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Retrieval of Spatial Join Pattern Instances from Sensor Networks

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Abstract

We study the continuous evaluation of spatial join queries and extensions thereof, defined by interesting combinations of sensor readings (events) that co-occur in a spatial neighborhood. An example of such a pattern is “a high temperature reading in the vicinity of at least four high-pressure readings”. We devise protocols for ‘in-network’ evaluation of this class of queries, aiming at the minimization of power consumption. In addition, we develop cost models that suggest the appropriateness of each protocol, based on various factors, including selectivity of query elements, energy requirements for sensing, and network topology. Finally, we experimentally compare the effectiveness of the proposed solutions on an experimental platform that emulates real sensor networks.

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Notes

  1. We assume that the locations of sensors are known to them. They could be constant and apriori defined (for stationary, manually placed sensors), or detected by GPS devices placed on the sensors.

  2. Without loss of generality, we assume that all sensors have the same communication range. Our protocols and filtering techniques can be easily adjusted for the generic case.

  3. Low-selectivity joins output few results while high-selectivity joins produce many results.

  4. In fact, if s i receives m > k messages, we have multiple query results, one for each \(m\choose k\) combination of border nodes. Nonetheless all these results can be compressed to a single tuple containing s i and all qualifying border nodes.

  5. A node that qualifies both predicates sends its message up to max {x,λ − x} hops.

  6. We employ low-power idle listening [15], where a node listens only for a short time interval for potential messages. See Table 1 for some typical operation costs.

  7. For a fixed product Sel(P 2)·Sel(P 1), the probability for a sensor to qualify either P 1 or P 2 (i.e., 1 − (1 − Sel(P 1))(1 − Sel(P 2))) is minimized when Sel(P 2) = Sel(P 1) and increases with r.

  8. Let p be the packet loss rate of each sensor node. Consider a fixed retransmission scheme, where each sensor node repeats z times its transmitting/receiving/idle listening operation. Thus, the probability of successfully transmitting a packet (between neighbors) increases rapidly from (1 − p) to (1 − p z). For example, suppose that the original packet loss rate is p = 0.2 (i.e., successful transmission probability of 0.8). At z = 2 (z = 3), we double (triple) the energy consumption of sensors and the successful transmission probability (between neighbors) rises to 0.96 (0.992). Thus, we are able to achieve a very high successful transmission probability, with only a small factor z in energy consumption.

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Authors and Affiliations

  1. Department of Computer Science, Aalborg University, 9220, Aalborg, Denmark

    Man Lung Yiu

  2. Department of Computer Science, University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong

    Nikos Mamoulis

  3. Department of Math. and Comp. Science, John Jay College, City University of New York, New York, NY, USA

    Spiridon Bakiras

Authors
  1. Man Lung Yiu
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  2. Nikos Mamoulis
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  3. Spiridon Bakiras
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Correspondence to Man Lung Yiu.

Additional information

Work supported by grant HKU 7155/06E from Hong Kong RGC. A preliminary version of this work appeared in [25], available at http://www.cs.aau.dk/∼mly/ssdbm07_senpat.pdf.

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Yiu, M.L., Mamoulis, N. & Bakiras, S. Retrieval of Spatial Join Pattern Instances from Sensor Networks. Geoinformatica 13, 57–84 (2009). https://doi.org/10.1007/s10707-007-0043-y

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  • DOI: https://doi.org/10.1007/s10707-007-0043-y

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