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Found 8 papers, 1 papers with code
A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles
We propose a convolutional autoencoder (CAE) that performs the semantic encoding of the road traffic signs.
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning for Digital Twins
Digital twin (DT) platforms are increasingly regarded as a promising technology for controlling, optimizing, and monitoring complex engineering systems such as next-generation wireless networks.
Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models
In this paper, we present a novel learning-based framework that estimates the traffic arrival of IoT devices based on Markovian events.
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach
In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices.
LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case
This paper presents a detailed description of the Smart Campus dataset based on LoRaWAN.
Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge
We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption.
A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks
Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion.
A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory
A Coupled Markov Modulated Poisson Process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques.
