Energy Management For Electric Vehicles in Smart Cities: A Deep Learning Approach

Laroui, Mohammed; Dridi, Aicha; Afifi, Hossam; Moungla, Hassine; Marot, Michel; Cherif, Moussa Ali

doi:10.1109/iwcmc.2019.8766580

Cited by 25 publications

(9 citation statements)

References 18 publications

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“…We propose to use long short-term memory (LSTM) algorithm to predict vehicles mobility inside the city [15]. The LSTM model is trained using real vehicles mobility dataset [16] in Rome city, Italy.…”

Section: A Prediction Algorithmmentioning

confidence: 99%

Prefetching of mobile devices information - a DNS perspective

Bernard¹,

Laroui

Marot

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: A Prediction Algorithmmentioning

confidence: 99%

Prefetching of mobile devices information - a DNS perspective

Bernard¹,

Laroui

Marot

et al. 2022

ICC 2022 - IEEE International Conference on Communications

Self Cite

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“…Among the main DL instances, we quote long short-term memory (LSTM) 48 and gated recurrent unit (GRU) 49 used for prediction and early network pattern detection. [50][51][52][53][54][55][56] Both LSTM and GRU recurrent neural network models are used to predict network parameters. These latter are required to feed the proposed SO-VMEC optimization algorithms.…”

Section: Deep Learning Models For Mobility and Energy Predictionmentioning

confidence: 99%

SO‐VMEC: Service offloading in virtual mobile edge computing using deep reinforcement learning

Laroui

Ibn‐Khedher

Cherif

et al. 2021

Trans Emerging Tel Tech

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Service offloading poses interesting challenges in current and next-generation networks. The classical network optimization algorithms are still painstakingly tune heuristics to get a sufficient solution. Classical approaches use data as input in order to output near-optimal solutions. These techniques show exponential computational time and deal only with small network scale. Therefore, we are motivated by replacing this tedious process with recent learning techniques to learn the behavior of the classical optimization algorithms while enhancing both the quality of service and satisfying the resources requirements of next-generation applications. Deep reinforcement learning (DRL) and machine learning (ML) can improve service offloading and network caching. An optimal service offloading in virtual mobile edge computing (SO-VMEC) use case algorithm is proposed using integer linear programming (ILP). Moreover, a service offloading protocol is presented to support the use case. We leverage software defined networking (SDN) and network function virtualization (NFV) concepts to control and virtualize network components. Then, a DRL-based offloading is proposed to deal with dense Internet of Things (IoT) networks. Extensive evaluations and comparison to state of the art techniques are carried out. Results show the efficiency of the proposed algorithms in terms of service offloading, resource utilization, and networking.

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“…Murphey et al (2012) applied the feedforward artificial neural network to predict the driving environment of the vehicle and based on this, designed the optimal vehicle output power control strategy. Laroui et al (2019) employed a recurrent neural network to solve the energy management of the electric vehicle in the smart city. These works applied the big data mining techniques to facilitate the operation of the electric vehicle at the microlevel.…”

Section: Integrated Power and Transportation Systemmentioning

confidence: 99%

Data mining for energy systems: Review and prospect

Liu

Zhao

Wang

2021

WIREs Data Min & Knowl

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An in‐depth study on big data mining is urgently needed for the next‐generation energy systems, which are characterized by a deep integration of cyber, physical, and social components. This paper presents an initial discussion on big data mining and its applications in intelligent energy systems. New progress in big data mining, such as deep learning, transfer learning, randomized learning, granular computing, and multisource data fusion, is introduced first. Some applications of data mining in energy systems, such as load forecasting and modeling, integrated power and transportation system, and electricity market forecasting and simulation, are discussed then. Moreover, some research problems in energy system data mining, such as cyber–physical–social system modeling and super‐resolution perception for smart meter data, which require further attention in the future, are also discussed. This article is categorized under: Application Areas > Business and Industry

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Energy Management For Electric Vehicles in Smart Cities: A Deep Learning Approach

Cited by 25 publications

References 18 publications

Prefetching of mobile devices information - a DNS perspective

Prefetching of mobile devices information - a DNS perspective

SO‐VMEC: Service offloading in virtual mobile edge computing using deep reinforcement learning

Data mining for energy systems: Review and prospect

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