wireless sensor network which provides low cost solution with long life time, low maintenance and good quality service as compared to the traditional method of wildfire detection. The hardware circuitry of proposed solution is based on Arduino board with ATmega328 microcontroller, temperature sensor and humidity sensor along with ZigBee and GPRS modules.
IRJET- Forest Fire Detection and Alerting SystemIRJET Journal
This document describes a forest fire detection and alerting system that uses sensors and IoT technology. The system is intended to detect forest fires using sensors like a flame detector, gas sensor, sound detection sensor and temperature sensor connected to an Arduino board. When a fire is detected, the system will send alert messages using a GSM or WiFi module to nearby areas like hospitals and fire stations. The system has advantages like low power consumption and providing an effective safety system. It can automatically trigger alerts. However, it also risks false alarms being triggered when no fire is actually present. The overall goal of the system is to help detect forest fires early and solve the problem before it worsens through sending timely alerts.
IRJET- Review on Image Processing based Fire Detetion using Raspberry PiIRJET Journal
The document describes a proposed image processing system using a Raspberry Pi to detect fires. The system would use a camera to capture images and the Raspberry Pi would process the images to detect fire signatures using heat patterns and colors. If a fire is detected, the system would sound an alarm. The proposed system aims to provide early fire detection without the need for additional sensors. It reviews existing fire detection methods and outlines the modules of the proposed system, including image capture, color-based segmentation, fire pattern recognition, and an emergency alarm trigger.
This document surveys automatic fire detection from the perspective of wireless sensor networks. It discusses fire detection techniques for residential areas and forests. For residential fires, the document reviews different sensor combinations and detection techniques, finding that using multiple sensors along with appropriate detection algorithms can reduce false alarms. For forest fires, it discusses indices like the Fire Weather Index system and National Fire Danger Rating System that are used for forest fire monitoring. The document also reviews how wireless sensor networks can contribute to early fire detection in various locations.
IRJET - An Automatic Forest Fire Detection using Lora Wireless Mesh TopologyIRJET Journal
This document describes an automatic forest fire detection system that uses LoRa wireless mesh topology. The proposed system uses a flame sensor integrated with an Arduino, LoRa, and GPS to detect forest fires and send alerts. When a fire is detected, the exact location is determined using GPS and an alert is sent via LoRa transmission to notify officials. The system aims to detect fires early to prevent widespread damage and allow immediate response.
IRJET- A Review of Fire Detection TechniquesIRJET Journal
This document reviews different techniques for fire detection. It discusses monitoring through observation towers, satellite-based monitoring, and systems using digital cameras and wireless sensor networks. Observation towers rely on human monitoring but have limitations. Satellite detection can find fires from space but provides intermittent coverage. Systems using cameras and sensors can automatically detect and verify fires with images. Wireless sensor networks are considered the best solution as sensor nodes can continuously monitor an area for temperature, smoke, and other indicators to detect fires early.
This document presents a wild fire monitoring system designed by Mahlalela John S. The system uses wireless sensors to detect fires and transmit information to a host computer. It has four operating modes depending on the received signals: normal, fire detected, power saving, and low battery. The system aims to use electronics to solve real-life problems, design a monitoring GUI and outdoor fire detection system, and transmit warning signals from the field to the host PC. It faces challenges in sensor design, power consumption minimization, and establishing communication between microcontrollers and the PC.
Wireless sensor network is emerging field because of its wide applications. It is a wireless network which subsist a group of small sensor nodes which communicate through radio interface. These sensor nodes are composed of sensing, computation, communication and power as four basic elements. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. But limited energy, communication capability, storage and bandwidth are the main resource constraints. The network should have self-organizing capabilities as the positions of individual nodes are not predetermined. The flexibility, fault tolerance, high sensing fidelity, low cost, and rapid deployment characteristics of sensor networks create many new and exciting application areas for remote sensing. Our survey is based on various aspects of routing protocols in wireless sensor networks.
This document summarizes an article that proposes an IoT-based intelligent modeling system for fire prevention and safety in smart homes. The system uses a wireless sensor network with multiple sensors to detect fires early and reduce false alarms by using Global System for Mobile Communications. It detects fires efficiently by requiring confirmation from two sources - the user responding to a GSM alert or two or more sensors detecting fire. The system was evaluated through simulation and showed it could detect fires early even if a sensor failed while keeping energy consumption low.
Data aggregation in wireless sensor network , 11751 d5811praveen369
The document discusses data aggregation in wireless sensor networks. It explains that sensor networks aim to gather and aggregate data in an energy efficient manner to extend network lifetime. It describes various data aggregation approaches like centralized, LEACH, and TAG. It also discusses cluster-based and tree-based aggregation where nodes aggregate and transmit data to parent nodes or cluster heads. The document outlines types of queries for sensor networks and benefits of data aggregation in reducing traffic and energy consumption while improving data accuracy.
Wireless Sensor networks are dense networks, which consist of small low cost
sensors having severely constrained computational and energy resources, which operate in
an adhoc environment. Sensor network combines the aspects of distributed sensing,
computing and communication. Despite the numerous applications of sensor networks in
various fields there are various issues which need to be explored and resolved such as
resource constraints, routing, coverage, security, information collection and gathering etc.
