MuSAC: Mutualistic Sensing and Communication for Mobile Crowdsensing

Sijie Ji, Lixiang Lian, Yuanqing Zheng, Chenshu Wu

Abstract: 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 Commu-nication), which seamlessly integrates the collection of sensory data with existing communication systems, without adding any extra communication overhead. 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 costs for sensory data collection while also mitigating privacy concerns and data heterogeneity in crowd-sensing. Our real-world experimental validation in Wi-Fi and cellular Massive MIMO communication scenarios demonstrates the effectiveness of the MuSAC framework, shedding light on efficient mobile crowdsensing for massive IoT data collection.