Robust sensor data collection over a long period using virtual sensing

Rachel Cardell-Oliver and Chayan Sarkar, "Robust sensor data collection over a long period using virtual sensing", First workshop on Time Series Analytics and Applications (TSAA 2016), Hobart, Australia. ACM.

Abstract

Virtual sensing is a technique in which sensor values in a sensor network are estimated some of the time instead of taking direct measurements. Virtual sensing conserves energy on the sensor nodes, and it can be used for recovering missing data due to node or network failure, and for estimating values where permanent sensor installation is infeasible. This paper considers the novel and challenging problem of designing an adaptive long-range virtual sensing (LRVS) framework that is able to estimate values for days or months. The framework does not require any a priori modeling of generating functions for the sensor time series. A new correlation metric, called "nearest hour of the day neighbor", finds similarity in multiple time series that is able to arbitrate the hidden contexts within the data. LRVS is evaluated on data from a building-monitoring sensor network with over 100 heterogeneous, noisy sensors from different suppliers.

BibTex entry

@inproceedings{sarkar2016lrvs,
    title={Robust sensor data collection over a long period using virtual sensing},
    author={Cardell-Oliver, Rachel and Sarkar, Chayan},
    booktitle={Proceedings of the Workshop on Time Series Analytics and Applications},
    series = {TSAA '16},
    pages={2--7},
    isbn = {978-1-4503-4820-1},
    year={2016},
    organization={ACM}
}