A hierarchical, scalable architecture for a real-time monitoring system for an electrocardiography, using context-aware computing

Journal of Biomedical Informatics 96 (2019) 103251

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A hierarchical, scalable architecture for a real-time monitoring system for an electrocardiography, using context-aware computing

T

Ahmad Malekian Borujeni, Mahmood Fathy , Nasser Mozayani ⁎

Iran University of Science and Technology, Tehran, Iran

ARTICLE INFO

ABSTRACT

Keywords: Cloudlet Context aware computing Edge computing Heart failure Internet of things Smart health care

Heart failure is one of the most common cause of death in the world. The real-time health monitoring system with the advent of the Internet of things has attracted growing attention in the health care industry, which can help reducing the death rate of heart failure. Despite the recent success of these efforts, there have been some limitations, such as response time, scalability, latency and fault tolerance. To address these issues, in this paper, we propose a hierarchical architecture with four layers to develop health care systems. In the proposed model, the vital signs of a patient are measured by means of a body sensor network and sent to a smart health care system. Each of these layers is related to a certain level of heart failure. Therefore, in the proposed model, simple and low-risk heart failure can be detected quickly before it reaches to a dangerous level. Empirical results confirm a significant improvement in terms of response time and scalability in comparison with the state of the art techniques.

1. Introduction Based on the World Health Organization (WHO) report in 2017, Cardiovascular Diseases (CVDs) take the lives of 17.7 million people every year. In other words, annually in the world about 31 percent of all deaths are caused by heart disease. Also, based on the prediction of same organization by 2030, about 23 million people will die from Heart Failure (HF) [1]. Therefore, regular and frequent monitoring of the symptoms of this disease such as increased blood pressure, reducing blood flow, etc is imperative [2]. Although the use of old methods like the pervasive health care for diagnosis and treatment of heart failure has been somewhat useful, today, with the advent of the Internet of Things, a major leap in pervasive health care has taken place. This makes health care services available to anyone at any time, anywhere, by removing the restraints of time and location [3]. This is because the rise of the Internet of things (IoT) has led to the emergence of Wireless Body Area Networks WBANs, Mobile Cloud Computing (MCC), edge computing and fog computing, which, in turn, has improved the efficiency and scalability of health services. In a wireless body area network a patient is equipped with a consisting of sensors that constantly measure vital signs, such as heart rate, respiration, temperature, blood pressure, Electrocardiogram (ECG), etc. These sensors, with several actuators, are considered as

nodes of a network in which they are connected via a wireless communication channel [4]. Several researches have been conducted to determine the best network topology or configuration of nodes and links. most of them suffer from the scalability, fault tolerance, and network latency. [5]. In this paper we overcome these limitations by proposing a hierarchical layer network topology. The proposed approach is to provide scalable and real-time services to support emergency Health Monitoring Systems (HMS) for heart failure. The proposed model uses four-layer architecture: smart mobile devices, edge computing, fog computing and the federated cloud using context-aware computing. In the first layer, the vital and electrocardiography signals that are collected by various sources, e.g. Smart Mobile Devices (SMD) are processed and results are forwarded as input to the next layers. In this architecture resource-rich cloudlets are used to facilitate the processing and storage operation between the smart phone and the federated cloud in the edge and fog layers. Most of the data-intensive computing tasks including pattern recognition, management, prediction, prevention, and control are performed in the federated cloud layer. Each level also has its own computational time. The preprocessing time of the underlying layers is less compared to the higher layers. So this layering allows physicians to provide appropriate medical guidelines for non-hazardous signs in the primitives layers.

⁎ Corresponding author at: Computer Engineering School, Iran University of Science and Technology, University Road, Hengam Street, Resalat Square, Narmak, Tehran Zip Code: 16846-13114, Iran. E-mail addresses: [email protected] (A. Malekian Borujeni), [email protected] (M. Fathy), [email protected] (N. Mozayani).

https://doi.org/10.1016/j.jbi.2019.103251 Received 24 October 2018; Received in revised form 5 July 2019; Accepted 12 July 2019 Available online 18 July 2019 1532-0464/ © 2019 Published by Elsevier Inc.

Journal of Biomedical Informatics 96 (2019) 103251

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The experimental results show the 70 percent performance improvement comparing cloud-based solutions. This improvement is about 30 percent compared to Fog computing approaches. The main contributions of this paper are as follows:

Table 2 Table of abbreviations.

1. We propose an IoT-based architecture to address scalability, fault tolerance, and availability issues of remote health care services by using hierarchical processing levels. 2. We consider the patient’s contexts and use context-aware computing to reducing the emergency response time. 3. We use multi-level processing technique, decompose the process to independent sub-tasks, and process data by using appropriate computing nodes with sufficient available resources. The rest of the paper is organized as follows: In Section 2, we describe the background and related works. Section 3 presents our proposed architecture including overview, architecture, and scenario. In Section 4, the simulation results are explained and thoroughly discussed. Finally, the discussion and conclusion are presented in Sections 5 and 6, respectively. 2. Background Sudden and unexpected death without any sign can happen by HF that health care service can play an important role in caring for these patients. The diagnosis of heart failure in early stages can lead to a better treatments which are summarized in Table 1. The HF diagnosis is based on health records. With the onset of heart failure symptoms and loss of awareness, death can occur within minutes [2] (see Table 2). Tracking of activities and optimizing patient management can lead to cost reduction in health care systems. Health monitoring is classified into Remote health monitoring systems, Mobile health monitoring systems, Wearable health monitoring system and Smart Health Monitoring Systems. Information, communication, technologies, software, hardware, and services are the foundation of digital health. Mobile health (M-health) is defined as the use of these foundations in a device through digital health. As a unique opportunity, M-health can improve the well-being of communities without any discrimination [7]. M-health technologies, incorporates smart devices, cloud computing and WBANs as the components of M-health technology are non-contact, wearable, context-aware, reliable, and secure sensors. It is worth noting that WBANs, despite the minimal source and limitations in processing capability, have a significant role in the Internet of Health [8]. While cloud is known for its cost-efficiency, servers that are deployed in the field near the patients, solve the delay and performance issues of the distant datacenter and cloud infrastructure [9]. Cloud federation is a collaborative organization hosted in order to execute a common process. The federated cloud is providing services to the group of the servers, that have been integrated to minimize management effort for better performance[10]. Modern health care systems rely on cloud computing and M-health, clouds and internet of things (IoT). The integration of IoT in health care is an important solution to the health care issues, which leads to increase in the Quality of Services (QoS), the effectiveness of health care services and bringing value to the patient and the elderly. health care

Description

Class I: Mild disease Class II: Moderate disease Class III: Moderately severe disease Class IV: Severe disease

Ordinary physical activity may not be limited Ordinary activity causes symptoms Less-than-ordinary activity causes symptoms

