Crowdsourcing and Sensing for Indoor Localization in IoT: A Review

Lashkari, Bahareh; Rezazadeh, Javad; Farahbakhsh, Reza; Sandrasegaran, Kumbesan

doi:10.1109/jsen.2018.2880180

Cited by 99 publications

(46 citation statements)

References 117 publications

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“…The method proposed in this work could be extended using crowdsourcing [16], as we did previously to detect a disabled AP [15]. The previously introduced methods allow the system to limit rebuilding frequency by waiting for a significant number of raised alarms to recreate the localisation model.…”

Section: State-of-the-artmentioning

confidence: 99%

Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

Luckner

Górak

2020

Sensors

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This paper faces the issue of changing the received signal strength (RSS) from an observed access point (AP). Such a change can reduce the Quality of Service (QoS) of a Wi-Fi-based Indoor Localisation System. We have proposed a dynamic system based on an estimator of RSS using the readings from other APs. Using an optimal threshold, the algorithm recognises an AP that has changed its characteristics. Next, the system rebuilds the localisation model excluding the changed AP to keep QoS. For the tests, we simulated a change in the analysed Wi-Fi network by replacing the measured RSS by an RSS obtained from the same AP model that lies in another place inside the same multi-floor building. The algorithm was evaluated in simulations of an isolated single-floor building, a single-floor building and a multi-floor building. The mean increase of the localisation error obtained by the system varies from 0.25 to 0.61 m after the RSS changes, whereas the error increase without using the system is between 1.21 and 1.98 m. The system can be applied to any service based on a Wi-Fi network for various kinds of changes like a reconfiguration of the network, a local malfunction or ageing of the infrastructure.

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Section: State-of-the-artmentioning

confidence: 99%

Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

Luckner

Górak

2020

Sensors

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“…According to a classification proposed by Lashkari et al [5] current indoor positioning systems can be grouped in two categories:  crowdsourcingsystems that imply an explicit interaction with the user, the information being harvested directly from the users  crowdsensingsystems that require implicit user interaction, where the data is collected from sensors rather than the users Besides the more extensive used techniques in indoor location that rely on external deployed equipment and wireless technologies to compose proximity based systems like WiFi, Zigbee, Bluetooth, acoustic signals, Radio Frequency Identification (RFID) or Ultra-wideband (UWB), this paper will focus more on those approaches that use software and common devices in detriment of dedicated hardware components. Comprehensive literature review of most important indoor location techniques that fall in the spectrum of the former category, like Received Signal Strength Indicator (RSSI), Angle of Arrival (AoA), Phase of Arrival (PoA), Time of Flight (ToF), Return Time of Flight (RToF), Line of Sight (LoS), might be found in Zafara et al [6], Magdid et al [7], Ferreira et al [8] and Xiao et al [9].…”

Section: Current Contextmentioning

confidence: 99%

Sensors for Web – Alternative for Traditional Mobile Applications

Obretin¹

2020

IJISRT

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This paper is providing an in-detail analysis of existing mobile based software solutions for indoor localization and identifies several issues to be tackled. The objective this research initiative is assuming is to provide an alternative approach for traditional sensorbased applications, an alternative that behaves better in terms of resource consumption, maintenance costs and degree of generality. The principles hereby described have been integrated into a web application that has been tested in two different environments, providing indoor localization accuracy under a meter

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“…Zhao et al [25] propose a crowdsourcing and multisource fusion-based fingerprint sensing where motion sensors are used to construct the radio map by volunteers and pedestrian dead reckoning based on motion sensors equipped in smartphones is used for positioning. Lashkari et al [26] study the level of user contribution in available crowd-powered techniques and propose a classification for crowd-powered indoor localization solutions to clarify which crowds-based approach is utilized in each indoor localization solution.…”

Section: Related Workmentioning

confidence: 99%

Effective Fingerprint Extraction and Positioning Method Based on Crowdsourcing

Zhao

et al. 2019

IEEE Access

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At present, WiFi fingerprinting indoor positioning technology is a research hotspot, and the construction of a radio map based on crowdsourcing can significantly reduce the amount of labor required. However, when users collect fingerprint information using crowdsourcing data in practical applications, the received signal strength (RSS) information collected by user phones updates slowly and will remain the same for a certain distance and time. Therefore, crowdsourcing data is inaccurate, and a radio map thereby established will also lead to inaccurate indoor positioning. In order to address this issue, this paper proposes a method for extracting effective RSS information from crowdsourcing data in order to establish the radio map and achieve positioning. By analyzing the crowdsourcing data characteristics, effective RSS information is identified and extracted. Based on inertial sensors data, the position of effective RSS information is determined. The effective crowdsourcing data associated with the position is weighted and fused in order to establish the radio map. Aimed at the issue of the RSS collected by phones updating slowly, a combined positioning method for position calculation is proposed in order to achieve effective positioning. Experiments were conducted in a real environment to validate the algorithm, and the positioning accuracy could reach 1.5 m by using crowdsourcing data to construct the radio map and executing an indoor positioning algorithm, which is close to the accuracy achieved when using manual data collected by professionals at a certain distance and saves a great deal of labor.

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Crowdsourcing and Sensing for Indoor Localization in IoT: A Review

Cited by 99 publications

References 117 publications

Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

Sensors for Web – Alternative for Traditional Mobile Applications

Effective Fingerprint Extraction and Positioning Method Based on Crowdsourcing

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