Biblio

We propose a distributed machine-learning architecture to predict trustworthiness of sensor services in Mobile Edge Computing (MEC) based Internet of Things (IoT) services, which aligns well with the goals of MEC and requirements of modern IoT systems. The proposed machine-learning architecture models training a distributed trust prediction model over a topology of MEC-environments as a Network Lasso problem, which allows simultaneous clustering and optimization on large-scale networked-graphs. We then attempt to solve it using Alternate Direction Method of Multipliers (ADMM) in a way that makes it suitable for MEC-based IoT systems. We present analytical and simulation results to show the validity and efficiency of the proposed solution.

With the development of big data and machine learning techniques, medical data sharing for the use of disease diagnosis has received considerable attention. Blockchain, as an emerging technology, has been widely used to resolve the efficiency and security issues in medical data sharing. However, the existing studies on blockchain-based medical data sharing have rarely concerned about the reasonable incentive mechanism. In this paper, we propose a cooperation model where medical data is shared via blockchain. We derive the topological relationships among the participants consisting of data owners, miners and third parties, and gradually develop the computational process of Shapley value revenue distribution. Specifically, we explore the revenue distribution under different consensuses of blockchain. Finally, we demonstrate the incentive effect and rationality of the proposed solution by analyzing the revenue distribution.

A novel method, consisting of fault detection, rough set generation, element isolation and parameter estimation is presented for multiple-fault diagnosis on analog circuit with tolerance. Firstly, a linear-programming concept is developed to transform fault detection of circuit with limited accessible terminals into measurement to check existence of a feasible solution under tolerance constraints. Secondly, fault characteristic equation is deduced to generate a fault rough set. It is proved that the node voltages of nominal circuit can be used in fault characteristic equation with fault tolerance. Lastly, fault detection of circuit with revised deviation restriction for suspected fault elements is proceeded to locate faulty elements and estimate their parameters. The diagnosis accuracy and parameter identification precision of the method are verified by simulation results.