Description

AHL AI AMI BP CVDs DBS ECG ED EPMS GPS HF HI HMS IF IoT LoS MCC M-health NFV PHR QoE QoS RMS SCTP SDN SLA SMAC SMD TCP UDP USL WBAN

At-Home Laboratory Artificial Intelligence Ambient Intelligence Blood Pressure Cardiovascular diseases Deep Brain Stimulation Electrocardiography (Electrocardiogram) Emergency Department Epilepsy Patients Monitoring System Global Positioning System Heart Failure Human Intelligence Health Monitoring Systems Influence Factor Internet of Things Length of Stay Mobile Cloud Computing Mobile health Network Function Virtualization Patient Health Record Quality of Experience Quality of Services Resource Management System Stream Control Transmission Protocol Software Defined Networking Service-level agreement Sensor Medium Access Control Smart Mobile Devices Transmission Control Protocol User Datagram Protocol Universal Scalability Law Wireless Body Area Networks

IoT is still in development phases and have received attentions in recent years. Real-time system and life-critical data can be reached by having an intelligent system and powerful algorithms [8]. Benefits of IoT in health care becomes prominent to ensure resource efficiency, emergency response time reduction and improvements in end-to-end service provisioning [11]. It is worth noting that scalability is one of the major issues for IoT Devices [4]. As we know, scaling means duplicating and adding resources to increase the availability of on-demand services, by adding additional resources, which is important for network performance [12]. Network performance, where refers to service quality measures of a network such as the number of users, the type of transmission medium, the connected hardware’s capabilities and software efficiency [13]. According to the WHO definition, in order to ensure the provision of quality services in health care systems, patients should be monitored in any time or anywhere. All of these services provide reliable information for decision-making and ensure the real-time service delivery policies for early intervention, prevention, and treatment [14]. It is noteworthy to mention that the golden time, is an essential concept in pre-hospital care and affected by to the health care, logistics, geographic, environmental and temporal variables [15]. Because of the type of medical data that is usually produced in a stream (e.g., heart rate), we use Stream Control Transmission Protocol (SCTP) for data transfer to be reliable in connection. The SCTP, like TCP, is reliable and connection-oriented protocol, but similar to UDP is message-oriented. This protocol has no significant effect on the latency of the network. It is worth noting that additional features such as multistreaming and multi-homing are available at that [16]. The characteristics of the patient’s system and services as an application or context can affect the quality of the experience of healthcare and, as a result, improve the quality of experience (QoE). There is no reference model for evaluating the QoE of IoT applications due to the lack of IOT

Table 1 Heart failure classes and Description [6]. Stage

Notation

Symptoms occur during rest and any physical activity makes them worse [2]

2

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architecture. Health monitoring and smart healthcare are categorized into a person-oriented IoT application, which builds on information and resources to improve the quality of healthcare experience, patient’s satisfaction level, and emergency services [17,18]. There are a number of studies, however, that demonstrate the quality of experiences [17–19], remote health monitoring systems [20–26], and systems to improve health services [20–29]. Also, IoT-based [5,17,18,23, 25–27,29–32], real-time solutions [5,28], cloud based monitoring systems [5,20,23,28,33], scalability [34], and edge computing, fog computing and related concepts [17,28,35,36] are studied. It is north worthy that the sensor networks considered in all of these studies. Lan et al. [20], proposed an end-to-end remote health monitoring and analytics system for heart failure patients.The authors designed A three-part solution and implemented to collect data through the smartphone-based gateway, a scalable cloud-based database, and ability of prognostic prediction in analytics engine. They offer to expand the WANDA analytics engine into task optimization, unsupervised learning, and individualized health guidance for patients, contextaware prediction, and long-term pattern discovery. Alshurafa et al. [21] developed the Wanda, a cardiovascular disease (CVD) system to predict in reducing detected CVD risk factors. They used the smartphone to transmit contextual baseline features and data such as activity blood pressure and questionnaire responses. The authors applied prediction tools to assist the clinicians and scientists, and to benefit from the remote health monitoring system. Also, they improved prediction by using intervention-based smartphone data. They present the AI usage in life science has aiming to achieve a higher quality of life and reshape the future of the health care system. They offer future studies to replicate their work in larger and more diverse groups using similar analysis to further validate the results. Nedungadi et al. [22] designed a personalized and commercial health monitoring system with improved in quality of life, using personalized and customized health services. This system supports community health workers on mobile devices in differential diagnosis and recommends additional diagnostic tests. Also, They show that, in a very uneven distribution of health resources, traditional clinical-based postoperative rehabilitation, should be implemented near the patients’ living areas, so e-Health could be a solution to the gap. Their health care monitoring system, enables clients to take charge of their own health and leads to an acceptable and good quality life. Their future efforts include large-scale testing in rural villages with integration in the adjacent to the health care center, also integrating the system with existing telemedicine solutions. Nguyen et al. [27], explore the use of IoT-based applications in the medical field and propose an IoT tiered architecture. Also, the authors introduce self-care system, data mining and machine learning as areas of IOT’s next generation of health care applications. They proposed a system to data collection and analysis for the development of the fall detection and prevention based on the IoT tiered architecture approach for future work. Verma et al. [5] pointed out that most contemporary researchers overlooked a suitable IoT analytics network design and most of the researches focused on the sensing and delivery networking technologies of the IoT system. The authors categorized existing massive IoT data analysis frameworks into real-time stream analysis and cloud computing, which have significant limitations on supporting delay-sensitive IoT application. They discuss the numbers of open research issues and future directions on real-time IoT analytics such as the challenges of analytics network scalability, agility, fault tolerance, spectral efficiency, and network delay. Satyanarayanan in [36], described edge computing by placing computational resources in close proximity to mobile devices as a reforms on the cloud hierarchy. The author describe that, new technologies and applications are shifting the function of centralized cloud computing into the edge devices of networks. Also, The author described the emergence of edge computing through three important

