Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

TL;DR

This paper surveys the current state of mobile crowdsensing, emphasizing the importance of ensuring Quality of Information given human unreliability, and proposes a new framework for QoI management.

Contribution

It introduces a novel framework for defining and enforcing QoI in mobile crowdsensing and analyzes current research challenges and future directions.

Findings

Existing methods struggle with human unreliability in crowdsensing.

A new framework for QoI can improve data reliability and trustworthiness.

Identifies key research challenges and potential solutions for QoI in mobile crowdsensing.

Abstract

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.