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

arXiv: https://arxiv.org/abs/1804.11147 · HTML: https://mlsanity.com/paper/1804.11147/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2018.

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## Abstract (arXiv)

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.