6 research outputs found
Data-aided Sensing for Gaussian Process Regression in IoT Systems
In this paper, for efficient data collection with limited bandwidth,
data-aided sensing is applied to Gaussian process regression that is used to
learn data sets collected from sensors in Internet-of-Things systems. We focus
on the interpolation of sensors' measurements from a small number of
measurements uploaded by a fraction of sensors using Gaussian process
regression with data-aided sensing. Thanks to active sensor selection, it is
shown that Gaussian process regression with data-aided sensing can provide a
good estimate of a complete data set compared to that with random selection.
With multichannel ALOHA, data-aided sensing is generalized for distributed
selective uploading when sensors can have feedback of predictions of their
measurements so that each sensor can decide whether or not it uploads by
comparing its measurement with the predicted one. Numerical results show that
modified multichannel ALOHA with predictions can help improve the performance
of Gaussian process regression with data-aided sensing compared to conventional
multichannel ALOHA with equal uploading probability.Comment: 10 pages, 8 figures, to appear in IEEE IoT
Energy and bandwidth-efficient Wireless Sensor Networks for monitoring high-frequency events
2013 10th Annual IEEE Communications Society Conference on Sensing and Communication in Wireless Networks, SECON 2013, New Orleans, LA, 24-27 June 2013Wireless Sensor Networks (WSNs) are mostly deployed to detect events (i.e., objects or physical changes) at a high/low frequency sampling that is usually adapted by a central unit (or a sink), thus requiring additional resource usage in WSNs. However, the problem of autonomous adaptive sampling regarding the detection of events has not been studied before. In this paper, we propose a novel scheme, termed 'event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)' by addressing the problem in two stages, which lead to reduced resource usage (e.g., energy, radio bandwidth) in WSNs. First, e-Sampling provides a solution to adaptive sampling that automatically switches between high- and low-frequency intervals to reduce the resource usage while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained WSNs. In the absence of an event, 'uninteresting' data is not transmitted to the sink. We apply e-Sampling to structural health monitoring (SHM), which is a typical application of high frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in expanding the capacity of WSNs for high data rate applications.Department of ComputingRefereed conference pape