Abstract
The widely used pull-based method for high-frequency sensor data acquisition from Sensor Observation Services (SOS) is not efficient in real-time applications; therefore, further attention must be paid to real-time mechanisms in the provision process if sensor webs are to achieve their full potential. To address this problem, we created a data provision problem model, and compare the recursive algorithm Kalman Filter (KF) and our two proposed self-adaptive linear algorithms Harvestor Additive Increase and Multiplicative Decrease (H-AIMD) and Harvestor Multiplicative Increase and Additive Decrease (H-MIAD) with the commonly used Static Policy, which requests data with an unchanged time interval. We also developed a comprehensive performance evaluation method that considers the real-time capacity and resource waste to compare the performance of the four data provision algorithms. Experiments with real sensor data show that the Static Policy needs accurate priori parameters, Kalman Filter is most suitable for the data provision of sensors with long-term stable time intervals, and H-AIMD is the steadiest with better efficiency and less delayed number of data while with a higher resource waste than the others for data streams with much fluctuations in time intervals. The proposed model and algorithms are useful as a basic reference for real-time applications by pull-based stream data acquisition.
Highlights
The detection and early warning of emerging natural hazards and man-made disasters require real-time geographic information to support effective and timely emergency response
Based on the analysis of dynamic problems and algorithms, which could perform forecasting tasks, we modeled the pull-based process of real-time sensor data provision from sensor observation services, and four policies are discussed in terms of the proposed model
We have found a time interval showing no observation in about five hours, which may be caused by the car inspection and maintenance or a rest of the driver
Summary
The detection and early warning of emerging natural hazards and man-made disasters require real-time geographic information to support effective and timely emergency response. Sensor observations must be acquired in real-time [3,10] for numerous applications through accessible data services; existing provision methods lack effective and efficient real-time data acquisition mechanisms. A dynamic model and adaptive algorithms, with high time-efficiency and low resource waste, are needed for real-time applications of high-frequency sensor data flows. Geo-Inf. 2016, 5, 51 sensors according to the energy levels These two algorithms, AIMD and MIAD, based on this analysis, could be adapted and used as linear algorithms for real-time forecasting in sensor data provision. Based on the analysis of dynamic problems and algorithms, which could perform forecasting tasks, we modeled the pull-based process of real-time sensor data provision from sensor observation services, and four policies are discussed in terms of the proposed model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have