Yonsei University · Computer Science
Professor JeongGil Ko's research lab specializes in wireless sensor networks, with a focus on enabling secure, reliable, and interoperable communication in resource-constrained environments. The lab's main research directions include the design and deployment of IP-based wireless sensor networks for healthcare and emergency response, the development of secure and privacy-preserving data collection systems, and the advancement of standards for low-power, lossy networks (LLNs) such as 6LoWPAN and RPL. The lab also investigates mobility support in sensor networks and the performance implications of protocol interoperability across different implementations.
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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
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