## 2 System Definition
### System Specifications  
The analysis in Section 2.1 shows that _in situ_ pervasive sensing is among the fastest and most reliable monitoring methods. A low false alert rate due to higher detection reliability can reduce the need for human analysis and the effort of the field personnel, thus reducing the exploitation cost. Earlier fire detection can help the firefighters to contain the risks and damages, increasing safety and reducing costs. WSN technology looks well suited for distributed sensing and reporting, but to be economically viable, it needs to scale well for large deployments and to have reduced initial and operation cost: low sensor node cost, reliable operation for extended periods and to require very few or no maintenance at all. Transducers on board the WSN nodes typically use energy sparingly to preserve their reserves and life time. Among the effects of the fire, heat is always present, and high temperatures are among its most devastating effects. The temperature of the surrounding air can be energy- and cost-effectively measured (e.g., using inexpensive negative temperature coefficient (NTC)transducers), and the WSN nodes can periodically sample it looking for abnormal temperature patterns that are highly correlated with convection heating from fires. For example, the nodes can alert when the absolute temperature value or its rate of increase rises above specific thresholds [53]. The thresholds should be set high enough to prevent false alerts due to naturally occurring temperature patterns and as low as possible to improve detector sensitivity and timely alerting. As discussed in Section 2.1.3, fire detection accuracy can increase by combining measurements from different types of transducers, each sensitive to different effects of the fire. For instance, abnormal air temperatures can be correlated with the levels of infrared radiation on wavelengths closely related to the presence of fire, for the same monitored area. Transducers of different types can be mounted on the same sensor node or on different, specialized nodes. A node with multiple transducers can correlate the measurements on board, reducing network traffic. However, multiple transducers also increase the node cost, energy consumption and resource requirements. They may also add deployment constraints, increasing its complexity and cost. Specializing the nodes by transducer type allows one to differentiate their deployment requirements to optimize their operation. For instance, nodes that sense infrared radiation can be attached to the underside of the lower branches of the trees, facing down, at heights of a few meters above the ground, while the temperature sensors can be attached on tree trunks, at 0.5-1 m from the ground, to have good thermal contact with the hot air rising from nearby fires. The deployment density may also differ by node type. Field data from nodes can be correlated upstream, e.g., on the gateway or server for a typical tiered structure, like the one shown in Figure 1.  
Application-specific trade-offs are usually made between the required detection accuracy and the implementation cost. The industrial partner initially considered fire detection solutions based on low-cost sensors, which consume no energy in standby mode and that are activated by fire (e.g., heat-activated power supplies [72] or conventional small batteries that supply the node through a bimetallic switch only when heated above a threshold [73]). However, the quality of service (QoS) cannot be monitored for these solutions, thus they cannot be considered reliable in the general case. Consequently, the specifications evolved towards more typical WSN platforms that use active sensor nodes and a tiered structure as shown in Figure 1. Sensor nodes are fit with transducers to sense the environment properties of interest and can communicate with the gateway (or sink) nodes using short-range in-field radio communications, either directly or through multihop message forwarding.