trends, include: Software Defined Networking (SDN) and the related concepts of Network Function Virtualization (NFV), ultra low latency, one of the proposed attributes for future 5G networks, and continuous improvement of the computing capabilities of wearables, smartphones, and other mobile devices. Bhatt et al. [23], introduced access control enforcement for the cloud-enabled IoT. They use an access control framework to develop cloud-enabled wearable internet of things. Also, they enhance the access control enforcement architecture by adding a layer called object abstraction. The authors developed their approach within a framework based on the interactions between different layers of the enhanced architecture. They introduced remote health care and fitness monitoring to represent the various framework aspects. They are going to develop access control models focusing on some of the interactions in their three framework access control categories, in particular, cloud access control models for future work. Hassan et al. [28], proposes a hybrid network model for body area networks for pervasive health care. They combined WBANs and cloud for valid data sharing and delivery, wand with goals of transmission WBANs media data in efficient and real-time manner. Also, ContentCentric Networking is integrated with adaptive streaming technology has been utilized to support uninterrupted media health care delivery to multiple patients and physicians. They proposed a method to improve the scalability of the network, as well as the various security aspects of the system in future works. Otoum et al. [24] in 2015, have develop an Epilepsy Patients Monitoring System (EPMS), using WSNs. They use Sensor Medium Access Control (SMAC) because of their low power consumption. Their EPMS designed with five MICAz sensor motes that acquire seizure information and pass it to the coordinator to send it to the receiver. Their system performance evaluated using NS2 simulator. The authors show that, the SMAC protocol has a lower average delay in packets compared to the ZigBee protocol. They offered to improve the monitoring system and health care services for patients by incorporating GPS and routing protocols. Otoum et al. [25] in 2017, presented a hybrid architecture to monitor anomalies in critical systems such as environment, medical, and smart grids that can also be used in IoT architecture. They proposed an architecture that consists of two subsystems, anomaly detection subsystem and signature detection subsystem for enhanced densitybased spatial clustering of applications. their architecture show the great performance in detecting both known and unknown intrusive behavior of sensor nodes in critical applications. In their new hybrid method, the detection rate is enhanced up to 99.73 percent while the accuracy enhanced 98.95 percent using Artificial Intelligence. In another study from Otoum et al. [26] in 2018, they proposed an Adaptive Surveillance and Clustered Hybrid IDS for wireless connected sensor clusters that monitor critical infrastructures. They are using machine learning approaches and adaptation strategy to detect dynamically known and unknown intrusions. Anomalies in data acquired by wireless sensor networks can be detected by continuous monitoring of the infrastructures and require advances that adapt to unknown attacks. The proposed method for monitoring systems in the sensor network performs at 98.9 percent detection rate and approx. 99.80 percent overall accuracy to detect known and unknown malicious behavior. They are focusing on implementing optimization models and investigating the effect of their heterogeneous cluster sizes on their method. Zhao et al. [34], modeled and analyzed the throughput capacities and scaling properties of multi-level and hybrid networks. They introduce the concept networks, consisting of multiple gateways and different three tiers of radio nodes using clustering. Also, they demonstrate the effectiveness of using clustering to increase network scalability. They validated their work, using Ns-2 simulation, scaling behavior, and performance as a function of key parameters. They analyze the scalability properties of their proposed network, which lead 3

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assisted environment and also presented a reference model for the development of the heart monitoring system. The authors in this systematic review have tried their best to assisted researchers and health professionals and discussed open issues for IoT-based techniques in online health monitoring. Also, they discussed valuable contributions for healthcare professionals to IoT solutions in collection, decision support and monitoring. Meigal et al. [33], proposed an At-Home Laboratory (AHL) used in surveillance, assessment, and recommendations construction. The authors focus on the connection between Human Intelligence (HI) and Artificial Intelligence (AI) and proposed the fourth brain as a new layer for the continued connection between them. They discussed the existing possibilities of AI methods and Ambient Intelligence (AMI) environments and finally proposed AHL design for its implementation. The author used a semantic network for joint analysis and CardiaCare application extended within their AHL system for abnormalities detection. They used monitoring, evaluation, warning, notifying, and corrective advice on their design to provide information services. Considering the above and other studies, an appropriate remote health monitoring system should have the ability to detect abnormalities in real time. In the development of remote health monitoring systems, scalability, fault tolerance, context-aware computing, and long-term pattern discovery for personalized health care services should be considered. The hierarchy of layers and process decomposition can improve the latency of the network despite the software overheads. Accurate and reliable information should be available in time to evaluate and manage patients with chronic diseases. Context-aware and real-time decision-support systems are used to prevent and early intervention in health care. With the increase in patients’ demands for real-time services, scalability, fault tolerance, real-time response and context-aware computing are critical issues in health care services. In our proposed hierarchical health care architecture, which is described in the next section, we consider these important issues to improve the quality of service and network performance.

to a linear increase in end-user throughput. Finally, they determined the value of adding forwarding nodes to improving scaling behavior and reducing the required number of access points relative to the twotier case. Soraia et al. [17], describe using Petri net to optimize the allocation of resources integrated with edge computing and cloud computing. They proposed a framework to solve the resource assignment, process modeling, and management of Emergency Departments (EDs) problems with Petri nets. As a result, they have reduced the waiting time and the patient’s Length of Stay (LoS) based on cloud-fog technology. The authors, for better performance and higher reliability, have integrated the Resource Preservation Net (RPN) with the edge computing framework. Oueida et al. [18], proposed a model that can provide improved delivery and utilization of resources to satisfying the patient, owner and medical resources in the medical care systems. The authors used the Maximum Reward Algorithm (MRA) to optimize the satisfaction factors of healthcare systems. Their algorithms provide reliability, efficiency and optimal resource utilization by improving healthcare resource allocation. Aloqaily et al. [19], proposed the solution for data and service management in crowded environments such as the stadium or the metro station. The authors highlighted the open issue related to data processing, storage and networking in high-density environments in terms of data/service management and delivery. Also, they combine different techniques and mechanisms to overcome challenges and issues in data and service management in a crowded mobile environment. Korzun et al. [29], presented an approach to collecting and mining health-related information on the patient based mobile multi-source sensed data. The authors provided an approach to collecting and mining patients’ health information from various medical devices that accompany the patient throughout her entire life. They used IoT technologies to provide a new generation of mobile healthcare services based on fusing the real (physical) and cyber (information) worlds. They used semantic data mining methods to analyze, and also data mining for making recommendations. Finally, they introduced the At-Home Laboratory (AHL) mobile healthcare as a vision for using their facility. Turcu et al. [30], discussed the open issues, research challenges and have examined the technologies and the emergence of new ones which will impact the healthcare business strategy. In this study, they have highlighted IoT-based applications that have an impact on healthcare services. They believe that these applications could improve the delivery of healthcare services in a time-saving and low-cost manner, which will also be reflected in the potential economic impact of IoT technology in healthcare. The authors explained the opportunities such as creating new business models and “quantified self” movement to get highly involve people in healthcare products as services. D’Antrassi et al. [31], designed, implemented, and tested the feasibility of a multi-source patient-centered acquisition system in a real clinical research study. They use a standards-based architecture ensures the fulfillment of the primary objectives for health data in these studies. Their solution provides patients as the direct digital data source, ensuring reliability, integrity, security, attributability, and auditability of data. They tested their reliable solution for the optimization of Deep Brain Stimulation (DBS) therapy in patients with Parkinson’s disease. The authors focus in tolerability, reliability, quality, and standardize the correlation of the patient data in healthcare system. Santos et al. [32], performed a survey based on medical care and

3. Proposed architecture 3.1. Architecture overview In this section, we conceptually outline our IoT based and hierarchical architecture for context-aware smart health care service provision. The inspiration behind our proposed approach comes from the limitations and defects observed in the previous work, in terms of scalability, network latency, and real-time response [5,21,27,36]. Fig. 1 shows an overview of the architecture. According to the this figure, vital signs measured by several sources including wireless body area networks (WBANs), Global Positioning System (GPS), Blood Pressure (BP), survey system, activity monitor, body temperature, and ECG signal detector, and then these data are sent to the smart health care services provision system. This system performs a multi-level process on input data streams and as a result, reports the abnormality and patient’s health records to the health care service providers. The multi-level processing is implemented in a hierarchical manner such that each level execute processes with a particular granularity. A more detailed process task is placed on the higher layers while more lightweight processes are assigned to the lower layers. This multi-level architecture will result in an improvement in early intervention based on Fig. 1. The overview of the proposed smart health care service provision architecture.

