A Scalable Technique for Large Scale, Real-Time Range Monitoring of Heterogeneous Clients

Erin J. Hastings; Jaruwan Mesit; Ratan K. Guha

3rd International ICST Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TridentCom)

Research Article

A Scalable Technique for Large Scale, Real-Time Range Monitoring of Heterogeneous Clients

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@INPROCEEDINGS{10.1109/TRIDENTCOM.2007.4444732,
    author={Erin J. Hastings and Jaruwan Mesit and Ratan K. Guha},
    title={A Scalable Technique for Large Scale, Real-Time Range Monitoring of Heterogeneous Clients},
    proceedings={3rd International ICST Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TridentCom)},
    proceedings_a={TRIDENTCOM},
    year={2010},
    month={5},
    keywords={range monitoring; multi-level spatial hashing; mobile object database; continuous query},
    doi={10.1109/TRIDENTCOM.2007.4444732}
}

Erin J. Hastings
Jaruwan Mesit
Ratan K. Guha
Year: 2010
A Scalable Technique for Large Scale, Real-Time Range Monitoring of Heterogeneous Clients
TRIDENTCOM
IEEE
DOI: 10.1109/TRIDENTCOM.2007.4444732

Erin J. Hastings¹^,*, Jaruwan Mesit¹^,*, Ratan K. Guha¹^,*

1: School of Electrical Engineering and Computer Science University of Central Florida, Orlando, FL 32816

*Contact email: hastings@cs.ucf.edu, jmesit@cs.ucf.edu, guha@cs.ucf.edu

Abstract

Range monitoring is the continuous query on location data of mobile, real-world objects in real-time. Such real world objects are typically wireless, low capability clients. Therefore, tracking techniques must limit client computation and memory overhead, allow for client/server heterogeneity, and most importantly, minimize wireless transmissions. This paper presents a technique for range monitoring based on multi-level spatial hashing. The technique addresses: (1) real-time queries on mobile object locations, (2) real-time query on the proximity of mobile objects in relation to each other, (3) user defined special query areas, and (4) allows for variable levels of mobile client capability (heterogeneity). The spatial hashing-based method presented here provides a level of scalability similar to the best existing methods for client processing requirements, transmission size, and transmission frequency. Additionally, it provides the flexibility of multiple tracking modes, proximity queries, and support for multiple server base stations which other methods may not. The results of a simulation that computes total transmission overhead and data server requirements based on mobile object characteristics are presented.

Keywords: range monitoring; multi-level spatial hashing; mobile object database; continuous query

Published: 2010-05-16
Modified: 2010-05-16

: http://dx.doi.org/10.1109/TRIDENTCOM.2007.4444732