Air pollution is a leading global health threat, yet many developing countries lack the dense monitoring infrastructure needed for accurate exposure assessment and informed policy. Optimal Sensor Placement (OSP) is a foundational challenge in expanding monitoring capacity. While mutual information (MI) offers a principled criterion for selecting informative sensor locations, its computational cost grows with both the number of placements and the density of the candidate grid. We present a scalable, continuous optimization framework that treats sensor coordinates as differentiable parameters and directly maximizes MI. Unlike standard approaches, our method is computationally efficient—its runtime is independent of both the number of placements and the size of the search grid—making MI-based acquisition feasible over large spatial domains. On a continental-scale PM2.5 dataset, our method outperforms random placement and the widely-used Maximum Predictive Variance heuristic. In a focused regional study, it approaches the performance of greedy MI while being orders of magnitude faster. Our framework enables practical, information-theoretic sensor placement for real-world environmental monitoring.

Air pollution was responsible for 2.6 million deaths across South Asia in 2021 alone, with brick manufacturing contributing significantly to this burden. In particular, the Indo-Gangetic Plain; a densely populated and highly polluted region spanning northern India, Pakistan, Bangladesh, and parts of Afghanistan sees brick kilns contributing 8–14% of ambient air pollution. Traditional monitoring approaches, such as field surveys and manual annotation using tools like Google Earth Pro, are time and labor-intensive. Prior ML-based efforts for automated detection have relied on costly high-resolution commercial imagery and non-public datasets, limiting reproducibility and scalability. In this work, we introduce SENTINELKILNDB, a publicly available, hand-validated benchmark of 62,671 brick kilns spanning threekiln types Fixed Chimney Bull’s Trench Kiln (FCBK), Circular FCBK (CFCBK), and Zigzag kilns - annotated with oriented bounding boxes (OBBs) across 2.8 million km2 using free and globally accessible Sentinel-2 imagery. We benchmark state-of-the-art oriented object detection models and evaluate generalization across in-region, out-of-region, and super-resolution settings. SENTINELKILNDB enables rigorous evaluation of geospatial generalization and robustness for low-resolution object detection, and provides a new testbed for ML models addressing real-world environmental and remote sensing challenges at a continental scale. Datasets and code are available in SentinelKilnDB Dataset and SentinelKilnDB Bench-mark, under the Creative Commons Attribution–NonCommercial 4.0 International License.

Air pollution poses serious health concerns in developing countries, such as India, necessitating large-scale measurement for correlation analysis, policy recommendations, and informed decision-making. However, fine-grained data collection is costly. Specifically, static sensors for pollution measurement cost several thousand dollars per unit, leading to inadequate deployment and coverage. To complement the existing sparse static sensor network, we propose a mobile sensor network utilizing lower-cost PM2.5 sensors mounted on public buses in the Delhi-NCR region of India. Through this exercise, we introduce a novel dataset AirDelhi comprising PM2.5 and PM10 measurements. This dataset is made publicly available, at https://www.cse.iitd.ac.in/pollutiondata, serving as a valuable resource for machine learning (ML) researchers and environmentalists. We present three key contributions with the release of this dataset. Firstly, through in-depth statistical analysis, we demonstrate that the released dataset significantly differs from existing pollution datasets, highlighting its uniqueness and potential for new insights. Secondly, the dataset quality been validated against existing expensive sensors. Thirdly, we conduct a benchmarking exercise (https://github.com/sachin-iitd/DelhiPMDatasetBenchmark), evaluating state-of-the-art methods for interpolation, feature imputation, and forecasting on this dataset, which is the largest publicly available PM dataset to date. The results of the benchmarking exercise underscore the substantial disparities in accuracy between the proposed dataset and other publicly available datasets. This finding highlights the complexity and richness of our dataset, emphasizing its value for advancing research in the field of air pollution.

According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses which causes four million deaths annually. Regular lung health monitoring can lead to prognoses about deteriorating lung health conditions. This article presents our system SpiroMask that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for continuous lung health monitoring. We evaluate our approach on 48 participants (including 14 with lung health issues) and find that we can estimate parameters such as lung volume and respiration rate within the approved error range by the American Thoracic Society (ATS). Further, we show that our approach is robust to sensor placement inside the mask.

