Accuracy and Resource Consumption in Tracking and Location Prediction

Wolfson, Ouri; Yin, Huabei

doi:10.1007/978-3-540-45072-6_19

Cited by 51 publications

(41 citation statements)

References 9 publications

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“…In [11], the authors introduced a deviation update policy. The deviation update policy updates the location whenever the deviation between the actual location and the expected location exceeds a given threshold.…”

Section: Location Update Policymentioning

confidence: 99%

RFID Based Indoor Positioning System Using Event Filtering

Bok¹,

Yoo²

2017

Journal of Electrical Engineering and Technology

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-Recently, location systems using RFID technology have been studied in indoor environments. However, the existing techniques require high computational cost to compute the location of a moving object because they compare the location proximity of all reference tags and objects. In this paper, we propose an RFID based location positioning scheme using event filtering, which reduces the computation cost of calculating the locations of moving objects while maintaining the accuracy of location estimation. In addition, we propose an incremental location update policy to reduce the location update cost for moving objects. We also compare the proposed scheme with one of the localization schemes, LANDMARC using a performance evaluation. As a result, the proposed scheme outperforms LANDMARC in terms of the computational cost of location estimation. The proposed scheme also reduces the cost of location update by using the RFID-based update policy.

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Section: Location Update Policymentioning

confidence: 99%

RFID Based Indoor Positioning System Using Event Filtering

Bok¹,

Yoo²

2017

Journal of Electrical Engineering and Technology

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“…Regarding the uncertainty, Wolfson et al [23] introduce a cost based approach to determine the size of the uncertainty area. A formal quantitative approach to the aspect of uncertainty in modeling moving objects is presented in [17].…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Access Control Enforcement under Uncertain Location Estimates

Shin

Atluri

2009

Data and Applications Security XXIII

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Abstract. In a mobile environment, user's physical location plays an important role in determining access to resources. However, because current moving object databases do not keep the exact location of the moving objects, but rather maintain their approximate location for reasons of minimizing the updates, the access request evaluation cannot always guarantee the intended access control policy requirements. This may be risky to the system's security, especially for the highly sensitive resources. In this paper, we introduce an authorization model that takes the uncertainty of location measures into consideration for specifying and evaluating access control policies. An access request is granted only if the confidence level of the location predicate exceeds the predefined uncertainty threshold level specified in the policy. However, this access request evaluation is computationally expensive as it requires to evaluate a location predicate condition and may also require evaluating the entire moving object database. For reducing the cost of evaluation, in this paper, we compute lower and upper bounds (Rmin and Rmax) on the region that minimize the region to be evaluated thereby allowing unneeded moving objects to be discarded from evaluation. We show how Rmin and Rmax can be computed and maintained, and provide algorithms to process access requests.

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“…Such definition of uncertainty is an orthogonal definition from the location uncertainty in moving objects that has been used extensively in the literatures (e.g., [3,16,17,45,[54][55][56]). Location uncertainty refers to the lower resolution and inaccuracy of location-detection devices where the system is not aware of the exact location of moving objects.…”

Section: Uncertainty In Solementioning

confidence: 99%

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

Mokbel

Aref

2007

The VLDB Journal

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This paper presents the Scalable On-Line Execution algorithm (SOLE, for short) for continuous and on-line evaluation of concurrent continuous spatiotemporal queries over data streams. Incoming spatiotemporal data streams are processed in-memory against a set of outstanding continuous queries. The SOLE algorithm utilizes the scarce memory resource efficiently by keeping track of only the significant objects. In-memory stored objects are expired (i.e., dropped) from memory once they become insignificant. SOLE is a scalable algorithm where all the continuous outstanding queries share the same buffer pool. In addition, SOLE is presented as a spatio-temporal join between two input streams, a stream of spatio-temporal objects and a stream of spatio-temporal queries. To cope with intervals of high arrival rates of objects and/or queries, SOLE utilizes a load-shedding approach where some of the stored objects are dropped from memory. SOLE is implemented as a pipelined query operator that can be combined with traditional query operators in a query execution plan to support a wide variety of continuous queries. Performance experiments based on a real implementation of SOLE inside a prototype of a data stream management system show the scalability and efficiency of SOLE in highly dynamic environments.

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Accuracy and Resource Consumption in Tracking and Location Prediction

Cited by 51 publications

References 9 publications

RFID Based Indoor Positioning System Using Event Filtering

RFID Based Indoor Positioning System Using Event Filtering

Spatiotemporal Access Control Enforcement under Uncertain Location Estimates

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

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