In this paper we aim to provide the detailed overview of the wireless sensor technologies and
issues related to them, such as advancement of sensor technology, architecture, applications,
issues and the work done in the field of routing, coverage and security.
This document discusses the design and implementation of a fire detection system using computer vision. It proposes using a system with multiple units including a motion detecting unit, color analyzing unit, computing unit, verifying unit, location unit, and alarm unit. Different algorithms are considered for fire detection including color-based approaches, motion-based approaches, and approaches using fuzzy logic and hidden Markov models. The system is intended to automatically identify and locate fires as part of a larger surveillance system with benefits of being cheaper and more accurate than existing sensor-based solutions.
Forest-Fires Surveillance System Based On Wireless Sensor NetworkIJERA Editor
We present the design and evaluation of a wireless sensor network for early detection of forest fires. Wild fires
cause to damage on forest and a mountain which have valuable natural resources during the dry winter season
Where it becomes very paramount to cover the area caused by fire by the forest fighters. Current surveillance
systems utilize a camera, an infrared sensor system and a satellite system. These systems cannot support
authentic time surveillance, monitoring and automatic alarm. Even though it gives information about fire caused
area,but asthe forest looks same in all areas as it is covered with dense trees it is very hard to recognize the exact
area andimage transmition through the transmitter to the officers computer takes too much time .It takes too
much time to load the image. Which in turns waste the time and fire caused area goes on increasing. Taking in
toconsideration all this faults of the prior system in the forest we have designed our modified project.In our
project, we develop a forest fires surveillance system.
Cloud Based Forest Fire Alert System using IoTijtsrd
The most common hazard in forest fire Accident are themselves destroy the forest and great threat for wildlife and peoples. The number of trees has reduced drastically so the forest are creates an unhealthy environment for animals to survive in the forest. Forest covers 31 of the world and 21 of the India. It has found in a survey that 80 of losses are caused due to fire. This project of a system fire was detected early stages. It was tracking and alarming for protection of trees against forest fire. Now IOT Internet of things devices and cloud based to forest fire alerting system. If cloud can used to storing the information of data to monitoring the different environmental variables such as temperature, smoke, moisture of the forest. So finally to prevent the fire to spread over a huge area and precaution the forest. Ajith. S | Chandru. S | Boopalan. E | Periyathambi. P ""Cloud Based Forest Fire Alert System using IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30070.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30070/cloud-based-forest-fire-alert-system-using-iot/ajith-s
The document discusses wireless sensor networks and their applications. It provides information on key aspects of wireless sensor networks including smart sensors, ad hoc network topology, wireless communication modalities, and applications such as environmental monitoring, civil structure monitoring, and health care. Distributed relaying is shown to decrease the power consumption per sensor node. Hierarchical data processing and communication architectures can improve energy efficiency in large sensor networks.
The document describes a cloud-based active health monitoring system with an optimal communication scheme between sensor, edge, and cloud layers called Sensor-Cloud Integration Platform as a Service (SC-iPaaS). SC-iPaaS uses a push-pull communication between layers to maximize available sensor data for cloud applications. It formulates an optimization problem to determine optimal data transmission rates for sensors and edge nodes across objectives of bandwidth consumption, energy consumption, and data yield. A simulation evaluates the system monitoring multiple patients using sensors like ECG and shows the optimization algorithm finds pareto-optimal communication configurations.
IRJET- Use of the Embedded System for Forest Fire Monitoring and DetectionIRJET Journal
1) The document proposes a forest fire monitoring and detection system using an embedded system with sensors to detect fires early.
2) The system uses temperature, humidity, and air quality sensors connected to nodes placed in the forest. If sensor readings exceed thresholds, the node passes information to other nodes until it reaches the main terminal.
3) The main terminal then uses a GSM modem to alert a forest fire monitoring center via cell phone if a fire is detected. The goal is to detect fires early to reduce damage and casualties from forest fires.
Forest fire detection & alarm thermal imaging camera Elsa Wang
The document describes a forest fire detection intelligent monitoring system developed by Jinan Hope Wish Photoelectronic Technology Co.,Ltd. The system uses thermal imaging cameras to detect fires and sends alarm signals to the monitoring center. It integrates technologies like image recognition, network monitoring and GIS to automatically detect and locate fires, then trigger alarms and lock onto the fire point. The system provides all-weather, 24/7 monitoring to prevent large forest fires and reduce fire damage.
A wireless sensor network consists of spatially distributed sensor nodes that monitor physical conditions and communicate wirelessly. Nodes sense data, process it, and transmit it to other nodes or a central gateway. The gateway provides a connection to the wired world to collect, process, analyze and present measurement data. Routers can extend the communication range between nodes and the gateway. Sensor nodes are small, require little power, are programmable and cost-effective to purchase and maintain.
Estimating Fire Weather Indices Via Semantic Reasoning Over Wireless Sensor N...IJwest
Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region. Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.
