State of Energy Prediction in Renewable Energy-driven Mobile Edge Computing using CNN-LSTM Networks

Ku, Yu-Jen; Sapra, Sandalika; Baidya, Sabur; Dey, Sujit

doi:10.1109/igessc50231.2020.9285102

Cited by 17 publications

(4 citation statements)

References 14 publications

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“…However, the accuracy of the machine learning approach is exceptionally reliant on the amount and quality of the training data and the fit- ting algorithms. Due to the existing onboard vehicle control unit hardware insufficiency, machine-learning techniques cannot be implemented on the vehicle [27,28].…”

Section: Rdr Related Termsmentioning

confidence: 99%

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Mei

Karimi

Huang

et al. 2023

IET Control Theory & Appl

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Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends.

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Section: Rdr Related Termsmentioning

confidence: 99%

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Mei

Karimi

Huang

et al. 2023

IET Control Theory & Appl

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“…The study's main objective is to minimize energy consumption at the MEC servers while simultaneously guaranteeing the tasks' delay requirements. The authors of [226] focus on one of the main aspects of the ZTM (i.e., prediction) by using ML techniques, such as CNN and LSTM, to predict the state of the energy at the MEC hosts. The idea of the study is to minimize energy consumption by correctly predicting the state of the energy on the MEC hosts and accordingly distributing the load on the applications hosted at the edge.…”

Section: Distributed Edgementioning

confidence: 99%

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

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Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since mobile networks are becoming progressively more heterogeneous and more complex. In this context, a promising approach is the adoption of novel network automation solutions and, in particular, of zero-touch management techniques. In this work, we refer to zero-touch management as a fully autonomous network management solution with human oversight. This survey sits at the crossroad between zero-touch management and mobile and wireless network research, effectively bridging a gap in terms of literature review between the two domains. In this paper, we first provide a taxonomy of network management solutions. We then discuss the relevant state-of-the-art on autonomous mobile networks. The concept of zero-touch management and the associated standardization efforts are then introduced. The survey continues with a review of the most important technological enablers for zero-touch management. The network automation solutions from the RAN to the core network, including end-toend aspects such as security, are then surveyed. Finally, we close this article with the current challenges and research directions.

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“…In [10], unmanned aerial vehicle (UAV) is associated with mobile edge computing, where a UAV equipped with an MEC server is deployed to serve multiple IoT devices in a fnite period. In [11], a real-world testbed consisting of edge computing devices and cellular base stations are developed, and a CNN-LSTM model is used to predict SoE, in order to save electricity on the grid, thereby reducing the carbon footprint.…”

Section: Introductionmentioning

confidence: 99%

Evolving LSTM Networks for Time‐Series Classification in EdgeIoT

Cui¹,

Jiang

et al. 2023

Mathematical Problems in Engineering

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We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC-optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing.

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State of Energy Prediction in Renewable Energy-driven Mobile Edge Computing using CNN-LSTM Networks

Cited by 17 publications

References 14 publications

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Evolving LSTM Networks for Time‐Series Classification in EdgeIoT

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