Secure IoT-Based, Incentive-Aware Emergency Personnel Dispatching Scheme with Weighted Fine-Grained Access Control

Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages.

Efficient and Privacy-preserving Fog-assisted Health Data Sharing Scheme

Pervasive data collected from e-healthcare devices possess significant medical value through data sharing with professional healthcare service providers. However, health data sharing poses several security issues, such as access control and privacy leakage, as well as faces critical challenges to obtain efficient data analysis and services. In this article, we propose an efficient and privacy-preserving fog-assisted health data sharing (PFHDS) scheme for e-healthcare systems. Specifically, we integrate the fog node to classify the shared data into different categories according to disease risks for efficient health data analysis. Meanwhile, we design an enhanced attribute-based encryption method through combination of a personal access policy on patients and a professional access policy on the fog node for effective medical service provision. Furthermore, we achieve significant encryption consumption reduction for patients by offloading a portion of the computation and storage burden from patients to the fog node. Security discussions show that PFHDS realizes data confidentiality and fine-grained access control with collusion resistance. Performance evaluations demonstrate cost-efficient encryption computation, storage and energy consumption.