SUPPORT VECTOR MACHINE-BASED FIRE OUTBREAK DETECTION SYSTEMijscai
This study employed Support Vector Machine (SVM) in the classification and prediction of fire outbreak based on fire outbreak dataset captured from the Fire Outbreak Data Capture Device (FODCD). The fire outbreak data capture device (FODCD) used was developed to capture environmental parameters values used in this work. The FODCD device comprised DHT11 temperature sensor, MQ-2 smoke sensor, LM393 Flame sensor, and ESP8266 Wi-Fi module, connected to Arduino nano v3.0.board. 700 data point were captured using the FODCD device, with 60% of the dataset used for training while 20% was used for testing and validation respectively. The SVM model was evaluated using the True Positive Rate (TPR), False Positive Rate (FPR), Accuracy, Error Rate (ER), Precision, and Recall performance metrics. The performance results show that the SVM algorithm can predict cases of fire outbreak with an accuracy of 80% and a minimal error rate of 0.2%. This system was able to predict cases of fire outbreak with a higher degree of accuracy. It is indicated that the use of sensors to capture real world dataset, and machine learning algorithm such as support vector machine gives a better result to the problem of fire management.
AN XGBOOST-BASED REGRESSION MODEL FOR WILDFIRE IMPACT PREDICTIONIRJET Journal
This document presents a machine learning model for predicting wildfire risk and impact using meteorological data. It proposes an XGBoost regression model that takes temperature, humidity, wind speed, and previous moisture codes as input to predict the area impacted by wildfires. The system calculates moisture codes like FFMC, DMC, and DC from weather data, which are then used along with indices like ISI and BUI by the risk predictor (logistic regression model) to calculate fire probability and the impact predictor (XGBoost model) to estimate the affected area. It collects and preprocesses historical wildfire datasets to train the models. The trained models along with a user interface could help forest departments take preventive actions based on predicted risk and impact
AN IMPROVED ALGORITHM TO FIRE DETECTION IN FOREST BY USING WIRELESS SENSOR NE...IAEME Publication
Wireless sensor network systems diffuse an intensive, array of small, low-cost
sensors that monitor the environment. The system can be extending anywhere, even in
the place which is inaccessible .This technology can provide observe for forest fires in
the real time. Fire Ignition can be determined quickly, depending on the wake/sleep
table of the system of rules nodes. This research focus on fire detection ability of a
wireless network system. Sub divided system in randomly-spread nodes change the
network from being randomly spread to being arranged, and minimize the period of
working and less energy consumption of each hop. Separate the network into many
sub-networks lead to increases battery lifetime network by 3.7% and increased
performance of power by 69% compared to traditional fire detection networks. The
proposed network requires all nodes to be equipped with temperature sensor. The
analysis of data from sensors can show the fire, also its, behavior, intensity and
direction of deployed, which can assist the firefighting efforts. Traditional fire
detection networks show the fire only so the proposed algorithms show the best result
A Review on Wireless Sensor Network Protocol for Disaster ManagementEditor IJCATR
Disasters management and emergency services
warning , landslide monitoring, earthquake rescue operation , volcano monitoring, and fire protection. Timely report and res
especially important for reducing the number of sufferers and damages from incidents. In such cases, the communicati
may not function well. This makes it hard to gain information about the incident, and then to respond to the incident rapi
properly. Sensor networks can provide a good solution to these problems through actively monitoring and
emergency incidents to base station. Our objective on this topic aim to study different sensor network protocols to resolve
technical problems in this area, thus identify the energy efficient wireless sensor network archite
disaster management . We also analyze the WSN protocol based on metrics such as Energy efficiency, location awareness, network
lifetime. It furthermore focuses the advantages and performance for disaster management.
Comparative study on machine learning algorithms for early fire forest detect...IJECEIAES
Forest fires have become a great risk for countries. To minimize their impact and prevent this phenomenon, scientific methods have emerged. Notably machine learning algorithms and decision-making Geographical Information Systems. Therefore, a competitive spatial prediction model for early fire forest detection system using geodata can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.
Android mobile application for wildfire reporting and monitoringriyaniaes
Peat fires cause major environmental problems in Central Kalimantan Province, Indonesia and threaten human health and effect the social-economic sector. The lack of peat fire detection systems is one factor that causing these reoccurring fires. Therefore, in this study, we develop an Android mobile platform application and a web-based application to support the citizen-volunteers who want to contribute wildfires reports, and the decision-makers who wish to collect, visualize, and evaluate these wildfires reports. In this paper, the global navigation satellite system (GNSS) and a global position system (GPS) sensor from a smartphone’s camera, is a useful tool to show the potential fire and smoke’s close-range location. The exchangeable image (EXIF) file image and GPS metadata captured by a mobile phone can store and supply raw observation to our devices and sent it to the data center through global internet communication. This work’s results are the proposed application easy-to-use to monitoring potential peat fire by location and data activity. This paper focuses on developing an application for the mobile platform for peat fire reporting and a web-based application to collect peat fire location for decision-makers. Our main objective is to detect the potential and spread of fire in peatlands as early as possible by utilizing community reports using smartphones.
