Nericell

Mohan, P. V. Ananda; Padmanabhan, Venkata N.; Ramjee, Ramachandran

doi:10.1145/1460412.1460444

Cited by 912 publications

(37 citation statements)

References 17 publications

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“…Smartphone: Nericell [6] used mobile Smartphone to monitor road and traffic conditions. It detected potholes, braking, bumps and honks using accelerometer, microphone, GSM radio and GPS sensors inbuilt in smart phones.…”

Section: Background and Related Workmentioning

confidence: 99%

Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach

Devapriya¹,

Babu²,

Srihari³

2016

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An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notification systems, Automatic road enforcement, Collision avoidance systems, Automatic parking, Map database management, etc. Advance Driver Assists System (ADAS) belongs to ITS which provides alert or warning or information to the user during driving. The proposed method uses Gaussian filtering and Median filtering to remove noise in the image. Subsequently image subtraction is achieved by subtracting Median filtered image from Gaussian filtered image. The resultant image is converted to binary image and the regions are analyzed using connected component approach. The prior work on speed bump detection is achieved using sensors which are failed to detect speed bumps that are constructed with small height and the detection rate is affected due to erroneous identification. And the smartphone and accelerometer methodologies are not perfectly suitable for real time scenario due to GPS error, network overload, real-time delay, accuracy and battery running out. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

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Section: Background and Related Workmentioning

confidence: 99%

Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach

Devapriya¹,

Babu²,

Srihari³

2016

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“…In [20], Bao and Choudhury introduce MoVi that employs smartphones to enable collaborative sensing using videos for recognizing socially interesting events. In [21], authors develop a smartphone-based solution to monitor road and traffic condition of a city. Line wait-time detection and estimation.…”

Section: Related Workmentioning

confidence: 99%

LineKing: Coffee Shop Wait-Time Monitoring Using Smartphones

Bulut

Demirbaş

Ferhatosmanoğlu

2015

IEEE Trans. on Mobile Comput.

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This article describes LineKing, a crowdsensing system for monitoring and forecasting coffee shop line wait times. LineKing consists of a smartphone component that provides automatic and accurate wait-time detection, and a cloud backend that uses the collected data to provide accurate wait-time estimation. LineKing is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in the University at Buffalo, SUNY. The novel wait-time estimation algorithms of LineKing deployed at the cloud backend provide median absolute errors of less than 3 minutes. © 2002-2012 IEEE

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“…Many have proposed efforts to collect data about traffic [30], pot holes [31], or noise pollution [32]. The Personal Environmental Impact Report provides a pollution-exposure report based on the a cell phone's location trace and public data from (stationary) air-quality sensors [33].…”

Section: Examples: Applicationsmentioning

confidence: 99%

Opportunistic sensing: Security challenges for the new paradigm

Kapadia

Kotz

Triandopoulos

2009

2009 First International Communication Systems and Networks and Workshops

110

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We study the security challenges that arise in opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people-not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic peoplecentric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm.

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Nericell

Cited by 912 publications

References 17 publications

Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach

Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach

LineKing: Coffee Shop Wait-Time Monitoring Using Smartphones

Opportunistic sensing: Security challenges for the new paradigm

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