Joint Optimization of Energy Conservation and Privacy Preservation for Intelligent Task Offloading in MEC-Enabled Smart Cities

Peng, Kai; Huang, Hualong; Liu, Peichen; Xu, Xiaolong; Leung, Victor C. M.

doi:10.1109/tgcn.2022.3170146

Cited by 12 publications

(4 citation statements)

References 37 publications

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“…Rui Chen, Ganesh Neelakanta Iyer [16,17], and others considered the interactions among market participants with different risk attitudes and incorporated risk-neutral computation with given weights. Kai Peng [18] studied the privacy risk of different edge devices to propose a privacy-preserving game without considering the impact of risk on the allocation. Jianbo Du [19] used the asynchronous advantage actor-critic (A3C) deep reinforcement learning algorithm to obtain resource pricing and allocation and explored the risk coefficients that may exist using reinforcement learning.…”

Section: Related Workmentioning

confidence: 99%

Risk Assessment Edge Contract for Efficient Resource Allocation

Sheng,

Wang,

et al. 2024

Mathematics

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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.

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Section: Related Workmentioning

confidence: 99%

Risk Assessment Edge Contract for Efficient Resource Allocation

Sheng,

Wang,

et al. 2024

Mathematics

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“…Game theory is an effective way to deal with the interactions between incentive structures of a competitive nature, particularly, it describes a reactive decision-ahead scenario where other competitors react to their own decisions, and then adjust the initial decision to make a better final decision [74]. In ref.…”

Section: Game Theorymentioning

confidence: 99%

A Survey of Energy Optimization Approaches for Computational Task Offloading and Resource Allocation in MEC Networks

Yang,

Shah,

Pezaros

2023

Electronics

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With the increased penetration of cloud computing and virtualization, a plethora of internet of things devices have been deployed globally. As a result, computationally intensive tasks are transmitted from the edge towards the centralized cloud for processing that leads to increased energy utilization in the cloud data centers while at the same increasing significant latency for critical applications. Recent years have witnessed a paradigm shift from centralized cloud computing towards mobile edge computing (MEC), where computational tasks are offloaded at the edge servers near user equipment (UE). This paradigm leads to lowering the energy utilization in the cloud data centers, along with low latency for UE and efficient resource utilization at the edge. In this context, the scale and complexity of the MEC networks is drastically increasing and, consequently, finding effective energy-efficient solutions for computational task offloading and resource allocation in MEC networks has become an ambitious task. To address the aforementioned challenges, this work surveys the state of the art in different categorizations of algorithm-based computational task offloading and resource allocation strategies focusing on energy utilization. It also provides a detailed cross-comparison of existing strategies in terms of their implementation specifications. Additionally, this paper also highlights open challenges and potential future research directions to facilitate efficient task offloading and resource allocation at the edge with reduced energy consumption at the centralized data centers. Our work also paves the way for the deployment of critical applications at the edge that require low latency and high service quality guarantees.

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“…The endpoint transmits the raw data to the edge node, which processes it and then returns the resultant data to the endpoint 3 . Although this type of data processing reduces the risk of privacy leakage during data transmission, edge devices can access and store large amounts of raw sensitive data of users, which brings new challenges to the issue of data privacy protection in MEC 4,5 . For example, smart sensors in smart homes can capture a large amount of private user information, such as dynamic activity information of family members and room layouts.…”

Section: Introductionmentioning

confidence: 99%

“…3 Although this type of data processing reduces the risk of privacy leakage during data transmission, edge devices can access and store large amounts of raw sensitive data of users, which brings new challenges to the issue of data privacy protection in MEC. 4,5 For example, smart sensors in smart homes can capture a large amount of private user information, such as dynamic activity information of family members and room layouts. If this information is accessed by unscrupulous elements, the safety of users' lives and property will be seriously threatened.…”

mentioning

confidence: 99%

Data privacy protection model based on blockchain in mobile edge computing

Wu,

Bu,

et al. 2024

Softw Pract Exp

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Mobile edge computing (MEC) technology is widely used for real‐time and bandwidth‐intensive services, but its underlying heterogeneous architecture may lead to a variety of security and privacy issues. Blockchain provides novel solutions for data security and privacy protection in MEC. However, the scalability of traditional blockchain is difficult to meet the requirements of real‐time data processing, and the consensus mechanism is not suitable for resource‐constrained devices. Moreover, the access control of MEC data needs to be further improved. Given the above problems, a data privacy protection model based on sharding blockchain and access control is designed in this paper. First, a privacy‐preserving platform based on a sharding blockchain is designed. Reputation calculation and improved Proof‐of‐Work (PoW) consensus mechanism are proposed to accommodate resource‐constrained edge devices. The incentive mechanism with rewards and punishments is designed to constrain node behavior. A reward allocation algorithm is proposed to encourage nodes to actively contribute to obtaining more rewards. Second, an access control strategy using ciphertext policy attribute‐based encryption (CP‐ABE) and RSA is designed. A smart contract is deployed to implement the automatic access control function. The InterPlanetary File System is introduced to alleviate the blockchain storage burden. Finally, we analyze the security of the proposed privacy protection model and statistics of the GAS consumed by the access control policy. The experimental results show that the proposed data privacy protection model achieves fine‐grained control of access rights, and has higher throughput and security than traditional blockchain.

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Joint Optimization of Energy Conservation and Privacy Preservation for Intelligent Task Offloading in MEC-Enabled Smart Cities

Cited by 12 publications

References 37 publications

Risk Assessment Edge Contract for Efficient Resource Allocation

Risk Assessment Edge Contract for Efficient Resource Allocation

A Survey of Energy Optimization Approaches for Computational Task Offloading and Resource Allocation in MEC Networks

Data privacy protection model based on blockchain in mobile edge computing

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