  Integrated sensing, computation, and communication (ISCC) has been recently
considered as a promising technique for beyond 5G systems. In ISCC systems, the
competition for communication and computation resources between sensing tasks
for ambient intelligence and computation tasks from mobile devices becomes an
increasingly challenging issue. To address it, we first propose an efficient
sensing framework with a novel action detection module. In this module, a
threshold is used for detecting whether the sensing target is static and thus
the overhead can be reduced. Subsequently, we mathematically analyze the
sensing performance of the proposed framework and theoretically prove its
effectiveness with the help of the sampling theorem. Based on sensing
performance models, we formulate a sensing performance maximization problem
while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve
it, we propose an optimal resource allocation strategy, in which the minimum
resource is allocated to computation tasks, and the rest is devoted to the
sensing task. Besides, a threshold selection policy is derived and the results
further demonstrate the necessity of the proposed sensing framework. Finally, a
real-world test of action recognition tasks based on USRP B210 is conducted to
verify the sensing performance analysis. Extensive experiments demonstrate the
performance improvement of our proposal by comparing it with some benchmark
schemes.
