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Dhruv Agarwal

Other affiliations: Microsoft
Bio: Dhruv Agarwal is an academic researcher from Ashoka University. The author has contributed to research in topics: Modulo & Software deployment. The author has an hindex of 2, co-authored 5 publications receiving 7 citations. Previous affiliations of Dhruv Agarwal include Microsoft.

Papers
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Proceedings ArticleDOI
TL;DR: Modulo is proposed -- a system to bootstrap drive-by sensing deployment by taking into consideration a variety of aspects such as spatiotemporal coverage, budget constraints, etc and is well-suited to satisfy unique deployment constraints.
Abstract: Ambient air pollution in urban areas is a significant health hazard, with over 4.2 million deaths annually attributed to it. A crucial step in tackling these challenge is to measure air quality at a fine spatiotemporal granularity. A promising approach for several smart city projects, called drive-by sensing, is to leverage vehicles retrofitted with different sensors (pollution monitors, etc.) that can provide the desired spatiotemporal coverage at a fraction of the cost. However, deploying a drive-by sensing network at a city-scale to optimally select vehicles from a large fleet is still unexplored. In this paper, we propose Modulo -- a system to bootstrap drive-by sensing deployment by taking into consideration a variety of aspects such as spatiotemporal coverage, budget constraints. Modulo is well-suited to satisfy unique deployment constraints such as colocations with other sensors (needed for gas and PM sensor calibration), etc. We compare Modulo with two baseline algorithms on real-world taxi and bus datasets. Modulo significantly outperforms the baselines when a fleet comprises of both taxis and fixed-route vehicles such as public transport buses. Finally, we present a real-world case study that uses Modulo to select vehicles for an air pollution sensing application.

7 citations

Proceedings ArticleDOI
TL;DR: This paper aims to create a principled and data-driven model to design the reservations policy in India, and uses statistical modeling to create the new framework, RAMSES (Rigorous and Adaptive Measurement of Socio-Economic Status).
Abstract: Affirmative action in the form of reservations is a divisive and contentious topic of policy in India. In this paper, we aim to create a principled and data-driven model to design the reservations policy in India. We look at some arguments against current policy and try to resolve them. We use statistical modeling to create our new framework, RAMSES (Rigorous and Adaptive Measurement of Socio-Economic Status). RAMSES measures the multidimensional disadvantage faced by an individual as an "adjusted income", which attempts to calibrate the quantum of compensatory aid in the form of reservations for that individual to have a level playing field. We illustrate our model using a case study.

3 citations

Proceedings ArticleDOI
10 Nov 2019
TL;DR: This paper describes a system to evaluate the coverage offered by different subsets of vehicles for sensor deployment based on historical vehicle mobility data and provides visualizations showing coverage to gauge the efficacy of different vehicle selections.
Abstract: Drive-by sensing has emerged as a popular way to achieve fine-grained sensing of physical phenomena. However, for it to be effective at a city-scale, there is a need to optimally select a subset of vehicles from a larger available fleet. These chosen vehicles must maximize coverage of the entire city. Simultaneously, they must fulfill other deployment requirements specific to the sensing application such as reference-monitor colocation instances for gas sensors. In this paper, we describe a system to evaluate the coverage offered by different subsets of vehicles for sensor deployment based on historical vehicle mobility data. Our system allows evaluation of different vehicle selection algorithms, and also provides two in-built baselines --- i) Random-MP, and ii) MaxPoints --- for comparison. Finally, we provide visualizations showing coverage to gauge the efficacy of different vehicle selections.

3 citations

Book ChapterDOI
TL;DR: This work provides the first comprehensive description of the Aadhaar infrastructure, collating information across thousands of pages of public documents and releases, as well as direct discussions with Aadhaar developers.

2 citations

Journal ArticleDOI
18 Oct 2021
TL;DR: Turn2Earn as discussed by the authors, a generic vehicular crowdsensing system that incentivizes drivers to take specific routes for data collection, was deployed with 13 auto-rickshaw drivers for two weeks in Bangalore.
Abstract: Smart city projects collect data on urban environments to identify problems, inform policymaking, and boost citizen engagement. Typically, this data is collected by static sensors placed around the city, which is not ideal for spatiotemporal needs of certain sensing applications such as air quality monitoring. Vehicular crowdsensing is an upcoming approach that addresses this problem by utilizing vehicles' mobility to collect fine-grained city-scale data. Prior work has mainly focused on designing vehicular crowdsensing systems and related components, including incentive schemes, vehicle selection, and application-specific sensing, without understanding the motivations and challenges faced by drivers and passengers, one of the two key stakeholders of any vehicular crowdsensing solution. Our work aims to fill this gap. To understand drivers' and passengers' perspectives, we developed Turn2Earn, a generic vehicular crowdsensing system that incentivizes drivers to take specific routes for data collection. Turn2Earn system was deployed with 13 auto-rickshaw drivers for two weeks in Bangalore, India. Our drivers took 709 trips using Turn2Earn covering 79.2% of the city's grid cells. Interviews with 13 drivers and 15 passengers revealed innovative information-based strategies adopted by the drivers to convince passengers in taking alternative routes, and passengers' altruism in supporting the drivers. We uncovered novel insights, including viability of offered routes due to road closure, issues with electric vehicles, and selection bias among the drivers. We conclude with design recommendations to inform the future of vehicular crowdsensing, including engaging and incentivizing passengers, and criticality-based reward structure.

