Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
GPS-free Positioning in Mobile Ad Hoc Networks
Cluster Computing
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Wireless localization using self-organizing maps
Proceedings of the 6th international conference on Information processing in sensor networks
Adaptive Distance Estimation and Localization in WSN using RSSI Measures
DSD '07 Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools
Two-Phase Stochastic Optimization to Sensor Network Localization
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Virtual raw anchor coordinates: a new localization paradigm
ALGOSENSORS'10 Proceedings of the 6th international conference on Algorithms for sensor systems, wireless adhoc networks, and autonomous mobile entities
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Many WSN 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 correlation between 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.