On the public citation graph

Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach

https://doi.org/10.1109/tvt.2017.2760281

0 signed · 604 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4615-3618-5_1openalex-snapshot

Introduction: The Challenge of Reinforcement Learning

inferredcites →https://doi.org/10.1007/bf00992695openalex-snapshot

Introduction: The challenge of reinforcement learning

inferredcites →https://doi.org/10.1016/j.vehcom.2015.06.002openalex-snapshot

From today's VANETs to tomorrow's planning and the bets for the day after

inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot

Human-level control through deep reinforcement learning

inferredcites →https://doi.org/10.1109/access.2016.2540520openalex-snapshot

Big Data Analytics in Mobile Cellular Networks

inferredcites →https://doi.org/10.1109/access.2017.2671030openalex-snapshot

From Theory to Experimental Evaluation: Resource Management in Software-Defined Vehicular Networks

inferredcites →https://doi.org/10.1109/comst.2014.2352118openalex-snapshot

Wireless Network Virtualization: A Survey, Some Research Issues and Challenges

inferredcites →https://doi.org/10.1109/comst.2015.2394307openalex-snapshot

A Survey of Green Information-Centric Networking: Research Issues and Challenges

inferredcites →https://doi.org/10.1109/infcom.1999.749260openalex-snapshot

Web caching and Zipf-like distributions: evidence and implications

inferredcites →https://doi.org/10.1109/infcomw.2017.8116363openalex-snapshot

Energy-efficient resource allocation in software-defined mobile networks with mobile edge computing and caching

inferredcites →https://doi.org/10.1109/mcom.2016.7402268openalex-snapshot

Information-centric networking for connected vehicles: a survey and future perspectives

inferredcites →https://doi.org/10.1109/mcom.2016.7588230openalex-snapshot

Vehicular delay-tolerant networks for smart grid data management using mobile edge computing

inferredcites →https://doi.org/10.1109/mcom.2017.1700246openalex-snapshot

Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach

inferredcites →https://doi.org/10.1109/mnet.2015.7113228openalex-snapshot

Information-centric network function virtualization over 5g mobile wireless networks

inferredcites →https://doi.org/10.1109/mnet.2016.7389832openalex-snapshot

When big data meets software-defined networking: SDN for big data and big data for SDN

inferredcites →https://doi.org/10.1109/mwc.2004.1325890openalex-snapshot

A new method to support UMTS/WLAN vertical handover using SCTP

inferredcites →https://doi.org/10.1109/surv.2013.070813.00063openalex-snapshot

A Survey of Information-Centric Networking Research

inferredcites →https://doi.org/10.1109/tits.2014.2298038openalex-snapshot

Finite-State Markov Modeling for Wireless Channels in Tunnel Communication-Based Train Control Systems

inferredcites →https://doi.org/10.1109/tits.2015.2495269openalex-snapshot

Network-Coding-Assisted Data Dissemination via Cooperative Vehicle-to-Vehicle/-Infrastructure Communications

inferredcites →https://doi.org/10.1109/tmc.2007.250676openalex-snapshot

Optimal Joint Session Admission Control in Integrated WLAN and CDMA Cellular Networks with Vertical Handoff

inferredcites →https://doi.org/10.1109/tnet.2012.2227338openalex-snapshot

Estimating Instantaneous Cache Hit Ratio Using Markov Chain Analysis

inferredcites →https://doi.org/10.1109/tnet.2015.2432804openalex-snapshot

Cooperative Data Scheduling in Hybrid Vehicular Ad Hoc Networks: VANET as a Software Defined Network

inferredcites →https://doi.org/10.1109/tvt.2010.2041803openalex-snapshot

Distributed Optimal Relay Selection in Wireless Cooperative Networks With Finite-State Markov Channels

inferredcites →https://doi.org/10.1109/tvt.2015.2480004openalex-snapshot

Toward Vehicle-Assisted Cloud Computing for Smartphones

inferredcites →https://doi.org/10.1109/tvt.2015.2483558openalex-snapshot

Software-Defined Device-to-Device (D2D) Communications in Virtual Wireless Networks With Imperfect Network State Information (NSI)

inferredcites →https://doi.org/10.1109/tvt.2016.2532863openalex-snapshot

Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

inferredcites →https://doi.org/10.1109/tvt.2016.2538461openalex-snapshot

Delay-Optimal Virtualized Radio Resource Scheduling in Software-Defined Vehicular Networks via Stochastic Learning

