Abstract
Mobile crowdsensing systems use the extraction of valuable information from the data aggregation results of large-scale IoT devices to provide users with personalized services. Mobile crowdsensing combined with edge computing can improve service response speed, security, and reliability. However, previous research on data aggregation paid little attention to data verifiability and time sensitivity. In addition, existing edge-assisted data aggregation schemes do not support access control of large-scale devices. In this study, we propose a time-sensitive and verifiable data aggregation scheme (TSVA-CP-ABE) supporting access control for edge-assisted mobile crowdsensing. Specifically, in our scheme, we use attribute-based encryption for access control, where edge nodes can help IoT devices to calculate keys. Moreover, IoT devices can verify outsourced computing, and edge nodes can verify and filter aggregated data. Finally, the security of the proposed scheme is theoretically proved. The experimental results illustrate that our scheme outperforms traditional ones in both effectiveness and scalability under time-sensitive constraints.
Highlights
Recent years have witnessed the proliferation of smart devices in all areas of people’s daily life. ese devices are deployed in different Internet of ings (IoT) units, such as smartphones [1], smart cameras [2], wearable devices [3], and environmental sensors [4, 5]. e number of IoT devices is still growing rapidly in the near future
According to the prediction of the Global Association for Mobile Communications Systems [6], the number of IoT devices will reach 25.1 billion in 2025. e rapid growth of IoT devices has promoted the development of mobile crowdsensing. e massive IoT devices will together provide mobile crowdsensing systems with more realtime and high-precision data, from which we can extract valuable information for personalized service provision to the majority of end consumers [7]
We proposed the TSVA-CP-ABE scheme for mobile crowdsensing. e proposed scheme supports key distribution and efficient data aggregation
Summary
Recent years have witnessed the proliferation of smart devices in all areas of people’s daily life. ese devices are deployed in different Internet of ings (IoT) units, such as smartphones [1], smart cameras [2], wearable devices [3], and environmental sensors [4, 5]. e number of IoT devices is still growing rapidly in the near future. To make data aggregation meet the requirements of time-sensitive tasks, some schemes introduce privacy protection mechanisms to achieve safe transmission. To achieve verifiable data aggregation, Shen et al [18] proposed a data aggregation scheme that supports fault tolerance and data integrity, but its inability to support outsourced calculations results in high node calculation overhead, and the scheme requires a specific structure. To address this problem, Bao and Lu [29] proposed an aggregation scheme that supports dynamic groups, but this scheme needs to set a separate secret key for each device, which increases the cost of key management, encryption, and decryption.
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