Abstract: ManyWSN algorithms and applications are based on knowledge
regarding the position of nodes inside the network area.
However, the solution of using GPS based modules in order
to perform localization in WSNs is a rather expensive solution
and in the case of indoor applications, such as smart
buildings, is also not applicable. Therefore, several techniques
have been studied in order to perform relative localization
in WSNs; that is, to compute the position of
a node inside the network area relatively to the position
of other nodes. Many such techniques are based on indicators
like the Radio Signal Strength Indicator (RSSI)
and the Link Quality Indicator (LQI). These techniques are
based on the assumption that there is strong correlationbetween
the Euclidian distance of the communicating motes
and these indicators. Therefore, high values of RSSI and
LQI should indicate physical proximity of two communicating
nodes. However, these indicators do not depend solely on
distance. Physical obstacles, ambient electromagnetic noise
and interferences from other wireless transmissions also affect
the quality of wireless communication in a stochastic
way. In this paper we propose, implement, experimentally
fine tune and evaluate a localization algorithm that exploits
the stochastic nature of interferences during wireless communications
in order to perform localization in WSNs. Our
algorithm is particularly designed for in-door localisation of
moving people in smart buildings. The localisation achieved
is fine-grained, i.e. the position of the target mote is successfully
computed with approximately one meter accuracy.
This fine-grained localisation can be used by smart Building
Management Systems in many applications such as room
adaptation to presence. In our scenario, our proposed algorithm is used by a smart room in order to localise the
position of people inside the room and adapt room illumination
accordingly.