Battery-Free Subsea Internet-of-Things

Bringing massive connectivity to low-cost, low-power ocean sensors is important for numerous oceanographic applications (across climate/weather modeling, marine biology, aquaculture, and defense). However, standard IoT technologies (e.g, Bluetooth, WiFi, GPS) cannot operate underwater, which has left 70% of our planet (the ocean) beyond their reach. In this thesis, I describe how we can change this reality by introducing IoT technologies that are inherently designed for the ocean. Specifically, I show how by rethinking the entire IoT technology stack in the context of oceans, we introduced low-cost (< $100), net-zero-power, scalable connectivity technologies that seamlessly operate underwater and pave the way for massive underwater sensing, networking, localization, and machine learning. The thesis makes four fundamental contributions: First, it introduces ultra-wideband underwater bacskcatter, a technology that enables scalable, battery-free underwater communication. Second, it demonstrates how we can push the network throughput of underwater backscatter through a family of techniques including higher-order modulation techniques, self-interference cancellation, and multi-access protocols. Third, it shows how we can leverage our underwater backscatter nodes to enable a battery-free underwater GPS for localization and navigation. Finally, it demonstrates the feasibility of battery-free inference and machine learning in underwater environments by developing a task-specific deep neural network (DNN) model and deploying it on our battery-free underwater nodes. I deliver these contributions by designing and building new algorithms, systems and protocols for ultra low-power and scalable underwater sensing, networking, localization and inference. I also implement and evaluate these systems in real underwater environments (including rivers and lakes) and challenging weather conditions (including snow and rain), and discuss how they pave the way for new applications in ocean climate monitoring, underwater navigation, ocean exploration, robotics, aquaculture, and marine discovery.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Afzal, Sayed Saad

Advisor dc:contributor.advisor

• Adib, Fadel

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145054

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145054