Design of a prototype for sending fire notifications in homes using fuzzy log...IJECEIAES
This paper highlights the need to address fire monitoring in densely populated urban areas using innovative technology, in particular, the internet of things (IoT). The proposed methodology combines data collection through sensors with instant notifications via text messages and images through the user’s email. This strategy allows a fast and efficient response, with message delivery times varying from 1 to 4 seconds on Internet connections. It was observed that the time to send notifications on 3G networks is three times longer compared to Wi-Fi networks, and in some 3G tests, the connection was interrupted. Therefore, the use of Wi-Fi is recommended to avoid significant delays and possible bandwidth issues. The implementation of fuzzy logic in the ESP32 microcontroller facilitates the identification of critical parameters to classify notifications of possible fires and the sending of evidence through images via email. This approach successfully validated the results of the algorithm by providing end users with detailed emails containing information on temperature, humidity, gas presence and a corresponding image as evidence. Taken together, these findings support the effectiveness and potential of this innovative solution for fire monitoring and prevention in densely populated urban areas.
DESIGN CHALLENGES IN WIRELESS FIRE SECURITY SENSOR NODES ijesajournal
A design of simple hardware circuit with different kind of fire sensors enables every user to use this
wireless fire security system. The challenges in designing the nodes with various types of fire sensors are
discussed and the methods to overcome design problems are also analyzed. The circuit is interfaced with
the different types of sensor to sense different fire sources such as gas leakage, smoke, and heat. The cost,
circuit components, design requirements, power requirements of sensor node are minimized. The methods
to improve the quality of system to detect fire are analyzed. The system is fully controlled by the PIC
microcontroller.
DESIGN CHALLENGES IN WIRELESS FIRE SECURITY SENSOR NODES ijesajournal
A design of simple hardware circuit with different kind of fire sensors enables every user to use this wireless fire security system. The challenges in designing the nodes with various types of fire sensors are
discussed and the methods to overcome design problems are also analyzed. The circuit is interfaced with
the different types of sensor to sense different fire sources such as gas leakage, smoke, and heat. The cost,
circuit components, design requirements, power requirements of sensor node are minimized. The methods
to improve the quality of system to detect fire are analyzed. The system is fully controlled by the PIC
microcontroller. All the sensors and detectors are interconnected to PIC microcontroller by using various
types of interface circuits. The PIC microcontroller will continuously monitor all the sensors and if it senses
any security problem then the microcontroller will send the information to the PC central monitoring station wirelessly for a short distance of 300m indoor/1500m outdoor using zigbee technology. The gas
sensor, light sensor, smoke detector sensor, IR sensor, temperature & humidity sensor, fire sensor are interfaced with microcontroller to detect abnormal fire conditions in the environment in all possible ways.
Low cost wireless sensor networks and smartphone applications for disaster ma...eSAT Publishing House
This document describes a low-cost wireless sensor network and smartphone application system for disaster management. The system uses an Arduino-based wireless sensor network comprising nodes with various sensors to monitor the environment. The sensor data is transmitted to a central gateway and then to the cloud for analysis. A smartphone app connected to the cloud can detect disasters from the sensor data and send real-time alerts to users to help with early evacuation. The system aims to provide low-cost localized disaster detection and warnings to improve safety.
Wireless sensor networks carry out cooperative activities due to limited resources and nowadays, the applications of these networks are copious, varied and the applications in agriculture are still budding. One interesting purpose is in environmental monitoring and greenhouse control, where the crop conditions such as weather and soil do not depend on natural agents. To control and observe the environmental factors, sensors and actuators are necessary. Under these conditions, these devices must be used to make a distributed measure, scattering sensors all over the greenhouse using distributed clustering mechanism. This paper reveals an initiative of environmental monitoring and greenhouse control using a sensor network.
Preliminary study of wireless balloon network using adaptive position trackin...TELKOMNIKA JOURNAL
Limited resources in post-disaster areas, one of which is a communication where coordination needed for aid distribution in disaster areas. Wireless balloon technology as a solution for use in post-disaster areas. Bandwidth limitations and high delay in communication systems on wireless balloons create limitations in aid coordination, especially mobile device tracking on BPBD volunteers or officers. This research develops an effective communication system at the wireless balloon to track personal device officers in disaster areas that use cellular devices. This mobile device tracking system utilizes a wireless balloon using a publish-subscribe system on their mobile devices, namely volunteers as publishers and those responsible for disasters or communities as subscribers. To overcome the limitations of communication resources on cellular devices and wireless balloons using the Adaptive method on publish-subscribe called UM-Disaster. The results of this study, the UM-Disaster system for multi-cell tracking has an average efficiency of 40-63% for bandwidth and processor use on mobile devices at 51-70%.
This document describes an IoT-based environmental monitoring system using a wireless sensor network. It discusses key components of the system including sensor nodes, a Raspberry Pi gateway, and an application server. The sensor nodes collect data from temperature, light, and moisture sensors. The Raspberry Pi processes and transmits the sensor data via a wireless network. An application server receives the data, stores it, and provides access to users and other IoT applications. The system aims to enable long-term, low-cost environmental monitoring for various IoT applications.
Earlier Detection of Forest Fire Using IoTIRJET Journal
This document describes a proposed system for the earlier detection of forest fires using Internet of Things (IoT) technology. The system uses sensors like a temperature sensor and flame sensor connected to an Arduino Nano microcontroller. When the temperature rises above a threshold or a flame is detected, the data is sent via a GSM module to a cloud server. This allows for fires to be detected sooner compared to existing manual monitoring methods. A literature review discusses several previous studies that used techniques like wireless sensor networks, image processing, and LoRa technology for forest fire detection and monitoring. The proposed system aims to provide faster and more accurate detection of fires to help reduce damage and inform firefighters.