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Fig. 2. The details of the proposed smart health care service provision architecture.

• Patients:

the input stream. The reason for this is that many diseases can detected early by light-weight processes that are running in lower layers. For instance, hypertension is a predictor for coronary heart disease. Therefore, the abnormal situation could be declared without further intervention. So the operational processing steps, in summary, are as follows:

•

• Preprocessing step: a set of lightweight and real-time processes with •

•

the ability to run in resource-constrained devices and abnormality detection such as smart mobile devices in the bottom layer. In this step, the golden time is preserved and the early medical procedures are prescribed. Initial processing step: a set of processes which cannot be executed in the mobile devices because of their limitations. In this step, powerful computing resources such as cloudlet should be used to support resource-intensive processes to reduce response time. Furthermore, these processes have relatively complex algorithms that process more parameters to detect diseases. By the way determining the appropriate cloudlets, to service provisioning, would be in terms of priority, Service Level Agreement (SLA), and patient’s context. Final processing step: this step contains resource-intensive tasks with the capability of running batch, data-intensive, and time-consuming processes. The Personal Health Record (PHR), history of the patient’s family, and the existing patterns of diseases could be interfered in these processes to improve the precision of detection.

•

– User profile: is digital identity including personal information, age, and situation addressed. – History: personal health record (PHR) and Family history. – Location and environmental measurement – Patient situation: class I to class IV described in Table 1 Cloudlets: – Workload – Geographical location – Network traffic – System utilization Services – Type and variety – SLA

QoE Evaluation process: In each layer, the QoE influence factors previously mentioned used to evaluate healthcare monitoring services. Use the context parameters and define QoE influence factors will lead to improved management and experience in healthcare services. Realtime, scalable and available emergency services will lead to improving the patient’s experiences. As mentioned earlier, the related parameters to the content, network, device, application, user expectations and goals, and the context of use, which influences on QoE, are used in different layers of the proposed architecture. These factors are used in decision making and better services provisioning. It is worth noting that scalability, network latency and real-time responding are the main objectives of the proposed approach which would be preserved in the proposed multi-layered architecture as are shown in Section 3. In the next section, details of the proposed architecture with related algorithms are explained.

Other background processes run for prediction, management, authentication, registration, and other related services. Context-aware computing is one of most important properties that have an influence on management, orchestration, load balancing, prediction, and task scheduling. Context-aware computing process: The parameters, such as Patient’s profile, Patient’s geographical location, Service & End User Level Agreements (SLA), and the patient’s situation are related to server allocation, prediction, and management process. Patient’s profiles can consist of risk factors such as situation, unhealthy lifestyle habits, age, environment, occupation, family history and genetics, race or ethnicity, sex, and other medical conditions that affects the awareness, diagnosis, and treatment. Predict the Patients’ demand and provide services are based on context-aware computing and are hosted in the federated cloud layer. This process used these parameters to support decisionmaking. The contextual parameters are a set of Patients, Cloudlets, and Services parameters are as follows:

3.2. Architecture description As it is mentioned above, the proposed architecture used a hierarchical design, as presented in Fig. 2. The proposed architecture consists of four layers, bottom up: patient, Edge computing, Fog computing, and Federated cloud layer, receptively. The data stream including vital and ECG signals are collected and sent by various sources such as Smart Mobile Devices (SMD) is processed and the output is being forward as input to the next layers. Resource-rich cloudlets are used to facilitate the processing and storage operation between the smart phone and the federated cloud in the edge and fog layers. Most of the data-intensive computing tasks including pattern recognition, management, prediction, prevention, and control are 5

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performed in the federated cloud layer. In the Fig. 2, we first describe the various layers and used components and then, for a better understanding of the relationship between them, the corresponding process is explained. Patient’s layer: the aim of the patient’s layer is to monitor the cardiac of patients in terms of the coronary heart disease information to the patient’s situation awareness. To this end, it collects, aggregates, preprocess, and finally transmits the patient’s data to the next layers. These processes are shown in Algorithm 1.

storing them for preprocessing and abnormality detecting. After that, it determines the appropriate layer and transmits data to it for further processing. Edge layer: The aim of this layer, similar to the patient’s layer, is to monitor the patient’s situations with differences in device capability. The used devices in this layer have not the limitations of SMDs. It applied dynamic cloudlets for scalability, fault tolerance and real-time responding. The cloudlet for health care services is deployed at the

Algorithm 1. Preprocessing step in SMD.

The algorithm receives data streams as input and preprocesses them for abnormally detection and patient’s cardiac monitoring. It works in a loop in which every cycle involves getting the input data stream, in a specified period of time (e.g. per hour, per day, …), and

patient’s home or in the stations close to it. The used algorithm is shown in Algorithm 2. Algorithm 2. Preprocessing step in edge layer.

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As can be seen, the algorithm is similar to algorithm 1 in its steps. It enters in a loop preprocessing and checking the abnormality. After that, it selects the proper layer and sends data to it. Fog layer: (Static cloudlets) the cloudlets in this layer are stable, persistent and deployed at the edge of the network with many resources. Processing, storing and network services between the first layer and the dynamic layer have been facilitated, also scalability and fault tolerances have been provisioned. These cloudlets store the patient’s data, perform the initial processing and finally transmitted the data to the federated cloud. The algorithm of the processes of this layer is shown in Algorithm 3.

As can be seen, the algorithm is similar to algorithm 2 in its steps. It enters in a loop to preprocessing, initial processing and checking the abnormality. After that, it sends data to the Federated cloud layer. Federated cloud layer: In this layer, the transmitted data from the previous layers are used to final processing for abnormality or disease detection. This layer is the backbone for IoT and health care services at an enterprise level. The Patient Health Records(PHR) and hospital data will be in this layer. Pattern recognition, decision support systems, and therapeutic strategies hosted in the Federated cloud. Data-intensive health care application is submitted to this layer. The benefits of this layer are knowledge-sharing, clinical trial management and improving decision support systems. Also, prediction and management by using

Algorithm 3. Processing steps in fog layer.

Fig. 3. Application Model for proposed health care architecture. 7

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context-aware computing are hosted in this layer. These processes are shown in Algorithm 4.

responding to the demands can cause accidents with serious injuries or death. The health care process and critical care services are hosted in the different layers and, if necessary, the data is offloaded to a resource-

Algorithm 4. Processing steps in the federated cloud layer.

