  In the era of 6G, with compelling visions of intelligent transportation
systems and digital twins, remote surveillance is poised to become a ubiquitous
practice. Substantial data volume and frequent updates present challenges in
wireless networks. To address these challenges, we propose a novel agent-driven
generative semantic communication (A-GSC) framework based on reinforcement
learning. In contrast to the existing research on semantic communication
(SemCom), which mainly focuses on either semantic extraction or semantic
sampling, we seamlessly integrate both by jointly considering the intrinsic
attributes of source information and the contextual information regarding the
task. Notably, the introduction of generative artificial intelligence (GAI)
enables the independent design of semantic encoders and decoders. In this work,
we develop an agent-assisted semantic encoder with cross-modality capability,
which can track the semantic changes, channel condition, to perform adaptive
semantic extraction and sampling. Accordingly, we design a semantic decoder
with both predictive and generative capabilities, consisting of two tailored
modules. Moreover, the effectiveness of the designed models has been verified
using the UA-DETRAC dataset, demonstrating the performance gains of the overall
A-GSC framework in both energy saving and reconstruction accuracy.
