), and actuators and robots, which perform the appropriate actions. Knowing the position of the sensors and the robots is fundamental to contextualize the information gathered selleck chemicals Palbociclib by the sensors and Inhibitors,Modulators,Libraries to control the robots in an efficient way. Some of the sensors may be mobile, as they could be associated with mobile objects or people. Furthermore, the robots may not be equipped with self-navigation techniques. As a consequence, the availability of robust, accurate and easily deployable location systems is a key enabler of intelligent spaces and still an open challenge.Although several technologies can be used to estimate the position of the different objects [1] (ultrasounds, artificial vision, infrared, GPS, etc.), radiofrequency localization techniques [2] have become very popular and suitable for this kind of sentient spaces, as they reuse the wireless infrastructure.
Location may be computed from different parameters, such as time-of-flight, angle of arrival or received signal strength (RSS). Nevertheless, only the latter parameter is feasible in most commercial wireless technologies without hardware or Inhibitors,Modulators,Libraries software modifications. As the RSS information can be easily collected with off-the-shelf equipment, it has become the basis for the most popular techniques for inferring the relative positions of the nodes in the wireless network.In the literature, two main approaches have been proposed to solve the localization problem using RSS measurements: channel modeling based methods and fingerprint strategies. In the first one [3�C9], a propagation channel model is used to establish Inhibitors,Modulators,Libraries a relation between the RSS and the distance between two nodes.
The location of a node can then be determined from a set of these distances using some positioning algorithm, such as the ones in Inhibitors,Modulators,Libraries [10] or [11]. Conversely, the second approach [9,10,12�C15] creates a radio map of the environment by gathering, for each node, a set of RSS measurements in different positions, uniformly spaced on a regular grid. These ��fingerprints�� are then stored in a database; when an unknown node needs to be localized, its RSS measurements are matched against the ones stored in the map in order to find the closest correspondence. The main drawback of this approach is that a large number of on site measurements are required in order to obtain fine-grained localization; this situation unavoidably entails an increase of the operational cost.
Additionally, AV-951 fingerprint methods require an exhaustive, periodic and non-reusable preliminary calibration phase, which is usually infeasible in Rapamycin WY-090217 practical deployments.With respect to channel model based techniques, they are built on the fact that a channel model is a theoretical, simplified and non-perfect approach to describe the behavior of a complex propagation environment.