JeongGil Ko
연세대학교 첨단융합공학부 · 컴퓨터과학
정길 Ko 교수의 연구실은 저전력·손실이 큰 네트워크(Low-Power and Lossy Networks, LLNs) 기반의 IP 기반 센서 네트워크 기술을 핵심으로 하며, 특히 의료 모니터링과 재난 상황에서의 실시간 환자 데이터 수집을 위한 무선 센서 네트워크 시스템 개발에 집중하고 있습니다. MEDiSN과 같은 실용적 응용 시스템을 기반으로 신뢰성, 개인정보 보호, 보안성, 그리고 네트워크 간 상호운용성 등 실제 환경에서의 구현 도전 과제를 해결하고자 합니다. 또한 IETF 표준(6LoWPAN, RPL 등)과 오픈소스 운영체제(TinyOS, Contiki) 기반의 프로토콜 구현 및 성능 평가를 통해 표준화와 실세계 적용 간 격차를 해소하고자 연구를 진행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Driven by the confluence between the need to collect data about people's physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to urban and the recent availability of the technologies that enable this data collection, wireless sensor networks for healthcare have emerged in the recent years. In this review, we present some representative applications in the healthcare domain and describe the challenges they introduce to wireless sensor network
Staff shortages and an increasingly aging population are straining the ability of emergency departments to provide high quality care. At the same time, there is a growing concern about hospitals' ability to provide effective care during disaster events. For these reasons, tools that automate patient monitoring have the potential to greatly improve efficiency and quality of health care. Towards this goal, we have developed MEDiSN , a wireless sensor network for monitoring patients' physiological
Many applications, ranging from wireless healthcare to energy metering on the smart grid, have emerged from a decade of research in wireless sensor networks. However, the lack of an IP-based network architecture precluded sensor networks from interoperating with the Internet, limiting their real-world impact. Given this disconnect, the IETF chartered the 6LoWPAN and RoLL working groups to specify standards at various layers of the protocol stack with the goal of connecting low-power and lossy ne
Responding to the increasing interest to connect wireless sensor networks (WSN) to the Internet, the IETF has proposed standards that enable IPv6-based sensor networks. Specifically, the IETF 6LoWPAN and RoLL working groups developed standards for encapsulating IPv6 datagrams in 802.15.4 frames, neighbor discovery, and routing that allow sensor networks to exchange IPv6 datagrams with Internet hosts. However, given that these standards, especially the RPL routing protocol, are relatively new, th
Recently, mobile devices have been introduced in various wireless sensor network (WSN) applications in order to solve complex tasks or to increase the data collection efficiency. However, the current generation of low-power WSN protocols is mainly designed to support data collection and address application-specific challenges without any particular considerations for mobility. In this paper, we introduce MoMoRo, a mobility support layer that can be easily applied to existing data collection prot
IP-based sensor networks provide interoperability, but experience shows that interoperability between different protocol implementations is not a binary property. Instead, subtle differences in implementation choices may affect the performance of the resulting system. We present our experiences with the Contiki and TinyOS implementations of the IPv6 stack for low-power and lossy (LLN) networks including the IETF 6LoWPAN adaptation layer and the IETF RPL protocol. Our results show two independent
RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, home and building automation, industrial and environmental monitoring, health care, wireless sensor networks, and the Internet of Things (IoT) in general with thousands and millions of nodes interconnected through multihop mesh networks. RPL constructs tree-like routing topology rooted at an LLN border router (LBR) and supports b
Multiple studies suggest that the level of patient care may decline in the future because of a larger aging population and medical staff shortages. Wireless sensing systems that automate some of the patient monitoring tasks can potentially improve the efficiency of patient workflows, but their efficacy in clinical settings is an open question. This article examines the potential of wireless sensor network (WSN) technologies to improve the efficiency of the patient-monitoring process in clinical
User needs and technology availability drive the introduction of wireless sensing applications in clinical environments. While these applications have the potential to improve efficiency and quality of care, very little is known about their performance during day-to-day use at the hospital. In this
The IEEE 802.15.4 MAC (medium access control) is a protocol used in many applications including the wireless sensor network. Yet the IEEE 802.15.4 MAC layer cannot support different throughput performance for individual nodes with the current specifications. However, if certain nodes are sending data more frequently compared to others, with the standard MAC, it is hard to achieve network efficiency. Therefore, we modified the IEEE 802.15.4 MAC and additionally proposed a new state transition sch
Interoperability is essential for the commercial adoption of wireless sensor networks. However, existing sensor network architectures have been developed in isolation and thus interoperability has not been a concern. Recently, IP has been proposed as a solution to the interoperability problem of low-power and lossy networks (LLNs), considering its open and standards-based architecture at the network, transport, and application layers. We present two complete and interoperable implementations of
ABSTRACT From ancient Europe, the renaissance and industrialisation eras, to the modern times, urban planning paradigms have evolved in many ways, advancing the environments where people live in. Nevertheless, the recent development of wireless and wired communication network technologies and low‐power miniature sensors for various application domains provide us with another chance of revolutionising cities by making them smarter . Smart cities , propelled by a city‐scale infrastructure, where i
With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena