Research Article
[Retracted] Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing
| MEC | Mobile edge computing |
| UE | User equipment | EU | End user | DL | Deep learning | AI | Artificial intelligence | RL | Reinforcement learning | LSTM | Long short-term memory | RNN | Recurrent neural network | SL | Supervised learning | MES | Mobile edge server | FC | Fog computing | EC | Edge computing | MCC | Mobile cloud computing | ETSI | European Telecommunications Standards Institute | UAV | Unmanned aerial vehicle | ITS | Intelligent transportation system | VANETs | Vehicular ad hoc networks | DRL | Deep reinforcement learning | DNN | Deep neural network | QoS | Quality of service | MDP | Markov decision process | TOT | Total offloading technique | ROT | Random offloading technique | EEDOT | Energy-efficient deep learning-based offloading technique | CEDOT | Comprehensive and energy effective deep learning-based offloading technique | LSTMOT | Long short-term memory offloading technique | WT | Waiting time |
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