## 4 Road Sensing
### Detecting Stationary Vehicles  
The detection of stationary vehicles is typically used in intelligent parking applications where the aim is to control which parking spaces are free. Unlike moving vehicles, where the time window for detecting a vehicle is small, stationary vehicles may remain close to the sensor node for a long period of time. This not only improves reliability in detections, since a positive occupancy can be reported after several measurements above a certain threshold, but it also enhances power savings by allowing lower sampling rates and thus more efficient duty-cycles. In addition, the use of adaptive sampling rates, which temporarily increase after the first above-threshold measurement, allows for additional reductions in consumption [30]. Furthermore, the long periods elapsed between samples let the sensor node enter deeper power saving modes, which require relatively long transitions to the active state and are otherwise unfeasible.  
Typically the detection is carried out by AMR sensors [27, 28, 30] despite the fact that there are other alternatives such as the use of light sensors [29]. Cameras and active sensors (ultrasonic, light with laser pointer) have also been tested in some works [27]; however, they show excessive power consumption. In addition, cameras generate a great amount of data to be transmitted and a more complex processing, while ultrasonic and light sensors with laser pointers cannot differentiate between humans and other objects from vehicles, needing to check extra attributes such as speed.  
AMR sensors, in the case of parking lots, can be affected by interferences (e.g., vehicles in contiguous parking spaces) which may lead to false positives. If nodes are placed under the vehicles, in the center of the parking spots, the acquired signal from vehicles parked at the place under consideration is noticeably higher than that originated by vehicles in neighbor parking spaces. This, along with the use of properly defined threshold values for the measured signal, can be applied to indentify when a vehicle is currently parked at the sampled space. It should be noted that the measured magnetic signal depends on the type of vehicle above (vehicle elevation, layout, _etc._); therefore threshold values must be chosen so that they are valid for the different kinds of vehicles using the parking.  
According to the open literature, light sensors may be considered either as a good option for detecting stationary vehicles [67] or as totally useless [27]. The huge difference in both of these studies is due to the location of the sensors. These sensors offer inaccurate results when placed in a lateral position (with respect to the vehicles), but provide good outcomes when placed under vehicles, since vehicles block all the incoming light, reporting a positive detection. However, light sensors are quite sensitive to environmental illumination, not being well-suited to work in shadow areas or at night. In [29] some additional reference nodes equipped with light sensors were used to eliminate the effect of environmental light variations. However, how the system behaves in dark conditions was not specified. A positive consideration about these sensors, though, is that they are available with most commercial sensing platforms, thus alleviating the complexity of installing additional hardware.