A fog‐based ubiquitous exercise healthcare monitoring framework for smart cities. Issue 1 (3rd August 2020)

• Record Type: Journal Article
• Authors: Wu, Jiang; Patrono, Luca
• Is Part Of: Internet technology letters. Volume 4:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-03
• Subjects: deep learning -- exercise healthcare monitoring -- fog‐based framework -- smart cities -- ubiquitous monitoring; Internet -- Periodicals; 004.67805
• DOI: 10.1002/itl2.199
• Languages: English
• ISSNs: 2476-1508

Abstract

Abstract : Fog‐based exercise healthcare monitoring (EHM) has attracted many interests in the field of smart cities (SCs) as an effective supplement to the cloud‐based applications to reduce the transmission delay and data quantity swarming from smart health devices toward the Internet. However, due to the amount of heterogeneous data from a variety of Internet of Things (IoT) devices, providing fast‐responsive or low‐latency service has always been a great challenge in EHM. Hence, to address these challenges, we propose a fog‐based framework for ubiquitous exercise monitoring capable of efficiently transmitting and processing data at the edge of the network. For this framework, a deep learning‐based traffic prediction method is proposed to ensure optimal network performance. The simulation results demonstrate that our proposed framework has better network performance, in terms of real‐time, average completion time and lost rate of packet queue, compared with several state‐of‐the‐art frameworks.