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Exploiting Fog Computing in Health Monitoring: Principles and Paradigms

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Abstract

This chapter exploits fog computing in health‐monitoring Internet‐of‐Things (IoT) systems for enhancing the quality of healthcare service. It shows an overview of the architecture of an IoT‐based system with fog computing. Fog computing services locating in a fog layer of smart gateways are diversified for serving IoT applications. The chapter discusses the fog computing services in smart e‐health gateways. The health‐monitoring IoT system consists of several wearable sensor nodes, smart gateways with fog services, cloud servers, and terminals. The chapter discusses detailed implementations of these components. It provides a case study, experimental results, and evaluation related to heart rate variability (HRV) analysis. The chapter presents the related applications in fog computing and discusses future research directions. Fog computing demonstrates that it is one of the most suitable candidates for augmenting IoT systems in healthcare and other domains.

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... Compared to traditional IoT applications which often rely on a 3-layer architecture (sensorcloud-terminal), a Fog-assisted IoT application has extra layers between the sensor nodes and the cloud. Depending on the application and type of acquired data, a different number of Edge/Fog layers can be deployed [7]- [9]. ...
... Similar to Edge gateways, Fog gateways can offer advanced services such as push notification, distributed data storage, security, data fusion, and data processing. More detailed information of Fog services and are discussed in our previous articles [7], [8], [26]- [28]. ...
... Therefore, power consumption is not an issue. In this paper, we reuse and adapt fog services, cloud applications, and enduser terminals which have been implemented in our previous papers [7], [8], [26]- [28]. The user interface has been modified for data visualization. ...
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... To overcome the high latency constraints of Cloud computing and meet the real-time processing requirements of critical healthcare data including IoT device-generated ECG signals, Fog computing solutions have been employed in many remote patient monitoring systems [17]. By acting as an intermediate layer between Cloud data centres and edge devices, the Fog paradigm brings the computing facilities in the vicinity of the data sources that reduce the data transfer delay and improve the overall response time in data processing [18]. ...
... In [17], the authors presented a Fog-based IoT system for remote health monitoring. The system had an advanced Fog architecture in which smart gateways are interconnected and communicate with each other. ...
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... However, tasks can be offloaded from end devices to fog nodes, which is closer to the end device/user and thus reduces energy consumption. The aforementioned important features make the fog computing paradigm to be used in different infrastructures such as smart cities (He et al., 2018;Jawhar et al., 2018;Javadzadeh and Rahmani, 2020;Naranjo et al., 2019;Santos et al., 2018), telesurveillance (Verma and Sood, 2018), healthcare (Verma and Sood, 2018;Andriopoulou et al., 2017;Ulusar et al., 2019;Akrivopoulos et al., 2019;Gia and Jiang, 2019;Negash et al., 2018;Paul et al., 2018;Dash et al., 2019;Prasad et al., 2019) and smart transportation (Neto et al., 2018). ...
... The integration of IoT and fog computing paradigm provides reliable medical service provisioning with high security and privacy of patients with fast and accurate treatment delivery, reduction of medical cost, improvement of doctor-patient contacts, and the delivery of personalised treatment-oriented to users need or preferences (Andriopoulou et al., 2017;Ulusar et al., 2019;Akrivopoulos et al., 2019;Gia and Jiang, 2019;Negash et al., 2018). Data management has an important role in fog computing enabled healthcare systems. ...
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... However, tasks can be offloaded from end devices to fog nodes, which is closer to the end device/user and thus reduces energy consumption. The aforementioned important features make the fog computing paradigm to be used in different infrastructures such as smart cities (He et al., 2018;Jawhar et al., 2018;Javadzadeh and Rahmani, 2020;Naranjo et al., 2019;Santos et al., 2018), telesurveillance (Verma and Sood, 2018), healthcare (Verma and Sood, 2018;Andriopoulou et al., 2017;Ulusar et al., 2019;Akrivopoulos et al., 2019;Gia and Jiang, 2019;Negash et al., 2018;Paul et al., 2018;Dash et al., 2019;Prasad et al., 2019) and smart transportation (Neto et al., 2018). ...
... The integration of IoT and fog computing paradigm provides reliable medical service provisioning with high security and privacy of patients with fast and accurate treatment delivery, reduction of medical cost, improvement of doctor-patient contacts, and the delivery of personalised treatment-oriented to users need or preferences (Andriopoulou et al., 2017;Ulusar et al., 2019;Akrivopoulos et al., 2019;Gia and Jiang, 2019;Negash et al., 2018). Data management has an important role in fog computing enabled healthcare systems. ...
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... These bring Cloud computing paradigms to the Edge of the network and help reduce the burden of Cloud providing faster services unsupported by Cloud computing. Edge and Fog computing help to reduce energy consumption of sensor nodes and diminish overall latency [10], [11]. The combination of Edge and Fog computing with IoT can provide a suitable approach for enhancing overall performance. ...