A STUDY ON LEA AND SEED ALGORITHMS FOR DATA PROTECTION OF SMARTPHONE BASED DI...IJNSA Journal
The number of disaster occurrences around the world based on the climate changes due to the global
warming has been indicating an increase. To prevent and cope with such disaster, a number of researches
have been actively conducted to combine the user location service as well as the sensor network
technology into the expanded IoT to detect the disaster at early stages. However, due to the appearance of
the new technologies, the scope of the security threat to the pre-existing system has been expanding. In this
thesis, the D-SASS using the beacon to provide the notification service to the disaster-involved area and
the safe service to the users is proposed. The LEA Algorithm is applied to the proposed system to design
the beacon protocol collected from the smartphone to safely receive the notification information. In
addition, for data protection of a notification system, LEA and SEED algorithms were applied, and a
comparative analysis was conducted.
High accuracy sensor nodes for a peat swamp forest fire detection using ESP32...IJICTJOURNAL
The use of smoke sensors in high-precision and low-cost forest fire detection kits needs to be developed immediately to assist the authorities in monitoring forest fires especially in remote areas more efficiently and systematically. The implementation of automatic reclosing operation allows the fire detector kit to distinguish between real smoke and non-real smoke successfully. This has profitably reduced kit errors when detecting fires and in turn prevent the users from receiving incorrect messages. However, using a smoke sensor with automatic reclosing operation has not been able to optimize the accuracy of identifying the actual smoke due to the working sensor node situation is difficult to predict and sometimes unexpected such as the source of smoke received. Thus, to further improve the accuracy when detecting the presence of smoke, the system is equipped with two digital cameras that can capture and send pictures of fire smoke to the users. The system gives the users choice of three interesting options if they want the camera to capture and send pictures to them, namely request, smoke trigger and movement for security purposes. In all cases, users can request the system to send pictures at any time. The system equipped with this camera shows the accuracy of smoke detection by confirming the actual smoke that has been detected through images sent in the user’s Telegram channel and on the graphical user interface (GUI) display. As a comparison of the system before and after using this camera, it was found that the system that uses the camera gives advantage to the users in monitoring fire smoke more effectively and accurately.
Development in the technology of sensor such as Micro Electro Mechanical Systems (MEMS), wireless communications, embedded systems, distributed processing and wireless sensor applications have contributed a large transformation in Wireless
Sensor Network (WSN) recently. It assists and improves work performance both in the field of industry and our daily life. Wireless Sensor Network has been widely used in many areas especially for surveillance and monitoring in agriculture and habitat monitoring. Environment monitoring has become an important field of control and protection, providing real-time system and control communication
with the physical world. An intelligent and smart Wireless Sensor Network system can gather and process a large amount of data from the beginning of the monitoring and manage air quality, the conditions of traffic, to weather situations.
Iaetsd implementation of a wireless sensor networkIaetsd Iaetsd
This document describes the design and implementation of a wireless sensor network platform for monitoring temperature in a forest area. Key requirements for the platform include low cost, ability to deploy a large number of sensors, long lifetime with low maintenance, and high quality of service. The document outlines the specifications for sensor nodes, gateway nodes, and the overall network architecture. It also provides details on the software and hardware design and implementation of the sensor nodes, gateway nodes, and monitoring system to meet the application requirements.
Intelligent flood disaster warning on the fly: developing IoT-based managemen...journalBEEI
The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network
is used.
Similar to Modelling of wireless sensor networks for detection land and forest fire hotspot (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
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2. Related Work
Environmental monitoring caused by forest fire can be done in many ways, most of
technology currently use is satellite images, by capture earth image to find hotspot for
environmental detection. In Indonesia, satellite used as well for detection forest fire by
government to monitor status of fire hotspot. A new technology use for hotspot detection is
wireless sensor and remote sensing, this technology ability to detect potential of fire by analyze
environmental changing. Proposes new method for forest fire detection and monitoring system
be able to give early warning system before fire disaster is happen, by analyze indicator of
environmental changing with various sensors and detection method this system able to give
accurate information of location as well early warning for fire prevention action. Figure 1 shows
a satellite image for Indonesia fire hotspot status by mid of year 2017, most of hotspot located in
central of Sumatera and west of Kalimantan Island.
Figure 1. Fire hotspot in Indonesia based on satellite image [5]
Wireless Sensor Networks (WSNs) can be apply in many applications, such as in
remote environmental monitoring, industrial automatic control, remote sensing and target
tracking. The similar application system is in environmental monitoring system which is for fire
hotspot detection that can make a real-time monitoring and detection. WSNs consists numerous
numbers of small nodes in most situations, which small nodes are deployed in remote and
inaccessible hostile environments or over large geographical areas. The large number of sensor
with small nodes sense environmental changes and report them to cluster head node or sensor
base station, then through a gate way to transfer data to the servers which the deployment and
maintenance should be easy and scalable [6].
