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Bret Hull

Bio: Bret Hull is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Wireless sensor network & Wireless network. The author has an hindex of 10, co-authored 11 publications receiving 3683 citations.

Papers
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Proceedings ArticleDOI
31 Oct 2006
TL;DR: CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year, and has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics.
Abstract: 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.

1,188 citations

Proceedings ArticleDOI
17 Jun 2008
TL;DR: This paper describes a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles, which they call the Pothole Patrol (P2), which uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions.
Abstract: This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.

1,126 citations

Proceedings ArticleDOI
03 Nov 2004
TL;DR: Fusion, a combination of hop-by-hop flow control, rate limiting source traffic when transit traffic is present, and a prioritized medium access control (MAC) protocol, can improve network efficiency by a factor of three under realistic workloads.
Abstract: Network congestion occurs when offered traffic load exceeds available capacity at any point in a network. In wireless sensor networks, congestion causes overall channel quality to degrade and loss rates to rise, leads to buffer drops and increased delays (as in wired networks), and tends to be grossly unfair toward nodes whose data has to traverse a larger number of radio hops.Congestion control in wired networks is usually done using end-to-end and network-layer mechanisms acting in concert. However, this approach does not solve the problem in wireless networks because concurrent radio transmissions on different "links" interact with and affect each other, and because radio channel quality shows high variability over multiple time-scales. We examine three techniques that span different layers of the traditional protocol stack: hop-by-hop flow control, rate limiting source traffic when transit traffic is present, and a prioritized medium access control (MAC) protocol. We implement these techniques and present experimental results from a 55-node in-building wireless sensor network. We demonstrate that the combination of these techniques, Fusion, can improve network efficiency by a factor of three under realistic workloads.

623 citations

Proceedings ArticleDOI
29 Sep 2006
TL;DR: The high-level conclusion is that grassroots Wi-Fi networks are viable for a variety of applications, particularly ones that can tolerate intermittent connectivity, and how the measurement results can improve transport protocols in such networks.
Abstract: The impressive penetration of 802.11-based wireless networks in many metropolitan areas around the world offers, for the first time, the opportunity of a "grassroots" wireless Internet service provided by users who "open up" their 802.11 (Wi-Fi) access points in a controlled manner to mobile clients. While there are many business, legal, and policy issues to be ironed out for this vision to become reality, we are concerned in this paper with an important technical question surrounding such a system: can such an unplanned network service provide reasonable performance to network clients moving in cars at vehicular speeds.To answer this question, we present the results of a measurement study carried out over 290 "drive hours" over a few cars under typical driving conditions, in and around the Boston metropolitan area (some of our data also comes from a car in Seattle). With a simple caching optimization to speed-up IP address acquisition, we find that for our driving patterns the median duration of link-layer connectivity at vehicular speeds is 13 seconds, the median connection upload bandwidth is 30 KBytes/s, and that the mean duration between successful associations to APs is 75 seconds. We also find that connections are equally probable across a range of urban speeds (up to 60 km/hour in our measurements). Our end-to-end TCP upload experiments had a median throughput of about 30 KBytes/s, which is consistent with typical uplink speeds of home broadband links in the US. The median TCP connection is capable of uploading about 216 KBytes of data.Our high-level conclusion is that grassroots Wi-Fi networks are viable for a variety of applications, particularly ones that can tolerate intermittent connectivity. We discuss how our measurement results can improve transport protocols in such networks.

542 citations

Proceedings ArticleDOI
22 Aug 2005
TL;DR: The current testbed setup is described, the current results are presented, and preliminary experimental results from both a 60-node sensor network deployment and a small-scale 802.11 deployment are presented.
Abstract: Carrier sense is a fundamental part of most wireless networking stacks in wireless local area- and sensor networks. As increasing numbers of users and more demanding applications push wireless networks to their capacity limits, the efficacy of the carrier sense mechanism becomes a key factor in determining wireless network capacity.We describe how carrier sense works, point out its limitations, and advocate an experimental approach to studying carrier sense. We describe our current testbed setup, and then present preliminary experimental results from both a 60-node sensor network deployment and a small-scale 802.11 deployment. Our preliminary results evaluate how well carrier sense works and expose its limitations.

159 citations


Cited by
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Journal ArticleDOI
Raghu K. Ganti1, Fan Ye1, Hui Lei1
TL;DR: The need for a unified architecture for mobile crowdsensing is argued and the requirements it must satisfy are envisioned.
Abstract: An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy.

1,833 citations

Proceedings ArticleDOI
04 Nov 2009
TL;DR: In this paper, the authors evaluate datapath validation and adaptive beaconing in CTP Noe, a sensor network tree collection protocol, on both interference-free and interference-prone channels.
Abstract: This paper presents and evaluates two principles for wireless routing protocols. The first is datapath validation: data traffic quickly discovers and fixes routing inconsistencies. The second is adaptive beaconing: extending the Trickle algorithm to routing control traffic reduces route repair latency and sends fewer beacons.We evaluate datapath validation and adaptive beaconing in CTP Noe, a sensor network tree collection protocol. We use 12 different testbeds ranging in size from 20--310 nodes, comprising seven platforms, and six different link layers, on both interference-free and interference-prone channels. In all cases, CTP Noe delivers > 90% of packets. Many experiments achieve 99.9%. Compared to standard beaconing, CTP Noe sends 73% fewer beacons while reducing topology repair latency by 99.8%. Finally, when using low-power link layers, CTP Noe has duty cycles of 3% while supporting aggregate loads of 30 packets/minute.

1,516 citations

Proceedings ArticleDOI
05 Nov 2008
TL;DR: Nericell is presented, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course, and 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.
Abstract: 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.

1,407 citations

Proceedings ArticleDOI
31 Oct 2006
TL;DR: CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year, and has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics.
Abstract: 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.

1,188 citations