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J. Sens. Actuator Netw. 2017, 6(3), 15; doi:10.3390/jsan6030015

On-Line RSSI-Range Model Learning for Target Localization and Tracking

1
Robotics Vision and Control Group, Universidad de Sevilla, Escuela Superior de Ingenieros, c/Camino de los Descubrimientos s/n, 41092 Seville, Spain
2
Universidad de Sevilla, Escuela Superior de Ingenieros, c/Camino de los Descubrimientos s/n, 41092 Seville, Spain
3
Central University of Venezuela, Ciudad Universitaria s/n, Caracas 1050, Venezuela
*
Author to whom correspondence should be addressed.
Received: 8 May 2017 / Revised: 30 July 2017 / Accepted: 5 August 2017 / Published: 10 August 2017
(This article belongs to the Special Issue QoS in Wireless Sensor/Actuator Networks and Systems)
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Abstract

The interactions of Received Signal Strength Indicator (RSSI) with the environment are very difficult to be modeled, inducing significant errors in RSSI-range models and highly disturbing target localization and tracking methods. Some techniques adopt a training-based approach in which they off-line learn the RSSI-range characteristics of the environment in a prior training phase. However, the training phase is a time-consuming process and must be repeated in case of changes in the environment, constraining flexibility and adaptability. This paper presents schemes in which each anchor node on-line learns its RSSI-range models adapted to the particularities of its environment and then uses its trained model for target localization and tracking. Two methods are presented. The first uses the information of the location of anchor nodes to dynamically adapt the RSSI-range model. In the second one, each anchor node uses estimates of the target location –anchor nodes are assumed equipped with cameras—to on-line adapt its RSSI-range model. The paper presents both methods, describes their operation integrated in localization and tracking schemes and experimentally evaluates their performance in the UBILOC testbed. View Full-Text
Keywords: Wireless Sensor Network; RSSI; localization Wireless Sensor Network; RSSI; localization
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MDPI and ACS Style

Ramiro Martínez-de Dios, J.; Ollero, A.; Fernández, F.J.; Regoli, C. On-Line RSSI-Range Model Learning for Target Localization and Tracking. J. Sens. Actuator Netw. 2017, 6, 15.

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