Cloudlet Selection in Cache-Enabled Fog Networks for Latency Sensitive IoT Applications

Basir, Rabeea; Qaisar, Saad; Ali, Mudassar; Naeem, Muhammad

doi:10.1109/access.2021.3092819

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…A horizontal computing model combines physical and virtual resources. With the standardization of fog computing-related concepts, the technical architecture of cloud-fog hybrid computing has become increasingly mature [ 23 ].…”

Section: Model Introductionmentioning

confidence: 99%

Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care

2022

Computational Intelligence and Neuroscience

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In order to alleviate the “difficulty in seeing a doctor” for the masses, continuously optimize the service process, and explore new financial service processes for admission and discharge, this study proposes a cloud-fog hybrid model UCNN-BN based on an improved convolutional neural network and applies it to financial services in smart medical care. Decision-making applications: this research improves and designs the UCNN network based on AlexNet and introduces small convolution layers to form convolution groups, making the network more adjustable. The network structure is simpler and more flexible, and it is easy to adjust the algorithm. The number of parameters is small, and it can be directly superimposed without having to add new network hidden layers. The experimental results show that the recognition rate of the UCNN network on the FER2013 and CK+ datasets is higher than that of other recognition methods, and the recognition rates on the FER2013 and CK+ datasets are 98% and 68.01%, due to other methods. This shows that the improved convolutional neural network used in this study for financial services in smart medical care has certain applicability, and small convolution kernels help to extract more subtle features, so as to identify more accurately.

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Section: Model Introductionmentioning

confidence: 99%

Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care

2022

Computational Intelligence and Neuroscience

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“…where P t m is the transmit power of TN m , σ is the white-noise power, and g k is the channel gain, which is inversely proportional to the distance between TN m and HN k [32].…”

Section: Sub-task Transmission Timementioning

confidence: 99%

Efficient Matching-Based Parallel Task Offloading in IoT Networks

Malik

Javed

Frnda

et al. 2022

Sensors

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Fog computing is one of the major components of future 6G networks. It can provide fast computing of different application-related tasks and improve system reliability due to better decision-making. Parallel offloading, in which a task is split into several sub-tasks and transmitted to different fog nodes for parallel computation, is a promising concept in task offloading. Parallel offloading suffers from challenges such as sub-task splitting and mapping of sub-tasks to the fog nodes. In this paper, we propose a novel many-to-one matching-based algorithm for the allocation of sub-tasks to fog nodes. We develop preference profiles for IoT nodes and fog nodes to reduce the task computation delay. We also propose a technique to address the externalities problem in the matching algorithm that is caused by the dynamic preference profiles. Furthermore, a detailed evaluation of the proposed technique is presented to show the benefits of each feature of the algorithm. Simulation results show that the proposed matching-based offloading technique outperforms other available techniques from the literature and improves task latency by 52% at high task loads.

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“…It is assumed that an active up-link with dedicated bandwidth B u mn exists between an IoT device d m and a fog node f n . The task uplink rate R u mn is calculated as [28]:…”

Section: ) Transmission Delaymentioning

confidence: 99%

SMRETO: Stable Matching for Reliable and Efficient Task Offloading in Fog-Enabled IoT Networks

et al. 2022

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Fog computing is a key technology that supports timely and efficient computation of different tasks in IoT networks. By using the nearby fog nodes for quick task computation, application related decisions by IoT devices can be taken within the delay requirements. Resource allocation in terms of task placement on the free computing resources of the fog nodes is a major challenge in IoT networks. In this paper, we consider task offloading from IoT devices to the logically partitioned fog computing resources known as Virtual Resource Units (VRUs) to reduce the number of task outages and energy consumption of the IoT and fog nodes. We propose a two phased task offloading algorithm to minimize the number of task outages. In the first phase, we utilize the task deadline to compute the minimum number of resources required for a task from the fog nodes. To meet the heterogeneous task computing requirements, we introduce the concept of variable sized VRUs in the fog nodes. Moreover, we propose a modified Deferred Acceptance Algorithm (DAA) for stable matching between IoT tasks and variable sized VRUs. In the second phase of the algorithm, the unmatched fog node resources are distributed among the previously matched IoT tasks. Simulation results show that the proposed algorithm outperforms available techniques in the literature in terms of task outages and energy efficiency.INDEX TERMS Fog computing, IoT, task offloading.

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Cloudlet Selection in Cache-Enabled Fog Networks for Latency Sensitive IoT Applications

Cited by 7 publications

References 46 publications

Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care

Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care

Efficient Matching-Based Parallel Task Offloading in IoT Networks

SMRETO: Stable Matching for Reliable and Efficient Task Offloading in Fog-Enabled IoT Networks

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