CarTel: a distributed mobile sensor computing system

By Bret Hull, V. Bychkovsky, Yang Zhang, Kevin Chen, Michel Goraczko, Allen K. L. Miu, Eugene Shih, Hari Balakrishnan, Samuel Madden. ACM International Conference on Embedded Networked Sensor Systems, 2006

1,209 citations recorded by the source corpus.

CarTel is a mobile sensor computing system designed to collect, process, deliver, and visualize data from sensors located on mobile units such as automobiles. A CarTel node is a mobile embedded computer coupled to a set of sensors. Each node gathers and processes sensor readings locally before delivering them to a central portal, where the data is stored in a database for further analysis and visualization. In the automotive context, a variety of on-board and external sensors collect data as users drive.CarTel provides a simple query-oriented programming interface, handles large amounts of heterogeneous data from sensors, and handles intermittent and variable network connectivity. CarTel nodes rely primarily on opportunistic wireless (e.g., Wi-Fi, Bluetooth) connectivity to the Internet, or to "data mules" such as other CarTel nodes, mobile phone flash memories, or USB keys-to communicate with the portal. CarTel applications run on the portal, using a delay-tolerant continuous query processor, ICEDB, to specify how the mobile nodes should summarize, filter, and dynamically prioritize data. The portal and the mobile nodes use a delay-tolerant network stack, CafNet, to communicat.CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year. It has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/fc0d466e44bb48ae86c4181c6d40d3307b682343

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In Computer Networks and Communications

Citations in the corpus

A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas (2017, methodology): “OnStar and Tesla offer telemetry services, as do platforms like CarTel [14], which relies on data buffering and opportunistic WiFi, or the CloudThink platform, which uses direct cellular vehicle mirroring to create vehicle “avacars” for open application development [57].”
An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks (2014, background): “They have wide-ranging applications, some of which include military [1]–[3], environment monitoring [4], [5], agriculture [6], [7], home automation [8], smart transportation [9], [10], and health [11].”
On truth discovery in social sensing: A maximum likelihood estimation approach (2012, background): “Some early applications include CenWits [16], a participatory sensor network to rescue hikers in emergency situations, CarTel [18], a vehicular sensor network for traffic monitoring and mitigation, and BikeNet [14], a bikers sensor network for sharing cycling related data and mapping the cyclist exp”
Named Data Networking (NDN) Project (2010, background): “Data collected for such applications includes GPS trajectories to monitor traffic patterns [31], pollution traces to assess environmental impact [32], vehicular fuel-efficiency measurements to find green routes [30], and health data.”
AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling (2017, background): “Typical HPAs include heads-up displays (HUDs) [2], field-ofview enhancement [3], and road sensing [10].”
Mobile crowdsensing: current state and future challenges (2011, methodology): “On the other hand, these GPS sensor measurements (from daily commutes) shared within a larger community can be used to obtain trafﬁc congestion levels in a given city [4].”
Deanonymizing mobility traces: using social network as a side-channel (2012, methodology): “, location, acceleration) generated by everyday mobile devices such as smartphones, tablets, and cars have become quite popular [3, 11, 15, 21, 32].”
Preserving privacy in gps traces via uncertainty-aware path cloaking (2007, background): “One example and the motivation for this paper is automotive traffic monitoring through probe vehicles [20, 14, 30], which infers traffic congestion from position and speed information periodically reported from GPS-equipped vehicles.”
The Rise of People-Centric Sensing (2008, background): “The MIT Cartel project [4] provides a mobile communications infrastructure based on car-mounted communication platforms exploiting open WiFi access points in a city, and provides urban sensing information such as traffic conditions.”
Travel time estimation of a path using sparse trajectories (2014, background): “Floating-Car-Data Approaches: Learning city transportation using floating car data has gained more attention recently [1, 5, 17, 23, 24].”
Analytical and grid-free solutions to the Lighthill-Whitham-Richards traffic flow model (2011, background): “For instance, most traffic estimation applications require high resolution output such as δx ≤ 200m, see for example [22, 36, 23, 31].”
Urban sensing systems: opportunistic or participatory? (2008, neutral): “Both Cartel [10] and the work by Microsoft Research [12] lean towards opportunism.”
Anonysense: privacy-aware people-centric sensing (2008, methodology): “Other major systems note these challenges but offer no solutions [1, 7, 18, 31, 8, 32].”
Improving energy efficiency of location sensing on smartphones (2010, background): “Some instances of current ly popular LBAs include mobile social networking [2, 3, 9, 10], hea lthcare [1], local traffic [7,22,23,29,36], and local restaura nts [6].”