Dynamic radius and confidence prediction in grid-based location prediction algorithms

Hazan, Itay; Shabtai, Asaf

doi:10.1016/j.pmcj.2017.10.007

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…In the future we intend to apply this method on additional grid-based location prediction algorithms (e.g., neural network), POI-based location prediction algorithms, and use it to improve the next location radius such as done in [44] in addition to the next location cell.…”

Section: Discussionmentioning

confidence: 99%

Improving Grid-Based Location Prediction Algorithms by Speed and Direction Based Boosting

Hazan

Shabtai

2019

IEEE Access

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Grid-based location prediction algorithms are widely researched and evaluated. These algorithms usually integrate the speed and the direction during the learning process as regular contextual features, for example, like the time of the day or the day of the week. Unfortunately, the way speed and direction are currently used does not fulfill their potential. In this paper, we propose an alternative approach for integrating the user's current speed and direction in a post-processing mechanism that highly improves the algorithms' accuracy. We dynamically update the probabilities of the predictions provided by the existing (base) algorithms by dividing the surface into four areas while boosting the probabilities in some areas and reducing the probabilities in others. We evaluated our method on three well-known grid-based location prediction algorithms and two different datasets and were able to show that our method improves the predictions made solely by the algorithms. Our improvement was stable during the entire experiment for long-term predictions and for greater prediction distances, particularly in the cold start phase which is considered more difficult to improve.

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Section: Discussionmentioning

confidence: 99%

Improving Grid-Based Location Prediction Algorithms by Speed and Direction Based Boosting

Hazan

Shabtai

2019

IEEE Access

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“…The currently available large-scale geolocation data from urban traffic including buses, trucks, and taxis equipped with global positioning system (GPS) equipment provide a reliable data source for traffic geography analysis. Such vehicle trajectory data have been applied for the extraction of points of interest within cities [1], map matching [2], road network map building and updating [3], location prediction [4], experiential optimal path selection [5] and other applications. The traffic patterns of urban residents can be understood across different spatiotemporal ranges via the examination and processing of these big data.…”

Section: Introductionmentioning

confidence: 99%

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

Chen

2021

IJGI

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The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.

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“…• Host-based: In this approach, an installed application can infer the location of the device by probing built-in sensors or evaluating data provided when a user checks in to a place on social media. Local sensors that can provide location data include hotspot (Wi-Fi) information such as the SSID and BSSID [1], connected cellular, as well as GPS [2]. The location can also be inferred by using various side-channel attacks such as power supply variance analysis [3].…”

Section: Introductionmentioning

confidence: 99%

“…In order to utilize location traces as a meaningful information source, it is imperative to analyze the data and aggregate it to location clusters that are important to the user, such as home, shopping, or work [2]. These locations are also known as the users' points of interest (POIs).…”

Section: Introductionmentioning

confidence: 99%

“…1 This privacy breach was also acknowledged during the 2018 DEFCON workshops, when applications of both iOS and Android-based devices were detected sending accurate location data in unencrypted formats. 2 In this research, we investigate the phenomenon of location data leakage in the Internet traffic of Android based smartphones. The main goals of this research are as follows:…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Location Data Leakage in the Internet Traffic of Android-based Mobile Devices

Sivan,

Bitton,

Shabtai

2018

Preprint

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In recent years we have witnessed a shift towards personalized, context-based applications and services for mobile device users. A key component of many of these services is the ability to infer the current location and predict the future location of users based on location sensors embedded in the devices. Such knowledge enables service providers to present relevant and timely offers to their users and better manage traffic congestion control, thus increasing customer satisfaction and engagement. However, such services suffer from location data leakage which has become one of today's most concerning privacy issues for smartphone users. In this paper we focus specifically on location data that is exposed by Android applications via Internet network traffic in plaintext (i.e., without encryption) without the user's awareness. We present an empirical evaluation, involving the network traffic of real mobile device users, aimed at: (1) measuring the extent of location data leakage in the Internet traffic of Android-based smartphone devices; and (2) understanding the value of this data by inferring users' points of interests (POIs). This was achieved by analyzing the Internet traffic recorded from the smartphones of a group of 71 participants for an average period of 37 days. We also propose a procedure for mining and filtering location data from raw network traffic and utilize geolocation clustering methods to infer users' POIs. The key findings of this research center on the extent of this phenomenon in terms of both ubiquity and severity; we found that over 85% of devices of users are leaking location data, and the exposure rate of users' POIs, derived from the relatively sparse leakage indicators, is around 61%.

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Dynamic radius and confidence prediction in grid-based location prediction algorithms

Cited by 5 publications

References 12 publications

Improving Grid-Based Location Prediction Algorithms by Speed and Direction Based Boosting

Improving Grid-Based Location Prediction Algorithms by Speed and Direction Based Boosting

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

Analysis of Location Data Leakage in the Internet Traffic of Android-based Mobile Devices

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