As can be seen, the algorithm is similar to the previous algorithm in its steps. It enters in a loop to preprocessing, initial processing, and checking the abnormality. Also, final processing will occur on whole data for further processing. Eventually, Get the results and store them in the PHR to provide patient care recommendations.

rich cloud or server and returns the result back to the health care service provider. Check on a patient’s health record (PHRs) and bring it up to date is so time-consuming and affect the scalability. These timeconsuming services/processes are running in the cloud to ensure the performance and scalability of real-time and light-weight processes running in SMD or near patients. The proposed scenario for the implementation of the architecture of health service is shown in Fig. 3.

3.3. Scenario This proposed architecture is independent of technologies and implementations, and with the goal of providing services in the cloud which are accessible by users at any time, any place, and anywhere. Due to the large numbers of patients and the demand for critical cardiac patients scalability, real-time, and fault-tolerance are critical issues related to resuscitation times to cardiopulmonary. Delaying or not

4. Simulation 4.1. Evaluation metrics Based on the proposed architecture described above, some of the 8

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performance evaluation metrics are explained.

appropriate layer can access this information and process them accordingly. We have considered a network area with 100 m × 100 m for 6 patients per each cluster for initial abnormality detection in near realtime. This process runs on dynamic cloudlets which is geographically close to the patients. Every 10 dynamic cloudlets register itself with its profiles in a static cloudlet without any limitation in time and resources. Eventually, between 10 to 30 servers are managed by one hub in the federated cloud, and all data will be queued in the federated cloud for data-intensive processes. The scenario has been implemented in the simulator for the input stream of the ECG data (22 MB) per user, per hour. Eventually, we are using the Monte Carlo simulation technique to repeat the experiment 1000 times and achieve a reasonable result. The input stream rate of ECG data is variable and be considered as in Eq. (5). The operational compression ratio of this data is 20 (20:1).

4.1.1. Emergency response time Emergency response time is defined as the interval between detecting abnormality and emergency health care services to prevent health hazards. Response time is one of the potential components for medical services to improve health care performance [37]. The response time in Eq. (1) is based on network latency and processing time. In the proposed architecture, the response time of the servers is reduced by reducing the distance between the patients and the computing nodes based on Eq. (1)

TR: Response time = 2*(Latency ) + Processing Delay

(1)

4.1.2. Network latency Latency introduced by these components in Eq. (2)[38]. It should be noted, due to the importance of End-to-end delay in the real-time application, propagation and queueing delay have a significant effect in response time (Eq. (1)) [39].

Latency = Propagation + Transmit + Queueing + Processing

Stream rate : 11

It is worth noting that the uniform distribution of mobility with the unknown location is used in these simulation models. Also, exponential distribution is used to generate a random node transmit. Tables 3, 4 contain the initial simulation settings that are used in this study. Finally, we compare our architecture with the commons alternative approaches and report the state-of-the-art results. Common approaches using cloud-based IoT architecture for monitoring electrocardiogram (ECG).

(2)

Propagation = Distance / Speed of light Trasnmit = Packet Size / Bandwidth Processing : the time of packet prcessing in network 4.1.3. Scalability

X (N ) =

1+

(N

(1

)/

Nmax =

N 1) + N (N

4.3. Simulation results In this section, we measure and discuss the response time, scalability and fault tolerance of the proposed approach according to the predefined scenario.

(3)

1)

Concurrency : Where X (N ) = N when and X (1) = Contention : Coherency: Xmax = X (Nmax )

=

=0

4.3.1. Response time Table 5 shows the measured results of network latency and response time for all the processes in different layers of the proposed architecture. In this table the columns show the evaluation metrics and the Table 3 Simulation parameters.

4.1.4. Queuing formula The aim of this paper is to reduce the waiting times of patients in queues. One of the leading solutions to reduce the mean service rate ( µ ) is adding processing capabilities. Also, reducing the utilization of the server (( )) by increasing the Inter-arrival time lead to reducing the mean arrival rate (( )). The Queuing formulas for the M/M/c queue considered in Eq. (1)

Lq = =

Component

Attributes

Values

Wi-Fi

Frequency Bitrate Range Interface Mobility speed Data rate Diameter Mobility Data rate SCTP SCTP server, SCTP Client Data

2.4 GHz Random (54Mbps) 150 m IEEE802.11 a/b/g 1mps 1Mbps/500Kbps/250Kbps 2Km 10mps 1G-10G – 22 Mb

LTE

P0 ( µ )c c! (1

(5)

22Mbit (perhour ) (pereachlead )

)2

(4)

Fiber

cµ

c: the number of identical servers = 1/ E [Inter arrival Time] mean rate of arrival. where E [] denotes the expectation operator . µ = 1/E [Service Time] mean service rate.

Table 4 Simulation settings.

4.2. Simulation scenario To evaluate the proposed architecture we have performed simulation in OMNeT++. The above-described scenario has been implemented using OMNeT ++, according to the architecture framework presented in this paper. The OMNeT++ engine runs simulating discrete, event-driven communicating of nodes event-driven on platforms [40]. In this simulation, the patient is able to send data from their smart devices to the appropriate layer by using the SCTP protocol. So the

Simulation parameter

Values

Number of patients nodes Number of clusters (EDGE) Number of clusters (FOG) Number of Hubs in federated cloud protocol Packet size Communication range

6 10 10 6 SCTP 11–22 bytes Based on technology in each layer (Table 3) 100 m × 100 m ECG sensors

Operational area of patients nodes Sensor types

9

Journal of Biomedical Informatics 96 (2019) 103251

2(2.83) + tp

2(17.35) + tp

–

–

–

tf

tp

1.49 + tp

– –

(min) (End-to-end delay) + Process delay

rows specify the latency time of the related process. As previously stated, the response time is affected by the network latency. To this aim, we first obtain the network latency and then calculate the response time based on Eq. (1). These values are shown in the last column of the table. In addition, to make a better comparison with previous methods, here we have calculated the values in the worst and average cases. In the following, the obtained results are described separately for different processes. Due to scalability in architecture and based on Resource Management System (RMS), the patients are classified into subgroups on dynamic cloudlets and also, dynamic cloudlets are divided between static cloudlets. In the hierarchy architecture, the static cloudlets, also categorized between hubs in federal cloud computing environment to better responding. In every step of sending data the response time is calculated based on Eq. (1). As mentioned above, the experiments are repeated using the Monte Carlo simulation technique, 1,000 times.

ti

0

60

500

10

10

:Remarkable value

4.3.3. Edge layer Fig. 4 and Table Table 5, SE illustrates the network latency of transmitting health data from SMD to edge layer. The patient’s data after the preprocessing step, are transmitted to the servers in the edge computing environment. More heart diseases can be detected by providing more processing capability to the preprocessing step. So, without the resources constraints for emergency services, the preprocessing time will be less than patient’s layer. Due to the small amount of propagation and transmission time, the network latency is negligible for this step. It is worth noting that due to the scale-up the computing resources, the processing time will significantly reduce. As mentioned earlier, based on dividing patients into small groups and assigning them to a cloudlet, queueing delay has an influence on response time but is insignificant and lead to the ability of abnormal detection in real time. In Eq. (3), due to the clustering of patients in small groups (every 1 to 6 patient) and associating them with a cloudlet, the queuing delay depends on the concurrency ratio ( ). Thus, the contention ratio ( ), due to the significant reduction in the waiting and queueing time for shared resources, is not significant and the coherency ratio ( ) due to the data inconsistency becomes zero. It is worth noting that the independence of the processes has an effect on reducing service time and arrival time in Eq. (4), and so in response time.

