Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System

2025-10-22

AI Summary

In real-world health studies, mobile data collection faces challenges including battery constraints, intermittent or weak network connectivity, and low participant adherence. To address these, this paper proposes MotionPI—a secure, resilient, and low-burden distributed smartphone sensing system. MotionPI innovatively integrates passive sensing (e.g., GPS, accelerometer) with activity-triggered ecological momentary assessment (EMA), leveraging BLE-enabled wristband collaboration for lightweight, context-aware sensing. It employs on-device caching, local encryption, and asynchronous encrypted upload to support offline data acquisition and robust synchronization under poor network conditions. Experimental evaluation demonstrates that MotionPI maintains high data availability and end-to-end privacy protection during long-term deployment. It significantly improves the robustness, scalability, and participant compliance of behavioral and health data collection in real-world settings.

Technology Category

• Machine Learning: Privacy
• Application Domains: Mobility, Driving & Flight
• Intelligent Robots: Motion and Path Planning

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Real-world health studies require continuous and secure data collection from mobile and wearable devices. We introduce MotionPI, a smartphone-based system designed to collect behavioral and health data through sensors and surveys with minimal interaction from participants. The system integrates passive data collection (such as GPS and wristband motion data) with Ecological Momentary Assessment (EMA) surveys, which can be triggered randomly or based on physical activity. MotionPI is designed to work under real-life constraints, including limited battery life, weak or intermittent cellular connection, and minimal user supervision. It stores data both locally and on a secure cloud server, with encrypted transmission and storage. It integrates through Bluetooth Low Energy (BLE) into wristband devices that store raw data and communicate motion summaries and trigger events. MotionPI demonstrates a practical solution for secure and scalable mobile data collection in cyber-physical health studies.

Problem

• Secure smartphone system collects health data passively
• Minimizes user interaction during continuous behavioral monitoring
• Operates reliably under battery and connectivity constraints

Innovation

• Smartphone-based system collects sensor and survey data
• Integrates passive data collection with Ecological Momentary Assessment
• Uses encrypted local and cloud storage with BLE connectivity