Wireless Power Transfer Meets Semantic Communication for Resource-Constrained IoT Networks: A Joint Transmission Mode Selection and Resource Management Approach

In this work, we consider the integration of energy harvesting (EH) and semantic communication strategies in resource-constrained Internet of Things (IoT) systems. The system empowers IoT devices to harvest energy from a base station, utilizing this harvested energy for the extraction and transmission of semantic information (e.g., scene graphs). To maximize the total transmission of image data or scene graphs to the central station, we formulate a comprehensive problem that jointly optimizes the EH duration, original image selection, transmit power, and channel allocation to IoT devices. The challenges arising from the dynamic environments and uncertain system parameters are effectively tackled by policy-based deep reinforcement learning algorithms, i.e., advantage actor-critic (A2C) and proximal policy optimization (PPO). Simulation results are implemented on the real data set clearly showing the superior performance achieved by our proposed algorithms compared to the baseline schemes. Notably, our approach enables IoT devices to transmit a greater number of original images and scene graphs with increased triplets to the central station, as highlighted in the simulation outcomes. This phenomenon showcases the potential of our strategy to enhance the capabilities of IoT systems in dynamic environments.