## 2 System Definition
### Wildfire Detection Methods
#### 2.1.3 Pervasive _In Situ_ Detection Methods  
_In situ_ pervasive wildfire detection has several advantages over remote detection [42]. First, the sensors are closer to the events (e.g., combustion); thus, their sensing is generally less susceptible to interference from external factors (e.g., wind, fog, obstructions). Second, _in situ_ detection is implicitly redundant to some extent, since the higher sensor density makes it likely that more than one of them detects an event within a short time window. Nevertheless, natural occurring, non-fire-related phenomena (such as fog, dust or insect nests) may still trigger false alerts, even for pervasive methods, or the effectiveness of some detection methods may be limited at night or in low visibility conditions. Moreover, it is important to note that the cost per unit of surface of _in situ_ monitoring is usually higher than that of remote sensing, both for the initial deployment and for the maintenance [43]. Thus,reducing these costs can significantly extend the application range that can be economically addressed using pervasive methods. Their finer spatial and temporal resolution and the generally higher detection accuracy can complement the remote detection methods and can be justified for areas with higher risk of fire or where humans or goods are more exposed, such as along roads or trails that cross forests or at the interface between human habitat or resources and forests. WSNs are among the best-suited systems for pervasive _in situ_ long-term environmental monitoring, even in wild and harsh conditions [44, 45, 46, 47, 48, 49, 50], although other techniques have been reported, as well, e.g., based on optical fiber [31]. WSN nodes typically include the transducers and processing power to autonomously detect specific fire effects within their range, such as light, smoke, heat and noise [51, 52, 53, 54, 55]. The nodes are deployed within the area to monitor using application-specific criteria [56] and periodically check for fire signatures using their on-board sensing and processing capabilities. The control center is notified of positive matches by using radio messages sent either directly to gateways or forwarded through a mesh network made by their neighboring nodes. Most fire detection techniques used by pervasive WSNs are shared with the remote methods (see Sections 2.1.1 and 2.1.2) with various degrees of detection confidence, power profile and cost [49, 57]. Low-power techniques are preferred, since the limited sensor node energy is an important selection factor, energy being a direct and indirect contributor to WSN solution cost (we will analyze this in detail in Section 3.1.1). Techniques based on low-power image processing [58] or radio-acoustic detection of the sound of burning fire [31] performed on board the sensor nodes are reported, but they generally require higher processing energy. Detection of infrared [59] or ultraviolet radiation [60] that are correlated with fires have a lower energy consumption profile and can be used alone or in conjunction with smoke detection for increased accuracy. Smoke particle detection can be combined with temperature sensing [53] (either as absolute value or variation speed). The detection of temperature patterns can be used alone (and this is arguably one of the most cost- and energy-effective techniques) [61], or correlated with humidity readings [44], or with humidity, wind and rainfall data that are periodically retrieved from the application server [48]. In general, techniques based on data fusion from multiple transducer types can increase detection reliability [35, 62, 63]. Reliability can be increased also using adaptive methods on sensor nodes [64, 65] or on sensor and gateway nodes, the latter benefiting from data aggregation from multiple sensor nodes [66]. Other methods are based on the detection of abnormal levels of combustion gases, standalone or combined with temperature detection [67]. However, gas transducers may require higher power and periodic recalibration.