Power efficiency through tuple ranking in wireless sensor network monitoring

  • Authors:

  • Panayiotis Andreou;Demetrios Zeinalipour-Yazti;Panos K. Chrysanthis;George Samaras

  • Affiliations:

  • Department of Computer Science, University of Cyprus, Nicosia, Cyprus 1678;Department of Computer Science, University of Cyprus, Nicosia, Cyprus 1678;Department of Computer Science, University of Pittsburgh, Pittsburgh, USA 5213-4034;Department of Computer Science, University of Cyprus, Nicosia, Cyprus 1678

  • Venue:
  • Distributed and Parallel Databases
  • Year:
  • 2011

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Abstract

In this paper, we present an innovative framework for efficiently monitoring Wireless Sensor Networks (WSNs). Our framework, coined KSpot, utilizes a novel top-k query processing algorithm we developed, in conjunction with the concept of in-network views, in order to minimize the cost of query execution. For ease of exposition, consider a set of sensors acquiring data from their environment at a given time instance. The generated information can conceptually be thought as a horizontally fragmented base relation R. Furthermore, the results to a user-defined query Q, registered at some sink point, can conceptually be thought as a view V. Maintaining consistency between V and R is very expensive in terms of communication and energy. Thus, KSpot focuses on a subset V驴(驴V) that unveils only the k highest-ranked answers at the sink, for some user defined parameter k.To illustrate the efficiency of our framework, we have implemented a real system in nesC, which combines the traditional advantages of declarative acquisition frameworks, like TinyDB, with the ideas presented in this work. Extensive real-world testing and experimentation with traces from UC-Berkeley, the University of Washington and Intel Research Berkeley, show that KSpot provides an up to 66% of energy savings compared to TinyDB, minimizes both the size and number of packets transmitted over the network (up to 77%), and prolongs the longevity of a WSN deployment to new scales.