Remote health care cyber‐physical system: quality of service (QoS) challenges and opportunities

Abstract

There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.

Multimedia Internet of Things: A Comprehensive Survey

“…Zafra et al [141] addressed the issue of coexistence wearable devices for e-health with traditional three-layered IoT architecture and proposed a pervasive layered architecture to integrate M2M communication between e-health wearable and IoT devices. Shah et al [142] highlight the challenges in analyzing big data generated from remote health monitoring and decision making by comparison with the patient's history. The authors also provided details of various QoS driven Cloud of Things (CoT) based data processing for remote health applications.…”

“…However, improvements are still required for deploying remote medical health systems as packets drop in critical medical condition is not tolerable. [142]), robotic ambulance [144], X-ray for robotic surgery ( [145], [146]), biomedical training for surgeons [147] and ocular diseases detection using smartphones [148], [149]. All these applications could be integrated using cloud service to be available for online and offline access.…”

Distributed Control and Quality-of-Service in Multihop Wireless Networks

“…The flows are F 10 : 7 → 9 → 10, F 4 : 7 → 8 → 2 → 4, F 11 : 1 → 2 → 4 → 11, F 13 : 9 → 10 → 13, F 12 : 1 → 3 → 6 → 12, F 15 : 5 → 14 → 15 and F 6 : 5 → 3 → 6. The constant k 0 = 0.01 in (2), and in the distributed optimization, the algorithm runs 15 cycles over the set of nodes with α = 0.0001 and the initial state is zero. The simulation runs for 10 5 slots, with a 1 = 0.2 and a 2 = 2 in (6).…”

Design an Adaptive Mechanism Model for Supporting the Prevention of SLAs Violation in a Context of COPD Patient Monitoring

“…Shah et al [16] discuss the concept of QoS for the health care system as a whole. The authors explain that these systems handle large volumes of data and that quality control mechanisms are needed to improve the quality of service.…”