Implementation of Fog computing for reliable E-health applications

Abstract

The 5th generation (5G) wireless communication network is expected to support up to 1000x more connections per cell with reduced latency below 1 ms. Maintaining uplink synchronization for each individual device as conventional 4G does, known as the Timing Advance adjustment, will lead to significant signaling overhead, especially for small traffic scenarios, like IoT services, "always-on-line" TCP connections, etc. This paper shows that the Timing Advance is not necessary if we introduce FBMC waveform in combination with suitable multiple access schemes. The resulting system is highly robust against timing misalignment while attaining high spectral efficiency.

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

“…Healthcare applications have been covered by Gu et al [110], Cao et al [109], and Craciunescu et al [111]. Gu et al [110] aim at optimizing resource utilization in the fog stratum for medical cyber-physical systems in order to minimize the communication cost as well as virtual machines deployment cost.…”

Real-Time Distributed Architecture for Remote Acoustic Elderly Monitoring in Residential-Scale Ambient Assisted Living Scenarios

“…In fact, the fog computing paradigm aims to decouple the data management process into two (or more) stages, which enables one to (1) conduct preliminary data processing and aggregation tasks close to where data are generated [ 48 ] (also referred to as edge computing) and (2) carry all the heavy load and intensive tasks in the cloud (also referred to as cloud computing) [ 44 , 47 , 49 ]. Hence, fog computing has resulted in a powerful tool to bridge the gap between the requirements of high performance computing for sensing environments and the features offered by clouds [ 50 ], especially in the eHealth field, where clinic-centric treatments are no longer feasible and a transition to patient-centric healthcare is required [ 51 ].…”

“…Therefore, they are often used to connect the sensing layer (i.e., data acquisition in an indoor environment) with the analytic/storage/processing/computing layer of AAL solutions [ 63 ]. Usually, [ 46 , 49 , 50 , 51 , 52 , 53 , 54 ], the computing layer is not physically located close to the patient facilities (e.g., cloud computing infrastructure); hence, the Internet is used to link both domains, which reduces the overall available bandwidth, makes the system prone to cyber security threats [ 64 ] and adds a considerable delay to the event detection process. It is worth considering that the negative effects of these issues (i.e., bandwidth, cyber security and delay) grow with the number of patients and/or area to be monitored since their associated sensed data also grow accordingly, as is the case of residential areas such as the FAM facilities under study.…”

“…Therefore, we propose the fog-inspired [ 50 ] distributed architecture depicted in Figure 3 to (1) support data collection from multiple sources; (2) provide a real-time early event detection layer and (3) refine the event detection process with a high-level event analysis layer. Hence, the proposed architecture is divided into three layers: Sensing layer: It is composed of all the wireless acoustic sensors that are deployed over the area where the patients need to be monitored.…”

Implementation of Fog computing in IoT-based healthcare system

“…The IoT-based healthcare system appears as an adequate approach to ensure the appropriate (effective and proactive) healthcare delivery based on parameters monitoring and useful medical knowledge obtained on the gathered and analyzed health-related data [9,19]. Its omnipresence in peoples' lives has resulted in the increasing number of diverse, smart medical devices and sensors, which more than ever before generate and transmit data.…”