In-body backscatter communication and localization
Name
ReMix_SIGCOMM_2018.pdf
Description
Accepted version
Size
5.09 MB
Format
Adobe PDF
Checksum (MD5)
ce301edc04a010761db8fd328e5f8e9d
Author(s)
Vasisht, Deepak
Zhang, Guo
Abari, Omid
Lu, Hsiao-Ming
Flanz, Jacob
Katabi, Dina
Date Issued
August 2018
Journal
Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication
Publisher
ACM Press
Citation
Vasisht, Deepak, et al. “In-Body Backscatter Communication and Localization.” Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication - SIGCOMM ’18, Budapest, Hungary, 20-25 August, 2018, ACM Press, 2018, pp. 132–46.
Version
Author's final manuscript
Abstract
Backscatter requires zero transmission power, making it a compelling technology for in-body communication and localization. It can significantly reduce the battery requirements (and hence the size) of micro-implants and smart capsules, and enable them to be located on-the-move inside the body. The problem however is that the electrical properties of human tissues are very different from air and vacuum. This creates new challenges for both communication and localization. For example, signals no longer travel along straight lines, which destroys the geometric principles underlying many localization algorithms. Furthermore, the human skin backscatters the signal creating strong interference to the weak in-body backscatter transmission. These challenges make deep-tissue backscatter intrinsically different from backscatter in air or vacuum. This paper introduces ReMix, a new backscatter design that is particularly customized for deep tissue devices. It overcomes interference from the body surface, and localizes the in-body backscatter devices even though the signal travels along crooked paths. We have implemented our design and evaluated it in animal tissues and human phantoms. Our results demonstrate that ReMix delivers efficient communication at an average SNR of 15.2 dB at 1 MHz bandwidth, and has an average localization accuracy of 1.4cm in animal tissues.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3230543.3230565
