:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Dey, Debasmita, Ghosh, Nirnay
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Networking and Internet Architecture
Accesso online:	https://arxiv.org/abs/2403.04416
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

How Resilient is QUIC to Security and Privacy Attacks?
di: Sengupta, Jayasree, et al.
Pubblicazione: (2024)

Intelligent Routing as a Service (iRaaS)
di: Ghosh, Saptarshi, et al.
Pubblicazione: (2024)

Learning Automata-Based Enhancements to RPL: Pioneering Load-Balancing and Traffic Management in IoT
di: Homaei, Mohammadhossein
Pubblicazione: (2024)

A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols
di: Voigt, Johannes, et al.
Pubblicazione: (2025)

RPLUW/M: Enabling RPL on the Internet of Underwater Things
di: Homaei, Mohammadhossein
Pubblicazione: (2024)

Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networks
di: Tarif, Mehran, et al.
Pubblicazione: (2024)

LiSec-RTF: Reinforcing RPL Resilience Against Routing Table Falsification Attack in 6LoWPAN
di: Goel, Shefali, et al.
Pubblicazione: (2025)

A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols in Mobile Networks
di: Gu, Peter J., et al.
Pubblicazione: (2024)

Li-MSD: A lightweight mitigation solution for DAO insider attack in RPL-based IoT
di: Verma, Abhishek, et al.
Pubblicazione: (2024)

Probabilistic Trust-Based Enhancement for simultaneous transmission in AOMDV Routing Protocol
di: Mishra, Nikhil
Pubblicazione: (2024)

Explainable Multi-Agent Reinforcement Learning for Extended Reality Codec Adaptation
di: Iturria-Rivera, Pedro Enrique, et al.
Pubblicazione: (2024)

Intelligible Protocol Learning for Resource Allocation in 6G O-RAN Slicing
di: Rezazadeh, Farhad, et al.
Pubblicazione: (2023)

Collaborative Optimization of Wireless Communication and Computing Resource Allocation based on Multi-Agent Federated Weighting Deep Reinforcement Learning
di: Wu, Junjie, et al.
Pubblicazione: (2024)

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving
di: Avanzi, Giacomo, et al.
Pubblicazione: (2025)

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey
di: Liu, Yinqiu, et al.
Pubblicazione: (2025)

Multi-Agent Deep Reinforcement Learning for Optimized Multi-UAV Coverage and Power-Efficient UE Connectivity
di: Cai, Xuli, et al.
Pubblicazione: (2025)

Trusted Routing for Blockchain-Enabled Low-Altitude Intelligent Networks
di: He, Sijie, et al.
Pubblicazione: (2025)

Applications of Multi-Agent Deep Reinforcement Learning Communication in Network Management: A Survey
di: Pi, Yue, et al.
Pubblicazione: (2024)

A Multi-Agent Reinforcement Learning Scheme for SFC Placement in Edge Computing Networks
di: Li, Congzhou, et al.
Pubblicazione: (2024)

AgentVNE: LLM-Augmented Graph Reinforcement Learning for Affinity-Aware Multi-Agent Placement in Edge Agentic AI
di: Zheng, Runze, et al.
Pubblicazione: (2026)

LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergence
di: Tan, Renxuan, et al.
Pubblicazione: (2025)

Model Context Protocol-based Internet of Experts For Wireless Environment-aware LLM Agents
di: Liu, Zongxi, et al.
Pubblicazione: (2025)

Designing, Developing, and Validating Network Intelligence for Scaling in Service-Based Architectures based on Deep Reinforcement Learning
di: Soto, Paola, et al.
Pubblicazione: (2024)

Intelligent Load Balancing Systems using Reinforcement Learning System
di: Singh, Raju
Pubblicazione: (2025)

OpenCLAW-Nexus: A Self-Reinforcing Trust Framework for Byzantine-Resilient Decentralized Federated Learning
di: Jia, Wenyang, et al.
Pubblicazione: (2026)

Energy-Efficient Routing Algorithm for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach
di: Soltani, Parham, et al.
Pubblicazione: (2025)

DRAMA: A Dynamic Packet Routing Algorithm using Multi-Agent Reinforcement Learning with Emergent Communication
di: Zhang, Wang, et al.
Pubblicazione: (2025)

NetWorld: Communication-Based Diffusion World Model for Multi-Agent Reinforcement Learning in Wireless Networks
di: Meng, Kechen, et al.
Pubblicazione: (2026)

LLM Agent Communication Protocol (LACP) Requires Urgent Standardization: A Telecom-Inspired Protocol is Necessary
di: Li, Xin, et al.
Pubblicazione: (2025)

GLo-MAPPO: Multi-Agent Deep Reinforcement Learning for Energy-Efficient UAV-Assisted LoRa Networks
di: Ahmed, Abdullahi Isa, et al.
Pubblicazione: (2025)

Mobility-Aware Resource Allocation for mmWave IAB Networks: A Multi-Agent Reinforcement Learning Approach
di: Zhang, Bibo, et al.
Pubblicazione: (2022)

Agent Communications toward Agentic AI at Edge -- A Case Study of the Agent2Agent Protocol
di: Duan, Qiang, et al.
Pubblicazione: (2025)

CP-AgentNet: Autonomous and Explainable Communication Protocol Design Using Generative Agents
di: Kwon, Dae Cheol, et al.
Pubblicazione: (2025)

Agent-OSI: A Layered Protocol Stack Toward a Decentralized Internet of Agents
di: Xu, Wenxin, et al.
Pubblicazione: (2026)

Predictive Importance Sampling Based Coverage Verification for Multi-UAV Trajectory Planning
di: Ghosh, Snehashish, et al.
Pubblicazione: (2026)

Extended Reality (XR) Codec Adaptation in 5G using Multi-Agent Reinforcement Learning with Attention Action Selection
di: Iturria-Rivera, Pedro Enrique, et al.
Pubblicazione: (2024)

Efficient Twin Migration in Vehicular Metaverses: Multi-Agent Split Deep Reinforcement Learning with Spatio-Temporal Trajectory Generation
di: Chen, Junlong, et al.
Pubblicazione: (2025)

Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning
di: Sani, Meisam Sharifi, et al.
Pubblicazione: (2025)

IE-RAP: An Intelligence and Efficient Reader Anti-Collision Protocol for Dense RFID Networks
di: Rezaei, Hadiseh, et al.
Pubblicazione: (2026)

Adaptive Vision-Based Coverage Optimization in Mobile Wireless Sensor Networks: A Multi-Agent Deep Reinforcement Learning Approach
di: Soltani, Parham, et al.
Pubblicazione: (2025)