Service Offloading With Deep Q-Network for Digital Twinning-Empowered Internet of Vehicles in Edge Computing

Xu, Xiaolong; Shen, Bowen; Ding, Sheng; Srivastava, Gautam; Bilal, Muhammad; Khosravi, Mohammad R.; Menon, Varun G.; Jan, Mian Ahmad; Wang, Maoli

doi:10.1109/tii.2020.3040180

Cited by 166 publications

(55 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) For the common multi-dimensional decisionmaking problems, weight is recruited to indicate the different significances of multiple dimensions [17][18][19][20][21][22][23] .…”

Section: Further Discussionmentioning

confidence: 99%

Privacy-aware examination results ranking for the balance between teachers and mothers

Yuan

Wang

Zhao

et al. 2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

As the main parent and guardian, mothers are often concerned with the study performance of their children.More specifically, most mothers are eager to know the concrete examination scores of their children. However, with the continuous progress of modern education systems, most schools or teachers have now been forbidden to release sensitive student examination scores to the public due to privacy concerns, which has made it infeasible for mothers to know the real study level or examination performance of their children. Therefore, a conflict has come to exist between teachers and mothers, which harms the general growing up of students in their study. In view of this challenge, we propose a Privacy-aware Examination Results Ranking (PERR) method to attempt at balancing teachers' privacy disclosure concerns and the mothers' concerns over their children's examination performance.By drawing on a relevant case study, we prove the effectiveness of the proposed PERR method in evaluating and ranking students according to their examination scores while at the same time securing sensitive student information.

show abstract

“…(2) For the common multi-dimensional decisionmaking problems, weight is recruited to indicate the different significances of multiple dimensions [17][18][19][20][21][22][23] .…”

Section: Further Discussionmentioning

confidence: 99%

Privacy-aware examination results ranking for the balance between teachers and mothers

Yuan

Wang

Zhao

et al. 2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Furthermore, spatial information of both users and service providers is not employed in our approach. erefore, we will further enhance our algorithm by taking more context factors such as periodicity [38,39] and location [40,41] into account.…”

Section: Discussionmentioning

confidence: 99%

Time-Aware Cross-Platform IoT Service Recommendation with Privacy Preservation

Zhang

Yan

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

IoT service recommendation techniques can help a user select appropriate IoT services efficiently. Aiming at improving the recommendation efficiency and preserving the data privacy, the locality-sensitive hashing (LSH) technique is adopted in service recommendation. However, existing LSH-based service recommendation methods ignore the intrinsic temporal feature of IoT services. In light of this challenge, we integrate the temporal feature into the conventional LSH-based method and present a time-aware approach with the capability of privacy preservation for IoT service recommendation across multiple platforms. Experiments on a real-world dataset are conducted to validate the advantage of our proposed approach in terms of accuracy and efficiency in recommendation.

show abstract

“…Edge devices can independently choose their computing strategy in terms of local processing and/or off-loading it to another edge device or edge server. Since the overall objective of SplitPred is to enable agile edge analytics, both local and collaborative data processing are modelled around their respective execution time [27].…”

Section: B Data Processingmentioning

confidence: 99%

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Bharti¹,

McGibney

2021

IEEE Access

View full text Add to dashboard Cite

The proliferation of Industry 4.0 has made modern industrial assets a rich source of data that can be leveraged to optimise operations, ensure efficiency, and minimise maintenance costs. The availability of data is advantageous for asset management, however, attempts to maximise the value of this data often fall short due to additional constraints, such as privacy concerns and data stored in distributed silos that is difficult to access and share. Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and offering new opportunities for a collaborative approach to asset management. Despite the benefits, FL has some challenges that need to be overcome to make it fully compatible for asset management or more specifically predictive maintenance applications. FL requires a set of clients that participate in the model training process, however, orchestration, device heterogeneity and scalability can hinder the speed and accuracy in the context of collaborative predictive maintenance. To address this challenge, this work proposes a split-learning-based framework (SplitPred) that enables FL clients to maximise available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning). Experiments performed on the benchmark C-MAPSS dataset demonstrate the advantage of applying SplitPred in the FL process in terms of efficient use of resources, i.e., model convergence time, accuracy, and network load.

show abstract

Service Offloading With Deep Q-Network for Digital Twinning-Empowered Internet of Vehicles in Edge Computing

Cited by 166 publications

References 31 publications

Privacy-aware examination results ranking for the balance between teachers and mothers

Privacy-aware examination results ranking for the balance between teachers and mothers

Time-Aware Cross-Platform IoT Service Recommendation with Privacy Preservation

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Contact Info

Product

Resources

About