CeCO: Cost-Efficient Computation Offloading of IoT Applications in Green Industrial Fog Networks

Hazra, Abhishek; Amgoth, Tarachand

doi:10.1109/tii.2021.3130255

Cited by 29 publications

(15 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The constituent parts of these applications can be simultaneously executed on different FSs or CSs. Several works such as [9,46,55,104] discuss applications with independent components or tasks in the Fog literature.…”

Section: Architectural Designmentioning

confidence: 99%

“…We study the FSs' resources and classified works based on their heterogeneity into 1) heterogeneous and 2) homogeneous accordingly. Many works have considered heterogeneous resources for FSs [9,39,46,102] while some works consider homogeneous resources for FSs [54,73,134].…”

Section: Heterogeneitymentioning

confidence: 99%

“…Multi objective proposals consider several parameters to simultaneously optimize them, incurring higher complexity. In the literature, several proposals targeted single objective optimization such as [56,69,114,157], while other proposals try to optimize several parameters such as [38,46,95,145].…”

Section: Objective Numbermentioning

confidence: 99%

“…Several works also have considered a set of optimization parameters, referred as hybrid. These works use any combination of above-mentioned parameters simultaneously [39,40,46,134].…”

Section: 34mentioning

confidence: 99%

“…2) energy such as [11,146], and 3) hybrid (i.e., any combination of deadline, energy, and cost) such as [22,46,100].…”

Section: Qos Constraintsmentioning

confidence: 99%

See 4 more Smart Citations

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

View full text Add to dashboard Cite

Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described. CCS Concepts: • General and reference → General literature; • Computer systems organization → Distributed architectures.

show abstract

Section: Architectural Designmentioning

confidence: 99%

Section: Heterogeneitymentioning

confidence: 99%

Section: Objective Numbermentioning

confidence: 99%

“…Several works also have considered a set of optimization parameters, referred as hybrid. These works use any combination of above-mentioned parameters simultaneously [39,40,46,134].…”

Section: 34mentioning

confidence: 99%

“…2) energy such as [11,146], and 3) hybrid (i.e., any combination of deadline, energy, and cost) such as [22,46,100].…”

Section: Qos Constraintsmentioning

confidence: 99%

See 3 more Smart Citations

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

View full text Add to dashboard Cite

show abstract

A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

et al. 2023

View full text Add to dashboard Cite

SummaryFederated learning (FL) is widely used in internet of things (IoT) scenarios such as health research, automotive autopilot, and smart home systems. In the process of model training of FL, each round of model training requires rigorous decryption training and encryption uploading steps. The efficiency of FL is seriously affected by frequent encryption and decryption operations. A scheme of key computation and key management with high efficiency is urgently needed. Therefore, we propose a group key agreement technique to keep private information and confidential data from being leaked, which is used to encrypt and decrypt the transmitted data among IoT terminals. The key agreement scheme includes hidden attribute authentication, multipolicy access, and ciphertext storage. Key agreement is designed with edge‐cloud collaborative network architecture. Firstly, the terminal generates its own public and private keys through the key algorithm then confirms the authenticity and mapping relationship of its private and public keys to the cloud server. Secondly, IoT terminals can confirm their cryptographic attributes to the cloud and obtain the permissions corresponding to each attribute by encrypting the attributes. The terminal uses these permissions to encrypt the FL model parameters and uploads the secret parameters to the edge server. Through the storage of the edge server, these ciphertext decryption parameters are shared with the other terminal models of FL. Finally, other terminal models are trained by downloading and decrypting the shared model parameters for the purpose of FL. The performance analysis shows that this model has a better performance in computational complexity and computational time compared with the cited literature.

show abstract

Machine Learning Techniques for Industrial Internet of Things

Sharma,

Hazra,

Tomar

2023

Learning Techniques for the Internet of Things

View full text Add to dashboard Cite

CeCO: Cost-Efficient Computation Offloading of IoT Applications in Green Industrial Fog Networks

Cited by 29 publications

References 19 publications

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

Machine Learning Techniques for Industrial Internet of Things

Contact Info

Product

Resources

About