# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 2 Related Work  
The proliferation and interconnection of numerous data sources across various IoT devices are speeding up the digital explosion in our everyday lives [22]. For example, the International Data Corporation estimates that about 80 billion devices will be connected to the Internet by 2025, and as a result, the globally generated data will be as tremendous as 163 zettabytes [23]. Since it is often impractical and even impossible to centralize the explosive data due to the bandwidth limits and privacy concerns, some research organization have predicted that the majority of the data (over 90 percent) will be stored and processed locally in a distributed manner [24]. Correspondingly, along with the revolutionary development of ubiquitous mobile equipment, wearable gadgets and smart sensors, innovative data analytical solutions are increasingly developed and deployed on the edge of the Internet.  
In particular, driven by the intensive interests in the \"intelligent edge\" [23], there is a clear trend in implementing machine learning techniques on various and distributed IoT devices on the edge. Although the implementation of edge machine learning is still at an early stage [25], it is evident that IoT and edge devices are complementary to cloud resources for scaling machine learning systems [24]. Furthermore, considering that the data in production for edge learning are generated gradually instead of being available all at once, the trained models must be updated \"in an online fashion to accommodate new data without compromising its performance on old data\" [26]. Thus, online machine learning has become a distinctive research topic in the edge learning field [27], which can be characterized by the real-time streaming processing computation model [28].  
In fact, the revival of online learning in edge computing can be traced back to sequential learning, which was studied decades ago [29]. Nevertheless, as identified in [15], those early studies may still require (estimating) the samples' statistical information in advance for their sequential learning implementations. On the other hand, although recent studies have tried to simplify the online learning mechanisms, the solutions seem to be bypassing, ignoring, or tolerating the missing of the statistical characteristics of data, e.g., pre-setting suitable skewness values for simulations [30; 31], using robust-to-skewness metrics to measure performance [32], etc.  
It should be clarified that the machine learning community is well aware of the importance of skewness. For example, since canonical machine learning techniques assume balanced datasets [19], not only class skewness but also skewed features will bias the classification of dataset samples [33]. More generally, it has been identified that machine learning models cannot do a good job with skewed data, no matter how well the parameter tuning is conducted [6]. Unfortunately, to our best knowledge, little research into online machine learning has taken into account the real-time awareness of skewness. The study [34] is the only one we have found to monitor data skewness at runtime, which aims to balance the skewness of labels to train weak classifiers more equally. However, its skewness calculation is not compatible with the de facto adjusted Fisher-Pearson skewness coefficient (cf. Equation (1)). In addition, this use case only corresponds to the cumulative version of running skewness (cf. Section 3).  
In contrast, this research investigates three running skewness versions and their calculation methods, by aligning with the adjusted Fisher-Pearson skewness coefficient. The proposed calculation methods can conveniently be converted into efficient algorithms and be implemented as skewness monitoring modules on IoT devices in order to satisfy the different needs of skewness-aware online edge learning.