A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> In this paper, we propose a wireless sensor network paradigm for real-time forest fire detection. The wireless sensor network can detect and forecast forest fire more promptly than the traditional satellite-based detection approach. This paper mainly describes the data collecting and processing in wireless sensor networks for real-time forest fire detection. A neural network method is applied to in-network data processing. We evaluate the performance of our approach by simulations. <s> BIB001 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> ††† † Summary Wireless Sensor Networks (WSNs) become an important issue such as environmental monitoring, home or factory automation, logistics and so on. Many wild fires cause to damage on forest and a mountain which have valuable natural resources during the dry winter season. Current surveillance systems use a camera, an infrared sensor system and a satellite system. These systems can not support real-time surveillance, monitoring and automatic alarm. In this paper, we develop a forest-fires surveillance system in South Korea Mountains. We call this system FFSS (Forest-Fires Surveillance System). The developed FFSS consists of WSNs, middleware and web application. The WSNs measure temperature and humidity, and detect smoke. The middleware program and the web application analyze the collected data and information. The FFSS is possible to detect the heat. It let to know early alarm in real time when the forest-fire occurs. <s> BIB002 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> In this paper we present FireWxNet, a multi-tiered portable wireless system for monitoring weather conditions in rugged wildland re environments. FireWxNet provides the re ghting community the ability to safely and easily measure and view re and weather conditions over a wide range of locations and elevations within forest res. This previously unattainable information allows re behavior analysts to better predict re behavior, heightening safety considerations. Our system uses a tiered structure beginning with directional radios to stretch deployment capabilities into the wilderness far beyond current infrastructures. At the end of our system we design and integrate a multi-hop sensor network to provide environmental data. We also integrate a web-camera to provide visual data. This paper describes a week long full system deployment utilizing 3 sensor networks and 2 web-cams in the Selway-Salmon Complex Fires of 2005. We perform an analysis of system performance and present observations and lessons gained from our deployment. <s> BIB003 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> We present the design and evaluation of a wireless sensor network for early detection of forest fires. We first present the key aspects in modeling forest fires. We do this by analyzing the Fire Weather Index (FWI) System, and show how its different components can be used in designing efficient fire detection systems. The FWI System is one of the most comprehensive forest fire danger rating systems in North America, and it is backed by several decades of forestry research. The analysis of the FWI System could be of interest in its own right to researchers working in the sensor network area and to sensor manufacturers who can optimize the communication and sensing modules of their products to better fit forest fire detection systems. Then, we model the forest fire detection problem as a k-coverage problem in wireless sensor networks. In addition, we present a simple data aggregation scheme based on the FWI System. This data aggregation scheme significantly prolongs the network lifetime, because it only delivers the data that is of interest to the application. We validate several aspects of our design using simulation. <s> BIB004 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> This paper presents the simulation environment for the EIDOS (equipment destined for orientation and safety) architecture applied to wildfire fighting operations. The development of this environment involves a multidisciplinary study, in which we have to handle several different types of information (geographical data, vegetation models, fire models, etc.) and devices (sensors, aerial vehicles, and mobile devices). Therefore, the simulation tool is actually composed of several independent and interconnected modules. <s> BIB005 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> Forest and rural fires are one of the main causes of environmental degradation in Mediterranean countries. Existing fire detection systems only focus on detection, but not on the verification of the fire. However, almost all of them are just simulations, and very few implementations can be found. Besides, the systems in the literature lack scalability. In this paper we show all the steps followed to perform the design, research and development of a wireless multisensor network which mixes sensors with IP cameras in a wireless network in order to detect and verify fire in rural and forest areas of Spain. We have studied how many cameras, sensors and access points are needed to cover a rural or forest area, and the scalability of the system. We have developed a multisensor and when it detects a fire, it sends a sensor alarm through the wireless network to a central server. The central server selects the closest wireless cameras to the multisensor, based on a software application, which are rotated to the sensor that raised the alarm, and sends them a message in order to receive real-time images from the zone. The camera lets the fire fighters corroborate the existence of a fire and avoid false alarms. In this paper, we show the test performance given by a test bench formed by