inferredcites →https://doi.org/10.1109/tvt.2016.2591558openalex-snapshot

Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey

inferredcites →https://doi.org/10.1109/tvt.2017.2751641openalex-snapshot

Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks

inferredcites →https://doi.org/10.1145/1721654.1721672openalex-snapshot

A view of cloud computing

inferredcites →https://doi.org/10.1145/2656877.2656887openalex-snapshot

Named data networking

inferredcites →https://doi.org/10.1609/aaai.v30i1.10295openalex-snapshot

Deep Reinforcement Learning with Double Q-Learning

inferredcites →https://doi.org/10.48550/arxiv.1312.5602openalex-snapshot

Playing Atari with Deep Reinforcement Learning

inferredcites →https://doi.org/10.48550/arxiv.1504.00702openalex-snapshot

End-to-End Training of Deep Visuomotor Policies

inferredcites →https://doi.org/10.48550/arxiv.1508.04186openalex-snapshot

Distributed Deep Q-Learning

inferredcites →https://doi.org/10.48550/arxiv.1509.02971openalex-snapshot

Continuous control with deep reinforcement learning

inferredcites →https://doi.org/10.48550/arxiv.1511.06581openalex-snapshot

Dueling Network Architectures for Deep Reinforcement Learning

inferredcites →https://doi.org/10.48550/arxiv.1603.00748openalex-snapshot

Continuous Deep Q-Learning with Model-based Acceleration

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4072706openalex-extracted

inferred← citeshttps://doi.org/10.2139/ssrn.4604459openalex-extracted

Task Graph Offloading Via Deep Reinforcement Learning in Mobile Edge Computing

inferred← citeshttps://doi.org/10.1002/9781119785873.ch16openalex-snapshot

Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions

inferred← citeshttps://doi.org/10.1002/9781119873747.ch8openalex-snapshot

DRL at the Application and Service Layer

inferred← citeshttps://doi.org/10.1002/9781119892199.ch19openalex-snapshot

Spectrum Challenges in the Internet of Things: State of the Art and Next Steps

inferred← citeshttps://doi.org/10.1002/cpe.5014openalex-snapshot

An opportunistic resource management model to overcome resource‐constraint in the Internet of Things

inferred← citeshttps://doi.org/10.1002/cpe.7741openalex-snapshot

Edge computing: A systematic mapping study

inferred← citeshttps://doi.org/10.1002/ett.4427openalex-snapshot

Machine learning for next‐generation intelligent transportation systems: A survey

inferred← citeshttps://doi.org/10.1002/ett.4792openalex-snapshot

Cooperative caching strategy based mobile vehicle social‐aware in internet of vehicles

inferred← citeshttps://doi.org/10.1002/ett.4842openalex-snapshot

Intelligent multimedia content delivery in 5G/6G networks: A reinforcement learning approach

inferred← citeshttps://doi.org/10.1007/978-3-030-10546-4_4openalex-snapshot

Deep Reinforcement Learning for Mobile Social Networks

inferred← citeshttps://doi.org/10.1007/978-3-030-17513-9_13openalex-snapshot

Improved Flow Awareness by Intelligent Collaborative Sampling in Software Defined Networks

inferred← citeshttps://doi.org/10.1007/978-3-030-19153-5_11openalex-snapshot

DQN Aided Edge Computing in Satellite-Terrestrial Network

inferred← citeshttps://doi.org/10.1007/978-3-030-22971-9_38openalex-snapshot

A Reinforcement Learning Based Task Offloading Scheme for Vehicular Edge Computing Network

inferred← citeshttps://doi.org/10.1007/978-3-030-37337-5_24openalex-snapshot

Cooperative Privacy Preservation for Internet of Vehicles with Mobile Edge Computing

inferred← citeshttps://doi.org/10.1007/978-3-030-38651-1_27openalex-snapshot

Edge Service Migration for Vehicular Networks Based on Multi-agent Deep Reinforcement Learning

inferred← citeshttps://doi.org/10.1007/978-3-030-56827-6_1openalex-snapshot

Introduction

inferred← citeshttps://doi.org/10.1007/978-3-030-56827-6_7openalex-snapshot

Deep Learning Based Autonomous Driving in Vehicular Networks

inferred← citeshttps://doi.org/10.1007/978-3-030-67490-8_17openalex-snapshot

Blockchain- and Deep Learning-Empowered Resource Optimization in Future Cellular Networks, Edge Computing, and IoT: Open Challenges and Current Solutions

inferred← citeshttps://doi.org/10.1007/978-3-030-70183-3_10openalex-snapshot

Edge Analytics and Deep Learning for Sustainable Development

inferred← citeshttps://doi.org/10.1007/978-3-030-88743-8_4openalex-snapshot

Collaborative Computing for Internet of Vehicles

inferred← citeshttps://doi.org/10.1007/978-3-030-96507-5_1openalex-snapshot

Introduction

inferred← citeshttps://doi.org/10.1007/978-3-030-96507-5_4openalex-snapshot

Intelligent Multi-Dimensional Resource Allocation in MVNETs

inferred← citeshttps://doi.org/10.1007/978-3-031-26118-3_8openalex-snapshot

A Blockchain-Based Distributed Machine Learning (BDML) Approach for Resource Allocation in Vehicular Ad-Hoc Networks

