MuSAC: Toward Communication-Free Sensory Data Acquisition

Sensing and communication are at the core of the Internet of Things, which usually function independently. For example, a smartphone can communicate over Wi-Fi or cellular networks while continuously acquiring sensory data from the environment through various sensors. This paper presents a novel framework, MuSAC (Mutualistic Sensing and Communication), which seamlessly integrates the collection of sensory data with existing communication systems, without adding any additional communication overhead (i.e., communication-free). The framework leverages the mutualistic relationship between specific communication data and sensory data to effectively crowdsource heterogeneous sensory data without harming communication performance in practical distributed systems. To embed massive sensory data into the current transmission of communication data, MuSAC presents novel neural networks to distill universal features from the raw data for compression at the sender side and then extract invariant features on the server side. By doing so, MuSAC eliminates additional communication overhead for sensory data collection while also mitigating privacy concerns and data heterogeneity in crowdsensing. To evaluate the performance of MuSAC, we first conduct system-level simulations in a distributed environment by embedding public human activity sensing datasets into cellular massive MIMO communication processes. The results demonstrate that MuSAC effectively supports heterogeneous sensory data collection over existing wireless communication links at zero additional communication overhead. We further implement a functional prototype of the MuSAC system using off-the-shelf devices, where real-world sensory data are seamlessly integrated into WiFi transmissions. This practical implementation demonstrates the viability of communication-free sensory data acquisition in real-world scenarios, paving the way for broader applications of MuSAC.