As more vehicles are being connected to the Internet and equipped with autonomous driving features, more robust safety and security measures are required for connected and autonomous vehicles (CAVs). Therefore, threat analysis and risk assessment are essential to prepare against cybersecurity risks for CAVs. Although prior studies have measured the possibility of attack and damage from attack as risk assessment indices, they have not analyzed the expanding attack surface or risk assessment indices that rely upon real-time resilience. This study proposes the PIER method to evaluate the cybersecurity risks of CAVs. We implemented cyber resilience for CAVs by presenting new criteria, such as exposure and recovery, in addition to probability and impact, as indices for the threat analysis and risk assessment of vehicles. To verify its effectiveness, the PIER method was evaluated with respect to software update over-the-air and collision avoidance features. Furthermore, we found that implementing security requirements that mitigate serious risks successfully diminishes the risk indices. Using the risk assessment matrix, the PIER method can shorten the risk determination time through high-risk coverage and a simple process.

Federated learning is a distributed machine learning framework that enables a large number of devices to cooperatively train a model without data sharing. However, because federated learning trains a model using non-independent and identically distributed (non-IID) data stored at local devices, the weight divergence causes a performance loss. This paper focuses on solving the non-IID problems and proposes Kalman filter-based clustering federated learning method called K-FL to get performance gain by providing a specific model with low variance to the device. To the best of our knowledge, it is the first clustering federated learning method that can train a model requiring fewer communication rounds under the premise that non-IID environment without any prior knowledge and an initial value set by the user. From simulations, we demonstrate that the proposed K-FL can train a model much faster, requiring fewer communication rounds than FedAvg and LG-FedAvg when testing neural networks using the MNIST, FMNIST, and CIFAR-10 datasets. As a numerical result, it is shown that the accuracy is improved in all datasets while the computational time cost is reduced by 1.43×, 1.67×, and 1.63× compared to FedAvg, respectively.

Purpose: The objective of this study was to show the prevalence and investigate treatment trends of pediatric nephrolithiasis based on a large population of U.S. insurance individual's data. Materials and Methods: This research involved a retrospective observational cohort study. Administrative claims data were extracted from the IBM ^® MarketScan ^® Research Database. We included all patients newly diagnosed with nephrolithiasis, aged <18 years old at the time of diagnosis from January 1, 2007, to December 31, 2014. The patient cohort with nephrolithiasis was selected using the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) code for nephrolithiasis. Each treatment method was searched by Current Procedural Terminology (CPT) code. Results: A total of 28,014 patients were found to have nephrolithiasis in our cohort. Of nephrolithiasis patients, 701 (2.5%) patients were treated by surgical methods. The mean age of patients at the time of treatment was 13 years old. Extracorporeal shockwave lithotripsy (SWL) was the most used treatment modality during the period. SWL was performed in 66% of patients. The number of cases of SWL did not tend to change according to year, whereas retrograde intrarenal surgery (RIRS) tended to increase from 15% to 31%. Percutaneous nephrolithotripsy (PCNL) decreased from 13% to <10 cases. The number of open surgeries was very small and did not show any tendency. Conclusion: During the study period, SWL is stable. RIRS has become more popular in treating renal stones, whereas PCNL has decreased. These results suggest that the RIRS has become more popular than PCNL in treating large renal stones.

High-definition (HD) maps determine the location of the vehicle under limited visibility based on the location information of safety signs detected by sensors. If a safety sign disappears or changes, incorrect information may be obtained. Thus, map data must be updated daily to prevent accidents. This study proposes a map update system (MUS) framework that maps objects detected by a road map detection system and the object present in the HD map. Based on traffic safety signs notified by the Korean National Police Agency, 151 types of objects, including traffic signs, traffic lights, and road markings, were annotated manually and semi-automatically. Approximately 3,000,000 annotations were trained based on the you only look once (YOLO) model, suitable for real-time detection by grouping safety signs with similar properties. The object coordinates were then extracted from the mobile mapping system point cloud, and the detection location accuracy was verified by comparing and evaluating the center point of the object detected in the MUS. The performance of the groups with and without specified properties was compared and their effectiveness was verified based on the dataset configuration. A model trained with a Korean road traffic dataset on our testbed achieved a group model of 95% mAP and no group model of 70.9% mAP.

The in-depth discussion on the development of green technology at the 25th Wireless World Research Forum (WWRF) Conference in November 2010 indicated the growing interest in energy conservation technology that needs to be applied in next generation mobile communication. Green Internet of Things (IoT) technology is expected to be included as part of green communication. In this study, to meet the low power-consumption requirements of IoT devices, IEEE 802.11ba—a standard that minimizes power consumption significantly—is introduced. IEEE 802.11ba minimizes the power consumption of wireless devices by using power-saving technology that activates wireless local area network (WLAN) chips only when necessary. Thus far, no discussions have been provided for wake-up radio (WUR) technology regarding non-sleep attacks, where the WUR receiver ends up waking up the main radios accidentally or remains in the non-sleep mode because of malicious attacks such as spoofing. Therefore, in this paper, a general operating procedure of WUR for ensuring low-power consumption and an algorithm for detecting malicious attacks are proposed. Further, an operating process for responding to malicious attacks is defined. The extensive simulation results show that the proposed anti-malicious attack WUR (AMA-WUR) protocol reduces the average packet delay by 62.84% compared with original WUR protocol, and reduces the average power consumption by 93.71% with original WUR protocol while the flooding attack vulnerabilities.