F2CDM: Internet of Things for Healthcare Network Based Fog-to-Cloud and Data-in-Motion Using MQTT Protocol

Al-Joboury, Istabraq M.; Al-Hemiary, Emad H.

doi:10.1007/978-3-319-68179-5_32

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Nevertheless, all the algorithms used were static without paying attention to the state of the server during the execution of tasks, which resulted in overhead and increased the execution time. Al-Joboury and Al-Hemiary [13] provided a mechanism to monitor healthcare data in real-time and reduce the congestion on the cloud network. This was achieved by sending the pulse (heartbeat) sensor messages by MQTT protocol to the fog server.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

Rajab

Younis

2021

eijs

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Internet of Things (IoT) contributes to improve the quality of life as it supports many applications, especially healthcare systems. Data generated from IoT devices is sent to the Cloud Computing (CC) for processing and storage, despite the latency caused by the distance. Because of the revolution in IoT devices, data sent to CC has been increasing. As a result, another problem added to the latency was increasing congestion on the cloud network. Fog Computing (FC) was used to solve these problems because of its proximity to IoT devices, while filtering data is sent to the CC. FC is a middle layer located between IoT devices and the CC layer. Due to the massive data generated by IoT devices on FC, Dynamic Weighted Round Robin (DWRR) algorithm was used, which represents a load balancing (LB) algorithm that is applied to schedule and distributes data among fog servers by reading CPU and memory values of these servers in order to improve system performance. The results proved that DWRR algorithm provides high throughput which reaches 3290 req/sec at 919 users. A lot of research is concerned with distribution of workload by using LB techniques without paying much attention to Fault Tolerance (FT), which implies that the system continues to operate even when fault occurs. Therefore, we proposed a replication FT technique called primary-backup replication based on dynamic checkpoint interval on FC. Checkpoint was used to replicate new data from a primary server to a backup server dynamically by monitoring CPU values of primary fog server, so that checkpoint occurs only when the CPU value is larger than 0.2 to reduce overhead. The results showed that the execution time of data filtering process on the FC with a dynamic checkpoint is less than the time spent in the case of the static checkpoint that is independent on the CPU status.

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Section: Related Workmentioning

confidence: 99%

Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

Rajab

Younis

2021

eijs

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“…It can be seen that the IoT layers are different from the Cloud layer and therefore it is essential to provide assurance connectivity [12]. We propose IoT architecture based on Cloud for storage, control, and monitoring the patient.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Architecture Based Cloud for Healthcare

Al-Joboury¹,

Al-Hemiary

2018

Iraqi Journal of ICT

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The Internet of Things (IoT) contains smart devices placed in different environments, connected with each other across networks and Internet. The integration between Things and Cloud Computing (CC) for monitoring and permanent storage is required for future IoT applications. Therefore, this paper proposes IoT architecture based Cloud for healthcare network when patients are remotely monitored by their family and physicians. This proposed architecture is different from the traditional IoT architecture that consists of Things, getaways, middleware, and application layers which in turn need connectivity insurance between them. The proposed architecture is designed and configured using Cisco Packet Tracer version 7.0 over two sites: Site 'A' located at smart home and site 'B' located at the smart hospital. The results show that the IoT based Cloud enhances the patient life style by using smart sensors and mobile application, as well as the physicians can remotely monitor the data in real time.

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“…Governments and societies benefit from these messages to improve the quality-of-life of end users [6]. Messages from Things can be analyzed and processed using Data in Motion (DM) technique that is introduced by IBM to provide analyzing messages without to need storing it [7][8]. Virtualization is a technology used to reduce power, cost, space and complexity by making multiple virtual servers on a single physical server called Virtual Machines (VMs).…”

Section: Introductionmentioning

confidence: 99%

IoT-F2CDM-LB: IoT Based Fog-to-Cloud and Data-in-Motion Architectures with Load Balancing

Al-Joboury¹,

Al-Hemiary²

2018

EAI Endorsed Trans IoT

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The work in this paper tries to enhance the performance of IoT by modifying the Cloud based architecture in terms of storage, processing, and Load Balancing (LB). The assumption is as follows: In a single Fog server, high traffic coming from Things may cause packet loss which in turn affect the overall IoT performance. To overcome such a situation, LB on Fog layer is proposed and implemented practically using virtualization technology. The proposed IoT based Fog-To-Cloud and Data-in-Motion with LB (IoT-F2CDM-LB) architecture consists of two load balancers; HAProxy and Server Load Balancing (SLB), are used to distribute messages from Things to four virtualized Fog servers according to Least Connection technique. The implementation is carried out using VirtualBox and GNS3. Message Queue Telemetry Transport (MQTT) protocol is used to transfer messages between layers. Both load balancers result in packet loss reduction by 50%, lower response time and higher throughput.

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F2CDM: Internet of Things for Healthcare Network Based Fog-to-Cloud and Data-in-Motion Using MQTT Protocol

Cited by 7 publications

References 12 publications

Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

Internet of Things Architecture Based Cloud for Healthcare

IoT-F2CDM-LB: IoT Based Fog-to-Cloud and Data-in-Motion Architectures with Load Balancing

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