Adaptive Learning-Based Secure and Energy-Aware Resource Management for Multi-Mode Low-Carbon PIoT

Liao, Haijun; Jia, Zehan; Wang, Zhao; Zhou, Zhenyu; Wang, Xiaoyan; Mumtaz, Shahid; Guizani, Mohsen

doi:10.1109/globecom48099.2022.10001389

Cited by 2 publications

(5 citation statements)

References 23 publications

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“…The quality of the training dataset may affect the training model's performance and accuracy [37]. In this work, for the Random Forest, XGBoost, and LSTM algorithms, 4 years of London area's PV electricity generation record from Sheffield open-source PV live data and weather data from MIDAS UK open weather data were used 1 . Both of the datasets (PV and weather) start from 2016/01/01 to 2019/12/31, recording every 60 minutes.…”

Section: Data Preparationmentioning

confidence: 99%

“…The integration of cutting-edge technologies, including 5G and artificial intelligence (AI), empowers microgrids to significantly enhance consumer electronics in various ways. These advanced technologies contribute to a more dependable, efficient, and sustainable power supply for electronic devices, promoting their optimal performance [1]. Electricity plays a pivotal role in fostering economic development and advancing technological progress, particularly in the context of rapid consumer electronics [2], [3].…”

Section: Introductionmentioning

confidence: 99%

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Live Power Generation Predictions via AI-Driven Resilient Systems in Smart Microgrids

Wang,

Li,

Iqbal

2024

IEEE Trans. Consumer Electron.

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The 5G technology can significantly benefit smart consumer devices powered by microgrids in several ways, enhancing their efficiency, reliability, and overall performance, which play a pivotal role in advancing consumer electronics by providing a more reliable, efficient, and sustainable source of power for these devices. The growing environmental awareness and emergence of new technologies have made smart microgrids a good renewable and resilient power to serve consumer electronics. This work developed a secure AI-driven predictable and resilient power generation system for efficient microgrid energy use and management. Specifically, we first developed an intelligent power generation forecasting model based on a joint distribution of power generation and weather data; then, a resilient eXtreme Gradient Boosting (XGBoost) power generation forecast model was proposed that allows incorporating the weather intermittency in the joint distribution. The scheme has been validated using real-time power generation data together with weather data. The experimental results show that the proposed scheme can provide a more accurate and robust prediction of the microgrid against weather intermittency.

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Section: Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Live Power Generation Predictions via AI-Driven Resilient Systems in Smart Microgrids

Wang,

Li,

Iqbal

2024

IEEE Trans. Consumer Electron.

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“…First, the matching preferences of device scheduling is constructed based on historical performances already known, and a sliding window is utilized to remove outdated historical performances from preference estimation to improve estimation accuracy and increase convergence speed. Second, a price rising mechanism based on device-side queue backlog and service priority is developed to eliminate matching conflict caused by device scheduling constraint defined in (1). The procedure of sliding windowbased matching preference list construction and price risingbased matching competition elimination are introduced as follows.…”

Section: Large-timescale Device Scheduling Based On Sliding Window Pr...mentioning

confidence: 99%

“…Otherwise, it does not send access request and stays unscheduled in this period. Afterwards, if the number of access requests received by each edge gateway exceeds , the constraint defined in (1). We develop a price rising mechanism with service priority awareness to eliminate conflict, which is introduced in the next subsection.…”

Section: Sliding Window Based Matching Preference List Constructionmentioning

confidence: 99%

“…Distribution grid is the key pillar of smart grid which serves as the bridge between high-voltage power grid and lowvoltage energy consumer. Massive internet of things (IoT) devices are deployed in distribution power grid to collect the operation data of major electric equipment [1]. These collected data are uploaded, processed, and mined to support novel multi-services such as source-grid-load-storage physical collaboration, power supply and utilization resource collaboration, operation and control service collaboration, as well as ecological collaboration of external and inner power grid.…”

Section: Introductionmentioning

confidence: 99%

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Cloud-edge-end Collaborative Multi-service Resource Management for IoT-based Distribution Grid

WANG,

WEN,

et al. 2024

IEICE Trans. Fundamentals

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The rapid advancement of cloud-edge-end collaboration offers a feasible solution to realize low-delay and low-energy-consumption data processing for internet of things (IoT)-based smart distribution grid. The major concern of cloud-edge-end collaboration lies on resource management. However, the joint optimization of heterogeneous resources involves multiple timescales, and the optimization decisions of different timescales are intertwined. In addition, burst electromagnetic interference will affect the channel environment of the distribution grid, leading to inaccuracies in optimization decisions, which can result in negative influences such as slow convergence and strong fluctuations. Hence, we propose a cloud-edge-end collaborative multi-timescale multi-service resource management algorithm. Large-timescale device scheduling is optimized by sliding window pricing matching, which enables accurate matching estimation and effective conflict elimination. Small-timescale compression level selection and power control are jointly optimized by disturbance-robust upper confidence bound (UCB), which perceives the presence of electromagnetic interference and adjusts exploration tendency for convergence improvement. Simulation outcomes illustrate the excellent performance of the proposed algorithm.

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Adaptive Learning-Based Secure and Energy-Aware Resource Management for Multi-Mode Low-Carbon PIoT

Cited by 2 publications

References 23 publications

Live Power Generation Predictions via AI-Driven Resilient Systems in Smart Microgrids

Live Power Generation Predictions via AI-Driven Resilient Systems in Smart Microgrids

Cloud-edge-end Collaborative Multi-service Resource Management for IoT-based Distribution Grid

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