Archit Goyal supervised by Dr. Sachin Chaudhari  received his Master of Science –  Dual Degree  in  (LCD). Here’s a summary of his research work on A Context-Based Quantitative Assessment of the Quality of Bias Benchmarks for Language Models:

 Intermittent Water Supply (IWS) systems, prevalent in many developing regions, pose significant challenges for urban water management due to their scheduled delivery and the resulting need for consumers to store water for non-supply periods. The lack of high-frequency, building-level consumption data in such systems hinders accurate demand profiling, leakage detection, and informed infrastructure planning. This thesis addresses these gaps by developing and deploying scalable, Internet of Things (IoT) enabled strategies to capture and analyse water consumption patterns in IWS settings, with a particular focus on the Indian context. The work is structured in two parts: first, improving the accuracy and reliability of IoT-based smart retrofit devices for analog water meter digitization; and second, constructing and analysing water consumption curves at different temporal resolutions for diverse building types using the collected data. Retrofitting existing analog water meters is a cost-effective approach to enable high-frequency, automated water usage monitoring without replacing established infrastructure. While image-based meter reading using IoT devices has shown promise, real-world deployments encounter significant challenges such as dew accumulation, scratches, smudges, and insect intrusion, all of which can degrade image quality and lead to digit detection errors. To address these issues, this thesis introduces a lightweight Hamming distance-based refinement algorithm that systematically corrects digit misclassifications by leveraging the expected range of meter readings and minimizing the number of differing digits. The algorithm is computationally efficient, requiring only basic arithmetic operations, and is thus well-suited for real-time deployment on edge devices like the Raspberry Pi. To further enhance data quality, a web based annotation tool was developed, enabling rapid, targeted manual validation of flagged anomalies and supporting the efficient correction and curation of large datasets. Building on these advancements, the thesis proposes two innovative, non-intrusive IoT-based strategies for capturing water consumption in IWS-serviced buildings. The first strategy involves installing smart flow meters at the outlets of Over Head Tank (OHT) to directly measure consumption. The second combines flow meters at the inlet with water level sensors inside OHTs, using mass conservation principles to infer consumption at high temporal resolution. These approaches minimize the need for intrusive instrumentation and are readily scalable to a variety of building types commonly found in Indian urban settings. A comprehensive field study was conducted at International Institute of Information Technology Hyderabad (IIIT-H), where seven IoT nodes were deployed across five buildings, including hostels and classroom blocks. Over 125,000 data points were collected at three-minute intervals, capturing detailed water usage dynamics. The data underwent rigorous cleaning, refinement, and synchronization, leveraging the developed algorithms and annotation tools to ensure high integrity and accuracy. Using a bottom-up approach, the thesis constructs instantaneous, diurnal, and weekly water consumption curves for each building, normalized using peak factors to facilitate comparison of demand volatility across user categories. The resulting consumption patterns reveal distinct signatures for residential and non-residential buildings, highlight the impact of scheduled events (such as sports screenings and hackathons), and quantify temporal variations in demand. These high-resolution insights enable utilities and building managers to detect leaks and unauthorized usage, optimize supply schedules, and inform the design of future-ready water distribution networks. The methodologies and findings also provide a robust foundation for transitioning from IWS to CWS systems and for upgrading conventional buildings to smart, resource-efficient infrastructure.

June 2025