Binary fingerprinting-based indoor positioning systems

Mizmizi, Marouan; Reggiani, Luca

doi:10.1109/ipin.2017.8115899

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…Indoor positioning systems are being widely studied recently, and providing robust indoor positioning and navigation solutions is a concern. Moreover, many researchers are studying the accuracy of indoor positioning systems [16]- [19] and how to reduce the server load with different algorithms and implementation techniques [20]- [22].…”

Section: Related Workmentioning

confidence: 99%

An Indoor Positioning and Navigation System Using Named Data Networking

et al. 2020

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Named data networking (NDN) is a new and promising Internet architecture, which aims to replace the current transmission control protocol/Internet protocol (TCP/IP)-based Internet. The NDN internet architecture has introduced several benefits in numerous applications. An NDN-based indoor positioning and navigation system can further optimize existing localization technologies with reduced the server load and faster response time. In this study, an NDN-based approach for the existing Wi-Fi fingerprinting-based indoor positioning and navigation was investigated. Among the many features of NDN, the network-level caching can reduce the computational load and response time of the localization and navigation server. The theoretical analysis of the runtime complexity shows that the NDN's network-level caching performance is better than those of the conventional algorithms. In this paper, naming methods for different services and a server-side algorithm for handling the NDN requests are proposed. The real-world implantation and testing results show a better overall performance than that of TCP/IP. This network-level optimization for indoor positioning and navigation opens new opportunities because it can be combined with other application-level optimization techniques for more efficient indoor positioning and navigation in the future.

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Section: Related Workmentioning

confidence: 99%

An Indoor Positioning and Navigation System Using Named Data Networking

et al. 2020

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“…Mizmizi et al proposed a binary fingerprinting-based indoor positioning systems [16], which quantified RSS values (ranging from −127∼30dBm) as briary codes. This method could quantify the traditional 8-bit data (including symbol bit) to a 1-bit data, and finally save 87.5% of the storage.…”

Section: Related Workmentioning

confidence: 99%

“…Wang X et al proposed a novelty method to train the dataset which is based on Deep Learning, and the model trained by Deep Learning can obtain high accuracy [17]. Gao W et al proposed an idea of RSS quantization, which was similar to [16], for the purpose of storage reduction. However, it utilized Genetic Algorithm (GA) to select the RSS borders in the range of RSS value to determine the quantization bits [18].…”

Section: Related Workmentioning

confidence: 99%

A Reliable Localization Algorithm Based on Grid Coding and Multi-Layer Perceptron

Sun¹,

Zhang

Ren³

2020

IEEE Access

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The traditional RSS-based fingerprint localization algorithm needs RSS values from all access points (AP) at each reference point (RP). In the large-scale indoor environment, the increasing of the number of APs will lead to establish a large-scale fingerprint database, which occupies a lot of storage space. In this paper, we propose a new reliable localization algorithm, which firstly utilizes quantized RSS to encode the monitoring region which has been divided into grids, so as to specify the grids that the interested target appears roughly. Then, we utilize Multi-Layer Perceptron (MLP) to train the grid regions in which the beacons deployment is non-isomorphic and obtain the accurate localization result. Due to the same deployment of isomorphic regions, it is imperative to train only one model to replace the others, which greatly reduces the computation of neural network. It can be concluded from the experimental results that compared with the traditional MLP-based fingerprint localization algorithm, the proposed algorithm reduces the size of fingerprint database over 80% with guarantee of localization accuracy. Moreover, our algorithm can obtain better localization accuracy compared with the other latest quantization based localization algorithm. INDEX TERMS Grid coding, multi-layer perceptron, indoor localization.

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“…The obtained results showed that the computational complexity can be reduced, without loss of precision, by adapting the RSSI quantization with respect to the variance of the measurement noise. In Mizmizi and Reggiani, 12 we have focused on the simplest quantization, with two levels, exploring its relations with binary encoding. We have introduced a new scheme of a specific binary representation of the RSSI signatures and the measures (binary fingerprinting or BFP).…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we consider the design presented in Mizmizi and Reggiani, 12 developing the analytical estimation of the space discrimination provided by this novel spatial labeling. Therefore, the main contributions of this article are…”

Section: Introductionmentioning

confidence: 99%

Analysis of space labeling through binary fingerprinting

Mizmizi

Reggiani

2019

International Journal of Distributed Sensor Networks

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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.

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Binary fingerprinting-based indoor positioning systems

Cited by 8 publications

References 9 publications

An Indoor Positioning and Navigation System Using Named Data Networking

An Indoor Positioning and Navigation System Using Named Data Networking

A Reliable Localization Algorithm Based on Grid Coding and Multi-Layer Perceptron

Analysis of space labeling through binary fingerprinting

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