Self-Localization of Wireless Sensor Nodes By Means of Autonomous Mobile Robots

  • Andrea Zanella
  • Emanuele Menegatti
  • Luca Lazzaretto
Part of the Signals and Communication Technology book series (SCT)

In general, a wireless sensor network consists of a large number of lowcost, static nodes that organize themselves in order to deliver events notification to a sink node in a multi-hop fashion. Typically, nodes are battery driven and are limited in terms of processing, storing and communication capabilities. On the contrary, an autonomous mobile robot is an expensive object, equipped with advanced interfaces and capable of performing complex tasks. The complementary capabilities of these two technologies can be integrated in a synergetic manner not only to enhance the performance of each single system, but also to create novel applications and services.

In this paper, we will describe the RAMSES2 project, which aims at investigating the potential benefits resulting from the integration of WSNs and AMRs. As case study, we present and analyze the first experimental results concerning the selflocalization problem, by which a wireless sensor node, placed in an unknown location, infers its own position by processing the information received by an AMR that moves in its proximity, thus acting as mobile beacon. The advantage of using mobile beacons for localization in WSN has been already reported in literature. However, most of the previous work refers to outdoor scenarios, while in this paper we report results obtained in a typical indoor environment. The localization problem in indoor environment represents a challenging benchmark to check the functionalities of the RAMSES2 hybrid platform and, despite the project is still in a very initial stage, the first results are promising and call for further investigation of this novel and interesting domain.


Sensor Node Wireless Sensor Network Mobile Robot Mobile Node Wireless Sensor Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Andrea Zanella
    • 1
  • Emanuele Menegatti
    • 1
  • Luca Lazzaretto
    • 1
  1. 1.Department of Information EngineeringUniversity of PadovaItaly

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