We are developing Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and energetically self-reliant devices that can be attached to objects that are traditionally not networked (e.g., books, furniture, walls, doors, toys, keys, clothing, and produce), thereby providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects (items in a store, boxes in transit), and determining locations of disaster survivors.
In order for EnHANTs to rely on harvested energy, they have to spend significantly less energy than Bluetooth, Zigbee, and IEEE 802.15.4a devices. Moreover, the harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using ultra-low-power circuits, the energy required to receive a bit is significantly higher than the energy required to transmit a bit). I will talk about our work in developing ultra-low power neighbor discovery algorithms, and some early ideas on higher-layer tracking protocols being developed for EnHANTs.
Dan Rubenstein is an associate professor in the Department of Computer Science at Columbia. His research interests are in network technologies, applications, and performance analysis. He is a former editor of IEEE/ACM Transactions on Networking, program chair of Association for Computing Machinery (ACM) Sigmetrics 2011, and has received an NSF CAREER Award, IBM Faculty Award, and several best paper awards. Rubenstein is also Co- Founder and former Chief Scientist at Infinio Systems, and has been a visiting scientist at Google.
Rubenstein received his Ph.D. in computer science from the University of Massachusetts, Amherst; an MA in mathematics from UCLA; and a BS in mathematics from MIT.