Indoor carbon dioxide (CO2) can rapidly accumulate to form invisible pollution hotspots, posing significant health risks due to its odorless and colorless nature. Despite growing interest in wearable or stationary sensors for pollutant detection, effectively visualizing CO2 levels and engaging individuals remains an ongoing challenge. In this paper, we develop a portable wrist-sized pollution sensor that detects CO2 in real time at any indoor location and reveals CO2 bubbles by highlighting sudden spikes. In order to promote better ventilation habits and user awareness, we also develop a smartphone-based augmented reality (AR) game for users to locate and disperse these high-CO2 zones. A user study with 35 participants demonstrated increased engagement and heightened understanding of CO2’s health impacts. Our system’s usability evaluations yielded a median score of 1.88, indicating its strong practicality.

In recent years, indoor air pollution has posed a significant threat to our society, claiming over 3.2 million lives annually. Developing nations, such as India, are most affected since lack of knowledge, inadequate regulation, and outdoor air pollution lead to severe daily exposure to pollutants. However, only a limited number of studies have attempted to understand how indoor air pollution affects developing countries like India. To address this gap, we present spatiotemporal measurements of air quality from 30 indoor sites over six months during summer and winter seasons. The sites are geographically located across four regions of type: rural, suburban, and urban, covering the typical low to middle-income population in India. The dataset contains various types of indoor environments (e.g., studio apartments, classrooms, research laboratories, food canteens, and residential households), and can provide the basis for data-driven learning model research aimed at coping with unique pollution patterns in developing countries. This unique dataset demands advanced data cleaning and imputation techniques for handling missing data due to power failure or network outages during data collection. Furthermore, through a simple speech-to-text application, we provide real-time indoor activity labels annotated by occupants. Therefore, environmentalists and ML enthusiasts can utilize this dataset to understand the complex patterns of the pollutants under different indoor activities, identify recurring sources of pollution, forecast exposure, improve floor plans and room structures of modern indoor designs, develop pollution-aware recommender systems, etc.

Detecting dangerous driving has been of critical interest for the past few years. However, a practical yet minimally intrusive solution remains challenging as existing technologies heavily rely on visual features or physical proximity. With this motivation, we explore the feasibility of purely using mm Wave radars to detect dangerous driving behaviors. We first study characteristics of dangerous driving and find some unique patterns of range-doppler caused by 9 typical dangerous driving actions. We then develop a novel Fused-CNN model to detect dangerous driving instances from regular driving and classify 9 different dangerous driving actions. Through extensive experiments with 5 volunteer drivers in real driving environments, we observe that our system can distinguish dangerous driving actions with an average accuracy of 97(±2)%. We also compare our approach with existing state-of-the-art baselines to establish their significance,

Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transport like public buses, allowing them to pre-plan their travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations where a public bus stops. Although straightforward factors like stay duration extracted from unimodal sources like GPS at these locations look erratic, a thorough analysis of public bus GPS trails for 1,335.365 km at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop , a system for extracting and characterizing the stay-locations from multi-modal sensing using commuters’ smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allows the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected in-house dataset indicates that the system works with high accuracy in identifying different stay-locations such as regular bus stops, random ad hoc stops, stops due to traffic congestion, stops at traffic signals, and stops at sharp turns. Additionally, we develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60 seconds from the ground-truth arrival time.

Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this article propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale.

Unmanned Aerial Vehicles (UAVs) can be utilized as aerial base stations to establish wireless communication networks in various challenging scenarios, such as emergency disaster areas and rural areas. Under large regions, the aerial communication networks would require UAVs to form wireless (backhaul) links among each other to provide end-to-end wireless services between two or more ground users (via one or more UAVs). Such UAV backhauling in aerial communication networks may be severely compromised if one or more UAVs are knocked off during the time of operation – it may be due to UAV hardware/software faults, limited battery, malicious attacks, etc. Deep reinforcement learning (DRL) has emerged as a powerful tool for learning tasks with large state and continuous action spaces. In this paper, we leverage emerging DRL to achieve reliable backhauling in an aerial communication network that remains functional and supports end-to-end wireless services even under various random and/or targeted UAV node failures. The proposed method (i) maximizes the reliability of UAV backhauling with joint consideration for communication coverage, (ii) learns the complex environment and its dynamics, and (iii) makes 3D positioning decisions for each UAV under the guidance of two deep neural networks. Our performance evaluation reveals that the proposed DRL approach outperforms the baseline method in terms of wireless coverage and network reliability against UAV failures.

Public city bus services across various developing cities inhabit multiple stay-locations on the routes due to ad-hoc bus stops to provide on-demand passenger boarding and alighting services. Characterizing these stay-locations is essential to correctly develop models for bus transit patterns used in various digital navigation services. In this poster, we create a deep learning-driven methodology to characterize ad-hoc stay-locations over bus routes based on crowd-sensing contextual information. Experiments over 720km of bus travel data in a semi-urban city in India indicate promising results from the model in terms of good detection accuracy.