Fog Computing Architectures and Frameworks for Healthcare 4.0

Nair, Anuja; Tanwar, Sudeep

doi:10.1007/978-3-030-46197-3_3

Cited by 11 publications

(9 citation statements)

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“…The algorithm uses the Thresholdbased algorithm to ensure efficient resource utilization, but also considers the real-time information to ensure optimal QoS. [32] • Hybrid Prediction-based: This algorithm combines the Prediction-based algorithm with dynamic information, such as resource utilization and network congestion, to determine how tasks should be assigned. The algorithm uses the Predictionbased algorithm to estimate future resource utilization and network congestion, but also considers the real-time information to ensure optimal QoS.…”

Section: Load Balancing In Fog Computingmentioning

confidence: 99%

Improved QoS with Fog computing based on Adaptive Load Balancing Algorithm

Saurabh

Dhanaraj

2023

IJRITCC

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As the number of sensing devices rises, traffic on the cloud servers is boosting day by day. When a device connected to the IoTwants access to data, cloud computing encourages the pairing of fog & cloud nodes to provide that information. One of the key needs in a fog-based cloud system, is efficient job scheduling to decrease the data delay and improve the QoS (Quality of Service). The researchers have used a variety of strategies to maintain the QoS criteria. However, because of the increased service delay caused by the busty traffic, job scheduling is impacted which leads to the unbalanced load on the fog environment. The proposed work uses a novel model which curates the features and working style of Genetic algorithm and the optimization algorithm with the load balancing scheduling on the fog nodes. The performance of the proposed hybrid model is contrasted with the other well-known algorithms in contrast to the fundamental benchmark optimization test functions. The proposed work displays better results in sustaining the task scheduling process when compared to the existing algorithms, which include Round Robin (RR) method, Hybrid RR, Hybrid Threshold based and Hybrid Predictive Based models, which ensures the efficacy of the proposed load balancing model to improve the quality of service in fog environment.

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Section: Load Balancing In Fog Computingmentioning

confidence: 99%

Improved QoS with Fog computing based on Adaptive Load Balancing Algorithm

Saurabh

Dhanaraj

2023

IJRITCC

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“…Earlier, the use of personal gateways as intermediate nodes of fog which are utilized between the healthcare cloud and IoT devices, has been reported [20]. An algorithm for clustering all cells could improve resource sharing between them [21].…”

Section: B Related Workmentioning

confidence: 99%

A Fog Computing Framework in IoT Healthcare Environment: Towards A New Method Based on Tasks Significance

Shanshool¹,

Abbas²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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The Internet-of-Things (IoT) is an important technology and is considered the future of the Internet. Healthcare is described as one of the important areas in IoT used for remote patient monitoring. Real-time remote monitoring health applications are important as delays in data transfer between the cloud, and the application may be unacceptable. Fog computing refers to a geographically distributed computing system with several devices connected to the same network to achieve flexible and collaborative computation, storage, and communication services. Fog computing is mainly used for efficient data processing between sensors and cloud computing as it reduces the volume of data exchanged between sensors and the cloud, thereby improving the whole system's efficiency. Wireless sensor networks (WSN) are also used in health monitoring systems to simultaneously transfer huge data volumes (of different priority levels and length values) to the fog computing system. Hence, there is a need to appropriately implement a task scheduling mechanism that can accurately prioritize tasks irrespective of their length. This study aims to systematically review the existing fog computing technologies in the Internet of things HealthCare (IoTH) systems and improve the performance of the available static task scheduling algorithms using the Tasks Classification (TC) method where task importance is paramount. The performance of the suggested approach was evaluated based on the Max-Min scheduling algorithm (SA).

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“…Nair and Tanwar [178] worked on the latency parameter by dividing the framework collection in the field of healthcare application related to fog computing. The authors have performed the experimentation using simulation software to reduce latency in healthcare application.…”

Section: ) Frameworkmentioning

confidence: 99%