Privacy-Preserving Online Task Allocation in Edge-Computing-Enabled Massive Crowdsensing

Zhou, Pan; Chen, Wenbo; Ji, Shouling; Jiang, Hao; Yu, Li; Wu, Dapeng

doi:10.1109/jiot.2019.2903515

Cited by 62 publications

(21 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the centralized Laplace mechanism, the workers' QL is regarded as the error distance between the workers' real position l i and the interference position l′ i . Assuming there are N workers, the QL of the centralized Laplace mechanism is expressed by Equation (21). Similarly, the QL of N workers using a centralized Gaussian white mechanism is expressed by ∑d l l QL = ( , ).…”

Section: Methodsmentioning

confidence: 99%

“…Selected participants only need to submit their real data along with the reconstructed data generated by the algorithm. Zhou et al 21 proposed an MCS task assignment framework based on edge computing. When workers upload sensing data, the Laplace noise mechanism and exponential mechanism are used to disturb the workers' location information to protect the workers' privacy.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

et al. 2021

View full text Add to dashboard Cite

With the rise of the Internet of Things (IoT) and fifth‐generation (5G) networks, which have led to a surge in data processing and increased data transfer time, traditional cloud computing could no longer meet the needs of workers, so edge computing has emerged. Edge computing could meet the demand for low time consumption by processing data at the edge of the network and then transmitting it to a third‐party platform. However, since the credibility of the third‐party platform is unknown which can easily leak the privacy of workers. For the transparent mechanism of blockchain, a two‐stage privacy protection mechanism based on blockchain is proposed to solve this problem. In the first stage, this paper proposes a double disturbance localized differential privacy (DDLDP) algorithm to disturb the location information of workers. In the second stage, all the sensing data are uploaded to the blockchain through edge nodes, processed by the edge cloud, and fed back to the requester. Blockchain technology not only guarantees the integrity of sensing data, but also prevents the possibility of third‐party platforms from leaking workers' privacy. Through extensive performance evaluation and comparative experiments on real data sets, the DDLDP algorithm could effectively protect the privacy of workers and has higher service quality and data availability.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In [265], the authors also discussed several open research problems, including how to leverage DL to detect privacy and security threats, how to reduce computational overhead in vastly and rapidly changing environments, and how to implement DL in mobile users for energy and cost efficiency. A hierarchical computing architecture for task allocation was proposed in [277], where the cloud layer does learning of participants' reputation and the edge layer communicates with participants for data collection and optimization.…”

Section: ) Mobile Crowdsensingmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

View full text Add to dashboard Cite

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

show abstract

“…Xia et al [36] proposes a new data aggregation architecture based on mobile edge computing, which has a significant improvement in dealing with frequent user location changes and reducing the number of specified sensing tasks. Zhou et al [37] proposes a novel context-aware task allocation framework in the context of edge computing. Task assignment is performed in the cloud computing layer and edge computing layer, which helps the crowd sensing platform handle a wide range of tasks efficiently and real-timely.…”

Section: Related Workmentioning

confidence: 99%

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

2020

Self Cite

View full text Add to dashboard Cite

To meet some real-time mobile crowd sensing (MCS) scenarios, there is a tendency to enhance the MCS system with mobile edge computing (MEC). One of the key challenges is how to select some satisfied participants in such an edge-cloud collaboration MCS system to effectively and real-timely handle dynamic and heterogeneous sensing tasks. In this paper, we propose a bilateral satisfaction aware participant selection mechanism in the edge-cloud collaboration MCS system. The participant selection process is coordinated by the cloud service platform and the MEC server. The cloud service platform sends the required data types to the MEC server and evaluates the user reputation through the user history task records. The MEC server generates a set of tasks and evaluates user fitness based on the user's real-time location. Then the MEC server obtains the user sensing cost based on the user status, and develops the task price model based on the user supply index and data demand index. Finally, the participant selection process is transformed into a game between users and the MEC server about the task reward, and the user who accepts the optimal task price is selected as the participant. The results show that the proposed participant selection strategy can effectively reduce the amount of data processed by the cloud platform, shorten the task completion time, and increase bilateral satisfaction. INDEX TERMS Crowd sensing, edge computing, participant selection, user characteristic, bilateral satisfaction.

show abstract

Privacy-Preserving Online Task Allocation in Edge-Computing-Enabled Massive Crowdsensing

Cited by 62 publications

References 40 publications

A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Bilateral Satisfaction Aware Participant Selection With MEC for Mobile Crowd Sensing

Contact Info

Product

Resources

About