R‐D ^P : A risk‐adaptive privacy protection scheme for mobile crowdsensing in industrial internet of things

Abstract

The integration of the Mobile Crowdsensing (MCS) and Industrial Internet of Things (IIoT) brings enormous volumes of data that generate significant commercial value. However, the data contain a wealth of sensitive information about devices' environmental situation and collective activities, which draws a flock of adversaries and poses an unprecedented security challenge. Furthermore, sensing gadgets deployed in the IIoT device are usually resource-constrained and often do not have adequate 3C resources (i.e. communication, computing, caching) to run sophisticated privacy-preserving methods, making them easier targets for attacks in data sharing. Therefore, a risk-adaptive privacy protection scheme R-D P for MCS-enabled IIoT gadgets is proposed, which comprises a closed-loop risk-awareness process and an adaptive privacy protection method D P (a dissemination process with perturbation). The closed-loop process dynamic awareness of risks and threats in MCS task feeds appropriate privacy protection advice to the decisionmakers for the task. In addition, D P was designed as a lightweight and risk-adaptive privacy protection method to meet the operational needs of 3C resource-constrained gadgets. The analysis and evaluation show that R-D P provides satisfactory privacy protection while the availability of statistical features reaches more than 96%, and the time complexity is only O (1) for sensing gadgets.

Design and implementation of fuzzy-based natural routing system for mobile Internet of Things

“…After the moving object receives the GPS signal, it is positioned and location information is obtained. The data is transmitted to the central server and passed through the transmission path, and algorithms are used to complete the interaction between the mobile terminal and the database [19][20].…”