Pervasive Computing and Communications Workshops, IEEE International Conference on (2007)
White Plains, New York, USA
Mar. 19, 2007 to Mar. 23, 2007
David Yates , Bentley College, USA
Erich Nahum , IBM T.J. Watson Research Center, USA
Jim Kurose , University of Massachusetts, USA
Prashant Shenoy , University of Massachusetts, USA
This research is motivated by emerging, real-world wireless sensor network applications for monitoring and control. We examine the benefits and costs of caching data for such applications. We propose and evaluate several approaches to querying for, and then caching data in a sensor field data server. We show that for some application requirements (i.e., when delay drives data quality), policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost savings. This win-win is because when system delay is sufficiently important, the benefit to both query cost and data quality achieved by using approximate values outweighs the negative impact on quality due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between query cost and data quality emerges. We also identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a sensor field query. Finally, we demonstrate that our results are robust to the manner in which the environment being monitored changes using two different sensor field models.
D. Yates, E. Nahum, J. Kurose and P. Shenoy, "Data Quality and Query Cost in Wireless Sensor Networks," Pervasive Computing and Communications Workshops, IEEE International Conference on(PERCOMW), White Plains, New York, USA, 2007, pp. 272-278.