FreeLoc: Calibration-free crowdsourced indoor localization

Yang, Sung Mo; Dessai, Pralav; Verma, Mansi; Gerla, Mário

doi:10.1109/infcom.2013.6567054

Cited by 219 publications

(108 citation statements)

References 21 publications

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“…FreeLoc addresses radiomap construction across heterogeneous devices by employing relative, rather than absolute RSS values, and uses techniques for maintaining a single fingerprint for each location in the radiomap, irrespective of any number of uploaded data sets for a given location, thus keeping the radiomap to a reasonable size [107]. Differential, instead of absolute, RSS values have been explored for fusing crowdsourced RSS data collected with heterogeneous devices to make the resulting radiomap completely device independent [108].…”

Section: Radiomap Construction Through Crowdsourcingmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

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Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

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Section: Radiomap Construction Through Crowdsourcingmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

View full text Add to dashboard Cite

show abstract

“…The signal strength sampled at one place will change and fluctuate in a small range with the time pass by. An experiment proves that the sequences of a pair of AP would be stable if the RSSI value exceed 10dBm between them [12].…”

Section: Introductionmentioning

confidence: 91%

A Weighted K-AP Query Method for RSSI Based Indoor Positioning

Huo

Liu

et al. 2016

Lecture Notes in Computer Science

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Abstract. The paper studies the establishment of offline fingerprint library based on RSSI (Received Signal Strength Indication), and proposes WF-SKL algorithm by introducing the correlation between RSSIs. The correlations can be transformed as AP fingerprint sequence to build the offline fingerprint library. To eliminate the positioning error caused by instable RSSI value, WF-SKL can filter the noise AP via online AP selection, meanwhile it also reduces the computation load. WF-SKL utilizes LCS algorithm to find out the measurement between the nearest neighbors, and it proposes K-AP (P,Q) nearest neighbor queries between two sets based on Map-Reduce framework. The algorithm can find out K nearest positions and weighted them for re-positioning to accelerate the matching speed between online data and offline data, and also improve the efficiency of positioning. According to a large scale positioning experiments, WF-SKL algorithm proves its high accuracy and positioning speed comparing with KNN indoor positioning.

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“…Both methods, aim to reduce the number of reference points (RP) and RSS measurements. Also, [14][15][16] explore crowdsourcing-based methods to reduce the deployment workload by engaging the users to participate in radio map construction.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Several works have been proposed to reduce the workload of the offline phase [11][12][13]. The crowdsourcing method has been shown to be a promising approach to solving this problem [14][15][16]. In a crowdsourcing-based system, each user can contribute to the construction and updating of the radio map.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

Zhang

Valaee

et al. 2017

Applied Sciences

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Abstract:Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

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FreeLoc: Calibration-free crowdsourced indoor localization

Cited by 219 publications

References 21 publications

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

A Weighted K-AP Query Method for RSSI Based Indoor Positioning

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

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