1,721,127 research outputs found
Adaptive ML-Enabled Edge-Cloud System Framework for Safe and Efficient Autonomous Systems
No full textA Conversational Approach for Modifying Service Mashup in IoT Environments
No full textExisting conversational approaches for Internet of Things (IoT) service mashup do not support modification because of the usability challenge, although it is common for users to modify the service mashups in IoT environments. To support the modification of IoT service mashups through conversational interfaces in a usable manner, we propose the conversational mashup modification agent (CoMMA). Users can modify IoT service mashups using CoMMA through natural language conversations. CoMMA has a two-step mashup modification interaction, an implicature-based localization step, and a modification step with a disambiguation strategy. The localization step allows users to easily search for a mashup by vocalizing their expressions in the environment. The modification step supports users to modify mashups by speaking simple modification commands. We conducted a user study and the results show that CoMMA is as effective as visual approaches in terms of task completion time and perceived task workload for modifying IoT service mashups
MultiFedRL: Efficient Training of Service Agents for Heterogeneous Internet of Things Environments
No full textThe Internet of Things (IoT) has gained more attention for enhancing users' daily lives in public spaces by providing services using shareable devices. However, uncertain factors and other services in the environment may affect the service severely, resulting in users' low satisfaction. Based on multiagent reinforcement learning and cluster-based federated learning, autonomous service agents may learn the complex influence of the factors from user feedback without sophisticated modeling and detection processes. However, conventional approaches are limited in dealing with multiple clustering dimensions of service agents and dynamic environmental contexts affecting the agents. In this work, we propose the multidimension and multiagent federated reinforcement learning (MultiFedRL) for efficient training of service agents in public IoT environments. First, we suggest a parallel structure of neural networks for multiple clustering dimensions to share parameters independently, solving the limitation of conventional cluster-based federated learning. Second, we suggest an environment-centric learnable communication protocol for the agents to summarize and interpret physical contexts consisting of static characteristics and dynamic states. To evaluate MultiFedRL, we developed a simulation framework for IoT services provided to mobile users in public spaces, imitating the user-service interaction based on crucial physics phenomena. Experimental results show that MultiFedRL increases user satisfaction by 82.9% and training efficiency by 24.5% compared to state-of-the-art cluster-based federated learning.
분산 ＩｏＴ 환경을 위한 공간-응집 서비스 검색 및 동적 서비스 이양
No full textSpatio-cohesive service discovery and handover methods for distributed IoT environments are disclosed. The spatio-cohesive service discovery and handover methods include discovering a set of IoT (internet of thing) resources for providing a set of services which is a set of functionalities necessary for a task in distributed IoT environments, wherein the discovering a set of IoT resources may discover the set of IoT resources through a spatio-cohesive method considering spatial distance between a user and a service and between two services
시각적 IoT 서비스를 위한 강화학습 및 효과도 기반 동적 미디어 선택을 위한 전자 장치 및 그의 동작 방법
No full textAn electronic device and an operating method thereof relate to effect-driven dynamic media selection for visual Internet of things (IoT) service using reinforcement learning, and may be configured to monitor a user in an Internet of things (IoT) service environment, predict a visual service effect of at least one service medium related to the user in the IoT service environment, select one of the at least one service medium based on the visual service effect, and provide service for the user through the selected service medium
모바일 엣지 컴퓨팅 환경에서 효율적인 서비스 마이그레이션을 위한 서비스 소비 계획을 제공하는 컴퓨터 시스템 및 그의 방법
No full textVarious embodiments relate to a computer system for providing a service consumption plan for efficient service migration in a mobile edge computing (MEC) environment, and a method thereof. The computer system and the method may be configured to construct a graph representing edges indicating a plurality of road segments and a plurality of MEC servers covering the road segments, detect the service consumption plan for maximizing a ratio of a utilized journey time, which is served through at least one of the MEC servers for a total journey time, with respect to paths between a starting vertex and a goal vertex set by a user by using the graph, and provide the user with the service consumption plan
Biological Mutualistic Models Applied to Study Open Source Software Development
No full textThe evolution of the Web has allowed the generation of several platforms for collaborative work. One of the main contributors to these advances is the Open Source initiative, in which projects are boosted to a new level of interaction and cooperation that improves their software quality and reliability. In order to understand how the group of contributors interacts with the software under development, we propose a
novel methodology that adapts Lotka-Volterra-based biological models used for host-parasite interaction. In that sense, we used the concept mutualism from social parasites. Preliminary results based on experiments on the Github collaborative platform showed that Open Source phenomena can be modeled as a mutualistic system, in terms of the evolution of the population of developers and repositories
소프트 랜딩 스케일러: 웹 트래픽 폭주에 대한 효율적 대응을 위한 적응적 자원 할당 전략
No full textThis research paper addresses managing sudden web traffic increases in cloud computing, emphasizing effective
resource allocation. Traffic surges can overwhelm servers, leading to service instability and dissatisfaction. The proposed
solution, the Soft Landing Scaler (SLS), dynamically adjusts resources based on traffic fluctuations using a Kubernetes-
based architecture. SLS is designed for optimal resource efficiency and adaptability, maintaining satisfactory response
times. The study analyzes traffic patterns such as Sharp Increase then Exponential Decrease (SIED), and Sharp Increase
then Linear Decrease (SILD), demonstrating SLS's performance. Results show improved resource efficiency and user
response times, highlighting SLS's effectiveness in handling diverse traffic surges. The study contributes to the field by
presenting an adaptive scaling system focused on response times, using real-world traffic data, and emphasizing buffer
resources and scaling size limits in downscaling strategies
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