15 research outputs found
Energy-efficient Wireless Analog Sensing for Persistent Underwater Environmental Monitoring
The design of sensors or "things" as part of the new Internet of Underwater
Things (IoUTs) paradigm comes with multiple challenges including limited
battery capacity, not polluting the water body, and the ability to track
continuously phenomena with high temporal/spatial variability. We claim that
traditional digital sensors are incapable to meet these demands because of
their high power consumption, high complexity (cost), and the use of
non-biodegradable materials. To address the above challenges, we propose a
novel architecture consisting of a sensing substrate of dense analog
biodegradable sensors over which lies the traditional Wireless Sensor Network
(WSN). The substrate analog biodegradable sensors perform Shannon mapping (a
data-compression technique) using just a single Field Effect Transistor (FET)
without the need for power-hungry Analog-to-Digital Converters (ADCs) resulting
in much lower power consumption, complexity, and the ability to be powered
using only sustainable energy-harvesting techniques. A novel and efficient
decoding technique is also presented. Both encoding/decoding techniques have
been verified via Spice and MATLAB simulations accounting for underwater
acoustic channel variations.Comment: 5 pages, IEEE UComms 201
UW-MARL: Multi-Agent Reinforcement Learning for Underwater Adaptive Sampling using Autonomous Vehicles
Near-real-time water-quality monitoring in uncertain environments such as
rivers, lakes, and water reservoirs of different variables is critical to
protect the aquatic life and to prevent further propagation of the potential
pollution in the water. In order to measure the physical values in a region of
interest, adaptive sampling is helpful as an energy- and time-efficient
technique since an exhaustive search of an area is not feasible with a single
vehicle. We propose an adaptive sampling algorithm using multiple autonomous
vehicles, which are well-trained, as agents, in a Multi-Agent Reinforcement
Learning (MARL) framework to make efficient sequence of decisions on the
adaptive sampling procedure. The proposed solution is evaluated using
experimental data, which is fed into a simulation framework. Experiments were
conducted in the Raritan River, Somerset and in Carnegie Lake, Princeton, NJ
during July 2019