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 timescales. 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.

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 linklayer 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-toend 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.

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.

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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 delaytolerant 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 delaytolerant network stack, CafNet, to communicate.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.

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Several emerging wireless sensor network applications must cope with a combination of node mobility (e.g., sensors on moving cars) and high data rates (media-rich sensors capturing videos, images, sounds, etc.). Due to their mobility, these sensor networks display intermittent and variable network connectivity, and often have to deliver large quantities of data relative to the bandwidth available during periods of connectivity. Unfortunately, existing distributed data management and stream processing are not appropriate for such applications because they assume that the network connecting nodes in the data processor is "always on," and that the absence of a network connection is a fault that needs to be masked to avoid failure. This thesis describes ICEDB (Intermittently Connected Embedded Database), a continuous query processing system for intermittently connected mobile sensor networks. ICEDB incorporates two key ideas: (1) a delay-tolerant continuous query processor, coordinated by a central server and distributed across the mobile nodes, and (2) algorithms for prioritizing certain query results to improve application-defined "utility" metrics. We describe the results of several experiments that use data collected from a deployed fleet of cabs driving in Boston.

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