Abstract
This book chapter discusses the concept of edge-assisted cloud computing and its relation to the emerging domain of “Fog-of-things (FoT)”. Such systems employ low-power embedded computers to provide local computation close to clients or cloud. The discussed architectures cover applications in medical, healthcare, wellness and fitness monitoring, geo-information processing, mineral resource management, etc. Cloud computing can get assistance by transferring some of the processing and decision making to the edge either close to client layer or cloud backend. Fog of Things refers to an amalgamation of multiple fog nodes that could communicate with each other with the Internet of Things. The clouds act as the final destination for heavy-weight processing, long-term storage and analysis. We propose application-specific architectures GeoFog and Fog2Fog that are flexible and user-orientated. The fog devices act as intermediate intelligent nodes in such systems where these could decide if further processing is required or not. The preliminary data analysis, signal filtering, data cleaning, feature extraction could be implemented on edge computer leading to a reduction of computational load in the cloud. In several practical cases, such as tele healthcare of patients with Parkinson’s disease, edge computing may decide not to proceed for data transmission to cloud (Barik et al., in 5th IEEE Global Conference on Signal and Information Processing 2017, IEEE, 2017) [4]. Towards the end of this research paper, we cover the idea of translating machine learning such as clustering, decoding deep neural network models etc. on fog devices that could lead to scalable inferences. Fog2Fog communication is discussed with respect to analytical models for power savings. The book chapter concludes by interesting case studies on real world situations and practical data. Future pointers to research directions, challenges and strategies to manage these are discussed as well. We summarize case studies employing proposed architectures in various application areas. The use of edge devices for processing offloads the cloud leading to an enhanced efficiency and performance.
Keywords
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This material is based upon work supported by the National Science Foundation under Grant No. (#1652538). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Barik, R.K. et al. (2018). Fog Assisted Cloud Computing in Era of Big Data and Internet-of-Things: Systems, Architectures, and Applications. In: Mishra, B., Das, H., Dehuri, S., Jagadev, A. (eds) Cloud Computing for Optimization: Foundations, Applications, and Challenges. Studies in Big Data, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-73676-1_14
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