A system develop as a simulator for approximates behavior of a wireless network of
temperature sensors deployed in the area affected by a wildfire. Based on a new signal
processing to approach in which the temperature experienced at a sensor due to a spreading of
fire front is modelled as the mixture of two-dimensional Gaussian distributions as
discussed [7, 8]. WSNs based Wildfire Hazard Prediction (WFHP) system is a systematic
description of architectural details and requirements of WSN for WFHP applications. The model
measure in terms of network latency, energy consumption, and scalability is analyzed through
simulation. Verification of model sanity and performance are carried out taking real weather
datasets and their corresponding wildfire hazard outputs as benchmarks and
elaborate in [9, 10].
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Modeling forest fires according to the Fire Weather Index (FWI) system which is one of
the most comprehensive forest fire danger rating systems. Then, a model the forest fire
detection problem as a node k-coverage problem (k≥1) in WSNs. Approximation algorithms for
the node k-coverage problem which is shown to be NP-hard. The simulation shows that
algorithms: activate near-optimal number of sensors, converge much faster than other
algorithms, significantly prolong (almost double) the network lifetime, and can achieve unequal
monitoring of different zones in the forest [11]. Development of WSNs based on multi-sensor
system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and
relative humidity) were integrated into one node of WSNs. An experiment was conducted using
burning materials from residual of forest to test responses of each node under no,
smoldering-dominated and flaming-dominated combustion conditions. For achieving higher
identification rate, an ANN model was built and trained with inputs of four sensor groups:
smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature as
discussed in [12, 13].
Several research on Wireless Sensor Network (WSN) as discuss in [14], the WSN
Simulator is developed based on proposed Sensor model and WSN model. The WSN Simulator
address important design issues as: coverage of the area under surveillance in relation to initial
sensor deployment, number of sensors needed for targeted deployment, and coverage change
as function of time. A new approach for forest fire monitoring and detection as discussed
in [15, 16] which using data aggregation in WSN. The proposed approach can provide faster
and efficiently reaction to forest fires while consuming economically WSN’s energy, which has
been validated and evaluated in extensive simulation experiments. Wireless sensor network be
able to provide better solution for disaster management and rescue operations such as
earthquake detection and alert system, flood detection, landslide detection, forest fire detection,
water level monitoring of Himalayan Rivers, monitoring of glaciers, pilgrimage and tourist
management are various examples where WSN can be used. Sensors are deployed for
measuring various parameters and on [17-19].
WSN algorithm to identify malicious data injections and build measurement estimates
that are resistant to several compromised sensors and even when they collude in the attack.
The methodology to apply this algorithm is in different contexts and evaluate its results on three
different datasets drawn from distinct WSN deployments [20, 21]. The others research has been
done is application of WSN in predicting natural disasters like hailstorm, fire, rainfall etc. by
WSN are infrequent and and stochastic [16]. As well as in design and implementation of a smart
fire detection system using a WSN and Global System for Mobile (GSM) communication to
detect fires effectively and reduce false positives, the system uses smoke and temperature
sensors [22]. Application of WSN in energy conservation, reducing data transmission delay and
improving the network lifetime. Used of cluster-chain mobile agent routing (CCMAR) for low
energy adaptive clustering hierarchy (LEACH) and power-efficient gathering in sensor
information systems (PEGASIS) [23].
3. The Proposed Scheme
Nowadays, many kind of monitoring system based on aim and objective as well as
parameters to be monitor. Environmental monitoring for fire hotspot detection is implemented in
some of institution or agency to monitor latest status of environmental. Current technology using
is mostly from satellite data to detect hotspot of forest fire, this technology has some weakness
and limitation such as only detect when fire happen and in some case for example in bad
weather or cloudy then satellite unable to penetration of cloud and image will not update.
Ground sensing technology which is WSNs enable to penetrate smoke environmental as well to
detect fire hotspot. WSNs sensor will deploy in the area with high risk of fire to collect data such
smoke detection, temperature, particle changing, etc. All the information collected by sensors
will send to sensor base station as gateway to transfer data to monitoring system (data center)
because the distance between sensor base station to monitoring system very far away more
than 100 km in some area to monitor data to analyze any changing of environmental image.
Beside new technology and smart sensors as elaborated in previous, common environmental
parameters such as temperature, humidity, wind speed and direction are applying in this
monitoring system as supporting data to analyze potential of fire.
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The use of WSNs sensors and base stations will setup at difference area to collect
information from environmental and sensors deploy surrounding. Information collected by
sensor forward to base station and will keep in internal database then send to monitoring
system (data center), because of sensor base station locate in rural area that far away up to
200 km then solar panel will use as power supply for system. Latest technology of
communication system also proposes such as 4G technology or even 5G technology for future
in order to achieve real-time data to display to monitoring system. In the end of this system
expected be able to gives early warning before fire is happen to authority for prevention action.
Figure 2 shows a map of fire hotspot detected in Riau Province in Indonesia.
Figure 2. Fire hotspot detected by satellite image in Riau Province, Indonesia
The impact of land and forest fire to the land as shows in Figure 3, there are a few fire
hotspots in smoke environmental. The point and typical of hotspot is very important to design
and model of sensors in detecting of hotspot on the field, whether fire is spreading or point to a
central of hotspot. Another model of fire in a forest with big fire is required to design WSNs
sensor to detect how big the fire spreading and impact to the forest as shows in Figure 4.
