## 2 Related Works  
In this section, we review several previous research activities related to the issues and enabling technologies for WSNs in forest fires monitoring and detection. Traditionally, forest fires are detected by using conventional techniques such as guard towers [9] and Osborne Fire Finder [10] which is a system comprising a topographic map of the area oriented and centered on a horizontal table with a circular rim graduated in degrees. However, these techniques cannot meet the requirement of early and accurate detection of forest fires due to the unreliability of human observation towers and difficult life conditions [11]. Therefore, some countries use satellite and image processing to detect forest fires, e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) used in Canada [12] and Advanced Very High Resolution Radiometer (AVHRR) used in China [13]. However, the satellite-based monitoring systems are limited by terrain, time of day and other environmental conditions [11]. Recently, quite extensive research has been done on WSNs including protocols, algorithms, and applications [14; 15; 16; 17; 18; 19]. Although these works may not involve specifically forest fire monitoring and detection, they are adaptive to various applications which include forest fire monitoring and detection. For fire monitoring and detection application of WSNs, several important design goals and features must be considered such as energy efficiency, accurate localization, early detection, forecast capability and adaptivity to harsh environment [9]. There is a considerable amount of research works from literature related to forest fires using WSNs [20; 21; 22; 23; 24; 25; 26]. The authors of [27] implemented a real-world forest fire monitoring and detection system in the San Francisco area. The system is composed of ten sensor nodes which are deployed with ranges up to 1 km. Each sensor node is equipped with a GPS interface and they sense and forward temperature, humidity, and pressure values to a base station. However, due to the long distance between each node in this system, the sensory data is not valuable enough when it arrives at the sink station. Moreover, with the aggravation of fires, some sensor nodes may be destroyed which may cause failure in delivering the data from the sensor node to the sink station. A South Korean forest fire surveillance system was proposed in [20]. In this system, an experimental approach based on a dynamic minimum cost path forwarding protocol was developed. After gathering sensory data, the sink station uses relative humidity, precipitation, and solar radiation to calculate forest fire risk level. However, there is no evaluation on the proposed approach which has been made by authors. Differentfrom this work, our proposed scheme processes the data not only at the sink station, but also at the cluster header. Meanwhile, we apply a low and fair energy consumption strategy by using the proposed intra-cluster and inter-cluster protocols rather than the approach in [20], which did not consider the energy constraints of sensor node. The authors of [21] proposed a general reliability-centric framework in which event reporting in WSNs also can be used in forest fire monitoring and detection. There is also a data aggregation algorithm for filtering important data and delay-aware data transmission for rapidly delivery. Works have been done [9] to detect forest fires early by means of Fire Weather Index (FWI) system which is one of the most comprehensive forest fire danger rating systems in US. In this work, authors used both simulation and test-bed approaches to validate their approach. Reference [22] presented a simulation environment which relies on sensory data and geographical information about the coverage to build a model for a fire. The estimation of the spread of a fire is sent to firefighters to help them fight fires. The authors of [23] surveyed fires detection research from three perspectives: residual fires, forest fires and contributions of WSNs applied in early fire detection.