NS2-VEHICULAR TECHNOLOGY 2022-2023
An IEEE Master’s is a solid choice for tech enthusiasts. It delves deep into cutting-edge topics like artificial intelligence, wireless communication, and robotics. The program is all about hands-on experience, letting you apply theoretical knowledge to real-world problems. You’ll be rubbing shoulders with top-notch professionals, expanding your network in the tech realm. The curriculum is dynamic, keeping up with the ever-evolving tech landscape. From coding to advanced algorithms, it’s a tech geek’s paradise. Plus, you get to work on cool projects that can potentially shape the future. No BS, just straight-up tech excellence. If you’re all about pushing boundaries and want to level up your game, an IEEE Master’s is where it’s at.
NS2-VEHICULAR TECHNOLOGY 2022-2023
| TITLE | DOWNLOAD |
|---|---|
| A Comprehensive Review of Computing Paradigms, Enabling Computation Offloading and Task Execution in Vehicular Networks | PDF/DOC |
| A Conditional Privacy-Preserving Certificateless Aggregate Signature Scheme in the Standard Model for VANETs | PDF/DOC |
| A Novel Blockchain Based Secured and QoS Aware IoT Vehicular Network in Edge Cloud Computing | PDF/DOC |
| A novelty of Hypergraph Clustering Model (HGCM) for Urban Scenario in VANET | PDF/DOC |
| A Survey of Vehicular Network Systems for Road Traffic Management | PDF/DOC |
| An Intelligent Machine Learning Based Routing Scheme for VANET | PDF/DOC |
| Cauchy Density-based Algorithm for VANETs Clustering in 3D Road Environments | PDF/DOC |
| CTMF Context-Aware Trust Management Framework for Internet of Vehicles | PDF/DOC |
| Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network | PDF/DOC |
| Data Dissemination in VANETs Using Clustering and Probabilistic Forwarding Based on Adaptive Jumping Multi-Objective Firefly Optimization | PDF/DOC |
| Energy and Age Pareto Optimal Trajectories in UAVassisted Wireless Data Collection | PDF/DOC |
| A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles | PDF/DOC |
| A Novel Deep Reinforcement Learning based Relay Selection for Broadcasting in Vehicular Ad hoc Networks | PDF/DOC |
| Communication-efficient Coordinated RSS-based Distributed Passive Localization via Drone Cluster | PDF/DOC |
| Detecting Sybil Attacks using Proofs of Work and Location in VANETs | PDF/DOC |
| Enhanced Wi-Fi RTT Ranging A Sensor-Aided Learning Approach | PDF/DOC |
| Impact of Block Data Components on the Performance of Blockchain-based VANET Implemented on Hyperledger Fabric | PDF/DOC |
| Index Coded – NOMA in Vehicular Ad Hoc Networks | PDF/DOC |
| Machine_Learning_Based_Misbehaviour_Detection_in_VA NET_Using_Consecutive_BSM_Approach | PDF/DOC |
| Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric LearMisbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learningning | PDF/DOC |