Nericell: rich monitoring of road and traffic conditions using mobile smartphones

By Prashanth Mohan, V. Padmanabhan, Ramachandran Ramjee. ACM International Conference on Embedded Networked Sensor Systems, 2008

1,522 citations recorded by the source corpus.

We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.

Subfields: Computer Science Applications, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/d153edfedadd1df29f799243f6720de7ea5f1634

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Citations in the corpus

Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones (2016, background): “Although there are many applications and systems based on crowdsensing [27], [28], [33], [36], [42], most of them require voluntary participation.”
Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces (2011, methodology): “In Nericell [10], the embedded accelerometer, microphone, and positioning system (GPS or GSM radio) are used to detect and localize traﬃc conditions and road conditions, e.g., potholes, bumps, or braking and honking (which are both implicit indicators of traﬃc congestion).”
Safe Driving Using Mobile Phones (2012, methodology): “Nericell [1] is a system researched and developed by Microsoft that detects traffic honking, bumps, and vehicle braking using external sensors.”
PRISM: platform for remote sensing using smartphones (2010, methodology): “We show that this application running on PRISM (within constraints such as forced amnesia) is as effective in detecting bumps accurately as a native application such as Nericell [31] without such constraints.”
Mobile crowdsensing: current state and future challenges (2011, methodology): “For example, in a pothole detection [ 5 ] application, a local analytic computes spikes from 3-axis acceleration sensor data to determine potential potholes.”
GeoCrowd: enabling query answering with spatial crowdsourcing (2012, methodology): “Examples of participatory sensing campaigns include [2, 10, 21, 26], which uses GPS-enabled mobile phones to collect traﬃc information.”
Using mobile phones to determine transportation modes (2010, background): “…to sharing sensor derived status information in online social networks, capturing the characteristics and dynamics of everyday activities such as the commute, and enabling queries associated with physical space [Miluzzo et al. 2007; Gaonkar et al. 2008; Li et al. 2008; Mohan et al. 2008].”
A framework of energy efficient mobile sensing for automatic user state recognition (2009, background): “sensing [14, 20, 21], activity recognition[22, 23, 24], and health monitoring [25, 26, 27].”
Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring (2015, methodology): “Then, using the Internet connection of the smartphone, fleet administrators may access real-time vehicle and driver information in order to optimize logistics and reduce overall fuel consumption.”
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones (2009, background): “Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full citation on the .rst page.”
SurroundSense: mobile phone localization via ambience fingerprinting (2009, methodology): “SurroundSense: Mobile Phone Localization via Ambience Fingerprinting Martin AzizyanIonut Constandache Romit Roy ChoudhuryDuke University Duke University Duke University Durham, NC, USADurham, NC, USADurham, NC, USAmartin.azizyan@duke.edu ionut@cs.duke.edu romit@ee.duke.edu ABSTRACT A growing numbe”
Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems (2018, background): “Nericell project [17] presents a system, in which users collect the traffic and road information by using carry-on smartphones.”
WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones (2011, background): “Prior work has focused on the sensing component of detecting various contextual items, such as honk detection and physical bump/brake/pothole detection [21].”
How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint (2014, background): “The great potential of mobile phone sensing offers a variety of novel, efficient ways to opportunistically collect data, enabling numerous mobile crowdsourced sensing (MCS) applications, such as Sensorly [2] for constructing cellular/WiFi network coverage maps, SignalGuru [3], Nericell [4] and VTrac”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “tem of context-aware apps such as behavior-based authentication [31], [47], [58], [18], [78], [94], fitness monitoring based on activity tracking [5], [76], [57], speech translation [48], [61] and traffic/environmental monitoring [3], [64], [83], [86].”