Figure 3. A case of fire hotspot detected in a land fire
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The area of fire hotspot modeling coverage assumes a set of WSNs sensors distributed
over a geographical region of land or forest area, in this case Riau Province in Indonesia is
model to monitor that coverage area. Coverage function P is given as:
(1)
where (x, y) are coordinates of sensor within the monitored region, and t is time. Model is using
a projection in the 2D space of a fire surveillance region, which is a 3D sphere. In the case of
network is stationary, without mobile WSNs sensors, but the sensor positions are time
dependent, since sensor nodes of WSNs are expected to stop operating in time. In this cease
operation can have different causes: hardware faults, accidental, battery depletion, and
intentional sensor removal, etc [24, 25].
Figure 4. A case of fire hotspot detected in a forest fire
Assume to define coverage index IP as a scalar value representing the percentage of
coverage for the area under the monitoring at a specific time as:
(2)
The basic model component is a WSN sensor node defined as a vector:
(3)
where d is a range of sensor transmission, or radius of transmission area, the area covered by
radio signal for data exchange with a neighboring node. E(t) is energy available for sensor
power supply. Assume a homogenous sensor network with n unified type sensors and one
hub-sensor for communication with a dispatcher node. Network parameters are described as
a vector:
(4)
where n is the number of sensors, fo is the frequency of regular transmissions, and ΔE is
energy consumption per transmission. Assume that sensor nodes periodically transmit the data
collected to the neighboring nodes. Energy consumption ΔE includes also energy spent in
sensing and data processing. Each node has two roles:
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a. sensing environmental data and its transmission.
b. receiving data from neighboring nodes and forwarding.
The sensing role is defined in accordance with the WSNs sensor network application,
and can be easily influenced with sensor node type selection. Energy consumption ΔE is thus
linked to the sensor node type and its value is listed in the sensor node data sheet.
The forwarding neighboring sensor node data role is primary defined by communication
protocol. WSNs simulator having knowledge of sensor nodes positions and defines paths for
data forwarding employing optimization algorithms. Assume that routing optimization would be
implemented in the real protocol as well. In order to simplify the model, each sensor node is
aware of its GPS co-ordinates, which are used in communication as an identification code. It is
also assumed that the hub node initially broadcasted across all the nodes. Based on these
assumptions it is possible to implement optimized routing algorithm. Energy consumption
needed for receiving and forwarding neighbor data is ΔE. Object under surveillance is modeled
as four-side stationary polygon defined as a set:
(5)
where A, B, C and D are polygon points with co-ordinates (x, y).
The role of WSNs hub sensor node is to collect data from each sensor node and
forward the data to base station or co-ordination center. Data package received and forwarded
by the hub node contains originator sensor node address and measurement values
(temperature, humidity and CO2). The WSN hub node has uninterrupted power supply and that
communication channel between the hub node and co-ordination center is unremitting. Hence,
simulation is treating the hub sensor as “constantly available”. The main objective of
the simulation is to optimize network routes for data transmission from sensor nodes to the hub
node [14].
In addition of the use and model of fire hotspot sensor to prevent incident of fire,
the environmental sensor in WSNs system to detect several of parameters that normally appear
because of land and forest fire such as Carbone Dioxide (Co2), Haze, Air Temperature and
Humidity. Figure 5 shows impact for environmental because of land or forest fire, case recorded
from Pekanbaru city in Riau Province of Indonesia.
Figure 5. Topology of WSN sensor nodes deploy in forest for fire detection
In actual practical the speed of wind related information can be determine either by
dedicated distributed wind sensors deployed as part of forest fire sensor beside temperature,
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pH, and carbon sensor to the area of forest to be protected, or the data can get to the local
meteorological stations in the same general region. The forest fire sensor in the front
characteristics (i.e. forest cell scale coordinates, flame size) are estimated periodically by a fire
spread prediction program. The forest temperature field modeling in this case might be thought
as an operation (and software component) evoked at the end of each iteration (time step) of a
fire spread simulation component which generates predictions for the new forest fire front to end
of locations and flame characteristics, using area specific static information and dynamic
information (wind speed and direction).
4. Performance Evaluation
Forest fires are a natural and recurrent phenomenon or manmade, in many cases of
the world. Burning areas are mainly located in temperature climates where its rainfall is high
enough to enable a significant level of vegetation, but in summers session are very hot and dry
environment, be able to create a dangerous fuel load. Global warming will contribute to increase
the number and importance of these disasters. In every season, not only are thousands of forest
hectares destroyed by wild land fires, but also properties, assets and public resources and
facilities are destroyed because of fire.
A forest fire in general a dynamic phenomenon that may changes its properties and
behavior by the time from one place to another and with the passage of time. In the fact that
the forest fuel available in a given location is limited, for a fire to continue it must spread to
neighboring fuel. This is performed through the complex heat spread to neighboring fuel and
performed through the complex fire behavior. Another approach is also based on the WSNs
paradigm has been designed and developed in the context of a research project that included
all the key actors in forest as well as fire fighting for operations. This unique proposed
ecosystem has provided the solution with a holistic perspective that results in a set of
distinguishing features, which all node types can include environment and meteorological
sensors. Figure 6 shows a WSNs proposed node scenario for land and forest fire detection.
