Collaborative Edge Computing for Social Internet of Things: Applications, Solutions, and Challenges

Dong, Peiran; Ge, Jingyi; Wang, Xiaojie; Guo, Song

doi:10.1109/tcss.2021.3072693

Cited by 27 publications

(8 citation statements)

References 72 publications

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“…The parameter r indicates the time discount degree, ranging from (0, 1], and is associated with the task information's timeliness. The gain factors λ 1 and λ 2 , with values in the range [1,2], are related to the value of the task information. The parameters θ 1 , θ 2 , and θ 3 , each ranging from (0, 5], represent the cost factor per unit time associated with the communication overhead caused by task information.…”

Section: A Simulation Settingsmentioning

confidence: 99%

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P²SPA: Privacy Preservation Strategy With Pseudo-Addresses for Edge Computing Networks

Du,

Zhang,

Yuan

et al. 2024

IEEE Access

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In recent years, edge computing networks have been widely adopted to achieve low latency, save bandwidth, and improve flexibility. However, most of the current Edge Nodes (ENs) are in semi-trusted or untrusted environments, where interactions among users are unsafe. Therefore, providing cost-effective protection strategies for ENs under resource limitations remains a great challenge. To cope with this, we propose an edge computing model with "End-Edge-Cloud" collaborative services, and a Privacy Preservation Strategy with Pseudo-Addresses (P 2 SPA) is constructed to maximize the cost-effectiveness while protecting the location privacy of the ENs. We quantify the privacy protection preference of user information using the Analytic Hierarchy Process (AHP) to select the optimal EN. Considering the dynamic change in the attack frequency, a pseudo-address selection and updating strategy is constructed based on the Stackelberg game theory; thus, the optimal pseudo-address update frequency is achieved. Numerical estimations are performed to verify the effectiveness of the proposed P 2 SPA strategy. Compared with the existing methods, P 2 SPA achieves a compromise service strategy with satisfactory performance on both the defense effect and defense cost.

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Section: A Simulation Settingsmentioning

confidence: 99%

“…As a consequence, users expect higher standards for network performance [1]. Despite the significant computational capabilities of cloud computing, it often fails to meet user demands for data transmission rates, latency, and overall service quality [2]. Consequently, Mobile Edge Computing (MEC) has emerged as a solution [3].…”

Section: Introductionmentioning

confidence: 99%

P²SPA: Privacy Preservation Strategy With Pseudo-Addresses for Edge Computing Networks

Du,

Zhang,

Yuan

et al. 2024

IEEE Access

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“…Its main features are: (1) It can handle many different cryptographic primitives, including shared and public key encryption (encryption and signing), hash functions, and Diffie-Hellman key agreement, specified as rewrite rules or equations. (2) It can handle an unlimited number of protocol sessions (even in parallel) and an unlimited message space. Using this tool, we can model different cryptographic elements correctly, and we can verify reachability properties, corresponding assertions, and observation equivalence.…”

Section: Automatic Formal Verification Of Security By Proverifmentioning

confidence: 99%

“…However, in federated learning within the Internet of Things (IoT), the model parameters encode user information, which means that the privacy of IoT devices can still be compromised through these parameters 2‐5 . Therefore, protecting the leakage of model parameters remains a challenge in federated learning.…”

Section: Introductionmentioning

confidence: 99%

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Anonymous federated learning framework in the internet of things

Du,

Liu,

Gao

2023

Concurrency and Computation

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With the continuous development of the internet of things (IoT), federated learning is being widely applied. This technology keeps data locally to protect data security. However, during the process of uploading gradients from local clients, there is a possibility of leaking sensitive information such as identities. To address this issue, this paper presents a secure and efficient anonymous federated learning framework. In our proposed, we first design a lightweight key‐sharing protocol based on elliptic curve cryptography (ECC) to generate shared keys and ensure secure communication. We then describe the improved process of federated learning, where clients communicate with the cloud server using pseudonyms to achieve anonymity. The security of our protocol is analyzed from both formal and informal perspectives, demonstrating that our proposed protocol satisfies “session key security.” We also employ the formal verification tool ProVerif to validate the security of the protocol in terms of mutual authentication and key configuration in the Dolve–Yao threat model. Finally, the computational and communication costs of the proposed anonymous federated learning framework are evaluated, showing that both the computational and communication expenses are relatively low. A comparison was made between the proposed federated learning process and three other federated learning processes, demonstrating the clear advantages of our proposed.

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The “Transitioning” from 5G to 6G

Detti,

Nitti

2023

The Road Towards 6G: Opportunities, Challenges, and Applications

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Collaborative Edge Computing for Social Internet of Things: Applications, Solutions, and Challenges

Cited by 27 publications

References 72 publications

P²SPA: Privacy Preservation Strategy With Pseudo-Addresses for Edge Computing Networks

P²SPA: Privacy Preservation Strategy With Pseudo-Addresses for Edge Computing Networks

Anonymous federated learning framework in the internet of things

The “Transitioning” from 5G to 6G

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