MuSAC: Mutualistic Sensing and Communication for Mobile Crowdsensing

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.

I. Introduction

Ubiquitous mobile devices, along with the rich set of sensors, have fostered mobile crowdsensing as a distributed paradigm to gather large-scale, highly diverse, and rich context information for various sensing applications. Despite its potential, mobile crowdsensing is hindered by challenges related to communication efficiency and user privacy. A solution like federated learning protects privacy, yet does not address the communication overhead as it requires multi-round exchanges of data or model parameters between devices and the server, rendering the communication inevitable and significant. Fur-thermore, in terms of performance, it is more promising to train a model with a large amount of sensory data on a central server, rather than on IoT devices with limited computing resources. Especially, the straggler effect of federated learning makes the performance confined by the computation capabili-ties of mobile devices [1]–[3]. Therefore, how to crowdsource massive sensory data with little communication overhead and preserved privacy remains a key enabler.