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A Decision Support System for Situation Management based on the Variability of Disaster Situations
Hyesun Lee, Sun-Wha Lim, Eun Joo Kim, Soyoung Park, Kang Bok Lee, Sang Gi Hong
http://doi.org/10.5626/JOK.2022.49.9.755
With increasing frequency and extent of disasters, the importance of prompt and accurate situation management is also increasing. Existing methods to support situation management decision-making can be applied only to specific situation management tasks in limited circumstances, making it difficult to support customized decision-making according to disaster situations. To address this problem, this paper proposed a variability-based situation management decision support method considering characteristics of disaster situations. The proposed method was based on the software product line engineering concept, constructing core information that could be configured by considering variabilities of disaster situation characteristics, thus providing situation management information from the core information according to disaster situations. This method could increase work efficiency by supporting systematic decision-making step by step based on the situation management work process according to the disaster situation. It could increase the speed and accuracy of decision-making by supporting decision-making automation. The feasibility of the method was validated by applying the method to situation management scenarios for different disaster situations.
Systematic Development of Mobile IoT Device Power Management : Feature-based Variability Modeling and Asset Development
Hyesun Lee, Kang Bok Lee, Hyo-Chan Bang
Internet of Things (IoT) is an environment where various devices are connected to each other via a wired/wireless network and where the devices gather, process, exchange, and share information. Some of the most important types of IoT devices are mobile IoT devices such as smartphones. These devices provide various high-performance services to users but cannot be supplied with power all the time; therefore, power management appropriate to a given IoT environment is necessary. Power management of mobile IoT devices involves complex relationships between various entities such as application processors (APs), HW modules inside/outside AP, Operating System (OS), platforms, and applications; a method is therefore needed to systematically analyze and manage these relationships. In addition, variabilities related to power management such as various policies, operational environments, and algorithms need to be analyzed and applied to power management development. In this paper, engineering principles and a method based on them are presented in order to address these challenges and support systematic development of IoT device power management. Power management of connected helmet systems was used to validate the feasibility of the proposed method.
A Conceptual Framework for Aging Diagnosis Using IoT Devices
Jae Yoo Lee, Jin Cheul Park, Soo Dong Kim
With the emergence of Internet-of-Things (IoT) computing, it has become possible to acquire users’ health-related contexts from various IoT devices and to diagnose their biological aging through analysis of the IoT health contexts. However, previous work on methods of aging diagnosis used a fixed list of aging diagnosis factors, making it difficult to handle the variability of users’ IoT health contexts and to dynamically adapt the addition and deletion of aging diagnosis factors. This paper proposes a design and methods for a dynamically adaptable aging diagnosis framework that acquires a set of IoT health contexts from various IoT devices based on a set of aging diagnosis factors of the user. By using the proposed aging diagnosis framework, aging diagnosis methods can be applied without considering the variability of IoT health contexts and aging diagnosis factors can be dynamically added and deleted.
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