  WiFi-enabled Internet-of-Things (IoT) devices are evolving from mere
communication devices to sensing instruments, leveraging Channel State
Information (CSI) extraction capabilities. Nevertheless, resource-constrained
IoT devices and the intricacies of deep neural networks necessitate
transmitting CSI to cloud servers for sensing. Although feasible, this leads to
considerable communication overhead. In this context, this paper develops a
novel Real-time Sensing and Compression Network (RSCNet) which enables sensing
with compressed CSI; thereby reducing the communication overheads. RSCNet
facilitates optimization across CSI windows composed of a few CSI frames. Once
transmitted to cloud servers, it employs Long Short-Term Memory (LSTM) units to
harness data from prior windows, thus bolstering both the sensing accuracy and
CSI reconstruction. RSCNet adeptly balances the trade-off between CSI
compression and sensing precision, thus streamlining real-time cloud-based WiFi
sensing with reduced communication costs. Numerical findings demonstrate the
gains of RSCNet over the existing benchmarks like SenseFi, showcasing a sensing
accuracy of 97.4% with minimal CSI reconstruction error. Numerical results also
show a computational analysis of the proposed RSCNet as a function of the
number of CSI frames.
