A Dependable Time Series Analytic Framework for Cyber-Physical Systems of IoT-based Smart Grid

Abstract

With the emergence of cyber-physical systems (CPS), we are now at the brink of next computing revolution. IoT (Internet of Things) based Smart Grid (SG) is one of the foundations of this CPS revolution and defined as a power grid integrated with a large network of smart objects. The volume of time series of SG equipment is tremendous and the raw time series are very likely to contain missing values because of undependable network transferring. The problem of storing a tremendous volume of raw time series thereby providing a solid support for precise time series analytics now becomes tricky. In this paper, we propose a dependable time series analytics (DTSA) framework for IoT-based SG. Our proposed DTSA framework is capable of proving a dependable data transforming from CPS to the target database with an extraction engine to preliminary refining raw data and further cleansing the data with a correction engine built on top of a sensor-networkregularization based matrix factorization (SnrMF) method. The experimental results reveal that our proposed DTSA framework is capable of effectively increasing the dependability of raw time series transforming between CPS and the target database system through the online lightweight extraction engine and the offline correction engine. Our proposed DTSA framework would be useful for other industrial big data practices.

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

“…Based on the results of experiments, our proposed MR-MGWO gave significantly better results compared to other similar algorithms, in a faster and scalable manner. Several research works are in progress for achieving higher QoS in offering services to consumers in the fields of Internet of Things [45][46][47] and Smart Grids. 48,49 Several optimization techniques are proposed to effectively tackle the energy management problems in smart grids.…”

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

“…Luo et al [ 45 ] surveyed the many platforms available in the literature for energy management system in smart buildings [ 46 , 47 , 48 , 49 , 50 ] and presented the development of an IoT-based platform that would produce day-ahead prediction of building energy demands. The predictive model would be based on the hybrid of k-means clustering and an artificial neural network.…”

“…The system was found to improve cooling capacity by 14%, coefficient of performance by 46.3%, and allow for better microclimate control within the building (Figure 11). Luo et al [45] surveyed the many platforms available in the literature for energy management system in smart buildings [46][47][48][49][50] and presented the development of an IoTbased platform that would produce day-ahead prediction of building energy demands. The predictive model would be based on the hybrid of k-means clustering and an artificial neural network.…”

Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions

“…The smart healthcare domain is one of the examples of a field that integrates IoT technologies to make patients' lives easier by adopting wearable devices in remote care and digital health programs in a modern model of hospital-centric care [109]. Similarly, IoT could be used and blended with cyber-physical systems of the smart grid to be more robust and resilient [110]. Likewise, IoT could keep up with the ubiquitous use of social networks in our daily lives by protecting the location privacy of the end-users and minimizing potential hacking, thefts of personal information and unauthorized access [111].…”