Joint Task Assignment, Power Allocation and Node Grouping for Cooperative Computing in NOMA-mmWave Mobile Edge Computing

Khazali, Azadeh; Bozorgchenani, Arash; Tarchi, Daniele; Shayesteh, Mahrokh G.; Kalbkhani, Hashem

doi:10.1109/access.2023.3309628

Cited by 2 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, some researchers have devised several resource allocation mechanisms to ensure user QoS, such as convex optimization-based approaches [8] and coordination through composite tables [9]. Additionally, some joint optimization models have been developed for task offloading and resource allocation [10] with several scheduling algorithms including deep Q-learning [11,12] and ADMM [13]. However, these joint optimization models work based on nonlinear programming and are typically NP-hard, being difficult to find optimal solutions in polynomial time [14,15].…”

Section: Related Workmentioning

confidence: 99%

Risk Assessment Edge Contract for Efficient Resource Allocation

Sheng,

Wang,

et al. 2024

Mathematics

View full text Add to dashboard Cite

The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among ECSs, ENs, and edge devices in high-workload environments, as well as the pricing of edge resources within the resource market framework, we propose a Risk Assessment Contract Algorithm (RACA) based on risk assessment theory. The RACA enables ECSs to assess risks associated with local users by estimating their future revenue potential and updating the contract autonomously at present and in the future. ENs acquire additional resources from ECSs to efficiently complete local users’ tasks. Simultaneously, ENs can also negotiate reasonable resource requests and pricing with ECSs by a Stackelberg game algorithm. Furthermore, we prove the unique existence of Nash equilibrium in the established game, implying that equilibrium solutions can stably converge through computational methods in heterogeneous environments. Finally, through simulation experiments on the dataset, we demonstrate that risk assessment can better enhance the overall profit capability of the system. Moreover, through multiple experiments, we showcase the stability of the contract’s autonomous update capability. The RACA exhibits better utility in terms of system profit capabilities, stability in high-workload environments, and energy consumption. This work provides a more dynamic and effective solution to the resource allocation problem in edge systems under high-workload environments.

show abstract

Section: Related Workmentioning

confidence: 99%

Risk Assessment Edge Contract for Efficient Resource Allocation

Sheng,

Wang,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

Location Privacy Protection in Edge Computing: Co-Design of Differential Privacy and Offloading Mode

Zhang,

Man

et al. 2024

Electronics

View full text Add to dashboard Cite

Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices and increases task processing efficiency. Nonetheless, the task offloading process can introduce a critical vulnerability, as adversaries may infer a user’s location through an analysis of their offloading mode, thereby threatening the user’s location privacy. To counteract this vulnerability, this study introduces differential privacy as a protective mechanism to obscure the user’s offloading mode, thereby safeguarding their location information. This research specifically addresses the issue of location privacy leakage stemming from the correlation between a user’s location and their task offloading ratio. The proposed strategy is based on differential privacy. It aims to increase the efficiency of offloading services and the benefits of task offloading. At the same time, it ensures privacy protection. An innovative optimization technique for task offloading that maintains location privacy is presented. Utilizing this technique, users can make informed offloading decisions, dynamically adjusting the level of obfuscation in response to the state of the wireless channel and their privacy requirements. This study substantiates the feasibility and effectiveness of the proposed mechanism through rigorous theoretical analysis and extensive empirical testing. The numerical results demonstrate that the proposed strategy can achieve a balance between offloading privacy and processing overhead.

show abstract

Joint Task Assignment, Power Allocation and Node Grouping for Cooperative Computing in NOMA-mmWave Mobile Edge Computing

Cited by 2 publications

References 47 publications

Risk Assessment Edge Contract for Efficient Resource Allocation

Risk Assessment Edge Contract for Efficient Resource Allocation

Location Privacy Protection in Edge Computing: Co-Design of Differential Privacy and Offloading Mode

Contact Info

Product

Resources

About