• ETRI Journal
• /
• v.40 no.6
• /
• pp.736-744
• /
• 2018

This study considers zone-based registration (ZBR), which is adopted by most mobile cellular networks. In ZBR, a user equipment (UE) registers its location area (or zone) in a network database (DB) whenever it enters a new zone. Even though ZBR is implemented in most networks for a UE to keep only one zone (1ZR), it is also possible for a UE to keep multiple zones. Therefore, a ZBR with two zones (2ZR) is investigated, and some mathematical models for 2ZR are presented. With respect to ZBR with three zones (3ZR), several studies have been reported, but these employed computer simulations owing to the complexity of the cases, and there have been no reports on a mathematical 3ZR model to analyze its performance. In this study, we propose a new mathematical model for 3ZR for the first time, and analyze the performance of 3ZR using this model. The numerical results for various scenarios show that, as the UE frequently enters zones, the proposed 3ZR model outperforms 1ZR and 2ZR. Our results help determine the optimal number of zones that a UE keeps, and minimize the signaling cost for radio channels in mobile cellular networks.

• Journal of Multimedia Information System
• /
• v.9 no.1
• /
• pp.11-20
• /
• 2022

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

• Journal of Multimedia Information System
• /
• v.9 no.1
• /
• pp.33-42
• /
• 2022

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

• Journal of Information Technology Services
• /
• v.20 no.5
• /
• pp.177-187
• /
• 2021

The global economy has been very difficult due to the recent impact of COVID-19. Korea is also pushing for strong quarantine policies such as K- quarantine and social distancing, but the economy is hardly recovering. In particular, the economic situation began to change rapidly depending on the export and domestic market, the public's interest in the economy increased, and companies became more sensitive. In order to estimate this rapidly changing economic situation, major advanced countries have also developed models that can periodically monitor the economy at the government level. Through this, by periodically reporting the economic trends, the public and companies can be aware of the economic trends to some extent. This study analyzed the cases of weekly business trends in advanced countries and developed a model of weekly economic activity suitable for Korea. To verify this, indices closely related to the economy such as mobility, industrial activity, face-to-face consumption, and psychology were discovered and estimated. As a result of the study, the weekly economic activity index was judged to be very useful in capturing short-term real economic activity. In the future, in order to secure the robustness and stability of the index and to increase the reflection of reality, model improvement and parameter estimation should be performed regularly.

• International Journal of Advanced Culture Technology
• /
• v.10 no.3
• /
• pp.127-145
• /
• 2022

Vaccine diplomacy is a manifestation of competition for political influence among great powers amidst the Covid-19 pandemic's blatant illustration of ineluctable interdependency across the global community. The reinforcement of trends bolstering global polity construction intensify concomitantly with nationalist populist value and attitude expressions increasing political polarization. The interdependency graphically illustrated in the Cold War-era's mutual assured destruction incentivized competition into indirect competitive intervention in the internal politics of third actors. Indirect international influence contestations included extended, de facto challenge competitions to generate soft power on behalf of the victor, e.g., the space race. The Covid-19 pandemic has intensified this competition to offer alternative development models while intense domestic political polarization undermines the mobilizational capacities for achieving sustainable development. In contrast to multinational and multiethnic states, nation states have an inherent mobilizational advantage because of the enhanced control capabilities available to the authorities without emphasizing coercion. Control through Gramscian hegemonic mechanisms is more readily feasible in nation states through the greater feasibility of commodification of social relations by states authorities regulating and channeling social competition to encourage social mobility and creativity. The regulation of the so-called private sector serves to manage and contain social competition while channeling it to develop the institutional capacities for control and allocation of developing societal human resources. It enhances developed state control mechanisms and international influence capacities. The appeal of offers of aid and assistance to the so-called developing world becomes ever more urgent amidst Anthropocene crises including its most recent, current Covid-19 pandemic disaster.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2377-2398
• /
• 2023

