Hybrid cache architectures (HCAs) containing both SRAM and non-volatile memory (NVM) have been proposed to overcome the disadvantages of NVM-based cache architecture. Most previous works have concentrated on managing write-intensive blocks by storing these blocks to SRAM to reduce the number of the write operations to NVM. However, they have not focused on reducing linefill operations which also occupy a large portion of overall NVM write counts in chip-multiprocessor (CMP) environments. This paper proposes an NVM way allocation scheme, taking into account the NVM linefill counts as well as cache miss rate and the NVM write hit counts during victim selection. Three metrics are introduced to estimate the effectiveness of NVM way allocation: Miss counts change (ΔM), write counts change (ΔW), and NVM write counts change (ΔNVMW). An algorithm to minimize the write counts of NVM based on these metrics is proposed as well. Our experimental results show that dynamic energy consumption is reduced by 37.5 percent on average.

For the secure deployment of network platforms tailored for IoT devices, the encryption of data transmission is equally as crucial as the process of authentication. In this context, we introduce the Secure and Scalable IoT network (SSI) network platform, designed to accommodate a diverse range of IoT devices. It provides scalability and implements effective many-to-many and end-to-end encryption across extensive regions. With the emergence of quantum computing, secure public key exchange mechanisms have become important. Among the various post-quantum cryptography (PQC) algorithms assessed, Nth Degree Truncated Polynomial Ring Units (NTRUs) have emerged as an optimally suited PQC algorithm for IoT devices constrained by limited computational capabilities. We have integrated NTRUs with SSI as a lightweight PQC solution. Moreover, SSI-PQM (Post-Quantum MACsec) enhances the SSI’s initial authentication structure to minimize PQC-TLS session attempts and protect the SSI’s important configuration information. When applying TLS with PQC for secret key exchange purposes, it was verified that this approach ensures stable performance in IoT environments. Upon the implementation of our proposed SSI-PQM on Raspberry Pi 3B+ based IoT devices, SSI-PQM exhibited acceptable performance at security levels from 80 to 128 and achieved a minimum speed improvement of 161% over RSA at security levels above 160. It can be concluded that SSI-PQM stands out as an effective Zero Trust-based IoT network platform, demonstrating its viability and efficiency in safeguarding data transmission against potential quantum computing threats.

The failure diagnosis of railway vehicle door system is carried out using a test bench and machine learning software for the fast and accurate classification. The signal length deviation exists in actual collected data of normal operation and abnormal failures with a time delay. The traditional data multi-segmentation technique for feature extraction has shortcomings by assuming that the measured time-based signals have the same operating time. However, the uniform data segmentation has a difficulty due to the deviation of measured data length. A method of converting time-based data into position-based data was performed to overcome the deviation problem. A method of optimized single-zone data using a genetic algorithm was proposed to improve the classification performance and to reduce computation time, instead of existing the multi-segmentation technique. A principal component analysis-based feature dimensional reduction with explained variance ratio was used to reduce the effect from multi-collinearity of features. Finally, the combination of the proposed methods was compared with individual methods to validate the classification performance by using support vector machine and other classifiers. It was confirmed that the proposed combination method shows the highest classification accuracy of 99.84%.