inferred← citeshttps://doi.org/10.1007/978-3-031-30101-8_15openalex-snapshot

Artificial Intelligence Advancement for 6G Communication: A Visionary Approach

inferred← citeshttps://doi.org/10.1007/978-3-031-33920-2_9openalex-snapshot

Reinforcement Learning-Based Resource Allocation in Fog Networks

inferred← citeshttps://doi.org/10.1007/978-3-031-49361-4_4openalex-snapshot

An Adaptive, Energy-Efficient DRL-Based and MCMC-Based Caching Strategy for IoT Systems

inferred← citeshttps://doi.org/10.1007/978-3-031-51819-5openalex-snapshot

Digital Twin

inferred← citeshttps://doi.org/10.1007/978-3-031-64076-6_6openalex-snapshot

Efficient Task Offloading in IoV Using DDPG and MEC with RIS Support

inferred← citeshttps://doi.org/10.1007/978-3-031-70606-6_8openalex-snapshot

Technologies for Service Capability Collaboration Interconnection

inferred← citeshttps://doi.org/10.1007/978-3-031-93251-9_6openalex-snapshot

Fast Adaptive Caching Algorithm for Mobile Edge Networks Based on Meta-reinforcement Learning

inferred← citeshttps://doi.org/10.1007/978-3-032-07667-0_5openalex-snapshot

End-Edge-Cloud Orchestration for Collaborative DNN Inference

inferred← citeshttps://doi.org/10.1007/978-3-032-18400-9_17openalex-snapshot

Inference Offloading and Resource Allocation with Large Models

inferred← citeshttps://doi.org/10.1007/978-3-319-32903-1_131-1openalex-snapshot

Secure Routing in Cognitive Radio Vehicular Ad Hoc Networks

inferred← citeshttps://doi.org/10.1007/978-3-319-78262-1_131openalex-snapshot

Secure Routing in Cognitive Radio Vehicular Ad Hoc Networks

inferred← citeshttps://doi.org/10.1007/978-981-15-4412-5_2openalex-snapshot

Background and Literature Survey

inferred← citeshttps://doi.org/10.1007/978-981-15-6186-3_4openalex-snapshot

Artificial Intelligence Applications on Edge

inferred← citeshttps://doi.org/10.1007/978-981-15-6186-3_8openalex-snapshot

Artificial Intelligence for Optimizing Edge

inferred← citeshttps://doi.org/10.1007/978-981-16-7118-0_40openalex-snapshot

Effective Resource Allocation in Network Slicing Using Artificial Intelligence

inferred← citeshttps://doi.org/10.1007/978-981-16-8892-8_59openalex-snapshot

Next Generation Wireless Communication: Facilitated by Machine Learning

inferred← citeshttps://doi.org/10.1007/978-981-19-8350-4_9openalex-snapshot

Cooperative Edge Caching Strategy Based on Mobile Prediction and Social-Aware in Internet of Vehicles

inferred← citeshttps://doi.org/10.1007/978-981-19-9582-8_10openalex-snapshot

Computation Offloading Algorithm Based on Deep Reinforcement Learning and Multi-Task Dependency for Edge Computing

inferred← citeshttps://doi.org/10.1007/978-981-96-1542-1_21openalex-snapshot

Enhancing Delay-Sensitive Task Offloading: A Multi-agent Deep Reinforcement Learning Algorithm for MEC-Based AIoT Systems

inferred← citeshttps://doi.org/10.1007/978-981-96-2186-6_3openalex-snapshot

Task Offloading Scheduling and Privacy Protection Optimization in Vehicular Edge Computing Based on Double Deep Q-Network

inferred← citeshttps://doi.org/10.1007/978-981-97-4390-2_11openalex-snapshot

A Survey of Edge Computing Resource Allocation and Task Scheduling Optimization

inferred← citeshttps://doi.org/10.1007/978-981-99-7545-7_52openalex-snapshot

Multi-agent Deep Reinforcement Learning with Hybrid Action Space for Resource Allocation of Vehicular Networks

inferred← citeshttps://doi.org/10.1007/s10586-022-03817-7openalex-snapshot

Edge resource slicing approaches for latency optimization in AI-edge orchestration

inferred← citeshttps://doi.org/10.1007/s10723-020-09530-2openalex-snapshot

A Survey on the Computation Offloading Approaches in Mobile Edge/Cloud Computing Environment: A Stochastic-based Perspective

inferred← citeshttps://doi.org/10.1007/s10723-021-09568-wopenalex-snapshot

A Computing Offloading Resource Allocation Scheme Using Deep Reinforcement Learning in Mobile Edge Computing Systems

inferred← citeshttps://doi.org/10.1007/s11036-019-01402-8openalex-snapshot

Improved Flow Awareness Among Edge Nodes by Learning-Based Sampling in Software Defined Networks

