Sensor networks are commonly adopted to collect a variety of measurements in indoor and outdoor settings. However, collecting such measurements from every node in the network, although providing high accuracy and resolution of the phenomena of interest, may easily cause sensors' battery depletion. In this article, we show that measurement correlation can be successfully exploited to reduce the amount of data collected in the network without significantly sacrificing the monitoring accuracy. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and we formulate an optimization problem to select the monitors under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. We also develop statistical approaches that automatically switch between learning and estimation phases to take into account the variability occurring in real networks. Simulations carried out on real-world traces show that our approach outperforms previous solutions based on compressed sensing, and it can be successfully applied to the real application of solar irradiance prediction of photovoltaics systems.
|Journal||ACM Transactions on Sensor Networks|
|State||Published - Dec 2018|
Bibliographical noteFunding Information:
This research was sponsored in part by the U.S. Army Research Laboratory and the U.K. Ministry of Defence, under Agreement Number W911NF-06-3-0001, by the NSF EPSCoR grant IIA-1355406, and by the NATO - North Atlantic Treaty Organization SPS grant G4936. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. We would like to thank Stefan Achleitner for the useful discussions on the solar irradiance prediction system, and for sharing the data of the irradiance dataset. Authors’ addresses: S. Silvestri, University of Kentucky, Davis Marksbury Building, 329 Rose Street, Lexington, KY 40506-0633, USA; email: email@example.com; R. Urgaonkar, Amazon Research, 410 Terry Ave. North, Seattle, WA, 98109-5210; email: firstname.lastname@example.org; M. Zafer, Nyansa Inc., 430 Cowper Street, Ste, 250, Palo Alto, CA 94301; email: email@example.com; B. Jun Ko, IBM T. J. Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598; email: firstname.lastname@example.org. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from email@example.com. © 2018 Association for Computing Machinery. 1550-4859/2018/12-ART4 $15.00 https://doi.org/10.1145/3272035
© 2018 Association for Computing Machinery.
- Change detection
- Estimation framework
- Measurement correlation
- Network monitoring
ASJC Scopus subject areas
- Computer Networks and Communications