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학술지 Decision-making of IoT device operation based on intelligent-task offloading for improving environmental optimization
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저자
Wenquan Jin, 임선환, 우성필, 박찬원, 김도현
발행일
202210
출처
Complex & Intelligent Systems, v.8 no.5, pp.3847-3866
ISSN
2199-4536
출판사
Springer
DOI
https://dx.doi.org/10.1007/s40747-022-00659-z
협약과제
22HR4500, 5G-IoT 기반 고신뢰 AI-데이터 커먼즈 프레임워크 핵심기술 개발, 임선환
초록
Computation offloading enables intensive computational tasks to be separated into multiple computing resources for overcoming hardware limitations. Leveraging cloud computing, edge computing can be enabled to apply not only large-scale and personalized data but also intelligent algorithms based on offloading the intelligent models to high-performance servers for working with huge volumes of data in the cloud. In this paper, we propose a getaway-centric Internet of Things (IoT) system to enable the intelligent and autonomous operation of IoT devices in edge computing. In the proposed edge computing, IoT devices are operated by a decision-making model that selects an optimal control factor from multiple intelligent services and applies it to the device. The intelligent services are provided based on offloading multiple intelligent and optimization approaches to the intelligent service engine in the cloud. Therefore, the decision-making model in the gateway is enabled to select the best solution from the candidates. Also, the proposed IoT system provides monitoring and visualization to users through device management based on resource virtualization using the gateway. Furthermore, the gateway interprets scenario profiles to interact with intelligent services dynamically and apply the optimal control factor to the actual device through the virtual resource. For implementing the improved energy optimization using the proposed IoT system, we propose two intelligent models to learn parameters of a user's residential environment using deep learning and derive the inference models to deploy in the intelligent service engine. The inference models are used for predicting a heater energy consumption that is applied to the heater. The heater updates the environment parameters to reach the user-desired values. Moreover, based on two energy consumption values, the decision-making model brings a smaller value to operate the heater to enable reducing the energy consumption as well as providing a user-desired environment.
KSP 제안 키워드
Autonomous Operation, Cloud Computing, Computation offloading, Computing resources, Consumption Values, Control factor, Decision-making Model, Energy optimization, Environmental optimization, High performance, Intelligent algorithms
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