Towards energy-aware fog-enabled cloud of things for healthcare

Mahmoud, Mukhtar M. E.; Rodrigues, Joel J. P. C.; Saleem, Kashif; Al‐Muhtadi, Jalal; Kumar, Neeraj; Korotaev, Valery V.

doi:10.1016/j.compeleceng.2018.02.047

Cited by 128 publications

(54 citation statements)

References 10 publications

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“…• Case 4: Comparison with other energy consumption algorithms. Authors Sun et al [8] have proposed IoT-fogcloud architecture with energy consumption and computation time of offloading and resource allocation (ETCORA) algorithm. Figure 8 provides comparison results between the proposed method and ETCORA to evaluate consumed energy for CloudIoT using the same parameters.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…According to Equation (8), the relationship between the energy consumption for processing tasks and power consumption in IoT is direct. When increasing the value of power consumption in IoT, the energy consumption increases.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Inspired by the fact that Cloud IoT is not the optimal solution to those cases that are sensitive to energy consumption and delay, Mahmoud et al [8] proposed a new fog-enabled Cloud IoT model. The entire idea is based on the energy allocation technique for populating tasks into a fog device.…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Energy Consumption for Cloud Internet of Things

et al. 2020

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The Internet of Things (IoT) and Cloud Computing are considered to be two of the greatest technology revolutions in the last few years. New technology raised in recent years is known as Cloud IoT or the Cloud of Things (CoT), which integrates cloud computing and the Internet of things. Dynamic and heterogeneous environments, energy efficiency, and delay-sensitivity are the major issues with the CoT. Energy efficiency is one of the basic requirements of IoT applications and resources in the cloud, which is a challenging issue in the CoT. This paper proposes the optimization of energy schemes for the CoT by applying a Genetic Algorithm (GA). Extensive numerical simulation is carried out to verify the effectiveness of the proposed method compared to the ETCORA algorithm. The analytical results present better performances of the proposed optimizing energy consumption technique. Results also show that the proposed method indeed outperforms the ETCORA in reducing the energy consumption of task requests.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Optimizing Energy Consumption for Cloud Internet of Things

et al. 2020

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“…Proposes a policy for service placement in Fog devices based on communities and transitive closures notions. [94] C R Proposes an energy-aware algorithm for mapping application components on Fog devices.…”

Section: Category Reference Requirementscontributionsmentioning

confidence: 99%

An Overview of Service Placement Problem in Fog and Edge Computing

2020

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To support the large and various applications generated by the Internet of Things (IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement in such infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new classification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.

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“…Furthermore, Mahmoud et al presented a task allocation strategy based on fog three‐layer architecture for remote patient monitoring systems in health care. This strategy was based on the remaining capacity of CPU and the energy available to fog servers to reduce energy consumption and to minimize delay for requested services in fog nodes.…”

Section: Classification Of the Selected Approachesmentioning

confidence: 99%

Quality of service‐aware approaches in fog computing

Kashani

Rahmani

Navimipour

2020

Int J Communication

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Summary In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.

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Towards energy-aware fog-enabled cloud of things for healthcare

Cited by 128 publications

References 10 publications

Optimizing Energy Consumption for Cloud Internet of Things

Optimizing Energy Consumption for Cloud Internet of Things

An Overview of Service Placement Problem in Fog and Edge Computing

Quality of service‐aware approaches in fog computing

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