Rain Sensing Network
ECE Capstone Design Project, Spring’12
Denis Poznykov
Walid Aljabowbi
Orestis Kotzias
Vladimir Samokhin
Advisor: Prof. Dario Pompili
Introduction: This project is a collaboration between electrical and computer engineering (Dr.
Pompili), and civil and environmental engineering (Dr. Hill) on sensing and modeling extreme
weather events. Such events have profound effects on the sustainability of urban centers. At the
same time, human activities are increasing the variability of the climate and increasing the
frequency of these events, driving the need for dynamic decision-making tools.
The overall objective of this research is to address urban sustainability through the development
of modeling methods suitable for forecasting environmental phenomena in a changing world,
and through the development of technology that can enable autonomous infrastructure to adapt to
rapidly evolving environmental conditions. This research will focus on rainfall estimation and
measure success by the ability to provide accurate rainfall estimates at resolutions higher than the
minimum threshold suggested by the literature.
Motivation: Currently all of the rainfall data is acquired either by radar or by satellite. These
techniques have been successfully used for many years. However, both radar and satellite predict
rainfall only over large areas, and quite often the actual precipitation varies largely on a higher
spatial resolution. One way to solve this problem and get accurate data about precipitation is to
place rain sensors directly in the area being considered. Those rain sensors can provide a very
accurate online reading and help in the early detection of flooding. Unfortunately, with quality
comes a high price tag. The proposed solution is to take advantage of ubiquitous rain sensors in
vehicles. Since 2006, many vehicles began
integrating simple precipitation sensors to automatically turn on the wipers. Those sensors are
cheap and ubiquitous, yet they are not very accurate. The idea however is to take advantage of
the multitude of those sensors, being deployed on more and more vehicles every year. The high
density of the sensors is expected to compensate for lack of precision, and still deliver useful
data.
Design: A network of portable and self-sufficiently powered sensors capable of transmitting data
for a long time in a desired area was constructed. Each sensor is able to collect and transmit data
to its parent router, which is then sent up the hierarchy on the network for further processing.
Sensing units are equipped with solar cells regenerating power supply. Web-based analysis of
data accessible to the public was developed. Both real time and previously recorded data from
any active sensor unit can be displayed with desired time frame and measurements. A distributed
adaptive sampling protocol, SILENCE, was implemented to reduce transmission overhead and to
prevent sending repetitive data from multiple sensors on the central network by leveraging data
redundancy due to spatial correlation and similarity.
Conclusion: This research focused on creating a real-time system for rainfall adaptive sensing
using ubiquitous sensors, which will permit to pursue the ambitious overall objective of enabling
predictive control of urban infrastructure during extreme weather events.