WiFi fingerprint positioning based on clustering in mobile crowdsourcing system

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only k-means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future.

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“…To sum up, most of the analyzed methods apply an off-line pre-processing stage [85]. It is devoted to create supporting data, e.g.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

Torres-Sospedra

Richter

Moreira

et al. 2022

IEEE Trans. on Mobile Comput.

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“…Crowdsourcing is a very promising solution to tackle these issues [65]. For example, the researchers in [66] divided the geography target area into several fingerprint clusters identified by Position Feature Vectors (PFVs) via crowdsourcing data for building the offline Wi-Fi fingerprint database. In the online phase, the detected Wi-Fi signal vector is first compared with PFVs to find the most matching cluster, and then the KNN algorithm is employed to calculate the accurate position.…”

Section: Single Rssi-based Positioningmentioning

confidence: 99%

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

Lin

Lyu

et al. 2022

Buildings

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With the increasing innovation and development of Wi-Fi technology, its penetration in the various fields of industry and academia is becoming more and more profound. As the core infrastructure of traffic data collection in the field of Intelligent Transportation Systems (ITS), Wi-Fi-based traffic detectors have great potential for use in traffic target positioning, perception, and pattern recognition due to their low-cost and extensive infrastructure deployment. This paper conducts a comprehensive review of three major Wi-Fi-based traffic detection applications in the field of ITS: target positioning, traffic parameter extraction, and travel mode identification. Among these, target positioning is one of the most widespread applications of Wi-Fi technology, which is also the basis for two other research aspects. Moreover, Wi-Fi-based positioning can be divided into two categories: ranging-based positioning and range-free one; in the field of transportation, it can also be categorized into pedestrian positioning and vehicle positioning based on travel mode. To further demonstrate the effectiveness of Wi-Fi-based ITS applications in practice, this study compares with the various Wi-Fi-involved models and algorithms around the world, as well as provides some ideas and inspiration along with this direction.

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“…For instance, the mobility information of users collected by MCS can provide navigation-based services or enable creating elaborative maps to bicycle riders. Yang et al [142] apply MCS to build a navigation dataset. Sun et al [110] proposed a secure and privacy-preserving finding system to detect different types of objects, such as children or old people.…”

Section: A Smart Navigationmentioning

confidence: 99%

Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges

Kong

Liu

Jedari

et al. 2019

IEEE Internet Things J.

108

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Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this article, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics. Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities.

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WiFi fingerprint positioning based on clustering in mobile crowdsourcing system

Cited by 15 publications

References 20 publications

A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges

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