Semantic-Aware UAV Command and Control for Efficient IoT Data Collection

TL;DR

This paper introduces a semantic-aware UAV control framework using DeepJSCC and reinforcement learning to optimize IoT image data collection, improving coverage and image quality under resource constraints.

Contribution

It presents a novel integration of semantic communication with UAV control via DDQN to enhance IoT data collection efficiency.

Findings

Outperforms baseline methods in device coverage.

Achieves higher semantic image reconstruction quality.

Effectively balances UAV trajectory and data quality.

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler technology for data collection from Internet of Things (IoT) devices. However, effective data collection is challenged by resource constraints and the need for real-time decision-making. In this work, we propose a novel framework that integrates semantic communication with UAV command-and-control (C&C) to enable efficient image data collection from IoT devices. Each device uses Deep Joint Source-Channel Coding (DeepJSCC) to generate a compact semantic latent representation of its image to enable image reconstruction even under partial transmission. A base station (BS) controls the UAV's trajectory by transmitting acceleration commands. The objective is to maximize the average quality of reconstructed images by maintaining proximity to each device for a sufficient duration within a fixed time horizon. To address the challenging trade-off and account for delayed C&C signals, we model the problem as a Markov Decision Process and propose a Double Deep Q-Learning (DDQN)-based adaptive flight policy. Simulation results show that our approach outperforms baseline methods such as greedy and traveling salesman algorithms, in both device coverage and semantic reconstruction quality.