Simulating Fog of Medical Things: Research Challenges and Opportunities

【Author】 Pati, Abhilash; Panigrahi, Amrutanshu; Parhi, Manoranjan; Kumar Pattanayak, Binod; Sahu, Bibhuprasad; Kant, Shashi

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Technology has a significant impact on medical applications at the current moment. Contemporary computers are capable of processing a lot of patient medical records quickly. Due to recent advancements in the Internet-of-Things (IoT) and medical applications, patient data may be dispersed over several places. Worldwide, the IoT connects numerous devices for e-healthcare systems. The medical data monitoring and tracking field, exercise programs, and remote medical help are expanding within the e-healthcare systems. IoT-based technologies are now being used in e-healthcare systems, which can relieve pressure on e-healthcare systems, lower medical expenses, and speed up computing and processing. In the IoT setting, cloud computing, which contains centralized data centers, was developed to manage more extensive and sophisticated e-healthcare data. The central server governs the data for all IoT devices. Problems with IoT and Cloud integration only include latency, bandwidth overuse, delays in real-time responses, security, privacy, integrity, etc. The ideas of fog computing and edge computing were developed to solve the above-mentioned problems. A thorough literature overview on Fog-based medical applications using IoT is provided in this article, i.e., Fog of Medical Things (FoMT), that explores the simulators that may be employed to create and assess new Fog-related theories as well as the key attributes of Fog computing frameworks. This review also emphasizes the difficulties in the field and some unanswered questions. This study can serve as a crucial road map for the future creation of Fog-based e-healthcare IoT applications.

【Keywords】FoMT; IoT; fog computing; cloud computing; simulation tools; e-healthcare systems; FoMT; IoT; fog computing; cloud computing; simulation tools; e-healthcare systems

【发表时间】2024

【收录时间】2024-10-28

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-雾计算