inferred← citeshttps://doi.org/10.1007/s11036-020-01624-1openalex-snapshot

Vehicular Edge Computing and Networking: A Survey

inferred← citeshttps://doi.org/10.1007/s11265-020-01572-9openalex-snapshot

Cost Optimization of Partial Computation Offloading and Pricing in Vehicular Networks

inferred← citeshttps://doi.org/10.1007/s11432-020-2955-6openalex-snapshot

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

inferred← citeshttps://doi.org/10.1007/s11704-023-1346-3openalex-snapshot

Multi-user reinforcement learning based task migration in mobile edge computing

inferred← citeshttps://doi.org/10.1007/s12083-020-00993-4openalex-snapshot

Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges

inferred← citeshttps://doi.org/10.1007/s12083-022-01353-0openalex-snapshot

DHL: Deep reinforcement learning-based approach for emergency supply distribution in humanitarian logistics

inferred← citeshttps://doi.org/10.1007/s13042-020-01077-8openalex-snapshot

An actor-critic reinforcement learning-based resource management in mobile edge computing systems

inferred← citeshttps://doi.org/10.1016/b978-0-44-330092-9.00014-8openalex-snapshot

Machine/deep-learning-based resource allocation

inferred← citeshttps://doi.org/10.1016/b978-0-44-330092-9.00024-0openalex-snapshot

Bibliography

inferred← citeshttps://doi.org/10.1016/j.adhoc.2022.102981openalex-snapshot

Cost optimization of UAV-MEC network calculation offloading: A multi-agent reinforcement learning method

inferred← citeshttps://doi.org/10.1016/j.array.2022.100136openalex-snapshot

Knowledge-defined networking: Applications, challenges and future work

inferred← citeshttps://doi.org/10.1016/j.comcom.2019.12.054openalex-snapshot

Intelligent resource allocation management for vehicles network: An A3C learning approach

inferred← citeshttps://doi.org/10.1016/j.comcom.2020.05.037openalex-snapshot

“DRL + FL”: An intelligent resource allocation model based on deep reinforcement learning for Mobile Edge Computing

inferred← citeshttps://doi.org/10.1016/j.comcom.2020.07.035openalex-snapshot

A taxonomy of AI techniques for 6G communication networks

inferred← citeshttps://doi.org/10.1016/j.comcom.2021.09.028openalex-snapshot

Deep Q-learning enabled joint optimization of mobile edge computing multi-level task offloading

inferred← citeshttps://doi.org/10.1016/j.comcom.2022.04.006openalex-snapshot

Task offloading in vehicular edge computing networks via deep reinforcement learning

inferred← citeshttps://doi.org/10.1016/j.comcom.2023.05.021openalex-snapshot

Task offloading scheme combining deep reinforcement learning and convolutional neural networks for vehicle trajectory prediction in smart cities

inferred← citeshttps://doi.org/10.1016/j.comcom.2024.03.005openalex-snapshot

A survey on integrated computing, caching, and communication in the cloud-to-edge continuum

inferred← citeshttps://doi.org/10.1016/j.comnet.2019.106904openalex-snapshot

When QoE meets learning: A distributed traffic-processing framework for elastic resource provisioning in HetNets

inferred← citeshttps://doi.org/10.1016/j.comnet.2020.107446openalex-snapshot

Joint task offloading and data caching in mobile edge computing networks

inferred← citeshttps://doi.org/10.1016/j.comnet.2020.107496openalex-snapshot

A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective

inferred← citeshttps://doi.org/10.1016/j.comnet.2021.108291openalex-snapshot

Joint beamforming and user association with reduced CSI signaling in mobile environments: A Deep Q-learning approach

inferred← citeshttps://doi.org/10.1016/j.comnet.2024.110247openalex-snapshot

Cooperative caching algorithm for mobile edge networks based on multi-agent meta reinforcement learning

inferred← citeshttps://doi.org/10.1016/j.comnet.2024.110687openalex-snapshot

Rethinking the mobile edge for vehicular services

inferred← citeshttps://doi.org/10.1016/j.comnet.2024.110791openalex-snapshot

Task offloading strategies for mobile edge computing: A survey

inferred← citeshttps://doi.org/10.1016/j.comnet.2026.112837openalex-snapshot

A hybrid DRL-based framework for secure IIoT resource allocation and digital twin-assisted MEC offloading

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2021.107530openalex-snapshot

IOT-based service migration for connected communities

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110836openalex-snapshot

Prioritized Experience Replay and Four-Trajectory Exploration - Multi-agents Proximal Policy Optimization: Multi-agents Reinforcement Learning-Based service migration strategy under vehicular edge computing

inferred← citeshttps://doi.org/10.1016/j.cose.2022.102602openalex-snapshot

Secure deduplication schemes for content delivery in mobile edge computing

inferred← citeshttps://doi.org/10.1016/j.dcan.2021.10.007openalex-snapshot

Machine learning in vehicular networking: An overview

inferred← citeshttps://doi.org/10.1016/j.dcan.2024.12.006openalex-snapshot

A knowledge graph-based reinforcement learning approach for cooperative caching in MEC-enabled heterogeneous networks

inferred← citeshttps://doi.org/10.1016/j.dcan.2025.06.005openalex-snapshot

Lightweight deep reinforcement learning for dynamic resource allocation in vehicular edge computing

inferred← citeshttps://doi.org/10.1016/j.dsp.2021.103014openalex-snapshot

An overview of deep reinforcement learning for spectrum sensing in cognitive radio networks

