Adaptive and Reconfigurable ResOurce Management for Wireless Sensors using Grid Technology

Rao, Imran; Imran, Nomica; Khan, Salman; Huh, Eui‐Nam; Chung, T. J.

doi:10.1109/comswa.2007.382470

Cited by 8 publications

(3 citation statements)

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“…So we propose a fast and dynamic event-matching algorithm that can meet the requirements of CBPSID system in Sensor-Grid. Also, we integrate our proposed ARROW resource management system [8] and OGSA-DAI [34] with the CBPSID system to efficiently process and disseminate sensor data to consumers respectively. The overall framework is shown in Fig.…”

Section: System Overviewmentioning

confidence: 99%

“…In our previous work [8], we addressed the first issue and proposed an adaptive and reconfigurable resource management model called ARROW (Adaptive and Reconfigurable ResOurce management for Wireless sensors using Grid technology) for real-time processing of huge sensor data by delegating of resource intensive task to the Grid through the mediation of a sensor-to-grid middleware. The design goal of ARROW was to adapt the variations in the resource requirements and find more resources or release unutilized resources to allay the resource management cost in Sensor-Grid framework.…”

Section: Introductionmentioning

confidence: 99%

“…Our algorithm focuses on the CBPSID system corresponding to OGF INFOD standard effort [10]. We also integrate the ARROW system of our previous work [8] with the CBPSID system to efficiently process sensor data and a data delivery mechanism called OGSA-DAI [34] to effectively disseminate sensor data to consumers from various geographically distributed heterogeneous Grid data sources in Sensor-Grid. The main contributions of this paper are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

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A dynamic and fast event matching algorithm for a content-based publish/subscribe information dissemination system in Sensor-Grid

2009

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In this paper, we discuss one of the most important issues in Sensor-Grid, i.e., to develop a fast and flexible content-based publish/subscribe information dissemination (CBPSID) system for automatic fusion, interpretation, sharing and delivery of huge sensor data to consumers as the entire Sensor-Grid environment is very dynamic. Existing works to develop the CBPSID system in Sensor-Grid mostly focus on reducing the effort to define and maintain subscriptions and to handle the difficulty of dynamic changes of publishers and consumers data. However, the performance of a CBPSID system in Sensor-Grid is bounded by the expensive matching/evaluation cost of events. Existing event-matching algorithms are not very efficient, especially for interval range predicates or overlapping predicates in subscriptions which are practical in Sensor-Grid as well as other application areas. So in this paper we discuss the above challenge and propose a dynamic and fast event-matching algorithm called CGIM for the CBPSID system in Sensor-Grid. The algorithm supports range predicates or overlapping predicates very well and provides single and composite event matching. It uses two approaches, called SGIM and DGIM, to group the subscriptions by the predicates and dynamically identifies appropriate number of groups considering different statistical distributions of subscriptions at run time. Also, we present an experimental evaluation of the proposed algorithm in a Sensor-Grid based u-Healthcare scenario using synthetic workloads and compare its performance with existing algorithms. The experimental results show that our algorithm significantly reduces the evaluation cost (on average using SGIM by 79% and DGIM by 88%) CBPSID system in Sensor-Grid 331 comparing with others and guarantees the scalability with respect to the number of subscriptions as well as the number of predicates and events. In addition, further experiments were conducted by applying the CGIM algorithm in other application areas, e.g. in the publish/subscribe system for online job sites, to show its diverse utilization and scalability.

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Section: System Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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