Abstract

In this paper, we assess the features of a rectangular constellation of four anchors on the position estimation accuracy of a mobile tag, operating under the IEEE 802.15.4 specifications. Each anchor implements a smart antenna with eight switched beams, which is capable to collect Received Signal Strength Indicator (RSSI) data, exploited to estimate the mobile tag position within a room. We also aim at suggesting a deployment criterion, providing the discussion of the best trade-off between system complexity and positioning accuracy. The assessment validation was conducted experimentally by implementing anchor constellations with different mesh sizes in the same room. Mean accuracies spanning from 0.32 m to 0.7 m on a whole 7.5 m × 6 m room were found by varying the mesh area from 1.19 m2 to 17 m2, respectively.

Highlights

  • It is widely recognized that user localization in Global Navigation Satellite Systems (GNSS)denied environments represents an essential feature for the capillary diffusion of the paradigm [1,2,3].This concept, which has recently been extended and referred to as the “Internet of Things” (IoT), consists of a complex system where physical objects are provided with sensors and actuators, as well as network connectivity

  • In the experiments described after, the mobile tag was installed on a tripod at a fixed height of studies

  • This paper has experimentally assessed the effects of the mesh area on localization accuracy

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Summary

Introduction

Denied environments represents an essential feature for the capillary diffusion of the paradigm [1,2,3] This concept, which has recently been extended and referred to as the “Internet of Things” (IoT), consists of a complex system where physical objects are provided with sensors and actuators, as well as network connectivity. This paradigm relies both on the devices low-energy demand, and their ability to reach an information network where they can contribute to distributed processes [4,5,6,7]. In many cases it is required for the transmitted data to be associated with the context in which the mobile agent operates to improve the information effectiveness, in particular to be linked to its localization [8].

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