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.123622openalex-snapshot

Deadline-aware task offloading in vehicular networks using deep reinforcement learning

inferred← citeshttps://doi.org/10.1016/j.future.2018.06.017openalex-snapshot

Proactive caching for edge computing-enabled industrial mobile wireless networks

inferred← citeshttps://doi.org/10.1016/j.future.2020.01.036openalex-snapshot

Classification of optimization problems in fog computing

inferred← citeshttps://doi.org/10.1016/j.future.2024.04.034openalex-snapshot

Task graph offloading via deep reinforcement learning in mobile edge computing

inferred← citeshttps://doi.org/10.1016/j.hcc.2024.100277openalex-snapshot

Learning-based cooperative content caching and sharing for multi-layer vehicular networks

inferred← citeshttps://doi.org/10.1016/j.icte.2022.07.002openalex-snapshot

Enabling technologies for AI empowered 6G massive radio access networks

inferred← citeshttps://doi.org/10.1016/j.inffus.2021.11.018openalex-snapshot

Information fusion for edge intelligence: A survey

inferred← citeshttps://doi.org/10.1016/j.iot.2022.100572openalex-snapshot

FogAI: An AI-supported fog controller for Next Generation IoT

inferred← citeshttps://doi.org/10.1016/j.iot.2024.101392openalex-snapshot

Fuzzy-based task offloading in Internet of Vehicles (IoV) edge computing for latency-sensitive applications

inferred← citeshttps://doi.org/10.1016/j.jestch.2024.101854openalex-snapshot

Deep reinforcement learning approach for multi-hop task offloading in vehicular edge computing

inferred← citeshttps://doi.org/10.1016/j.jii.2020.100189openalex-snapshot

Method towards reconstructing collaborative business processes with cloud services using evolutionary deep Q-learning

inferred← citeshttps://doi.org/10.1016/j.jiixd.2024.02.005openalex-snapshot

Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing

inferred← citeshttps://doi.org/10.1016/j.jksuci.2022.10.026openalex-snapshot

Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing

inferred← citeshttps://doi.org/10.1016/j.jnca.2024.103941openalex-snapshot

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

inferred← citeshttps://doi.org/10.1016/j.phycom.2018.12.016openalex-snapshot

Cache-aided multi-hop UAV-relaying networks

inferred← citeshttps://doi.org/10.1016/j.phycom.2025.102683openalex-snapshot

Fair channel allocation in IEEE 802.11p for high throughput and low-latency

inferred← citeshttps://doi.org/10.1016/j.rico.2022.100163openalex-snapshot

Status update frequency optimization for In-vehicle LAN route based on deep Q-learning

inferred← citeshttps://doi.org/10.1016/j.rineng.2025.106100openalex-snapshot

Edge-driven resource allocation in vehicular networks: A joint framework of multi-agent reinforcement learning and demand-supply predictive modeling

inferred← citeshttps://doi.org/10.1016/j.simpat.2024.103025openalex-snapshot

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

inferred← citeshttps://doi.org/10.1016/j.suscom.2021.100555openalex-snapshot

Decentralized adaptive resource-aware computation offloading & caching for multi-access edge computing networks

inferred← citeshttps://doi.org/10.1016/j.tcs.2024.114462openalex-snapshot

Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency

inferred← citeshttps://doi.org/10.1016/j.techfore.2020.120102openalex-snapshot

Cybersecurity threats in the auto industry: Tensions in the knowledge environment

inferred← citeshttps://doi.org/10.1016/j.trc.2022.103987openalex-snapshot

Modified DDPG car-following model with a real-world human driving experience with CARLA simulator

inferred← citeshttps://doi.org/10.1016/j.vehcom.2021.100353openalex-snapshot

A survey of data dissemination schemes in vehicular named data networking

inferred← citeshttps://doi.org/10.1016/j.vehcom.2021.100398openalex-snapshot

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

inferred← citeshttps://doi.org/10.1016/j.vehcom.2022.100476openalex-snapshot

A journey towards fully autonomous driving - fueled by a smart communication system

inferred← citeshttps://doi.org/10.1016/j.vehcom.2022.100512openalex-snapshot

A survey on Vehicular Fog Computing: Current state-of-the-art and future directions

inferred← citeshttps://doi.org/10.1016/j.vehcom.2022.100569openalex-snapshot

Artificial Intelligence and Machine Learning as key enablers for V2X communications: A comprehensive survey

