Exploitation of Physical Constraints for Reliable Social Sensing

This paper develops and evaluates algorithms for exploiting physical constraints to improve the reliability of social sensing. Social sensing refers to applications where a group of sources (e.g., individuals and their mobile devices) volunteer to collect observations about the physical world. A key challenge in social sensing is that the reliability of sources and their devices is generally unknown, which makes it non-trivial to assess the correctness of collected observations. To solve this problem, the paper adopts a cyber-physical approach, where assessment of correctness of individual observations is aided by knowledge of physical constraints on both sources and observed variables to compensate for the lack of information on source reliability. We cast the problem as one of maximum likelihood estimation. The goal is to jointly estimate both (i) the latent physical state of the observed environment, and (ii) the inferred reliability of individual sources such that they are maximally consistent with both provenance information (who claimed what) and physical constraints. We evaluate the new framework through a real-world social sensing application. The results demonstrate significant performance gains in estimation accuracy of both source reliability and observation correctness.

I. Introduction

This paper investigates the exploitation of physical constraints to improve the reliability of social sensing applications. We refer by social sensing to a broad set of applications, where sources, such as humans and digital devices they operate, collect information about the physical world for purposes of mutual interest. In social sensing, humans can play different roles by acting as sensor carriers [21] (e.g., opportunistic sensing), sensor operators [4] (e.g., participatory sensing) or sensor themselves [36]. The proliferation of mobile devices with sensors, such as smart phones, has significantly increased the popularity of social sensing. Examples of recent applications include optimization of daily commute [18], [44], reduction of carbon footprint [10], [20], disaster response [17], [33] and pollution monitoring [24], [28], to name a few. Due to the inclusive nature of data collection in social sensing (i.e., anyone can participate) and the unknown reliability of information sources, much recent work focused on estimating the likelihood of correctness of collected data [26], [36], [42].