StarTrack

Ananthanarayanan, Ganesh; Haridasan, Maya; Mohomed, Iqbal; Terry, Douglas B.; Thekkath, Chandramohan A.

doi:10.1145/1555816.1555838

Cited by 48 publications

(7 citation statements)

References 22 publications

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“…The GC streaming window contains tree grid clusters. Now, consider that the incoming DenseCellCluster event is the hashed cell with index equal to (4,4). DG2CEP first verifies if the incoming cell event is already contained within a given grid cluster.…”

Section: Grid Add Update and Mergementioning

confidence: 99%

DG2CEP: a near real-time on-line algorithm for detecting spatial clusters large data streams through complex event processing

Roriz

Olivieri

Endler

2019

J Internet Serv Appl

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Spatial concentrations (or spatial clusters) of moving objects, such as vehicles and humans, is a mobility pattern that is relevant to many applications. Fast detection of this pattern and its evolution, e.g., if the cluster is shrinking or growing, is useful in numerous scenarios, such as detecting the formation of traffic jams or detecting a fast dispersion of people in a music concert. On-Line detection of this pattern is a challenging task because it requires algorithms that are capable of continuously and efficiently processing the high volume of position updates in a timely manner. Currently, the majority of approaches for spatial cluster detection operate in batch mode, where moving objects location updates are recorded during time periods of a certain length and then batch-processed by an external routine, thus delaying the result of the cluster detection until the end of the time period. Further, they extensively use spatial data structures and operators, which can be troublesome to maintain or parallelize in on-line scenarios. To address these issues, in this paper we propose DG2CEP, a parallel algorithm that combines the well-known density-based clustering algorithm DBSCAN with the data stream processing paradigm Complex Event Processing (CEP) to achieve continuous and timely detection of spatial clusters. Our experiments with real-world data streams indicate that DG2CEP is able to detect the formation and dispersion of clusters with small latency while having higher similarity to DBSCAN than batch-based approaches.

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Section: Grid Add Update and Mergementioning

confidence: 99%

DG2CEP: a near real-time on-line algorithm for detecting spatial clusters large data streams through complex event processing

Roriz

Olivieri

Endler

2019

J Internet Serv Appl

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“…It can be achieved by using StarTrack framework [33]. In StarTrack framework, location of a user will be captured periodically using smartphones.…”

Section: Location Predictionmentioning

confidence: 99%

Survey on Three Components of Mobile Cloud Computing: Offloading, Distribution and Privacy

Paranjothi¹,

Khan²,

Nijim³

2017

JCC

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Mobile Cloud Computing (MCC) brings rich computational resource to mobile users, network operators, and cloud computing providers. It can be represented in many ways, and the ultimate goal of MCC is to enable execution of rich mobile application with rich user experience. Mobility is one of the main characteristics of MCC environment where user can be able to continue their work regardless of movement. This literature review paper presents the state-of-the-art survey of MCC. Also, we provide the communication architecture of MCC and taxonomy of mobile cloud in which specifically concentrates on offloading, mobile distribution computing, and privacy. Through an extensive literature review, we found that MCC is a technologically beneficial and expedient paradigm for virtual environments in terms of virtual servers in a distributed environment, multi-tenant architecture and data storing in a cloud. We further identified the drawbacks in offloading, mobile distribution computing, privacy of MCC and how this technology can be used in an effective way.

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“…All these systems demand time-consuming calibration and are not suitable for a large-scale area, such as urban environments. StarTrack [41] provides a framework for track-based applications, which does not require precise location. However, our work mainly focuses on providing accurate realtime location in outdoor environments, which provides fundamental techniques for location-based service and navigation.…”

Section: Wireless Localizationmentioning

confidence: 99%

SmartLoc: sensing landmarks silently for smartphone-based metropolitan localization

Cheng

Jung

Mao

et al. 2016

J Wireless Com Network

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We present SmartLoc, a system to estimate the location and the traveling distance of a vehicle in urban environments by leveraging the lower-power inertial sensors (e.g., accelerometer, gyroscope, compass) embedded in smartphones. It has been well documented that the inherent noise in the data measured from these sensors will result in a huge drift in the final estimated moving distance and hurt the performance of location-based applications. In SmartLoc, we designed, tested, and evaluated a sequence of techniques to address this notoriously challenging task of noise canceling. By exploiting the intermittent strong GPS signals, we use the linear regression to build a prediction model from the trace estimated from inertial sensors and the trace computed from GPS. We extensively rely on automatically detected landmarks (e.g., bridge, traffic lights) and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensors data to improve the localization accuracy when the GPS signal is weak. Our evaluations of SmartLoc in a large city demonstrate its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately within 20 m for 90 % of the time. SmartLoc system can also reduce the energy consumption for localization by carefully turning off GPS under certain scenarios.

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StarTrack

Cited by 48 publications

References 22 publications

DG2CEP: a near real-time on-line algorithm for detecting spatial clusters large data streams through complex event processing

DG2CEP: a near real-time on-line algorithm for detecting spatial clusters large data streams through complex event processing

Survey on Three Components of Mobile Cloud Computing: Offloading, Distribution and Privacy

SmartLoc: sensing landmarks silently for smartphone-based metropolitan localization

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