four wireless IP cameras in several situations and the energy consumed when they are transmitting. Moreover, we study the energy consumed by each device when the system is set up. The wireless sensor network could be connected to Internet through a gateway and the images of the cameras could be seen from any part of the world. <s> BIB006 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> The paper integrated the Geographical Information System with Cellular Automata (CA) and made use of VB and ArcGIS as development tools to design and implement the system of forest fire spread in Inner Mongolia Region. It realized the simulation of the 2D forest fire spread based on CA and visualization of the 3D real-time images transmitted from the fire site. In the 2D simulation process, combing the physical model and CA model and considering the factors affecting the forest fire, such as the meteorological, terrain and fuel type in order to more realistically simulate the spread of forest fire. At the same time, finding the short path to the fire site for fighter during the forest fire. By the analysis results, the decisionmakers can make the scientific and effective fire extinguishing decisions. <s> BIB007 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> Wireless Sensor Networks (WSNs) have a broad application range in the area of monitoring and surveillance tasks. Among these tasks, disaster detection or prevention in environmental scenarios is one typical application for WSN. Disasters may for example be forest fires, volcano outbreaks or flood disasters. Here, the monitored events have the potential to destroy the sensor devices themselves. This has implications for the network lifetime, performance and robustness. While a fairly large body of work addressing routing in WSNs exists, little attention has been paid to the aspect of node failures caused by the sensed phenomena themselves. This paper presents a proactive routing method that is aware of the node's destruction threat and adapts the routes accordingly, before node failure results in broken routes, delay and power consuming route re-discovery. The performance of the presented routing scheme is evaluated and compared to OLSR based routing in the same scenario. <s> BIB008 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> Wireless sensor-actuator networks (WSANs) greatly enhance the existing wireless sensor network architecture by introducing powerful and possibly even mobile actuators. The actuators work with the sensor nodes, but can perform much richer application-specific actions. To act responsively and accurately, an efficient and reliable reporting scheme is crucial for the sensors to inform the actuators about the environmental events. Unfortunately, the low-power multi-hop communications in a WSAN are inherently unreliable; frequent sensor failures and excessive delays due to congestion or in-network data aggregation further aggravate the problem. In this paper, we propose a general reliability-centric framework for event reporting in WSANs. We argue that the reliability in such a real-time system depends not only on the accuracy, but also the importance and freshness of the reported data. Our design follows this argument and seamlessly integrates three key modules that process the event data, namely, an efficient and fault-tolerant event data aggregation algorithm, a delay-aware data transmission protocol, and an adaptive actuator allocation algorithm for unevenly distributed events. Our transmission protocol adopts smart priority scheduling that differentiates event data of non-uniform importance. We further extend the protocol to handle node and link failures using an adaptive replication algorithm. We evaluate our framework through extensive simulations; the results demonstrate that it achieves desirable reliability with minimized delay. <s> BIB009 </s> A Review on Forest Fire Detection Techniques <s> Wireless Sensor Networks <s> The forest is considered as a precious and indispensable nature resource, but forest fire which can destroy forest resource safety and threaten human-living environment is considered as one of the severest disasters. How to monitor and collect information of forest fire at any time, it is a difficult problem for Forest Fire Prevention Departments to urgently solve. With the development of sensor technology, MEMS and wireless communications, wireless sensor network (WSN) has wide application in all kinds of fields. In order to prevent forest fire occurrence, this paper designs a monitoring system for forest fires based on wireless sensor network and GPRS network. The system gives the hardware design of wireless sensor nodes and software implementations, and chooses CC2531 to achieve the process of data acquisition and transmission, then sends the data through GPRS module to the remote monitoring center. By means of WSN and GPRS network, the system accomplishes data acquisition and long distance transmission. <s> BIB010
