Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things

Abstract

The increasing use of wearables in smart telehealth generates heterogeneous medical big data. Cloud and fog services process these data for assisting clinical procedures. IoT based ehealthcare have greatly benefited from efficient data processing. This paper proposed and evaluated use of low-resource machine learning on Fog devices kept close to the wearables for smart healthcare. In state-of-the-art telecare systems, the signal processing and machine learning modules are deployed in the cloud for processing physiological data. We developed a prototype of Fog-based unsupervised machine learning big data analysis for discovering patterns in physiological data. We employed Intel Edison and Raspberry Pi as Fog computer in proposed architecture. We performed validation studies on real-world pathological speech data from in-home monitoring of patients with Parkinson's disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smartwatches worn by the patients with PD. Results showed that proposed architecture is promising for low-resource clinical machine learning. It could be useful for other applications within wearable IoT for smart telehealth scenarios by translating machine learning approaches from the cloud backend to edge computing devices such as Fog.

Energy saving scheduling in a fog-based IoT application by Bayesian taskclassification approach

“…This fact reveals the importance of paying attention to FC and scheduling in this environment. Cloud can connect to an IoT device through fog nodes [18].…”

“…Feature engineering is the first step of every analysis based on ML. This is the process of selecting the correct data metrics to represent them as input to the ML algorithms [18]. In [19], the facility of resource management has been discussed by the ML in large scale distributed systems.…”

“…In [18], the use of low-resource ML has been evaluated on the wearable health-care fog devices which are kept close to users. In a big data analysis to discover patterns in the physiological data, they have developed a prototype of fog-based traditional unsupervised ML.…”

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

“…In all fog-based applications [2,3,[11][12][13][14][15][16][17][18] an intermediary fog layer is built between the physical layer, where edge devices like the sensors deployed in smart health care devices [3,12,19], smart phones, and wearable devices like smart watches, bands from where the row data are collected and the top layers where cloud servers are present. The generalized architecture of fog computing is comprised of three levels [20,21] is depicted in Figure 1.…”

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

“…The research discussed in this manuscript was supported by National Institute of Health Grant: R01MH108641. the locations [1]. Health data are heterogeneous that lead to challenges in integrating it with existing healthcare facilities, interoperability etc.…”