Intelligent task offloading in vehicular edge computing networks
Recently, traditional transportation systems have been gradually evolving to ITS, inspired by
both artificial intelligence and wireless communications technologies. The vehicles get …
both artificial intelligence and wireless communications technologies. The vehicles get …
A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies
The operation and scheduling management of smart grids are important aspects, and wind
speed forecasting modules are indispensable in wind power system management …
speed forecasting modules are indispensable in wind power system management …
Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IoT
X Guo, H Lin, Z Li, M Peng - IEEE Internet of things journal, 2019 - ieeexplore.ieee.org
Recently, with the proliferation of communication devices, Internet of Things (IoT) has
become an emerging technology which facilitates massive devices to be enabled with …
become an emerging technology which facilitates massive devices to be enabled with …
EnLSTM-WPEO: Short-term traffic flow prediction by ensemble LSTM, NNCT weight integration, and population extremal optimization
Accurate and stable short-term traffic flow prediction is an indispensable part in current
intelligent transportation systems. In this paper, a novel short-term traffic flow forecasting …
intelligent transportation systems. In this paper, a novel short-term traffic flow forecasting …
Joint task assignment, transmission, and computing resource allocation in multilayer mobile edge computing systems
In this paper, we propose a multilayer data flow processing system, ie, EdgeFlow, to
integrally utilize the computing capacity throughout the whole network, ie, the cloud center …
integrally utilize the computing capacity throughout the whole network, ie, the cloud center …
Integration of D2D, network slicing, and MEC in 5G cellular networks: Survey and challenges
With the tremendous demand for connectivity anywhere and anytime, existing network
architectures should be modified. To cope with the challenges that arise due to the …
architectures should be modified. To cope with the challenges that arise due to the …
Joint parallel offloading and load balancing for cooperative-MEC systems with delay constraints
Mobile-edge computing (MEC) has been recognized as a promising solution to provide
efficient communication and computation capabilities for mobile users (MUs). However, the …
efficient communication and computation capabilities for mobile users (MUs). However, the …
Multi-agent deep reinforcement learning based spectrum allocation for D2D underlay communications
Device-to-device (D2D) communication underlay cellular networks is a promising technique
to improve spectrum efficiency. In this situation, D2D transmission may cause severe …
to improve spectrum efficiency. In this situation, D2D transmission may cause severe …
Federated learning for automatic modulation classification under class imbalance and varying noise condition
Automatic modulation classification (AMC) is a promising technology for identifying
modulation types, and deep learning (DL)-based AMC is one of its main research directions …
modulation types, and deep learning (DL)-based AMC is one of its main research directions …
A distributed mobile fog computing scheme for mobile delay-sensitive applications in SDN-enabled vehicular networks
With the rapid development of intelligent transportation systems, enormous amounts of delay-
sensitive vehicular services have been emerging and challenge both the architectures and …
sensitive vehicular services have been emerging and challenge both the architectures and …