The line of sight and the early stage of the fire process problem could be solved with the second type of sensors. A new technology called wireless sensor network (WSN) is nowadays receiving more attention and has started to be applied in forest fire detection. The wireless nodes integrate on the same printed circuit board, the sensors, the data processing, and the wireless transceiver and they all consume power from the same source batteries. Unlike cell phones, WSN do not have the capability of periodic recharging. The sensors are devices capable of sensing their environment and computing data. The sensors sense physical parameters such as the temperature, pressure and humidity, as well as chemical parameters such as carbon monoxide, carbon dioxide, and nitrogen dioxide. The sensors operate in a self-healing and self-organising wireless networking environment. One type of wireless technology is ZigBee which is a new industrial standard based on IEEE 802.15.4. This technology emphasises low cost battery powered application and small solar panels and is suited for low data rates and small range communications. Wireless sensor networks have seen rapid developments in a large number of applications. This kind of technology has the potential to be applied almost everywhere; this is why the research interest in sensor networks is becoming bigger and bigger every year. Forest fire detection and prevention are another real problem faced by a number of countries. Different methods for monitoring the emergence of fires have been proposed. The early methods were based on manned observation towers but this technique was inefficient and not entirely effective. Subsequently, camera surveillance systems and satellite imaging technologies were tried but this also proved ineffective at being able to efficiently monitor the initial start of the surface fire. For example, camera networks can be installed in different positions in the forests but these provide only line of sight pictures and may be affected by weather conditions and/or physical obstacles. The revolution of WSN technology in recent years has made it possible to apply this technology with a potential for early forest fire detection. These sensors need to be selforganised and follow an efficient algorithm, interfaced with other technologies or networks. A number of studies have considered using WSN in wood fire systems. Lloret et al. BIB006 suggested deploying a mesh network of sensors provided with internet protocol (IP) cameras in Spain. Here, the sensors detect the fire at the beginning and send an alarm signal to the sink. The sink then sends back a message to switch the closest camera "on" to provide real images of the fire and avoid false alarms. Their paper is based on testing the performance of four IP cameras and their energy consumption. The problem with this system is that the transfer of images is a heavy load for wireless sensor networks in relation to their limited resources of power, memory, and buffer rather than IP cameras which can only provide a line of sight images and are not efficient in dark and in foggy and raining weather. IP cameras require installing each one of them manually in the appropriate position. Son et al. BIB002 propose a project for fire detection in South Korean Forest Fire Surveillance System (FFSS) using wireless sensor networks connected to the internet. They used a MCF (minimum cost path forwarding) as a routing protocol, which required a routing table for each node to find the minimum path to the sink. General information about the network architecture was provided in their paper. Sensor nodes sense the temperature, humidity, and illumination to forward it to the base station node and then to the gateway. The gateway is connected with a middleware and web application database to create some calculations based on daily humidity records in order to evaluate the hazards of the fire. Son's network is a scalable and real-time system, but their paper is more concerned with fire calculations and decision making than the network communication reliability. They did not discuss the network coverage and sensors distribution or fire localisation issues. Hartung et al. BIB003 in their paper present FireWxNet, a multitiered portable wireless system for monitoring weather conditions in rugged wildland fire environments. They used wireless sensor network for wood fire detection as a hybrid with web cameras. The main target of their studies was to investigate fire behaviour in forests rather than detecting fire in early stages. They used wireless sensor network to provide data for weather status and web cameras to provide images for the fire. FireWxNet provides the fire fighters with the ability to measure fire and weather conditions. The system uses a tiered structure which starts with directional antennas on the top of mountains and ends with multihope sensor network to observe the required environmental parameters. Hartung et al. stated that they used web cameras to provide vision data as well and that they equipped the sensors with a small GPS device to provide the location information. See Figure 6 . Doolin and Sitar  experimented a network of 10 Crossbow Mica2 mote sensors operated by TinyOS operating systems written in nesC language. The motes were provided with GPS devices and tested on grassy areas, about ten/acre to sense temperature and humidity pressure and send this data back to the base station. The base station was connected to a MySQL database and clients for alarm monitoring. The problem with this system is that the distance between sensors is too far (approximately 1 km); in case of node failure, a connection between some sensors and the sink might be lost and that could leave a gap in the network coverage; plus, adding a GPS device to each sensor will make the network more expensive and reduce the network life time as a GPS will consume more power. Furthermore, in Doolin's project, each node has to be installed and fixed manually and this is almost impossible for large forests. Aslan  proposed a good fire detection framework which consists of (i) Sensor deployment scheme: represented in the distance between sensors, minimum collision, and minimum number of sensors deployed with full coverage. (ii) Network architecture: the cluster scheme used as network topology. (iii) Intracluster communication protocol: the communication between nodes and clusters divided into: initialisation phase, risk free phase, fire threat phase, and progressed fire phase. Nodes enter or change their phase