2.36 Description:

0.33 0.72

4.94 Fog layer to Federated cloud

Patient’s SMD

Patient’s SMD to Edge layer Edge layer to Fog layer

0

0

.0003 .0003

0

0

0

6

6

0

tp < ti

tf ,

ti

4.3.2. Patient’s layer In this step, by means of collecting patients ’data from the sensors, the abnormality detection preprocessing will be execute immediately on the patients’ SMD. Scale out is done by adding more processing nodes using SMD’s, result in real time abnormality detection. As we know the preprocessing time is related to the device resources constraint. It is worth noting that the propagation, transmission, and queueing delays estimate to be near zero. The light-weight processes and reduced network latency have led to a greater ability to diagnose and detect in real time. In the patient’s layer, the queueing delay in the abnormality detection preprocessing is near the zero. It is worth noting that the concurrency ratio ( ) in Eq. (3), within the proposed architecture, is equal to the number of patients’ devices. The other parameters in Eq. (3), the ratios of contention and coherency with , in this scenario are equal to zero. The light-weight processes are executed in SMD and each patient has her own devices. It is worth noting that independent processes will be led to reducing service time and inter-arrival time in Eq. (4). These items have a remarkable impact on real-time response and scalability.

tp

tp 2.82

17.27 3

3

–

tp

3

2

0

2(1.46)

tp

–

–

–

2 Sequential sub process Pre-process Initial Final improves Process Process queue Inter-arrival- Service-time time User in queue Queuing (sec) Worst-case Average

Network latency Step description

Table 5 Simulation Result of the proposed architecture

Network latency Propagation Transmission

End-to-end delay (sec)

Max time (min)

Process delay

process Full process

Emergency response time

A. Malekian Borujeni, et al.

4.3.4. Fog layer Fig. 4, and Table 5 illustrates the network latency of transmitting data between the edge layers and the fog layer for initial processing 10

Journal of Biomedical Informatics 96 (2019) 103251

A. Malekian Borujeni, et al.

Fig. 5. Comparison of the simulation results for the Network latency.

have negative impacts on the network latency and the response time. Also in Eq. (4), the remarkable values of service time and inter-arrival time are increasing the queuing delay and, as a result, led to increasing the emergency response time. Figs. 4 and 5 show the details of the simulation results for network latency. Tables 5 and 6 summarize the results of these figures, respectively. The processing time, queueing time, emergency response time and network parameters like latency, end-to-end delay for the steps of the proposed architecture are summarized in these tables. The network delay column represents the worst-case values and average scenarios. The end-to-end delay includes network latency between each node and the processing queue and the other column represents the time of abnormality detection in processing steps. The final column is the time of emergency response. In the patients’ SMD row, the patients’ data processed locally on SMD and thus the network latency is zero. Service time is deterministic and the inter-arrival time is fixed so that the queuing delay of this step is zero. It’s worth noting that in this step, only the processing time is taken into account. As shown in Fig. 4 and Table 5, the network latency (either worstcase or average) between the patients’ nodes and edge layers is negligible and about 0.0003 s and the End-to-end delay is near 1.46 min in worst-case. In the edge layer to fog layer row, patients’ data are transmitted to the edge computing environment which the preprocessing time and the queuing delay are significant. Although, due to local processing, network latency is insignificant. The calculated values for transmitting patient data between the nodes in the edge layer and the fog layer are respectively 0.70 and 0.32 s for the worst-case and average scenario, and the end-to-end latency is near 2.82 min. In the fog layer to the federated cloud row, processing time, network latency and queuing delay have a significant impact on emergency response time. As shown, all of these parameters have a significant impact on emergency response time due to the distance between previous layers, processing queue, request number, and final processing step. It’s worth noting that, the preprocessing time for abnormality detection is insignificant, but the processing time of other steps is remarkable. Table 6 and Fig. 5 summarizes the results to compare the proposed architecture in the best-case, average-case, worst-case scenario, Cloudbased and Fog-based computing architectures. In the cloud-based architecture, the patients’ data are transmitted to the destination data center to execute the processing for abnormality detection. The optimistic result for the end-to-end delay is significant, due to excessive queuing delay and the high number of requests. This

Fig. 4. Simulation results of the network latency in the proposed architecture.

step. As shown, propagation, transmission and queuing delay are significant and have an influence on the response time. Increasing the geographical distance between two layers, the amount of data that has been collected and processed, and the number of patients’ demands explains these results. Scale-up of the computing resource capability in the highest layers reduces preprocessing time, but the initial processing, which is a heavyweight and time-consuming process, is executed in these layers to detect more abnormalities. As previously mentioned, clustering is used to group the dynamic cloudlets of the edge layer into the fog layer. So, dynamic cloudlets in the edge layer are divided into groups and each group is assigned to a static cloudlet in the fog layer and send their requests to these static cloudlets for further processing. The high number of requests which come from the below layers have an influence on queueing behavior and may result in the response time to be either fast or slow. As described in the simulation scenario, every 10 cloudlets in the edge computing environment are assigned to the one powerful cloudlets in the fog computing environment, which has the ability to run multi-task. But the service time and the inter-arrival time in Eq. (4) are significant, which leads to an increase in emergency response time. 4.3.5. Federated cloud layer Fig. 4 and Table 5 illustrates the latency of transmitting data between fog layers and the federated cloud for final processing. Given the reasons mentioned above and due to data integration, resource-intensive tasks, and time-consuming requests, the values of propagation, transmission, and queuing delay are more remarkable for final processing in the federated cloud. Long-length queues are needed to aggregate, store and process a large number of requests and a huge amount of data in the federated cloud. The delay of the above queues has an impact on emergency response time. As previously mentioned, the final processing step is dataintensive processing and requires more time for completing the process. Due to the resource sharing among tasks, a large number of patients, and geographical distance, the ratios of contention ( ), coherency ( ) and concurrency ( ) are remarkable in equation Eq. (3). So, these items 11

