In the era of Internet of Things (IoT), the data generated by a large number of devices usually relies on cloud servers for processing, but the traditional cloud server architecture has problems such as high latency and bandwidth bottlenecks. Mobile Edge Computing (MEC) effectively enhances the real-time data processing capability by sinking computing power to the network edge. However, the heterogeneity of MEC devices brings complex security challenges, such as risks of information leakage and privacy infringement, and it is urgent to build an efficient identity authentication mechanism to ensure system security. Nevertheless, the existing identity-based and physical unclonable function-based identity verification protocols show limitations, such as inability to ensure anonymity and non-reproducibility, vulnerability to the impact of challenge responses pairs on leakage, and susceptibility to various attacks caused by temporary secret leakage and key leakage. To address these issues, this paper proposes a PUF-enhanced lightweight three-factor authentication and key agreement protocol named PUF3FAka tailored for MEC environments. For the first time, CRO-PUF resilient to machine learning attacks is integrated with a fuzzy-verification mechanism into a three-factor authentication framework. Without storing any challenge–response pairs (CRPs) on the server side, the protocol eliminates the risk of CRPs leakage attacks from the root. The design satisfies ten edge-computing security evaluation criteria and withstands various attacks such as offline password guessing, ephemeral-secret leakage attacks and key-compromise impersonation attacks. Performance evaluations demonstrate that PUF3FAka outperforms existing protocols by 82.86% in computational cost and 96.7% in communication overhead on average, making it well suited for resource-constrained IoT terminals and edge servers.

Due to the limitations of symmetric-key techniques, authentication and key agreement (AKA) protocols based on public-key techniques have attracted much attention, providing secure access and communication mechanism for various application environments. Among these public-key techniques used for AKA protocols, chaotic-map is more effective than scalar multiplication and modular exponentiation, and it offers a list of desirable cryptographic properties such as un-predictability, un-repeatability, un-certainty, and higher efficiency than scalar multiplication and modular exponentiation. Furthermore, it is usually believed that three-factor AKA protocols can achieve a higher security level than single- and two-factor protocols. However, none of existing three-factor AKA protocols can meet all security requirements. One of the most prevalent problems is how to balance security and usability, and particularly how to achieve truly three-factor security while providing password change friendliness. To deal with this problem, in this article we put forward a provably secure three-factor AKA protocol based on extended chaotic-maps for mobile lightweight devices, by adopting the techniques of “Fuzzy-Verifiers” and “Honeywords”. We prove the security of the proposed protocol in the random oracle model, assuming the intractability of extended chaotic-maps Computational Diffie-Hellman problem. We also simulate the protocol by using the AVISPA tool. The security analysis and simulation results show that our protocol can meet all 13 evaluation criteria regarding security. We also assess the performance of our protocol by comparing with seven other related protocols. The evaluation results demonstrate that our protocol offers better balance between security and usability over state-of-the-art ones.

Due to the inherent disturbances associated with flow structures, measurement of the complicated flow parameters in multiphase flows remains a challenging problem of significant importance. The flow dynamical behaviors are still elusive. In this paper, a multichannel complex impedance measurement system is designed to cope with this difficult issue. First, the geometry of the distributed multielectrode impedance sensor is optimized and a matched hardware measurement system is developed. After performance evaluation, a convolutional neural network and long short-term memory based measurement model is formulated for measuring flow parameters with high accuracy. The mean absolute error is only 0.36% for water cut and 0.77% for total flow velocity. Further, from the perspective of Lempel-Ziv complexity and mutual information, the relationship between the diverse flow structures and spatial flow behaviors is explored, leading to a deeper understanding of oil-water flows. All the experimental and analytical results demonstrate that the combination of deep learning and the designed impedance sensor measurement system allows measuring the complicated flow parameters, thereby characterizing the flow structures and behaviors. This opens up a new venue for exploring industrial multiphase flows and serving for an efficient oilfield exploitation as well.