Abstract
The conventional method of manually reading analog meters to track consumption trends is both
laborious and costly. Moreover, it falls short in effectively managing sustainable water supplies, neces-
sitating accurate monitoring techniques to provide real-time insights into water usage for consumers.
While digital water meters have been introduced, their high cost makes them impractical for widespread
adoption, and they lack analytical capabilities for interpreting consumption patterns. In contrast, tradi-
tional analog water meters boast simplicity, low power consumption, durability, and reliability, but they
still rely on manual readings, which is inconvenient. To address this issue, an automated data-capturing
system is required to transmit real-time meter readings to a cloud server. Smart water meters, powered
by robust machine learning (ML) and deep learning (DL) algorithms for meter reading detection, can
analyze the data collected to gain valuable insights into water consumption patterns and detect leaks,
facilitating more efficient water management. Ultimately, smart water monitoring devices can empower
users to reduce their water usage and contribute to water conservation efforts. Keeping all these aspects
in mind, the thesis can be divided into three parts:
Firstly, this thesis introduces an IoT based economic retrofitting setup for digitising the analog water
meters to make them smart. The setup contains a Raspberry-Pi microcontroller and a Pi-camera mounted
on top of the analog water meter to take its images. The captured images are then preprocessed to
estimate readings using a ML model. The employed ML algorithm is trained on a rich dataset that
includes digits from the images of water meters captured by the hardware setup for ten days. The
readings are posted on a cloud server in real-time using Raspberry-Pi. High temporal resolution plots
of flow rate and volume are generated to derive inferences. The collected data can be used for deriving
water consumption patterns and fault detection for efficient water management.
After that, the thesis proposes a DL-based algorithm which is used for improving the performance
of digit detection from IoT-based analog water meters. The DL algorithm is trained on a rich dataset of
over 160,000 images collected from six water nodes deployed at locations with different environmental
conditions. A detailed comparison between the proposed DL and ML algorithm is made based on de-
tection accuracy, feature analysis, error analysis, and computational complexity analysis. It is observed
that compared to the ML model, the proposed DL model maintained a higher detection accuracy and is
more generalized in terms of feature extraction, which makes the algorithm robust.
Finally, the thesis presents a comprehensive analysis of water supply behaviour on an educational
campus, focusing on two distinct regions: student hostels and faculty/staff quarters. The investigation delves into the impact of water supply patterns on a monthly and weekly basis. Notably, it highlights
how each month, with its unique characteristics such as holidays, exams, and class schedules, influences
the water supply in both regions. One key difference between the two regions is that students reside in
one, leading to significant variations in water usage based on the number of holidays. Conversely,
the other region accommodates families, resulting in a consistent water requirement regardless of col-
lege holidays. The findings from this analysis are crucial for understanding water distribution patterns,
particularly within intermittent water supply (IWS) systems, with the ultimate goal of enhancing the
efficiency and robustness of water distribution. By thoroughly examining the water supply behaviour in
an educational campus and considering various factors that influence it, this work contributes to a better
understanding of water management on campus.