Journal of Biomedical Informatics 96 (2019) 103251

70.32

2(51.64) + tp

33.38

2(32.35) + tp

2(21.55)+ tp

2(7.18)+ tp

2(0)+ tp

(End-to-end delay) + Process delay

occurs because of centralized processing, which increases system load and thus is not suitable for healthcare services. Due to the distance between the patients and the processing nodes in the data center, a large number of patients request for abnormal detection and a large number of tasks in the queue, processing time, queuing delay and network latency are increased and have been significant. In the fog-based computing architecture, the end-to-end delay is improved in comparison with cloud-based architecture. This improvement is due to a reduction in patient’s distance and two-stage processing which leads to a reduction in the network latency, queuing delay and also emergency response time. In the proposed architecture, due to the local processing, classifying the patients, dividing the task into independent processes, reducing the transmitted data volume, queueing delay and network latency, the end-to-end delay in the best-case is 7.18 min. The most important parameters that affect these results are hierarchical layers and independent processing on patient’s devices which achieve a significant reduction in queueing delay, emergency response time and improve QoE requirements. Table 6 shows the improvement of about 67 percent on average in network latency, which represented more responsive and reliable services. As shown in this table, the proposed architecture significantly reduces emergency response time compared to other scenarios. The emergency response time improves by 70 percent and 33 percent, respectively in cloud computing and fog computing solution. As explained, the abnormality detection process requires computational resources. However, the computational resources are shared with other process and assigned only when needed. More formally, let P = {p1 , ...,pn } be a set of patients and let R = {r1, ...,rm} be a set of existing resources. Due to the Pigeonhole principle, there must be n patients and m resources, which states that there is no bijection in n > m . As mentioned above, each patient in the proposed architecture has its own computing resources. The abnormality detection process must be processed in these resources. So, it is clear that there is a bijection between the patients and computing resources. It is obvious that the horizontal scaling used to achieve linear scalability. Finally, the proposed architecture addresses the scalability issue by building a largescale distributed processing layer.

t fp

tp < ti 0 0

10.40 18.77

70.21

66.3 60

64.92

7.99 14.17

74.13

2.69 5.66

69.84

Improvement to Cloud-based (percent) Improvement in Average: Description:

0.67 1.84

0 0 0

The Best-case of proposed architetcure The Average-case of proposed architetcure The Worst-case of proposed architetcure Patient’s SMD to Fog Computing Improvement to Fog Computing (percent) Patient’s SMD to Cloud-based

The improvement of our proposed architecture in comparison with other approaches are shown in bold value.

3 200

tf ,

51.32

:Remarkable value

tp

ti

tf

–

tf

ti

tp 3 9 100

32.35

–

tf

ti

tp – – –

21.55

–

tp tp tp – – –

7.18

– – –

tp – – –

0

Full process Final Process Initial improves Pre-process ServiceTime Inter-arrivalTime User in Queue Worst-Case

Average

Propagation

Transmission

Queuing

Process queue (Seconds)

Network Latency Step description

Table 6 Comparison of simulation result.

Network Latency

End-to-end delay (Seconds)

Max time (minute)

Sequential sub process

Process delay

process

2

Emergency Response Time (minutes)

A. Malekian Borujeni, et al.

5. Discussion As explained before, in the end-to-end remote patient monitoring solution, data is transmitted through devices to a back-end server. Specially in the cloud-based architecture, patients’ information and demands are sent to a centralized cloud for storing, processing, and analysing. So, as shown in Figs. 4 and 5, patients’ location (geographical distance), a large number of patients, network latency, and processing queue delay have a negative influence on the emergency response time of transmitting data between patients’ SMD and the distant data-center. In this scenario, the patients’ data are added to the queues for abnormality detection processing in a centralized cloud. Due to the large distance and numerous demands and amount of patients’ data, the network latency is affected by the propagation, transmission and queuing delay. So by limiting the resources allocated to critical processing and the increase in network latency, there has been unsatisfactory influences on response time. It is worth noting that due to the resource constraints on patient’s devices, most of the processing requests are sent to the centralized cloud, and emergency services will not be responsive in real time. Similarly, fog computing architecture facilitates the storage and processing operation for patients. It is worth noting that the improvement in response time is seen due to geographical distance, dividing patients into groups and by assigning their demands to the computing nodes close to the patients’ location in the fog environment. These are well demonstrated in Fig. 5 and Table 6. In this scenario, abnormality detection processes execute in the fog layer, which can be close to the 12

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A. Malekian Borujeni, et al.

patient’s current location, or not. The processes can be execute in the fog or in the cloud environment. Based on patients’ location and proximity to the fog computing nodes, all of them are split between fog nodes for abnormality detection. So, the propagation, transmission and queuing delay in network latency are remarkable. The latency and processing delay caused by patients’ data and requests in fog computing nodes have an effect on response time. As a result, the average emergency response time is increased. We have addressed the shortcomings of previous works and proposed a hierarchical architecture in which patient data are transmitted between designed layers. These data are processed in each layer and are forwarded to the next layer for further consideration. This scenario will give us a lot of capability:

In addition to the study the network latency is improved by selecting the appropriate transmission medium and scalable hierarchical layers. The average waiting time in the queue will decrease because of shorter service time with process decomposition. Also, processing time has been affected by executing the process in the SMD, edge and fog layers, respectively. The end-to-end delay calculation in this architecture is done using the SCTP protocol. The results show a significant improvement in the proposed architecture compared to conventional health care architecture. The experimental results show the 70 percent improvement in emergency response time comparing cloud-based solutions. This improvement is about 30 percent compared to Fog computing approaches. Declaration of Competing Interest

• the patients can be clustered according to a priority, that can be specified by context-aware computing and situational awareness. • the health care process can be divided into sequential tasks. • the light weight processes can be brought close to the patients. • the queuing delay of the emergency services can be reduced.

