Sungkyunkwan University · Computer Science
Professor Hyunseung Choo's research lab specializes in intelligent networking and distributed systems, with a focus on wireless sensor networks, IoT integration, and UAV communications. The lab develops energy-efficient and scalable solutions for dynamic network management, including density control, processor allocation in 3D torus topologies, and reliable IoT gateway systems. Recent work emphasizes AI-driven optimization using deep reinforcement learning for UAV handover decisions and anomaly detection in industrial systems through advanced deep learning models.
Figures are computed from collected data and may differ slightly.
In wireless sensor networks, density control is an important technique for prolonging a network's lifetime. To reduce the overall energy consumption, it is desirable to minimize the overlapping sensing area of the sensor nodes. In this paper, we study the problem of energy-efficient area coverage by the regular placement of sensors with adjustable sensing and communication ranges. We suggest a more accurate method to estimate efficiency than those currently used for coverage by sensors with adju
The Internet of Things (IoT) will include new devices specifically designed for IoT compatibility and systems that are already in place today and operate outside of IoT networks. However, the path to creating cloud networks of interconnected devices requires a means for devices that are not IP-based to connect without having to bear the cost of a full Ethernet or WiFi interface with the accompanying protocol stack. This can be achieved through the use of IoT gateways that bridge these devices to
Multicomputer systems achieve high performance by utilizing a number of computing nodes. Recently, by achieving significant reductions in communication delay, the three-dimensional (3D) torus has emerged as a new candidate interconnection topology for message-passing multicomputer systems. In this paper, we propose an efficient processor allocation scheme-scan search scheme-for the 3D torus based on a first-fit approach. The scan search scheme minimizes the average allocation time for an incomin
Abstract The development of large‐scale wireless sensor networks engenders many challenging problems. Examples of such problems include how to dynamically organize the sensor nodes into clusters and how to compress and route the sensing information to a remote base station. Sensed data in sensor systems reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. Noteworthy efficient clustering schemes and data compressing techniques proposed re
The applications of Unmanned Aerial Vehicles (UAVs) are rapidly growing in domains such as surveillance, logistics, and entertainment and require continuous connectivity with cellular networks to ensure their seamless operations. However, handover policies in current cellular networks are primarily designed for ground users, and thus are not appropriate for UAVs due to frequent fluctuations of signal strength in the air. This paper presents a novel handover decision scheme deploying Deep Reinfor
The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing defects, are common and serious issues in manufacturing processes. Bearings provide accurate and smooth movements and play essential roles in mechanical equipment with shafts. Given their importance, bearing failure diagnosis has been extensively studie
Sensor nodes transmit the sensed information to the sink through wireless sensor networks (WSNs). They have limited power, computational capacities and memory. Portable wireless devices are increasing in popularity. Mechanisms that allow information to be efficiently obtained through mobile WSNs are of significant interest. However, a mobile sink introduces many challenges to data dissemination in large WSNs. For example, it is important to efficiently identify the locations of mobile sinks and
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