1 citations


Cited by
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Proceedings ArticleDOI
TL;DR: Modulo is proposed -- a system to bootstrap drive-by sensing deployment by taking into consideration a variety of aspects such as spatiotemporal coverage, budget constraints, etc and is well-suited to satisfy unique deployment constraints.
Abstract: Ambient air pollution in urban areas is a significant health hazard, with over 4.2 million deaths annually attributed to it. A crucial step in tackling these challenge is to measure air quality at a fine spatiotemporal granularity. A promising approach for several smart city projects, called drive-by sensing, is to leverage vehicles retrofitted with different sensors (pollution monitors, etc.) that can provide the desired spatiotemporal coverage at a fraction of the cost. However, deploying a drive-by sensing network at a city-scale to optimally select vehicles from a large fleet is still unexplored. In this paper, we propose Modulo -- a system to bootstrap drive-by sensing deployment by taking into consideration a variety of aspects such as spatiotemporal coverage, budget constraints. Modulo is well-suited to satisfy unique deployment constraints such as colocations with other sensors (needed for gas and PM sensor calibration), etc. We compare Modulo with two baseline algorithms on real-world taxi and bus datasets. Modulo significantly outperforms the baselines when a fleet comprises of both taxis and fixed-route vehicles such as public transport buses. Finally, we present a real-world case study that uses Modulo to select vehicles for an air pollution sensing application.

7 citations

22 Jul 2022
TL;DR: This study investigates an innova-tive approach of drive-by sensing, which leverages large-scale ridesourcing vehicles (RVs) to monitor and infer the states of urban road networks and proposes an optimal rerouting model to simultaneously maximize the sensing coverage and reliability.
Abstract: The monitoring of the urban road network contributes to the efficient operation of the urban transportation system and the functionality of urban systems. Current practices of sensing urban road networks mainly depend on inductive loop sensors, roadside cameras, and crowdsourcing data from massive urban travelers (e.g., Google Map). These data have the drawbacks of high costs, limited coverage, or low reliability due to insufficient user penetration. This study investigates an innova-tive approach of drive-by sensing, which leverages large-scale ridesourcing vehicles (RVs) to monitor and infer the states of urban road networks. With RV traversing over road networks, we examine the RV fleet sensing performance based on the unique number of road segments explicitly visited, the sensing reliability as a result of repeated visits, and the information that can be implicitly inferred given explicit data and road network topology. We propose an optimal rerouting model to simultaneously maximize the sensing coverage and sensing reliability, which can be efficiently solved using a heuristic algorithm to guide the cruising trajectory of unoccupied RVs sequentially. To validate the effectiveness of the proposed model, comprehensive experiments and sensitivity analyses are performed using real-world RV data of more than 20,000 vehicles in New York City (NYC). Our approach is shown to cover up to 75.5% of all road segments which leads to an implicit coverage of 97.2%. More importantly, the coverage are obtained with at least 31.1% improvements in sensing reliability as compared to the baseline scenario.

1 citations

Journal ArticleDOI
TL;DR: In this article , the authors present an optimization-oriented summary of recent literature by presenting a four-step discussion, namely (1) quantifying the sensing quality (objective); (2) assessing the sensing power of various fleets (strategic); (3) sensor deployment (strategies); and (4) vehicle maneuvers (tactical/operational).
Abstract: Pervasive and mobile sensing is an integral part of smart transport and smart city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is gaining popularity in both academic research and field practice. The DS paradigm has an inherent transport component, as the spatial-temporal distribution of the sensors are closely related to the mobility patterns of their hosts, which may include third-party (e.g. taxis, buses) or for-hire (e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is therefore essential to understand, assess and optimize the sensing power of vehicle fleets under a wide range of urban sensing scenarios. To this end, this paper offers an optimization-oriented summary of recent literature by presenting a four-step discussion, namely (1) quantifying the sensing quality (objective); (2) assessing the sensing power of various fleets (strategic); (3) sensor deployment (strategic/tactical); and (4) vehicle maneuvers (tactical/operational). By compiling research findings and practical insights in this way, this review article not only highlights the optimization aspect of drive-by sensing, but also serves as a practical guide for configuring and deploying vehicle-based urban sensing systems.

1 citations