FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems

TL;DR

FIRST is a framework designed to improve the reliability of mobile crowdsensing data by optimally using trusted participants, significantly reducing security threats and achieving high classification accuracy in real-world scenarios.

Contribution

The paper introduces FIRST, a novel framework that determines the optimal number of trusted participants needed for reliable sensing report classification in mobile crowdsensing.

Findings

Achieves nearly 80% classification accuracy in real-world tests.

Effectively mitigates corruption, on/off, and collusion attacks.

Reduces security threats in crowdsensing systems.

Abstract

Mobile crowdsensing allows data collection at a scale and pace that was once impossible. One of the biggest challenges in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To this end, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST), that leverages mobile trusted participants (MTPs) to securely assess the reliability of sensing reports. FIRST models and solves the challenging problem of determining before deployment the minimum number of MTPs to be used in order to achieve desired classification accuracy. We extensively evaluate FIRST through an implementation in iOS and Android of a room occupancy monitoring system, and through simulations with real-world mobility traces. Experimental results demonstrate that FIRST reduces significantly the impact of three security attacks (i.e., corruption, on/off, and collusion), by achieving a classification accuracy of almost 80% in the considered scenarios. Finally, we discuss our ongoing research efforts to test the performance of FIRST as part of the National Map Corps project.