In our daily lives, we engage in a variety of tasks that rely on our senses, such as seeing. Blindness is the absence of the sense of vision. According to the World Health Organization, 2.2 billion people worldwide suffer from various forms of vision impairment. Unfortunately, blind people face a variety of indoor and outdoor challenges on a daily basis, limiting their mobility and preventing them from engaging in other activities. Blind people are very vulnerable to a variety of hazards, including falls. Various barriers, such as stairs, can cause a fall. The Internet of Things (IoT) is used to track falls and send a warning message to the blind caretakers. One of the gaps in the previous works is that they were unable to differentiate between falls true and false. Treating false falls as true falls results in many false alarms being sent to the blind caretakers and thus, they may reject the IoT system. As a means of bridging this chasm, this paper proposes an intelligent shoe that is able to precisely distinguish between false and true falls based on three sensors, namely, the load scale sensor, the light sensor, and the Flex sensor. The proposed IoT system is tested in an indoor environment for various scenarios of falls using four models of machine learning. The results from our system showed an accuracy of 0.96%. Compared to the state-of-the-art, our system is simpler and more accurate since it avoids sending false alarms to the blind caretakers.

• Journal of Smart Tourism
• /
• v.3 no.4
• /
• pp.23-31
• /
• 2023

This paper explores the concept of enhanced smart tourism as a response to the challenges and opportunities arising in the post-pandemic tourism landscape. The COVID-19 pandemic has not only halted the global tourism industry but also prompted a reevaluation of its sustainability, technological integration, and impact on local communities. The need for a paradigm shift in tourism is emphasized, focusing on digitalization, innovation, and resilience. Enhanced smart tourism is characterized by a shift from traditional practices to innovative governance models, increased emphasis on sustainability, and the integration of technology for better management and visitor experiences. The paper discusses the four pillars of enhanced smart tourism - Technology, Sustainability, Accessibility/Mobility, and Innovation/Creativity, and their expansion in the post-pandemic era. Furthermore, the significant role of data in smart tourism is examined, highlighting the importance of data valuation, management, and ethics. The paper proposes frameworks and methods for data valuation and emphasizes the necessity of a comprehensive approach to data within the smart tourism ecosystem. The conclusion points to the need for further empirical and conceptual research to fully realize the potential of enhanced smart tourism.

• ETRI Journal
• /
• v.45 no.5
• /
• pp.836-846
• /
• 2023

Mobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian-rich spaces. This study proposes real-time, accurate, three-dimensional (3D) multi-pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected-component algorithm. The multi-pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.

• International Journal of Computer Science & Network Security
• /
• v.23 no.12
• /
• pp.13-26
• /
• 2023

Recently, the development of wireless network technologies has been advancing in several directions: increasing data transmission speed, enhancing user mobility, expanding the range of services offered, improving the utilization of the radio frequency spectrum, and enhancing the intelligence of network and subscriber equipment. In this research, a series of contradictions has emerged in the field of wireless network technologies, with the most acute being the contradiction between the growing demand for wireless communication services (on operational frequencies) and natural limitations of frequency resources, in addition to the contradiction between the expansions of the spectrum of services offered by wireless networks, increased quality requirements, and the use of traditional (outdated) management technologies. One effective method for resolving these contradictions is the application of artificial intelligence elements in wireless telecommunication systems. Thus, the development of technologies for building intelligent (cognitive) radio and cognitive wireless networks is a technological imperative of our time. The functions of artificial intelligence in prospective wireless systems and networks can be implemented in various ways. One of the modern approaches to implementing artificial intelligence functions in cognitive wireless network systems is the application of fuzzy logic and fuzzy processors. In this regard, the work focused on exploring the application of fuzzy logic in prospective cognitive wireless systems is considered relevant.

• International Journal of Computer Science & Network Security
• /
• v.24 no.9
• /
• pp.30-40
• /
• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.