Figure 6. A WSN sensor nodes propose use ZigBee standard
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Scenario as in figure 6 shows a schematically structure proposed of the development
ZigBee-WSNs-based system for land and forest fire detection and protection management,
consisting of multi-sensor nodes, coordinators, cluster heads, routers and remote decision
server. This cluster-tree network topology structure proposes design to reduce the loss of
energy and data package while transferring. ZigBee technique is a global standard based on
IEEE 802.15.4 applicable for low-rate wireless Personal Area Networks (PAN). ZigBee is one of
the wireless network standards targeted at low power sensor that apply in multi frequencies
868 MHz or 915 MHz and 2.4 GHz. The technical advantage proposes of ZigBee is to offer a
system with long battery life, small size, low-cost, high reliability and automatic or
semi-automatic installation. Therefore, in this development design WSNs node to achieve an
optimal choice for forest fire detection and monitoring [26]. Figure 7 shows actual fabricated
sensor ready to deploy, before sensor node deploy in the field the sensor nodes have to
configure based on design and requirement.
Figure 7. A WSN sensor nodes propose use ZigBee standard
Actual hardware on WSNs node for fire hotspot detection and monitoring can be found
in many types in the market. Where temperature, humidity, smoke and carbon sensor installed
in the node to detect all the parameter that high relation to the forest fire. All the nodes will send
a data or message to the WSNs coordinator that has function to receive all information from
node scattered. Proposed monitoring system expected to detect any abnormality in
environmental for land and forest fire, monitoring system normally used by government
institution or agency assign to do a monitoring. With a new technology proposed with smart
sensors, the system may adopt by many company to detect and monitoring environmental
based on they are purpose. For example, a paper and pub company may use this monitoring
system for detection fire or hotspot at they are farming area. Furthermore, the monitoring
system can be used for community for they are to know environmental status such as air
quality, temperature, humidity, etc. A mobile application can be done based of data collected
then community be able to check environmental by remote in mobile phone or others mobile
device. The application and product potential for market and new novelty based on smart
sensor developed, a decision easier to do because have some background and real data.
During research and development of smart monitoring system, government and some private
institution such as industrial and community have to involve in this project. Information of area
with high risk and placement of sensor base station in correct location is very important to
achieve faster and accurate data to send to monitoring system. Thus, some information from
local community is really helping to determine sensor location. Government institution as well
because to get license to enter in some of area that under control of government for example
protected forest area and special land for industrial etc.
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5. Conclusion
Development of WSNs nodes for land and forest fire detection, furthermore for
monitoring have been modelled. In this case the design and analysis use mathematical
approach according to the area have to cover which in the whole Riau Province in Indonesia. Air
temperature and humidity, haze and Co2 sensor are high light in this case because of those
parameters are basic parameters to the fire hotspot case either in the land or forest area.
Proposed design sensors node use ZigBee model, with low power then sensor nodes can use
in long life as node powered by battery. In order to cover the whole of Riau province, minimum
have to create network coordinator in each of area and a gateway to access in server (cloud
database) as well monitoring computer. Theoretical proposed concept of WSNs very applicable
to use for detection forest fire, especially in Riau Province in Indonesia. Estimation of number
sensor nodes to cover an area discussed to determine how big the WSN system and accuracy
for detection forest fire.
Acknowledgement
Authors would like to say thank you to Ministry of Research and Higher Education
(RISTEKDIKTI) of Indonesia and Universiti Teknologi Petronas, Malaysia for funding this
research as well as Universitas Islam Riau to support the facilities and the laboratory for
simulation and testing.
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10. TELKOMNIKA ISSN: 1693-6930 ◼
Modelling of wireless sensor networks for detection land and forest… (Evizal Abdul Kadir)
2781
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technique: A perspective. 2015 Third International Conference on Image Information Processing
(ICIIP). 21-24 Dec. 2015.
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Himalayan region of Uttarakhand. 2017 3rd International Conference on Advances in
Computing,Communication & Automation (ICACCA) (Fall). 15-16 Sept. 2017.
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Networks. IEEE Transactions on Network and Service Management. 2015; 12(3): 496-510.
[21] Kadir EA, Rosa SL, Gunawan H. Application of RFID technology and e-seal in container terminal
process. 2016 4th International Conference on Information and Communication Technology (ICoICT).
25-27 May 2016.
[22] Saoudi M, Bounceur A, Euler R, Kechadi T, Cuzzocrea A. Energy-Efficient Data Mining Techniques
for Emergency Detection in Wireless Sensor Networks. 2016 Intl IEEE Conferences on Ubiquitous
Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and
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(UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld). 18-21 July 2016.
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aggregation in wireless sensor network. Journal of Communications and Networks. 2017; 19(4):
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Hotspot. The 18th International Conference on Electronics, Information, and Communication (ICEIC
2019); Auckland, New Zealand IEEE. 22-25 January 2019
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International Conference on Electrical Engineering, Computer Science and Informatics (EECSI).
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