Identifying “who is around” is key in a plethora of smart scenarios. Our work in this area investigates how to use off-the-shelf devices and technologies (e.g., WiFi direct and Bluetooth Low Energy) to enable lightweight and energy efficient device-to-device continuous discovery. While many solutions exist, they often take a theoretical approach, reasoning about protocol behavior with an abstract model that makes simplifying assumptions about the environment. This approach creates a gap between protocol implementations and the models used during design and analysis. Our work, in contrast is focused on solutions that can be realistically implemented and deployed on real devices and in support of real applications in a way that honestly considers real-world constraints. For instance, our approaches consider the very real effects of packet collisions, which have a real and measurable impact on applications relying on device-to-device neighbor discovery. To our knowledge, this is a first in this domain. Our ultimate goal is to directly empower developers with the ability to determine the optimal protocol configuration for their applications.
- Chenguang Liu, Christine Julien, Amy L. Murphy. "PINCH: Self-Organized Context Neighborhoods for Smart Environments," IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO). 2018.
- Matteo Saloni, Christine Julien, Amy L. Murphy, Gian Pietro Picco. "LASSO: A Device-to-Device Group Monitoring Service for Smart Cities," Proceedings of the IEEE International Smart Cities Conference (ISC2). 2017.
- Christine Julien, Chenguang Liu, Amy L. Murphy, Gian Pietro Picco. "Blend: Practical continuous neighbor discovery for bluetooth low energy," Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks. 2017.