... Although Edge computing can bring advantages such as reduction in latency, bandwidth conservation, improvement in application robustness and security [11], [27], [28], there are inherent challenges. As the processing of the data stays near the Edge, specific data-oriented applications which require comparatively higher user interaction will have insignificant performance gain. ...
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... In this chapter, the authors present an architecture that aims to provide a reliable solution to handle the challenge of security and privacy for EHR [53]. In order to present the architecture, we firstly present the various kinds of attack [54]. The major security attacks are as follows [55]: ...
Chapter
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... Nevertheless access to remote cloud servers increases delay and energy consumption. As a solution towards this problem, fog computing based health monitoring has been discussed in [5,6,7,8,21,22,9,23]. In fog based health care system, the intermediate devices between the end node and cloud servers, such as switch, router etc participate in data processing. ...
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... Due to the scope of the paper, Fog services are not discussed. More detailed information of the Fog services is discussed in our previous papers [19]- [22]. ...
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There is an increasing trend to integrate sensor networks into the Internet, eventually resulting in an Internet of Things. Recent efforts of porting IPv6 to sensor networks turn sensor nodes into equitable Internet peers and RESTful Web Services on sensor nodes allow a distribution of the application logic among sensor nodes and more powerful Internet nodes. The touching point between a sensor network and the Internet is the gateway which translates between the link-layer protocols used in the Internet (Ethernet, Wi-Fi) and sensor networks (IEEE 802.15.4). So far, the functionality of those gateways was fixed and simple. We propose to turn these gateways into smart gateways by enabling them to execute application code. As only the gateway has full knowledge of and control over both the sensor network and the Internet, smart gateways can act as performance-enhancing proxies and intelligent caches to preserve the limited resources of the sensor network. Also, the smart gateway can perform application-specific protocol conversion between highly optimized but non-standard protocols in the sensor network and standardized, but less efficient protocols in the Internet. In this paper we present the design of a middleware for smart gateways that allows the execution of application code on the gateway by offering simplified interfaces to the sensor network and the Internet. We also report preliminary performance results for key functions of the middleware.
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Falls are a well-known source of morbidity and mortality in the elderly. Fall-related injury severity in this group, however, is less clear, particularly as it relates to type of fall. Our purpose is to explore the relationship between mechanism of fall and both pattern and severity of injury in geriatric patients as compared with a younger cohort. Our trauma registry was queried for all patients evaluated by the trauma service over a 412-year period (1994-1998). Two cohorts were formed on the basis of age greater than 65 or less than or equal to 65 years and compared as to mechanism, Injury Severity Score (ISS), Abbreviated Injury Scale score, and mortality. Over the study period, 1,512 patients were evaluated, 333 greater than 65 years and 1,179 less than or equal to 65 years of age. Falls were the injury mechanism in 48% of the older group and 7% of the younger group (p < 0.05). Falls in the older group constituted 65% of patients with ISS >15, with 32% of all falls resulting in serious injury (ISS >15). In contrast, falls in the younger group constituted only 11% of ISS >15 patients, with falls causing serious injury only 15% of the time (both p < 0.05). Notably, same-level falls resulted in serious injury 30% of the time in the older group versus 4% in the younger group (p < 0.05), and were responsible for an ISS >15 30-fold more in the older group (31% vs. <1%; p < 0.05). Abbreviated Injury Scale evaluation revealed more frequent head/neck (47% vs. 22%), chest (23% vs. 9%), and pelvic/extremity (27% vs. 15%) injuries in the older group for all falls (all p < 0.05). The mean ISS for same-level falls in the older group was twice that for the younger group (9.28 vs. 4.64, p < 0.05), whereas there was no difference in mean ISS between multilevel and same-level falls within the older group itself (10.12 vs. 9.28, p > 0.05). The fall-related death rate was higher in the older group (7% vs. 4%), with falls seven times more likely to be the cause of death compared with the younger group (55% vs. 7.5%) (both p < 0.05). Same-level falls as a cause of death was 10 times more common in the elderly (25% vs. 2.5%, p < 0.05). Falls among the elderly, including same-level falls, are a common source of both high injury severity and mortality, much more so than in younger patients. A different pattern of injury between older and younger fall patients also exists.
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  • I. Azimi
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