Goal and path prediction based on user's moving path data

Yoon, Taebok; Lee, Jee-Hyong

doi:10.1145/1352793.1352892

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…In the method proposed in [ 7 ], the user’s movement route data, distance, and time are collected, and then, the degree of similarity of the path with other frequently used paths is measured using the direction element. The method then selects the highest path similarity.…”

Section: Related Workmentioning

confidence: 99%

A Network Coverage Information-Based Sensor Registry System for IoT Environments

Jung

Jeong

Lee

et al. 2016

Sensors

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The Internet of Things (IoT) is expected to provide better services through the interaction of physical objects via the Internet. However, its limitations cause an interoperability problem when the sensed data are exchanged between the sensor nodes in wireless sensor networks (WSNs), which constitute the core infrastructure of the IoT. To address this problem, a Sensor Registry System (SRS) is used. By using a SRS, the information of the heterogeneous sensed data remains pure. If users move along a road, their mobile devices predict their next positions and obtain the sensed data for that position from the SRS. If the WSNs in the location in which the users move are unstable, the sensed data will be lost. Consider a situation where the user passes through dangerous areas. If the user’s mobile device cannot receive information, they cannot be warned about the dangerous situation. To avoid this, two novel SRSs that use network coverage information have been proposed: one uses OpenSignal and the other uses the probabilistic distribution of the users accessing SRS. The empirical study showed that the proposed method can seamlessly provide services related to sensing data under any abnormal circumstance.

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Section: Related Workmentioning

confidence: 99%

A Network Coverage Information-Based Sensor Registry System for IoT Environments

Jung

Jeong

Lee

et al. 2016

Sensors

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“…In addition to predicting the location, other information was also predicted, for example, the duration of a user’s stay in an actual location or at a destination which was derived by extracting user mobility patterns in conjunction with contextual information (current location and time) (Do and Gatica-Perez, 2012; Scellato et al , 2011). Other studies have simply investigated the user’s destination or vehicle (Patterson et al , 2003; Krumm and Horvitz, 2007; Yoon and Lee, 2008).…”

Section: Related Workmentioning

confidence: 99%

Contextual location prediction using spatio-temporal clustering

Guessoum

Miraoui

Tadj

2016

IJPCC

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Purpose The prediction of a context, especially of a user’s location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive adaptation for context-aware applications. This study/paper aims to propose a methodology that predicts a user’s location on the basis of a user’s mobility history. Design/methodology/approach Contextual information is used to find the points of interest that a user visits frequently and to determine the sequence of these visits with the aid of spatial clustering, temporal segmentation and speed filtering. Findings The proposed method was tested with a real data set using several supervised classification algorithms, which yielded very interesting results. Originality/value The method uses contextual information (current position, day of the week, time and speed) that can be acquired easily and accurately with the help of common sensors such as GPS.

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“…Such approaches deal with the problem of path similarity, which consists into finding the distance between two paths. The work presented in [15] introduce a simple and efficient similarity formula for comparing two paths, but it is unclear how representative paths are selected from the set of observed paths and how locations of interest are defined from the GPS traces. The work presented in [9] is based on a more complex DNA sequence alignment techniques for comparing paths.…”

Section: Introductionmentioning

confidence: 99%

S pinel

Billet

Issarny

2017

ACM Trans. Internet Technol.

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Nowadays, various static wireless sensor networks (WSN) are deployed in the environment, for many purposes: traffic control, pollution monitoring, etc. The willingness to open these legacy WSNs to the users is emerging, by integrating them to the Internet network as part of the future Internet of Things (IoT), for example in the context of smart cities and open data policies. While legacy sensors can not be directly connected to the Internet in general, emerging standards such as 6LoWPAN are aimed at solving this issue but require to update or replace the existing devices. As a solution to connect legacy sensors to the IoT, we propose to take advantage of the multi-modal connectivity as well as the mobility of smartphones to use phones as opportunistic proxies, that is, mobile proxies that opportunistically discover closeby static sensors and act as intermediaries with the IoT, with the additional benefit of bringing fresh information about the environment to the smartphones' owners. However, this requires to monitor the smartphone's mobility and further infer when to discover and register the sensors so as to guarantee the efficiency and reliability of opportunistic proxies. To that end, we introduce and evaluate an approach based on mobility analysis that uses a novel path prediction technique to predict when and where the user is not moving, and thereby serves anticipating the registration of sensors within communication range. We show that this technique enables the deployment of low-cost resource-efficient mobile proxies to connect legacy WSNs with the IoT.

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Goal and path prediction based on user's moving path data

Cited by 6 publications

References 6 publications

A Network Coverage Information-Based Sensor Registry System for IoT Environments

A Network Coverage Information-Based Sensor Registry System for IoT Environments

Contextual location prediction using spatio-temporal clustering

S pinel

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