inferred← citeshttps://doi.org/10.1016/j.vehcom.2024.100806openalex-snapshot

Fully distributed multi-agent processing strategy applied to vehicular networks

inferred← citeshttps://doi.org/10.1016/j.vehcom.2024.100844openalex-snapshot

Joint optimization for service-caching, computation-offloading, and UAVs flight trajectories over rechargeable UAV-aided MEC using hierarchical multi-agent deep reinforcement learning

inferred← citeshttps://doi.org/10.1016/j.vehcom.2024.100856openalex-snapshot

Boosting vehicular connectivity through resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization

inferred← citeshttps://doi.org/10.1016/j.vehcom.2025.100895openalex-snapshot

Resource allocation strategy for vehicular communication networks based on multi-agent deep reinforcement learning

inferred← citeshttps://doi.org/10.1038/s41598-025-15982-xopenalex-snapshot

Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning

inferred← citeshttps://doi.org/10.1038/s41598-026-44686-zopenalex-snapshot

A performance-optimized V2V task offloading framework for real-time vehicular communication

inferred← citeshttps://doi.org/10.1049/cmu2.12657openalex-snapshot

Vehicle clustering based edge caching scheme in internet of vehicles

inferred← citeshttps://doi.org/10.1049/cth2.12754openalex-snapshot

Aircraft human‐machine interaction assistant design: A novel multimodal data processing and application framework

inferred← citeshttps://doi.org/10.1049/iet-com.2020.0451openalex-snapshot

Deep reinforcement learning based optimal channel selection for cognitive radio vehicular ad‐hoc network

inferred← citeshttps://doi.org/10.1049/iet-com.2020.0630openalex-snapshot

Congestion‐aware adaptive decentralised computation offloading and caching for multi‐access edge computing networks

inferred← citeshttps://doi.org/10.1049/itr2.12489openalex-snapshot

Joint resource allocation and security redundancy for autonomous driving based on deep reinforcement learning algorithm

inferred← citeshttps://doi.org/10.1051/wujns/2023281035openalex-snapshot

Adversarial Example Generation Method Based on Sensitive Features

inferred← citeshttps://doi.org/10.1080/03081079.2026.2626399openalex-snapshot

Reliable vehicle edge computing-assisted unmanned aerial vehicle computation offloading: an improved online deep reinforcement learning approach

inferred← citeshttps://doi.org/10.1109/6gsummit49458.2020.9083926openalex-snapshot

DDPG-Based Radio Resource Management for User Interactive Mobile Edge Networks

inferred← citeshttps://doi.org/10.1109/access.2018.2809581openalex-snapshot

Deep Reinforcement Learning Based Dynamic Channel Allocation Algorithm in Multibeam Satellite Systems

inferred← citeshttps://doi.org/10.1109/access.2018.2881964openalex-snapshot

Deep Reinforcement Learning for Resource Management in Network Slicing

inferred← citeshttps://doi.org/10.1109/access.2018.2882000openalex-snapshot

Resource Allocation Algorithm With Multi-Platform Intelligent Offloading in D2D-Enabled Vehicular Networks

inferred← citeshttps://doi.org/10.1109/access.2019.2891073openalex-snapshot

Artificial Intelligence for Vehicle-to-Everything: A Survey

inferred← citeshttps://doi.org/10.1109/access.2019.2915959openalex-snapshot

Computation Offloading for Mobile Edge Computing Enabled Vehicular Networks

inferred← citeshttps://doi.org/10.1109/access.2019.2923021openalex-snapshot

Autonomous Cache Resource Slicing and Content Placement at Virtualized Mobile Edge Network

inferred← citeshttps://doi.org/10.1109/access.2019.2924030openalex-snapshot

Demand Response Management for Industrial Facilities: A Deep Reinforcement Learning Approach

inferred← citeshttps://doi.org/10.1109/access.2019.2924798openalex-snapshot

Electromagnetic Signal Classification Based on Deep Sparse Capsule Networks

inferred← citeshttps://doi.org/10.1109/access.2019.2937490openalex-snapshot

MIMO Signal Multiplexing and Detection Based on Compressive Sensing and Deep Learning

inferred← citeshttps://doi.org/10.1109/access.2019.2938410openalex-snapshot

Cell Fault Management Using Machine Learning Techniques

inferred← citeshttps://doi.org/10.1109/access.2019.2942052openalex-snapshot

Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach

inferred← citeshttps://doi.org/10.1109/access.2019.2950898openalex-snapshot

Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach

inferred← citeshttps://doi.org/10.1109/access.2019.2958175openalex-snapshot

Green Internet of Vehicles: Architecture, Enabling Technologies, and Applications

inferred← citeshttps://doi.org/10.1109/access.2019.2963051openalex-snapshot

Contract-Based Computing Resource Management via Deep Reinforcement Learning in Vehicular Fog Computing

inferred← citeshttps://doi.org/10.1109/access.2020.2968595openalex-snapshot

Deep Deterministic Policy Gradient (DDPG)-Based Resource Allocation Scheme for NOMA Vehicular Communications

