A Novel Edge Computing Architecture Based on Adaptive Stratified Sampling

Abstract

With the development of the Internet of Things technology, the current amount of data generated by the Internet of Things system is increasing, and these data are continuously transmitted to the data center. The data processing and analysis of the traditional Internet of Things system are inefficient and cannot handle such a large number of data streams. In addition, the IoT smart device has a resource-limited feature, which cannot be ignored when analyzing data. This paper proposes a new architecture ApproxECIoT (Approximate Edge Computing Internet of Things, ApproxECIoT) suitable for real-time data stream processing of the Internet of Things. It implements a self-adjusting stratified sampling algorithm to process real-time data streams. The algorithm adjusts the size of the sample stratums according to the variance of each stratum while maintaining the given memory budget. This is beneficial to improve the accuracy of the calculation results when resources are limited. Finally, the experimental analysis was performed using synthetic datasets and real-world datasets, the results show that ApproxECIoT can still obtain high-accuracy calculation results when using memory resources similar to simple random sampling. In the case of synthetic data streams, when the sampling ratio is 10%, compared with CalculIoT, the accuracy loss of ApproxECIoT is reduced by 89.6%; compared with SRS, the accuracy loss of ApprxoECIoT is reduced by 99.8%. In the case of using the real data stream of the wireless sensor network, the performance of ApproxECIoT is not the best, but as the sampling ratio increases, the accuracy loss of ApproxECIoT decreases more than other frameworks.