according to danger rate calculation, which depends on NFDRS (National Fire Danger Rating System), temperature, and humidity ranges. (iv) Intercluster protocol: the main target for this protocol is the power balancing for cluster heads. Hefeeda and Bagheri BIB004 present a very smart system. They based their network action on fire weather index (FWI). This index includes the probability of fire ignition and fire spread rate as well. FWI provides the moisture content in relation to weather observation and the fuel codes which is divided into the following three types to describe the soil content of forest ground. (i) Fine Fuel Code (FFMC) represents the litter and fine fuels for 2 cm deep. (ii) Duff Moisture Code (DMC) represents moisture content of decomposing organic material for 5-10 cm deep. (iii) Drought Code (DC) represents the moisture organic content for 10-20 cm deep. See Figure 7 . Hafeeda's system required a lot of work before deploying the network. It required a long analysis of the forest surface component in the forest's different parts to understand how to build the fuel code model for this area. Their work is more concerned with weather and fuel modelling for fire danger assessment than the network and coverage issues. Conrad et al.  produced a business case for the Enhanced Forest Fire Detection System with a GPS project in Pennsylvania. They claimed that forest fires damage 2,554 acres per year in Pennsylvania, which causes economic loss and potential loss of human life and environment. They proposed to create a fire detection system using fire sensors and GPS devices instead of the present fire detection systems such as the public phone lines, fine look-out towers, and ground or aerial patrol. They claimed that these techniques were outdated and it is time to use the technology for this problem. They targeted two aims for this project: (a) to use the already existing technology and (b) replace all the existing fire detection techniques with more efficient ones. Conard et al. planned to purchase the GPS handheld devices and radio wave sensors, where the sensor reacts to a fixed temperature by transmitting signals to the GPS system. Each signal would carry the devices unique identity code to increase the system efficiency and allow the system to identify the position. The project planned on installing 12,000 units within forty-eight months, 4,000 devices every 12 to 15 months. When fire or smoke was detected, the sensor would send a signal to GPS satellite, and then the GPS satellite would duplicate the signal to the handheld GPS device and the central monitoring database to display the fire location on the installed map for that area. See Figure 8 . It looks like a very interesting work; unfortunately, the file does not contain any results or proof of the project efficiency. The project expenditure is very high as they have to install each one of the units manually after they finalise the configuration and calibration. GPS devices consume more energy and reduce the nodes life time. The researchers defined more than 27 mathematical models to describe the fire behaviour where they stated that those models developed according to different countries experience of forest fire and each model is different according to the input parameters and the environments nature (fuel indexing   ). The researchers of forest fires manage to use some of these models in simulations or even create their own methods to create maps that can be used to analyse the fire behaviour at any time in the future so that they can help the fire fighters to determine the best method to extinguish the fire, such as BehavePlus, FlamMap, FARSITE, Geodatabase, and ArcSDE. On the contrary, researchers are trying to initiate a reliable technology that can detect the fire, localise the fire, and help in decision making in terms of requiring an immediate reaction in case of crisis possibility or a high fire risk situation. As a result, the fire can be extinguished in early stages within a short time to minimise the damage save lives, environment, fire fighter equipment, time and effort. Hongye et al. BIB007 in their research of forest fire in inner magnolia discussed a very smart structure for the system which is divided into monitoring system of the forest fire, database system of information management, and decision making system of management of the forest fire proofing. Where the monitoring system at the end provides a quick location for the fire by simulating the fire in 2D map, images come from the field used into 3D maps by using Geodatabase and ArcSDE programs for fire simulation. Although the logical architecture is smart, this system is not easy to build and image observation is not the smartest method for fire detection and simulation. Ngai et al. BIB009 provide a general reliability-centric framework for event reporting in wireless sensor networks which can also be used in forest fire detection systems. They consider the accuracy, importance, and freshness of the reported data in environmental event detection systems. They present a data aggregation algorithm for filtering important data and a delay-aware data transmission protocol for rapidly carrying the data to the sink node. Wenning et al. BIB008 propose a proactive routing method for wireless sensor networks to be used in disaster detection. The protocol is developed to be aware of a node's destruction threat and it can adapt the routes in case of a sensor node's death. The method can also adapt the routing state based on a possible failure threat indicated by a sensed phenomenon. García et al. BIB005 present a simulation environment that can create a model for a fire by analysing the data reported by sensor nodes and by using some geographical information