Air pollution is a global problem and severely impacts human health. Fine-grained air quality (AQ) monitoring is important in mitigating air pollution. However, existing AQ station deployments are sparse. Conventional interpolation techniques fail to learn the complex AQ phenomena. Physics-based models require domain knowledge and pollution source data for AQ modeling. In this work, we propose a Gaussian processes based approach for estimating AQ. The important features of our approach are: a) a non-stationary (NS) kernel to allow input depended smoothness of fit; b) a Hamming distance-based kernel for categorical features; and c) a locally periodic kernel to capture temporal periodicity. We leverage batch-wise training to scale our approach to a large amount of data. Our approach outperforms the conventional baselines and a state-of-the-art neural attention-based approach.

Air pollution killed 1.67M people in India in 2019. Previous work has shown that accurate public perception can help people identify the health risks of air pollution and act accordingly. News media influence how the public defines a social problem. However, news media analysis on air pollution has been on a small scale and regional. In this work, we gauge print news media response to air pollution in India on a larger scale. We curated a dataset of 17.4K news articles on air pollution from two leading English daily newspapers spanning 11 years. We performed exploratory data analysis and topic modeling to reveal the news media response to air pollution. Our study shows that, although air pollution is a year-long problem in India, the news media limelight on the issue is periodic (temporal bias). News media prefer to focus on the air pollution issue of metropolitan cities rather than the cities which are worst hit by air pollution (geographical bias). Also, the air pollution source contributions discussed in news articles significantly deviate from the scientific studies. Finally, we analyze the challenges raised by our findings and suggest potential solutions as well as the policy implications of our work.

Air pollution is a global challenge for cities across the globe. Understanding the public perception of air pollution can help policymakers engage better with the public and appropriately introduce policies. Accurate public perception can also help people to identify the health risks of air pollution and act accordingly. Unfortunately, current techniques for determining perception are not scalable: it involves surveying few hundred people with questionnaire-based surveys. Using the advances in natural language processing (NLP), we propose a more scalable solution called Vartalaap to gauge public perception of air pollution via the microblogging social network Twitter. We curated a dataset of more than 1.2M tweets discussing Delhi-specific air pollution. We find that (unfortunately) the public is supportive of unproven mitigation strategies to reduce pollution, thus risking their health due to a false sense of security. We also find that air quality is a year-long problem, but the discussions are not proportional to the level of pollution and spike up when pollution is more visible. The information required by Vartalaap is publicly available and, as such, it can be immediately applied to study different societal issues across the world.

Sensing is central to the SenSys and related communities. However, fine-grained spatial sensing remains a challenge despite recent advancements, owing to cost, maintenance, among other factors. Thus, estimating the sensed phenomenon at unmonitored locations and strategically installing sensors is of prime importance. In this work, we introduce Polire - an open-source tool that provides a suite of algorithms for spatial interpolation and near-field passive sensor placements. We replicate two existing papers on these two tasks to show the efficacy of Polire. We believe that Polire is an essential step towards lowering entry barriers towards sensing and scientific reproducibility.

COVID-19 has severely impacted millions of lives around the world. In this note, we explore the impact of COVID-19 on the electricity consumption of 93 households across two tier-2 cities in India. Given the work from home restrictions, we would expect electricity consumption to increase as people spend more time at home. Contrary to the expectations, we found that electricity consumption decreased during the lockdown as compared to previous years. On further follow-up with households, we found several reasons for decreased usage: i) inability to get air conditioners serviced due to movement restriction, ii) advisories on minimising AC usage, and iii) reducing energy to compensate for reduced income.

Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.

research-article Share on A Tree-Structured Neural Network Model for Household Energy Breakdown Authors: Yiling Jia University of Virginia, USA University of Virginia, USAView Profile , Nipun Batra IIT Gandhinagar, India IIT Gandhinagar, IndiaView Profile , Hongning Wang University of Virginia, USA University of Virginia, USAView Profile , Kamin Whitehouse University of Virginia, USA University of Virginia, USAView Profile Authors Info & Claims WWW '19: The World Wide Web ConferenceMay 2019 Pages 2872–2878https://doi.org/10.1145/3308558.3313405Published:13 May 2019Publication History 14citation306DownloadsMetricsTotal Citations14Total Downloads306Last 12 Months61Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.

