Broader Impact

Educational Contributions:

The project has provided a training ground for multiple PhD students at Stony Brook (4 from ECE and 2 from CS, so far). Several of them now graduated and found R&D level employment in high tech industry or academia. The project results have been communicated to journals and conferences, listed below. The project helped develop a laboratory for experimenting with backscattering technology in general. A high school student (Shane Kim) worked in the lab on summers and his work led to poster and workshop papers related to human activity recognition using RF signals. This experience benefited him greatly and contributed to his college applications.

At UTD, this time, the project allows 2 students and one post-doc to continue their professional development. There is a plan to add a new Ph.D. student to the research group who will be working on portions of the project agenda.

Project Impact:

This project (the contributions of UTD) is expected to make the following contributions to the passive network of tags (RIBBN) for smart spaces: (a) Architectural innovations: We will develop an adaptive energy aware tag architecture that will allow for high efficiency of energy harvesting; (b) Routing and information processing: We will develop scalable and adaptable distributed multi-hop routing for diverse RIBBN -specific topologies through novel hybrid routing approaches. We will also develop novel solutions for coping with inference in tag-to-tag communications; (c) Demonstration and evaluation of RIBBN; (d) We will apply our results to application scenarios in a realistic smart home-like setup, created by interactions between a number of objects tagged with developed prototypes to demonstrate the robustness of the developed algorithms for network-wide inferencing.

Additional Broader Impacts:

The development of the methodology and algorithms for optimization of routing could be used in other disciplines where route optimization is required. The development of efficient algorithms for finding Distributed Independent Set in graphs is likely to be used in numerous other fields and disciplines, as Independent Set in graphs is a generic problem that occurs in many studies. The development of energy harvesting techniques for passive tag networks will be applicable to sensor networks, especially those which are embedded in environments where battery-powering is prohibitive.

Impact on Resources:

Similarly to the "physical resources that form infrastructure," we envision that objects in working (institutional) spaces will be tagged with miniature battery-less RF-powered tags, which can autonomously interact among themselves and the environment around them and communicate over low-power wireless links without the need for any centralized control. These interactions will enable "Smart Office Spaces" wherein objects can collaboratively perceive the surrounding spaces and recognize other objects, their relationships, dynamic activities and events therein. Due to their exceptionally low-power design the tags do not have an on-board radio transmitter and communicate via backscattering the same RF signal that powers them.