The sensing and communication layers are both integral parts of the Internet-of-Things. The vast majority of recent studies on sensory status update treat the information sensing and sensory data communication problems separately (i.e., a decoupled approach) and optimize specific latency metrics such as age of information relying on simplified models of communication networks or sensory traffic. In this paper, we propose a deeply integrated sensing and communication scheduling (S2) framework based on status-error-triggered update, focusing specifically on multiaccess wireless networks. We first analyze a motivating example consisting of two-state Markov sensors, showing that when both optimized, S2 outperforms the decoupled approach significantly by capturing the key sensory status variation. For sensors with random-walk state transitions, whose scaling limit is Wiener processes, the closed-form Whittle��s index with arbitrary status tracking error functions is presented and indexability established. Furthermore, a mean-field approach is applied such that the decentralized status update medium access control design is solved explicitly, for both homogeneous nodes and heterogeneous nodes in terms of status transition behaviors. In addition to numerical results which validate the optimality of the proposed S2 scheme and its advantage over the decoupled approach, a potential application of dynamic Channel State Information (CSI) update is presented, with CSI generated by a commercial ray-tracing simulator.