Digital-Twin-Enabled 6G Mobile Network Video Streaming Using Mobile Crowdsourcing

Qi, Lianyong; Xu, Xiaolong; Wu, Xiaotong; Ni, Qiang; Yuan, Yuan; Zhang, Xuyun

doi:10.1109/jsac.2023.3310077

Cited by 25 publications

(5 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Xu et al [28] designed an attention-based neural network model, GainTrust, by combining the users' trusted neighbors with the users' overall temporal characteristics obtained via the LSTM network to predict the trust social relationship among users. In addition, Ren et al [29] proposed a collaborative filtering method based on dynamic trust decay, which not only adapted the neighbor selection mechanism but also redefined the neighbor effect by introducing the concept of trust decay.…”

Section: Social Relationship Predictionmentioning

confidence: 99%

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Liu,

Li,

Kou

et al. 2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

Cross-Platform Social Relationship Prediction (CPSRP) aims to utilize users' data information on multiple platforms to enhance the performance of social relationship prediction, thereby promoting socioeconomic development. Due to the highly sensitive nature of users' data in terms of privacy, CPSRP typically introduces various privacy-preserving mechanisms to safeguard users' confidential information. Although the introduction mechanism guarantees the security of the users' private information, it tends to degrade the performance of the social relationship prediction. Additionally, existing social relationship prediction schemes overlook the interdependencies among items invoked in a user behavior sequence. For this purpose, we propose a novel privacy-preserve Federated Social Relationship Prediction with Contrastive Learning framework called FSRPCL, which is a multi-task learning framework based on vertical federated learning. Specifically, the users' rating information is perturbed with a bounded differential privacy technology, and then the users' sequential representation information acquired through Transformer is applied for social relationship prediction and contrastive learning. Furthermore, each client uploads their respective weight information to the server, and the server aggregates the weight information and distributes it purposes to each client for updating.Numerous experiments on real-world datasets prove that FSRPCL delivers exceptional performance in social relationship prediction and privacy preservation, and effectively minimizes the impact of privacy-preserving technology on social relationship prediction accuracy.

show abstract

Section: Social Relationship Predictionmentioning

confidence: 99%

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Liu,

Li,

Kou

et al. 2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Meanwhile the task offloading and resource allocation processes are optimized collaboratively through the Isotonic Action Generation Technique (IAGT) and the dynamic update strategy. Qi et al [16] employs network duals as central controllers in combination with crowdsourcing techniques to incentivize mobile users to adhere to predefined paths for sharing network resources. The creation of these paths is depicted as an optimization problem in user recruitment with cost constraints.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-based 6G task offloading and cooperative computing resource allocation study

Tian,

Zhang,

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. Simulation results show that the WF-CSA algorithm significantly reduces the total delay by up to 42.58% compared to the fish swarm algorithm, wolf pack algorithm and binary particle swarm optimisation algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.

show abstract

“…With the onset of the big data era, data transmission and collaboration have become widespread, driving significant advancements in information technology research [1][2][3][4]. In the context of Mobile Edge Computing (MEC), the emergence of the Industrial Internet heralds the inception of profound integration between the new generation of information technology and the industrial sector [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A mobile edge computing-focused transferable sensitive data identification method based on product quantization

Zhao,

Yuan,

Qiu

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the risk of data leakage during transmission, thus enhancing overall security measures in MEC environments.

show abstract

Digital-Twin-Enabled 6G Mobile Network Video Streaming Using Mobile Crowdsourcing

Cited by 25 publications

References 45 publications

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Blockchain-based 6G task offloading and cooperative computing resource allocation study

A mobile edge computing-focused transferable sensitive data identification method based on product quantization

Contact Info

Product

Resources

About