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Self-organizing virtual macro sensors

Published:04 May 2012Publication History
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Abstract

The future large-scale deployment of pervasive sensor network infrastructures calls for mechanisms enabling the extraction of general-purpose data at limited energy costs. The approach presented in this article relies on a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence to spatial regions characterized by similar sensing patterns, and to let distributed aggregation of sensorial data take place on a per-region basis. The result of this process is that a sensor network can be modeled as a collection of virtual macro sensors, each associated to a well-characterized region of the physical environment. Within each region, each physical sensor has the local availability of aggregated data about its region and is able to act as an access point to such data. This feature promises to be very suitable for a number of emerging usage scenarios. Our approach is described and evaluated in both a simulation environment and a real test bed, and quantitatively compared with related works in the area. Current limitations and areas of future development are also discussed.

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            • Published in

              cover image ACM Transactions on Autonomous and Adaptive Systems
              ACM Transactions on Autonomous and Adaptive Systems  Volume 7, Issue 1
              Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
              April 2012
              365 pages
              ISSN:1556-4665
              EISSN:1556-4703
              DOI:10.1145/2168260
              Issue’s Table of Contents

              Copyright © 2012 ACM

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 4 May 2012
              • Accepted: 1 May 2011
              • Revised: 1 August 2010
              • Received: 1 May 2009
              Published in taas Volume 7, Issue 1

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