Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909864641429504 |
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| author | Namjoo, Foad Wan, Neng Mallory, Devan Chang, Yuyi Sugavanam, Nithin Lee, Long Yin Xiong, Ning Ertin, Emre Phillips, Jeff M. |
| author_facet | Namjoo, Foad Wan, Neng Mallory, Devan Chang, Yuyi Sugavanam, Nithin Lee, Long Yin Xiong, Ning Ertin, Emre Phillips, Jeff M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System Namjoo, Foad Wan, Neng Mallory, Devan Chang, Yuyi Sugavanam, Nithin Lee, Long Yin Xiong, Ning Ertin, Emre Phillips, Jeff M. Cryptography and Security Distributed, Parallel, and Cluster Computing Human-Computer Interaction Software Engineering 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. |
| title | Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing Human-Computer Interaction Software Engineering |
| url | https://arxiv.org/abs/2510.19938 |