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Found 10 papers, 0 papers with code
Privacy-preserving transactive energy systems: Key topics and open research challenges
This manuscript aims to formalize and conclude the discussions initiated during the PriTEM workshop 22-23 March 2023.
Balancing Explainability-Accuracy of Complex Models
Furthermore, we provide an upper bound of the computation complexity of our proposed approach for the dependent features.
Autonomous Driving
Explainable Artificial Intelligence (XAI)
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks
In this paper, we introduce the Digital Twin Wireless Networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane.
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
In this paper, we integrate DRL and permissioned blockchain into vehicular networks for intelligent and secure content caching.
Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond
In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption.
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks
Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency.
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids
To cope with this growth, intelligent management of the consumption profile of the households is necessary, such that the households can save the electricity bills, and the stress to the power grid during peak hours can be reduced.
Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks
Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i. e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources.
Deep Learning for Secure Mobile Edge Computing
Our proposed model can be used to detect malicious applications at the edge of a cellular network, which is a serious security threat.
Social Computing for Mobile Big Data in Wireless Networks
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain.
