Task Offloading Scheme for Latency Sensitive Tasks In 5G IOHT on Fog Assisted Cloud Computing Environment

Khan, Md. Tabrej; Barik, Lalbihari; Adholiya, Ashish; Patra, Sudhansu Shekhar; Brahma, Aditya Narayan; Barik, Rabindra K.

doi:10.1109/incet54531.2022.9824699

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…By providing cloud-like processing and storage capabilities with a low latency and less bandwidth, fog computing's architecture and latency benefit mobile nodes and 5G IoHT devices. Fog computing is a method for offloading tasks from programs operating on edge devices as a result of this added behavior [39].…”

Section: Methods and Backgroundmentioning

confidence: 99%

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

Ranjith,

Mahantesh,

Abhilash

2024

Journal of Robotics and Control

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In the present era, the number of Internet of Health Things (IoHT) devices and applications has drastically expanded. Security and attack are major issues in the IoHT domain because of the nature of its architecture and sorts of devices. Over the recent few years, network attacks have dramatically increased. Many detection and encryption techniques are existing however they lack accuracy, training stability, insecurity, delay etc. By the above concerns, this manuscript introduces a novel deep learning technique called Agnostic Spiking Binarized neural network with Improved Billiards optimization for accurate detection of network attacks and Light Weight integrated Puzzle War Elliptic Curve Cryptographic framework for secure data transmission with high security and minimal delay. Optimal features from the datasets are selected by volcano eruption optimization algorithm with better convergence for reducing the overall processing time. Wilcoxon Rank Sum and Mc Neymar’s tests are performed for proving the statistical analyses. The outcomes show that the introduced approach performs with an overall accuracy of 99.93% which is better than the previous techniques demonstrating the effectiveness.

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Section: Methods and Backgroundmentioning

confidence: 99%

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

Ranjith,

Mahantesh,

Abhilash

2024

Journal of Robotics and Control

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“…M.T. Khan et al [16] consider the fog environment as dynamic and address the problem of how to choose the optimal fog node in the fog computational offloading. An improved sine-cosine optimization technique is utilized to propose a task offloading technique to enhance quality of service (QOS), but it lacks consideration of cloud-fog collaboration.…”

Section: Related Workmentioning

confidence: 99%

“…The constraints in the above equation are interpreted as: (15) the range of guaranteed offload ratio, (16) the range of guaranteed user offload only to cloud or fog, (17) the range of ensuring that the sum of allocated computing resources cannot exceed the computing capacity of FS, (18) the range of user transmit power, and (19) the range of ensuring that the upper layer resources are greater than the lower layer resources.…”

Section: Question Formulationmentioning

confidence: 99%

Jointly Optimize Partial Computation Offloading and Resource Allocation in Cloud-Fog Cooperative Networks

Bai

Wang

2023

Electronics

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Fog computing has become a hot topic in recent years as it provides cloud computing resources to the network edge in a distributed manner that can respond quickly to intensive tasks from different user equipment (UE) applications. However, since fog resources are also limited, considering the number of Internet of Things (IoT) applications and the demand for traffic, designing an effective offload strategy and resource allocation scheme to reduce the offloading cost of UE systems is still an important challenge. To this end, this paper investigates the problem of partial offloading and resource allocation under a cloud-fog coordination network architecture, which is formulated as a mixed integer nonlinear programming (MINLP). Bring in a new weighting metric-cloud resource rental cost. The optimization function of offloading cost is defined as a weighted sum of latency, energy consumption, and cloud rental cost. Under the fixed offloading decision condition, two sub-problems of fog computing resource allocation and user transmission power allocation are proposed and solved using convex optimization techniques and Karush-Kuhn-Tucker (KKT) conditions, respectively. The sampling process of the inner loop of the simulated annealing (SA) algorithm is improved, and a memory function is added to obtain the novel simulated annealing (N-SA) algorithm used to solve the optimal value offloading problem corresponding to the optimal resource allocation problem. Through extensive simulation experiments, it is shown that the N-SA algorithm obtains the optimal solution quickly and saves 17% of the system cost compared to the greedy offloading and joint resource allocation (GO-JRA) algorithm.

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“…By analysing the number of research papers that have been published and the number of times that search results have been returned on Google Scholar, the purpose of this study is to conduct an analysis of the current research trends in FC. This study gives a complete design as well as a full study of various different FC architectures [17][18][19]. Prior research mostly concentrated on presenting high-level structures, and as a result, it lacked in-depth details.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Review of Internet of Things Model Workload Distribution Techniques in Fog Computing Networks

Puttaswamy,

Murthy,

Degha

2024

IDA

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In the realm of fog computing (FC), a vast array of intelligent devices collaborates within an intricate network, a synergy that, while promising, has not been without its challenges. These challenges, including data loss, difficulties in workload distribution, a lack of parallel processing capabilities, and security vulnerabilities, have necessitated the exploration and deployment of a variety of solutions. Among these, software-defined networks (SDN), double-Q learning algorithms, service function chains (SFC), virtual network functions (VNF) stand out as significant. An exhaustive survey has been conducted to explore workload distribution methodologies within Internet of Things (IoT) architectures in FC networks. This investigation is anchored in a parameter-centric analysis, aiming to enhance the efficiency of data transmission across such networks. It delves into the architectural framework, pivotal pathways, and applications, aiming to identify bottlenecks and forge the most effective communication channels for IoT devices under substantial workload conditions. The findings of this research are anticipated to guide the selection of superior simulation tools, validate datasets, and refine strategies for data propagation. This, in turn, is expected to facilitate optimal power consumption and enhance outcomes in data transmission and propagation across multiple dimensions. The rigorous exploration detailed herein not only illuminates the complexities of workload distribution in FC networks but also charts a course towards more resilient and efficient IoT ecosystems.

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Task Offloading Scheme for Latency Sensitive Tasks In 5G IOHT on Fog Assisted Cloud Computing Environment

Cited by 8 publications

References 16 publications

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

Jointly Optimize Partial Computation Offloading and Resource Allocation in Cloud-Fog Cooperative Networks

A Comparative Review of Internet of Things Model Workload Distribution Techniques in Fog Computing Networks

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