Problem
Research questions and friction points this paper is trying to address.
UAV
IoT data collection
semantic communication
image reconstruction
command and control
Semantic-Aware UAV Command and Control for Efficient IoT Data Collection
🤖 AI Summary
This work addresses the challenge of image acquisition in unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) systems under stringent resource constraints and real-time requirements. It pioneers the integration of semantic communication with UAV control by proposing a delay-aware, semantics-driven flight strategy. The approach leverages DeepJSCC to generate compact semantic representations of images and formulates UAV trajectory optimization as a Markov decision process, solved via a delay-aware Double Deep Q-Network (Double DQN) that adaptively balances coverage and timeliness. By jointly optimizing semantic fidelity and flight dynamics, the proposed method significantly enhances semantic image reconstruction quality while maintaining high device coverage, outperforming baseline strategies such as greedy and traveling salesman algorithms.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Semantic Communication
DeepJSCC
UAV Trajectory Optimization
Double Deep Q-Learning
IoT Data Collection
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