The authors declared that there is no conflict of interest. References [1] G.A. Roth, C. Johnson, A. Abajobir, F. Abd-Allah, S.F. Abera, G. Abyu, M. Ahmed, B. Aksut, T. Alam, K. Alam, et al., Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015, J. Am. Coll. Cardiol. 70 (1) (2017) 1–25. [2] T. Jaarsma, J. Cameron, B. Riegel, A. Stromberg, Factors related to self-care in heart failure patients according to the middle-range theory of self-care of chronic illness: a literature update, Curr. Heart Fail. Rep. 14 (2) (2017) 71–77. [3] D. Haluza, D. Jungwirth, Ict and the future of healthcare: aspects of pervasive health monitoring, Informatics Health Soc. Care 43 (1) (2018) 1–11. [4] S. Li, L. Da Xu, S. Zhao, 5g internet of things: a survey, J. Ind. Inf. Integr. 10 (2018) 1–9. [5] S. Verma, Y. Kawamoto, Z.M. Fadlullah, H. Nishiyama, N. Kato, A survey on network methodologies for real-time analytics of massive iot data and open research issues, IEEE Commun. Surv. Tut. 19 (3) (2017) 1457–1477. [6] G. Savarese, L.H. Lund, Global public health burden of heart failure, Cardiac Fail. Rev. 3 (1) (2017) 7. [7] J. Michael, C. Steinberger, Context modeling for active assistance, ER Forum/ Demos, vol. 1979, 2017, pp. 207–220. [8] N. Dey, A.S. Ashour, C. Bhatt, Internet of things driven connected healthcare, Internet of Things and Big Data Technologies for Next Generation Healthcare, Springer, 2017, pp. 3–12. [9] S. Wang, X. Zhang, Y. Zhang, L. Wang, J. Yang, W. Wang, A survey on mobile edge networks: convergence of computing, caching and communications, IEEE Access 5 (2017) 6757–6779. [10] S. Shenai, M. Aramudhan, A federated cloud computing model with self-organizing capability using trust negotiation, 2017 International Conference on IoT and Application (ICIOT), IEEE, 2017, pp. 1–6. [11] V. Vippalapalli, S. Ananthula, Internet of things (iot) based smart health care system, 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), IEEE, 2016, pp. 1229–1233. [12] E. Nygren, R.K. Sitaraman, J. Sun, The akamai network: a platform for high-performance internet applications, ACM SIGOPS Oper. Syst. Rev. 44 (3) (2010) 2–19. [13] A.B. Forouzan, Data Communications & Networking (sie), Tata, McGraw-Hill Education, 2007. [14] D.M. Berwick, E. Kelley, M.E. Kruk, S. Nishtar, M.A. Pate, Three global health-care quality reports in 2018, Lancet 392 (10143) (2018) 194–195. [15] I. Khan, B. Asima, S.A. Khan, Operations throughput as a determinant of goldenhour in mass-gathering medicine, Int. J. Med. Med. Res. 3 (1) (2017) 52–58. [16] K. Ono, H.G. Schulzrinne, The impact of sctp on server scalability and performance. (2008). [17] S. Oueida, Y. Kotb, M. Aloqaily, Y. Jararweh, T. Baker, An edge computing based smart healthcare framework for resource management, Sensors 18 (12) (2018) 4307. [18] S. Oueida, M. Aloqaily, S. Ionescu, A smart healthcare reward model for resource allocation in smart city, Multimedia Tools Appl. (2018) 1–22. [19] M. Aloqaily, I. Al Ridhawi, H.B. Salameh, Y. Jararweh, Data and service management in densely crowded environments: challenges, opportunities, and recent developments, IEEE Commun. Mag. 57 (4) (2019) 81–87. [20] M. Lan, L. Samy, N. Alshurafa, M.-K. Suh, H. Ghasemzadeh, A. MacabascoO’Connell, M. Sarrafzadeh, Wanda: An end-to-end remote health monitoring and analytics system for heart failure patients, Proceedings of the Conference on Wireless Health, ACM, 2012, p. 9. [21] N. Alshurafa, C. Sideris, M. Pourhomayoun, H. Kalantarian, M. Sarrafzadeh, J.A. Eastwood, Remote health monitoring outcome success prediction using baseline and first month intervention data, IEEE J. Biomed. Health Informatics 21 (2) (2017) 507–514. [22] P. Nedungadi, A. Jayakumar, R. Raman, Personalized health monitoring system for managing well-being in rural areas, J. Med. Syst. 42 (1) (2018) 22. [23] S. Bhatt, F. Patwa, R. Sandhu, An access control framework for cloud-enabled wearable internet of things, 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), IEEE, 2017, pp. 328–338.

obviously, all of the above have a positive impact on emergency response time. dividing the processes into a chain of independent tasks and distributing them into different layers would reduce the emergency services’ delay. also assigning the abnormality detection lightweight process to the patient’s device would improve scalability through concurrency, and then outperform the emergency response time. It is worth noting that the division of the processes into a chain of the sub-task and assigning them into an appropriate layer have a positive effect on service time, inter-arrival time and finally on queueing delay. Clearly, all of them would reduce the emergency response time. In addition, proposed architecture can provide fault tolerance using the redundancy of cloudlets in the same or other layers. Thus, occurring a failure on hardware or software in each cloudlet, the tasks would be assigned, with a less performance, to other ones. Furthermore, the proposed architecture addresses the scalability issue by building a large-scale distributed processing layer. As mentioned, abnormality detection process requires computational resources. However, the computational resources could be shared with other processes and assigned only when needed. More formally, let P = {p1 , …, pn } be a set of n patients and let R = {r1, …, rm} be a set of m existing resources. Due to the Pigeonhole principle, which states that there is no bijection in n > m . So, each patient in the proposed architecture should have its own computing resources for preprocessing. Hence the abnormality detection process can be assigned to these resources. As a result, there would be a bijection between the patients and computing resources. These activities as a horizontal scaling would guarantee the linear scalability. As future works, we intend to reduce the software overhead in this architecture, implement the different strategies in application module placement policies, and improve end-to-end delay and service time. Future developments will address improving the efficiency of powerconsuming for devices, distributing the context-aware computing, and security issues. 6. Conclusion In this paper, we propose a hierarchical IoT based architecture for a mission-critical application, the ECG Health Monitoring system. We propose an architecture that consists of four hierarchical layers for realtime abnormality detection including patient’s layer, edge layer, fog layer, and federated cloud layer. It is worth noting that context-aware computing has been used for precise diagnosis and treatment and improving service provision. Our aim is to improve the response time for emergency health care services and increase the scalability of early intervention. The response time is composed of network latency, queuing delay, and processing time. 13

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A. Malekian Borujeni, et al. [24] S. Otoum, M. Ahmed, H.T. Mouftah, Sensor medium access control (smac)-based epilepsy patients monitoring system, 2015 IEEE 28th Canadian conference on electrical and computer engineering (CCECE), IEEE, 2015, pp. 1109–1114. [25] S. Otoum, B. Kantarci, H.T. Mouftah, Detection of known and unknown intrusive sensor behavior in critical applications, IEEE Sens. Lett. 1 (5) (2017) 1–4. [26] S. Otoum, B. Kantarci, H. Mouftah, Adaptively supervised and intrusion-aware data aggregation for wireless sensor clusters in critical infrastructures, 2018 IEEE International Conference on Communications (ICC), IEEE, 2018, pp. 1–6. [27] H.H. Nguyen, F. Mirza, M.A. Naeem, M. Nguyen, A review on iot healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback, 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, 2017, pp. 257–262. [28] M.M. Hassan, K. Lin, X. Yue, J. Wan, A multimedia healthcare data sharing approach through cloud-based body area network, Fut. Gener. Comput. Syst. 66 (2017) 48–58. [29] D. Korzun, A. Meigal, Multi-source data sensing in mobile personalized healthcare systems: Semantic linking and data mining, 2019 24th Conference of Open Innovations Association (FRUCT), IEEE, 2019, pp. 187–192. [30] C. Turcu, C. Turcu, Improving the quality of healthcare through internet of things, arXiv preprint arXiv:<1903.05221>. (2019). [31] P. D’Antrassi, M. Prenassi, L. Rossi, R. Ferrucci, S. Barbieri, A. Priori, S. Marceglia, Personally collected health data for precision medicine and longitudinal research,

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