Abstract
In the context of fingerprinting applications, this article presents the performance analysis of a type of space labeling based on the binary quantization of the received signal strength indicator. One of the common drawbacks of fingerprinting is the large data size and consequently the large search space and computational load as a result of either vastness of the positioning area or the finer resolution in the fingerprinting grid map. Our approach can be considered, for example, when we use very small, inexpensive beacons, like those based on bluetooth low energy technology, radio frequency identification, or in the future context of the Internet of Things. One of the interesting properties of this deployment is that it can be interpreted as a form of space labeling or encoding since space is divided into cells, and each cell is associated to a binary codeword with the corresponding scalability of the spatial resolution. Here, it developed the performance estimation, exploiting the association of this deployment to an error correcting code. The analysis and numerical and experimental results allow a deeper understanding of the impact of the proposed solution and show that it is robust and computationally efficient with respect to the traditional fingerprinting technique.
Highlights
Due to recent developments in hardware electronics and communications, wireless personal area networks (WPANs) and wireless sensor networks (WSNs) have found outstanding importance in diverse applications such as industrial, medical, public services, and many other fields
The wireless local area network (WLAN) technology offers the best solutions in terms of costs and this thanks to the reuse of available infrastructures it has many limitations in terms of accuracy and reliability
We have introduced a new scheme of a specific binary representation of the received signal strength indicator (RSSI) signatures and the measures
Summary
Due to recent developments in hardware electronics and communications, wireless personal area networks (WPANs) and wireless sensor networks (WSNs) have found outstanding importance in diverse applications such as industrial, medical, public services, and many other fields. A radio map containing stored LD parameters measured over predetermined points (grid points) is built during an off-line or training phase and the target position is estimated via pattern matching between measured LD parameters and those previously recorded. In He and Chan,[5] it is possible to find some recent trends in two of the major research areas for FP localization: advanced localization techniques and efficient system deployment. Taking advantage of LD features of the radio signal, there can exist a radio map containing LD parameters measured in predetermined points called grids so that target position can be estimated using pattern matching algorithms. The estimate is a convex combination of the calibration points Pk, that is
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