Homes constitute roughly one-third of the total energy usage worldwide. Providing an energy breakdown – energy consumption per appliance, can help save up to 15% energy. Given the vast differences in energy consumption patterns across different regions, existing energy breakdown solutions require instrumentation and model training for each geographical region, which is prohibitively expensive and limits the scalability. In this paper, we propose a novel region independent energy breakdown model via statistical transfer learning. Our key intuition is that the heterogeneity in homes and weather across different regions most significantly impacts the energy consumption across regions; and if we can factor out such heterogeneity, we can learn region independent models or the homogeneous energy breakdown components for each individual appliance. Thus, the model learnt in one region can be transferred to another region. We evaluate our approach on two U.S. cities having distinct weather from a publicly available dataset. We find that our approach gives better energy breakdown estimates requiring the least amount of instrumented homes from the target region, when compared to the state-of-the-art.

Homes constitute more than one-thirds of the total energy consumption. Producing an energy breakdown for a home has been shown to reduce household energy consumption by up to 15%, among other benefits. However, existing approaches to produce an energy breakdown require hardware to be installed in each home and are thus prohibitively expensive. In this paper, we propose a novel application of feature-based matrix factorisation that does not require any additional hard- ware installation. The basic premise of our approach is that common design and construction patterns for homes create a repeating structure in their energy data. Thus, a sparse basis can be used to represent energy data from a broad range of homes. We evaluate our approach on 516 homes from a publicly available data set and find it to be more effective than five baseline approaches that either require sensing in each home, or a very rigorous survey across a large number of homes coupled with complex modelling. We also present a deployment of our system as a live web application that can potentially provide energy breakdown to millions of homes.

The first step to saving energy in the home is often to create an energy breakdown: the amount of energy used by each individual appliance in the home. Unfortunately, current techniques that produce an energy breakdown are not scalable: they require hardware to be installed in each and every home. In this paper, we propose a more scalable solution called Gemello that estimates the energy breakdown for one home by matching it with similar homes for which the breakdown is already known. This matching requires only the monthly energy bill and household characteristics such as square footage of the home and the size of the household. We evaluate this approach using 57 homes and results indicate that the accuracy of Gemello is comparable to or better than existing techniques that use sensing infrastructure in each home. The information required by Gemello is often publicly available and, as such, it can be immediately applied to many homes around the world.

Buildings across the world contribute significantly to the overall energy consumption. Targeted feedback can help occupants optimise energy consumption. In our first work we present techniques for actionable feedback across fridges and air conditioning (HVAC) units, which can save upto 25% of fridge energy and identify homes needing feedback on HVAC setpoint schedule with 84% accuracy. In our next work, we do an extensive sensor deployment in a home in Delhi, India; monitoring appliance level power, home aggregate power and other ambient parameters. Our study presents various insights unseen in the developed world, such as: frequent voltage brownouts, poor network reliability, long lasting blackouts, heavy dominance of fridge and HVAC to overall energy consumption. Our study verifies that measuring appliance level power scales poorly in cost and maintenance. Non-intrusive load monitoring (NILM) is viewed as a viable alternative where machine learning techniques are used to break down aggregate household energy consumption into contributing appliances. Despite the existence of a rich volume of literature in NILM, it remained virtually impossible to compare NILM works due to: i) lack of existence of benchmarks; ii) previous work tested on single data set; iii) inconsistent metrics. To address these challenges we developed an open source toolkit: Non-intrusive load monitoring toolkit (NILMTK), designed specifically to enable the comparison of NILM algorithms. While many new NILM techniques have been proposed in recent times, it is not clear if these can enable energy saving and whether higher accuracy translates to higher energy saving. We explore these questions in our recent work and find that existing energy disaggregation techniques do not provide power traces with sufficient fidelity to support the feedback techniques we developed in our earlier work. Our results indicate a need to revisit the metrics by which disaggregation is evaluated.

Residential buildings contribute significantly to the overall energy consumption across most parts of the world. While smart monitoring and control of appliances can reduce the overall energy consumption, management and cost associated with such systems act as a big hindrance. Prior work has established that detailed feedback in the form of appliance level consumption to building occupants improves their awareness and paves the way for reduction in electricity consumption. Non-Intrusive Load Monitoring (NILM), i.e. the process of disaggregating the overall home electricity usage measured at the meter level into constituent appliances, provides a simple and cost effective methodology to provide such feedback to the occupants. In this paper we present Improved Non-Intrusive load monitoring using load Division and Calibration (INDiC) that simplifies NILM by dividing the appliances across multiple instrumented points (meters/phases) and calibrating the measured power. Proposed approach is used together with the Combinatorial Optimization framework and evaluated on the popular REDD dataset. Empirical results demonstrate significant improvement in disaggregation accuracy, achieved by using INDiC based Combinatorial Optimization, demonstrate significant improvement in disaggregation accuracy.