Context-aware Cyber-physical Systems Research Articles

A Fast CP-ABE System for Cyber-Physical Security and Privacy in Mobile Healthcare Network

A healthcare network as a new context-aware cyber-physical system (CPS) includes embedded sensors, Internet of Things technology, and cloud computing. With the intellectualization of mobile terminal devices, mobile healthcare networks are automatically generated and can provide convenient healthcare services and real-time communication. However, two problems in the CPS need to resolved. A solution to the first problem is to mitigate the computational and storage cost of a mobile terminal. A solution to the second problem is to provide the cyber-physical privacy and security for healthcare data. Ciphertext-policy attribute-based encryption (CP-ABE) can provide data privacy and secure sharing in an open environment. In this article, a fast CP-ABE scheme is provided in order to satisfy the use of mobile terminal devices with low computational and storage power. In the proposed scheme, the work of a system can deliver expensive computational tasks to semitrusted third parties, ensuring a constant number of simple local computations. The scheme can also verify the correctness of the decryption result by using a Boneh–Lynn–Shacham short signature scheme. The CP-ABE scheme not only ensures the cyber-physical security of healthcare data, but also reduces the local load from the efficiency analysis and experimental results.

An Energy-Efficient Context Aware Solution for Environmental Assessment

The paper focuses on presenting the advantages of context aware cyber-physical systems through an experimental platform capable of assessing its surroundings and self-performing decisions. The context aware paradigm is present in the control law implementation with various advantages such as energy efficiency as well as in the environmental measurements that trigger the robot to perform context-relevant decisions. The platform provides high versatility and the results presented throughout the study can be adapted to a manifold of multidisciplinary fields. Copyright (C) 2020 The Authors.

Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine

An adaptive multi-tiered framework, that can be utilised for designing a context-aware cyber physical system to carry out smart data acquisition and processing, while minimising the amount of necessary human intervention is proposed and applied. The proposed framework is applied within the domain of offshore asset integrity assurance. The suggested approach segregates processing of the input stream into three distinct phases of Processing, Prediction and Anomaly detection. The Processing phase minimises the data volume and processing cost by analysing only inputs from easily obtainable sources using context identification techniques for finding anomalies in the acquired data. During the Prediction phase, future values of each of the gas turbine’s sensors are estimated using a linear regression model. The final step of the process— Anomaly Detection—classifies the significant discrepancies between the observed and predicted values to identify potential anomalies in the operation of the cyber physical system under monitoring and control. The evolving component of the framework is based on an Artificial Neural Network with error backpropagation. Adaptability is achieved through the combined use of machine learning and computational intelligence techniques. The proposed framework has the generality to be applied across a wide range of problem domains requiring processing, analysis and interpretation of data obtained from heterogeneous resources.