about the area. The use of topography of the environment distinguishes the study from some other solutions. The estimation of the spread of a fire is sent to handheld devices of fire fighters to help them in fighting the fire in field. The FIRESENSE FP7 project aims to implement an automatic early warning system to remotely monitor areas of archaeological and cultural interest from the risk of fire and extreme weather conditions. FIRESENSE is a very complicated system; it consists of multisensors, optical, IR, and PTZ cameras in addition to temperature sensors, and weather stations. In this system, International Journal of Distributed Sensor Networks each sensor collects the data and applies some processing techniques and different models and data fusion algorithms in order to provide a clear understanding for the event to the local authority. Demonstrator deployments will be operated in selected sites in Greece, Turkey, Tunisia, and Italy. See Figure 9 . This project builds on very complicated scientific models, algorithms, concepts, and comparisons, such as the following. (i) Scene model: the fire and smoke, heat flux or emitted thermal (Planck's radiation formula), the fire flickering, the reflectance, absorption emission lines, and analysis of the atoms (e.g., potassium) and the molecules (water and carbon dioxide) are characteristics to be investigated. (ii) The background emits the thermal heat, the reflectance of sunlight, the clouds (clouds shadow) the buildings and the sky polarisation. (iii) The atmosphere has a number of gases (N 2 , O 2 , CO, CO 2 , H 2 O, etc.); each one has its own absorption and reflection behaviour. Water vapour concentration could vary as a result. Carbon dioxide is more uniformly distributed but its value is larger over industrial cities and vegetation fields than over oceans and deserts. Figure 10 describes the physical aspects related to forest fire detection. The atmosphere strongly influences the radiation transfer of the forest fire by absorbing and scattering radiation. The simplest form of detection is to compare two subsequent images of the same scene using an image processing system. If a difference is found between two images above a certain threshold which is defined by the noise in the images, an alarm is generated. After that, further processing is applied to the images by using Markov model. In this model Markov defines three states for each flame and nonflame moving pixel. See Figure 11 . S1 S2 S3 S1 S2 S3 Figure 11 : Three-state Markov models for flame and nonflame moving pixels, respectively  . Yu et al. BIB001 present a real-time forest fire detection system by using neural wireless sensor networks. They relied on the clustering algorithm as routing techniques to collect the measurement from the scattered sensors such as humidity, temperature, smoke, and wind speed where this data was used as input for The United States National Fire Danger Rating System. High probability of faulty alarms. Any existing satellite-based observations for forest fire suffer from severe limitations resulting in a failure in speedy and effective control for forest areas. The satellite coverage of the full region of the forest may not be available or the coverage may be only intermittent (not continuous in time), with substantial gaps in time when the satellite is not within the field of view from certain regions or spots of the forest. The optical and the infrared (heat) spectral radiation emitted by a small flame, the early phase of a forest prior to its spread over a wide region, may be too feeble in intensity to be detected by a satellite. The intensity decreases as the inverse square of the distance, in addition to being sensitive to the angle between the direction of the arriving beam of radiation and the normal to the receiving surface-mirror, camera, antenna, and detector so that position and orientation of the satellite may usually be far from optimal for detecting a forest fire at an early phase. The satellite may not be equipped with transponders and antennas designed to perform the reception, amplification, regeneration, frequency translation, and downlink transmission to the ground-optimally suited for detection of forest fires. In fact there may not yet be formal allocation of the appropriate frequency and bandwidth for forest fire detection and pertinent information transmission and processing (onboard or at the ground terminal or earth station). A satellite is usually designed to perform many diverse functions (telecommunications, remote-sensing for broad features of the earth's surface or the atmosphere, etc.) and it is not cost-effective to add to it the capability to detect forest fire. The operation of a satellite system may not be real time in order to instantaneously provide information about the occurrence of a forest fire anywhere within the forest region. The operation of a satellite system is bound by many national and international regulations and agreements and may be less than suitable for the task of forest fire observation. Optical sensors or camera systems in general need to be improved in order to reduce the number of false alarms due to various dynamic phenomena, such as wind-tossed trees, cloud shadows, reflections, and human activity. The difficulties of processing landscape images are due to their varying nature and to the large number of dynamic events that may appear under various illumination conditions, depending on weather, distance, time of day, masking objects, and so forth. These events produce dynamic envelopes, which are not always caused by motion, and consist of time-varying gray levels of connected pixels in several image regions. This kind of technology only provides a line of sight vision; where high trees or the