Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks

Jung, Suk-Hoon; Moon, Byungchul; Han, Dongsoo

doi:10.1109/tmc.2015.2506585

Cited by 88 publications

(38 citation statements)

References 28 publications

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“…In [9], a probabilistic radio map construction method is propose with crowdsourcing collection, taking into account both accuracy and survey costs. In order to obtain the need for location labels, Jung et al [10] propose the unsupervised learning method to calibrate a localization model based on a global-local optimization scheme. In this hybrid scheme, an efficient global-local interaction reduces the task complexity drastically.…”

Section: Related Workmentioning

confidence: 99%

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

Zhai

Wang

2019

Wireless Communications and Mobile Computing

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Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications (e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing paradigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for a single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can nish a part of the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore, the probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task crowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task partition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. en, using a Markov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task partition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workers' arrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task assignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing potential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.

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Section: Related Workmentioning

confidence: 99%

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

Zhai

Wang

2019

Wireless Communications and Mobile Computing

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“…For example, the methods by Chintalapudi et al [15] and Makki et al [16] generates the RSSI propagation model acquired by smartphones when phone holders walk in the building. Their technique is able to build an RSSI propagation model in the building without any annotation [17].…”

Section: Indoor Localizationmentioning

confidence: 99%

EasyTrack: Zero-Calibration Smart-Home Tracking System

Kitbutrawat

Yamaguchi

Higashino

2019

Journal of Information Processing

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Location-based services in household enable not only estimating activities but also detecting the accident location of residents including children, parents and elderly people. Furthermore, home management systems make use of location information of residents for encouraging residents to live comfortably in their own homes. Passive infrared binary motion sensors are widely used in home tracking systems because of low energy consumption and flexibility on deployment. However, it's hard for non-specialists like residents to manage such location information of sensors which are very important for tracking systems. To mitigate human efforts and errors, we propose a sensor localization method to automatically identify the location of multiple binary motion sensors in a house from the observed motion detection event sequences by binary sensors. The method finds the movement patterns and characteristics of sojourn time of residents to identify the rooms where those sensors are located as well as the proximity relations among sensors assisted by prior knowledge such as from floorplan. The experimental results in actual houses show that our method can estimate the location of sensors placed close to the anchor locations within a one-day observation, and the accuracy of our approach is above 80% after three-day observation.

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“…EZ [6] and LARM [30] try to model the signal strength distribution using learning-based methods, but they still need some fingerprints labelled with location information. UCMA [22] is also a learningbased crowdsourcing system but requires no labelled fingerprints and no inertial data. It proposes a method that integrates a memetic algorithm and a segmental k-means algorithm in a hybrid globallocal optimisation scheme to locate unlabelled fingerprints to the floor plan.…”

Section: Related Workmentioning

confidence: 99%

Semi-Automated Signal Surveying Using Smartphones and Floorplans

Gao

Harle

2018

IEEE Trans. on Mobile Comput.

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Abstract-Location fingerprinting locates devices based on pattern matching signal observations to a pre-defined signal map. This paper introduces a technique to enable fast signal map creation given a dedicated surveyor with a smartphone and floorplan. Our technique (PFSurvey) uses accelerometer, gyroscope and magnetometer data to estimate the surveyor's trajectory post-hoc using Simultaneous Localisation and Mapping and particle filtering to incorporate a building floorplan. We demonstrate conventional methods can fail to recover the survey path robustly and determine the room unambiguously. To counter this we use a novel loop closure detection method based on magnetic field signals and propose to incorporate the magnetic loop closures and straight-line constraints into the filtering process to ensure robust trajectory recovery. We show this allows room ambiguities to be resolved. An entire building can be surveyed by the proposed system in minutes rather than days. We evaluate in a large office space and compare to state-of-the-art approaches. We achieve trajectories within 1.1 m of the ground truth 90% of the time. Output signal maps well approximate those built from conventional, laborious manual survey. We also demonstrate that the signal maps built by PFSurvey provide similar or even better positioning performance than the manual signal maps.

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Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks

Cited by 88 publications

References 28 publications

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

EasyTrack: Zero-Calibration Smart-Home Tracking System

Semi-Automated Signal Surveying Using Smartphones and Floorplans

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