# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 6 Conclusions and Future Work  
It is known that machine learning can never do a good job with skewed data. Thus, pre-processing the whole datasets for curing high skewness (e.g., log transformation) has been a common practice before applying machine learning technologies. In edge computing, however, there is no way to conduct such data pre-processing, because it is impractical and impossible to store all the past observations and foresee all the future observations. For the same reason, machine learning at the Internet edge is generally implemented in an online fashion. Correspondingly, it will be valuable to monitor the running skewness of continuous observations before being able to take suitable actions to cure high skewness at runtime. Nevertheless, when trying to integrate skewness monitoring to online edge learning, we identified a surprising gap between practitioners' needs and the scientific research in running skewness algorithms. This research bridges the gap by developing a set of IoT-friendly statistical methods to facilitate monitoring skewness at runtime. These methods are essentially based on [PERSON]'s algorithm, which is the most efficient way to calculate running variance. We believe that as an innovation in computer science, [PERSON]'s algorithm might have been overlooked in the statistics community. More importantly, this research has initially validated the usefulness and significance of being aware of skewness in the implementations of online edge learning. To help boost this promising research field, our future work will unfold along two directions. Firstly, we plan to keep exploring and employing more datasets to strengthen the current validation  
Figure 3: The redundancy mechanism for skewness-aware online edge learning. results. Secondly, we will try to collaborate with more online learning researchers on developing real-time skewness remedies. Conceptualization, [PERSON] and [PERSON]; methodology, Z.L. ; software, Z.L. ; validation, [PERSON] and [PERSON]; formal analysis, Z.L. ; investigation, [PERSON] and [PERSON]; resources, Z.L. ; data curation, [PERSON]; writing--original draft preparation, Z.L. ; writing--review and editing, [PERSON] and [PERSON]; visualization, Z.L. ; funding acquisition, Z.L. Both authors have read and agreed to the published version of the manuscript. This research was funded by Chilean National Research and Development Agency (ANID, Chile) under grant FONDECYT Iniciacion 11180905. Not applicable. We reused the New York City taxi passenger dataset (i.e., nyc_taxi.csv) and the implementation of online-sequential extreme learning machine (OS_ELM) algorithm from the open source project at [[https://github.com/chickenbestlover/Online-Recurrent-Extreme-Learning-Machine](https://github.com/chickenbestlover/Online-Recurrent-Extreme-Learning-Machine)]([https://github.com/chickenbestlover/Online-Recurrent-Extreme-Learning-Machine](https://github.com/chickenbestlover/Online-Recurrent-Extreme-Learning-Machine)), accessed on 10 August 2021. The authors would like to thank [PERSON] who is the owner of GoL ([[https://github.com/alexander-yu/stream](https://github.com/alexander-yu/stream)]([https://github.com/alexander-yu/stream](https://github.com/alexander-yu/stream)), accessed on 10 August 2021) for his professional help and friendly communication. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: