Abstract
We present a new adaptive method to calculate the path loss exponent (PLE) for microcell outdoor dynamic environments in the 2.4 GHz Industrial, Scientific, and Medical (ISM) frequency band. The proposed method calculates the PLE during random walks by recording signal strength measurements from Radio Frequency (RF) transceivers and position data with a consumer-grade GPS receiver. The novelty of this work lies in the formulation of signal propagation conditions as a parametric observation model in order to estimate first the PLE and then the distance from the received RF signals using nonlinear least squares. GPS data is used to identify long term fading from the received signal's power and helps to refine the power-distance model. Ray tracing geometries for urban canyon (direct line of sight) and nonurban canyon (obstacles) propagation scenarios are used as the physics of the model (design matrix). Although the method was implemented for a lightweight localization algorithm for the 802.11b/g (Wi-Fi) standard, it can also be applied to other ISM band protocols such as 802.15.4 (Zigbee) and 802.15.1 (Bluetooth).
Highlights
The emergence of context-aware computing applications, and location-based services and the proliferation of portable electronic devices have motivated an extensive research on the topic of node or device localization for wireless networks operating under 2.4 GHz ISM band protocols such as Bluetooth, Zigbee, and especially Wi-Fi
In this paper we propose a path loss exponent (PLE) estimation method which uses the Received Signal Strength Indicator (RSSI) provided by a 802.11b/g transceiver in combination with data collected from a commercial grade GPS receiver
In the following literature review we focus on localization systems for outdoor environments since PLE estimation was first introduced for such environments
Summary
The emergence of context-aware computing applications, and location-based services and the proliferation of portable electronic devices have motivated an extensive research on the topic of node or device localization for wireless networks operating under 2.4 GHz ISM band protocols such as Bluetooth, Zigbee, and especially Wi-Fi. RSS levels are highly unpredictable and the formulation of a path loss model (PLM) as function of distance is a complex issue This complexity is derived from the fact that the received power level is a combination of different signal propagation mechanisms such as reflection, diffraction, and scattering and absorption losses. The algorithm proposed in this work is tailored to microcell RF environments with indoorto-outdoor coverage configurations; models such as Okumura-Hata, Lee, or Walfisch-Bertoni are not treated here From this point forward, when we refer to a blind node, we mean a device (smart phone, hand held, tablet, or laptop) whose position needs to be estimated; when we refer to anchor or beacon or landmark nodes, we mean access points (APs) for whose position are already known.
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