Energy-Efficient Sensory Data Collection Based on Spatiotemporal Correlation in IoT Networks

Abstract

The Internet of Things (IoT) is currently in a stage of rapid development. Hundreds of millions of sensing nodes and intelligent terminals undertake the tasks of sensing and transmitting data. Data collection is the key to realizing data analysis and intelligent application of IoT. The life cycle of IoT is limited by the energy of the IoT nodes in the network. A complex computing model will bring serious or even unbearable burdens to IoT nodes. In this study, we use the data prediction method to explore time correlation data and adjust the appropriate spatial sampling rate on the basis of the spatial correlation of sensory data to further reduce data. Specifically, the improved and optimized DNA-binding protein (DBP) data prediction method can increase the time interval of sensing data to further reduce energy consumption. Based on the spatial characteristics of the sensing data, substituting the data of similar nodes can reduce the sampling rate. The probabilistic wake-up strategy is also adopted to adjust the spatial correlation of the sensing data. On the basis of node priority, an optimized greedy algorithm is proposed to select the appropriate dominating node for eliminating redundant nodes and improving network energy utilization. Experiments have proven that our scheme reduces network energy consumption under the premise of ensuring data reliability.

A Study on Landscape VR Design of Special Neighborhood Based on Traditional Culture

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