inferred← citeshttps://doi.org/10.1109/access.2020.2971090openalex-snapshot

MIGRATE: Mobile Device Virtualisation Through State Transfer

inferred← citeshttps://doi.org/10.1109/access.2020.2989870openalex-snapshot

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

inferred← citeshttps://doi.org/10.1109/access.2020.2991057openalex-snapshot

Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

inferred← citeshttps://doi.org/10.1109/access.2020.3001277openalex-snapshot

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

inferred← citeshttps://doi.org/10.1109/access.2020.3010329openalex-snapshot

Cooperative Edge Caching: A Multi-Agent Deep Learning Based Approach

inferred← citeshttps://doi.org/10.1109/access.2020.3013714openalex-snapshot

Joint Connection Modes, Uplink Paths and Computational Tasks Assignment for Unmanned Mining Vehicles’ Energy Saving in Mobile Edge Computing Networks

inferred← citeshttps://doi.org/10.1109/access.2020.3014427openalex-snapshot

Deep Q-Network Learning Based Downlink Resource Allocation for Hybrid RF/VLC Systems

inferred← citeshttps://doi.org/10.1109/access.2020.3027707openalex-snapshot

Cognitive Caching at the Edges for Mobile Social Community Networks: A Multi-Agent Deep Reinforcement Learning Approach

inferred← citeshttps://doi.org/10.1109/access.2020.3033828openalex-snapshot

Computation Offloading for Vehicular Environments: A Survey

inferred← citeshttps://doi.org/10.1109/access.2020.3038605openalex-snapshot

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

inferred← citeshttps://doi.org/10.1109/access.2021.3051411openalex-snapshot

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

inferred← citeshttps://doi.org/10.1109/access.2021.3092381openalex-snapshot

Deep Reinforcement Learning for Task Offloading in Edge Computing Assisted Power IoT

inferred← citeshttps://doi.org/10.1109/access.2021.3118731openalex-snapshot

Implementing Practical DNN-Based Object Detection Offloading Decision for Maximizing Detection Performance of Mobile Edge Devices

inferred← citeshttps://doi.org/10.1109/access.2021.3131339openalex-snapshot

DQN Learning Based Defense Against Smart Primary User Emulation Attacks in Cooperative Sensing Systems

inferred← citeshttps://doi.org/10.1109/access.2022.3140719openalex-snapshot

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

inferred← citeshttps://doi.org/10.1109/access.2023.3269980openalex-snapshot

A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends

inferred← citeshttps://doi.org/10.1109/access.2024.3483455openalex-snapshot

Enhancing Edge-Linked Caching in Information-Centric Networking for Internet of Things With Deep Reinforcement Learning

inferred← citeshttps://doi.org/10.1109/access.2024.3498911openalex-snapshot

Cooperative Dew Computing for Computational Offloading in Healthcare Monitoring

inferred← citeshttps://doi.org/10.1109/aiars63200.2024.00040openalex-snapshot

Automatic Driving Decision Model Based on Deep Reinforcement Learning Algorithms in Complex Environments

inferred← citeshttps://doi.org/10.1109/ants52808.2021.9936947openalex-snapshot

Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding

inferred← citeshttps://doi.org/10.1109/asiancon62057.2024.10837855openalex-snapshot

Optimal Bandwidth Utilization Techniques for IoT Device Networks using Edge Computing

inferred← citeshttps://doi.org/10.1109/bmsb58369.2023.10211424openalex-snapshot

Resource Allocation Using Deep Reinforcement Learning in GEO Multibeam Satellite System

inferred← citeshttps://doi.org/10.1109/cac59555.2023.10451920openalex-snapshot

V2V Composite Routing Algorithm for Internet of Vehicles Based on Improved Q-Learning

inferred← citeshttps://doi.org/10.1109/case48305.2020.9216876openalex-snapshot

The Ecological Model and Global optimization Algorithm for Service Internet

inferred← citeshttps://doi.org/10.1109/ccgrid49817.2020.00-34openalex-snapshot

An efficient service dispersal mechanism for fog and cloud computing using deep reinforcement learning

inferred← citeshttps://doi.org/10.1109/cis58238.2022.00099openalex-snapshot

Load balance and time delay efficient algorithm for VNF service chain deployment based on DQN

inferred← citeshttps://doi.org/10.1109/compauto55930.2022.00018openalex-snapshot

Application Of Intelligent Construction In Architectural Decoration Industry

inferred← citeshttps://doi.org/10.1109/compauto55930.2022.00032openalex-snapshot

A Vehicle-Cloud Collaborative Strategy for State of Energy Estimation based on CNN-LSTM Networks

inferred← citeshttps://doi.org/10.1109/comst.2017.2758763openalex-snapshot

Integration of Networking, Caching, and Computing in Wireless Systems: A Survey, Some Research Issues, and Challenges

