With the evolutionary development of the Internet of Things (IoT), the demand for delay-sensitive applications has increased. In this manner, mobile-edge computing (MEC) has emerged as a promising technology to run these applications on mobile devices. Several studies have been conducted on pricing schemes in the MEC environment, and they have focused on the amount of offloaded data in pricing. However, under the previously proposed pricing policy, the server resource usage of users is not properly reflected in the payment, so users only try to occupy the server resources as much as possible without any consideration. To solve this problem, this article newly proposes a differential pricing scheme in which the unit price per second is determined based on the user’s usage of server computational resources. Also, it suggests the user’s optimal offloading strategy and server’s equilibrium pricing strategy by formulating Stackelberg game based on the proposed pricing scheme and execution delay. Several numerical results show that the proposed pricing scheme and strategies can solve the problems and can increase the efficiency of edge server’s computational resources.

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer’s gait and diagnose balance issues. This approach can be utilized to improve a user’s rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.

The integration of Internet of Things (IoT) technologies into modern vehicles has facilitated the emergence of the Internet of Vehicles (IoV), revolutionizing the automotive industry by enabling advanced connectivity and data-driven functionalities. Among the many applications made possible by these advancements, accurate driver identification has become essential for enhancing vehicle security, personalizing user experiences, and supporting usage-based services. However, existing driver identification methods often struggle to maintain accuracy across various road environments, as driving behavior varies with road characteristics. This paper introduces a novel driver identification framework that dynamically adapts to varying road geometries by integrating road curvature analysis to improve both accuracy and robustness across diverse road environments. Using global positioning system (GPS) sensor data, road curvature is estimated using the Menger curvature method, and road curvature segments are classified into distinct types through k-means clustering with dynamic time warping. Separate driver identification models are then developed for each road type using machine learning algorithms, including Random Forest, XGBoost, and LightGBM, to capture the subtle differences in driving behavior with varying road types. Extensive experiments using real-world driving data demonstrate that the proposed method achieves up to 86.02% accuracy on unseen road environments and outperforms existing methods by up to 18.64%. These experimental results highlight the improved generalization capability and comprehensive validation of the proposed model, emphasizing its effectiveness for robust driver identification in realistic scenarios.

This paper proposes a building energy management model in the Korean electric tariff system with the consideration of prediction errors in load consumption and renewable energy generation. The increase in controllable power resources has enabled demand-side managements in the practical environment, and various approaches to optimize power consumption in the building are studied and implemented. However, demand side management has a problem in that it is difficult for power consumers to control power consumption due to the lack of consideration of actual use environment. To resolve this issue, we propose penalty factor to minimize the gap between power scheduling results and usual power usage patterns. In addition, the prediction error of power consumption in real environment causes difficulty in power operation. To resolve the issue, this paper proposes a method to control power demand resources and energy storage system based on a set of reserve portion. The proposed model includes analysis on prediction errors of power data, and set of proper reserve proportion in demand response and energy storage system operation. The simulation results show that the electricity rate of consumers in the building is reduced by more than 21% per month.