hills and mountains can block the vision; plus it might be impossible to provide images for ignition place. Optical systems were designed to cover large areas with minimum numbers of camera towers; each tower has to detect smoke in range of 15-80 Km, where it requires a long delay after the ignition to produce a watchable smoke cloud that can be detected by the camera. Weather condition and night vision reflect on the camera performance. Camera surveillance systems with short distance links were tried, but this also proved an ineffective method for fire detection in regard to the need for manual installment for each camera in an appropriate position rather than the line of sight images, night images, bad weather images problems, and high possibility of false alarms because of the daily motion of sun light, clouds, atmospheric extinction, and vegetation. Finally, these systems are very expensive; the camera tower can be worth more than thirty thousand dollars per tower, and there is a need to build these towers and install a communication infrastructure in the remote areas inside the forests. Best available solution for forest fire detection. They can provide all required information that influences the environment at any moment accurately. They can cover any area size, plus its scalable network. They can observe and influence the physical world around them They can be connected to many devices and can add many kinds of sensors to measure different parameters. There is no need to build towers or set up complicated communication links. They can be deployed anywhere even in inaccessible places. Wang and Meng network required a very high density of deployed nodes and a very complicated routing and processing procedure to send the required data for the central unit. The main focus of this study is data aggregation methods; hence energy consumption issues are not discussed. Zhu et al. BIB010 created a monitoring system for forest fires based on wireless sensor networks and GPRS network. They relied on clustering systems to provide real-time monitoring for smoke, temperature, humidity, and some other environmental parameters. The collected data from the nodes will be transmitted in multihop fashion to the central nodec and then to the monitoring center to produce reports, graphs, and curves to help the fire fighters make an appropriate decision. Libelium  is one of the wireless sensor network companies based in spain. They call their nodes Waspmote and they introduce many applications for their products such as smart cities, forest fire detection, and many other applications. In forest fire application, they used their Waspmote nodes and a GPS device to localise those nodes. They provided the nodes with gas boards to measure temperature, humidity, carbon monoxide (CO), and carbon dioxide (CO 2 ) to detect the fire. Libelium created a very good system; as their experiment shows, they deployed 90 sensors to measure the four parameters every five minutes, and they attached solar panels for power scavenging. Waspmote nodes and the gas board made the device in bulky size, which was required to be installed manually one by one in appropriate positions; this is not efficient in large areas. The gas board will lead to more power consumption, thus reflecting on the network life span. See Figure 12 . 5.1. Discussion. The previous part details several of the principal study sources that have inspired the development of the research work in this thesis. It proves that the best available solution for forest fire detection is using sensor networks, because it can provide all required information that influences the environment at any moment accurately. Wireless sensor network technology normally deploys large number of small, low cost sensors fairly densely that can observe and influence the physical world around them by gathering physical information, transforming it into electrical signals, sending it to a remote location to do some analysis, and deploying the results in different applications. By this way there is no need to build towers or set up complicated communication links such as microwave and satellite. It can be deployed anywhere even in inaccessible places. This technology can provide real-time monitoring, where it can provide information at the ignition instance or at very small delays, depending on the node used in wake-up/sleep schedule. This technology works on short communication links fashion. As a result, more accurate information with less delay can be provided for the fire fighters. Using this technology for forest fire application requires a large number of randomly deployed nodes to provide a reliable network. Based on what has been discussed above, it can define the key issues of this network for this application in the following points. (i) Localisation: all the previous work used a GPS or fixed the nodes in a known place. (ii) Coverage: the nodes deployed randomly a full coverage almost impossible. (iii) Network life span: For sensor nodes working on batteries, it is impossible to go back to each node in the forest and recharge it again. (iv) Fire detection method: this is the heart of the application; it should be precise and reliable. In the previous work they used other kinds of sensors like IP cameras or other technologies like satellite images or superoptical tower cameras. They integrated the sensor nodes information with data bases, weather stations, fuel and weather indexing models, and many other models and processing techniques to reduce the number of false alarms. In order to detect the fire, each node was provided with many sensors to get the environment parameters such as temperature, wind speed, relative humidity, and fire flickering to define a fire incident in the first place.