  The development of emerging applications, such as autonomous transportation
systems, are expected to result in an explosive growth in mobile data traffic.
As the available spectrum resource becomes more and more scarce, there is a
growing need for a paradigm shift from Shannon's Classical Information Theory
(CIT) to semantic communication (SemCom). Specifically, the former adopts a
"transmit-before-understanding" approach while the latter leverages artificial
intelligence (AI) techniques to "understand-before-transmit", thereby
alleviating bandwidth pressure by reducing the amount of data to be exchanged
without negating the semantic effectiveness of the transmitted symbols.
However, the semantic extraction (SE) procedure incurs costly computation and
storage overheads. In this article, we introduce an edge-driven training,
maintenance, and execution of SE. We further investigate how edge intelligence
can be enhanced with SemCom through improving the generalization capabilities
of intelligent agents at lower computation overheads and reducing the
communication overhead of information exchange. Finally, we present a case
study involving semantic-aware resource optimization for the wireless powered
Internet of Things (IoT).