inferred← citeshttps://doi.org/10.1109/comst.2019.2916583openalex-snapshot

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

inferred← citeshttps://doi.org/10.1109/comst.2019.2924243openalex-snapshot

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

inferred← citeshttps://doi.org/10.1109/comst.2020.2964534openalex-snapshot

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

inferred← citeshttps://doi.org/10.1109/comst.2020.2965856openalex-snapshot

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

inferred← citeshttps://doi.org/10.1109/comst.2020.2970550openalex-snapshot

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

inferred← citeshttps://doi.org/10.1109/comst.2020.2975048openalex-snapshot

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

inferred← citeshttps://doi.org/10.1109/comst.2020.2988367openalex-snapshot

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

inferred← citeshttps://doi.org/10.1109/comst.2020.3020854openalex-snapshot

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

inferred← citeshttps://doi.org/10.1109/comst.2021.3065237openalex-snapshot

A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges

inferred← citeshttps://doi.org/10.1109/comst.2021.3073036openalex-snapshot

Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey

inferred← citeshttps://doi.org/10.1109/comst.2021.3089688openalex-snapshot

Comprehensive Survey on Machine Learning in Vehicular Network: Technology, Applications and Challenges

inferred← citeshttps://doi.org/10.1109/comst.2022.3149714openalex-snapshot

A Survey of Collaborative Machine Learning Using 5G Vehicular Communications

inferred← citeshttps://doi.org/10.1109/comst.2022.3224279openalex-snapshot

Reinforcement Learning-Based Physical Cross-Layer Security and Privacy in 6G

inferred← citeshttps://doi.org/10.1109/comst.2023.3315374openalex-snapshot

A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications

inferred← citeshttps://doi.org/10.1109/cyber59472.2023.10256477openalex-snapshot

Learning to Hybrid Offload in Space-Air-Ground Integrated Mobile Edge Computing for IoT Networks

inferred← citeshttps://doi.org/10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00090openalex-snapshot

Edge Computing: A Systematic Mapping Study

inferred← citeshttps://doi.org/10.1109/edge53862.2021.00019openalex-snapshot

VECFrame: A Vehicular Edge Computing Framework for Connected Autonomous Vehicles

inferred← citeshttps://doi.org/10.1109/eeccis62037.2024.10839871openalex-snapshot

Scalable Resource Management for Latency-Sensitive Applications in Edge Computing

inferred← citeshttps://doi.org/10.1109/eucnc/6gsummit54941.2022.9815597openalex-snapshot

Reducing Storage and Communication Latencies in Vehicular Edge Cloud

inferred← citeshttps://doi.org/10.1109/fmec54266.2021.9732562openalex-snapshot

Distributed Spatial-Temporal Demand and Topology Aware Resource Provisioning for Edge Cloud Services

inferred← citeshttps://doi.org/10.1109/globecom38437.2019.9013366openalex-snapshot

Joint Optimization of Networking and Computing Resources for Green M2M Communications Based on DRL

inferred← citeshttps://doi.org/10.1109/globecom38437.2019.9013434openalex-snapshot

Joint Resource Optimization for Adaptive Multimedia Services in MEC-Based Vehicular Networks

inferred← citeshttps://doi.org/10.1109/globecom38437.2019.9013486openalex-snapshot

Towards Big Data Processing in IoT: Network Management for Online Edge Data Processing

inferred← citeshttps://doi.org/10.1109/globecom38437.2019.9014294openalex-snapshot

Deep Reinforcement Learning-Based Dynamic Service Migration in Vehicular Networks

inferred← citeshttps://doi.org/10.1109/globecom42002.2020.9322537openalex-snapshot

Resource Allocation based on Graph Neural Networks in Vehicular Communications

inferred← citeshttps://doi.org/10.1109/globecom42002.2020.9348271openalex-snapshot

Bring Intelligence among Edges: A Blockchain-Assisted Edge Intelligence Approach

inferred← citeshttps://doi.org/10.1109/globecom52923.2024.10901701openalex-snapshot

A Quantum Temporal Difference Learning Method Based on Quantum World Model

inferred← citeshttps://doi.org/10.1109/globecom52923.2024.10901756openalex-snapshot

A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

inferred← citeshttps://doi.org/10.1109/globecom54140.2023.10436901openalex-snapshot

Task Offloading and Resource Allocation for SLAM Back-End Optimization: A Rewardless Active Inference Approach

inferred← citeshttps://doi.org/10.1109/globecom54140.2023.10437017openalex-snapshot

Knowledge-Driven Resource Allocation for Efficient Task Offloading in Connected Autonomous Vehicles

inferred← citeshttps://doi.org/10.1109/globecom54140.2023.10437345openalex-snapshot

Resource Allocation for Cognitive Radio Inspired Non-Orthogonal Multiple Access Networks: A Quantum Soft Actor-Critic Method

showing the first 200 of 566

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.