S
Samuel Madden
Researcher at Massachusetts Institute of Technology
Publications - 413
Citations - 49321
Samuel Madden is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Wireless sensor network & Computer science. The author has an hindex of 95, co-authored 388 publications receiving 46424 citations. Previous affiliations of Samuel Madden include Lawrence Berkeley National Laboratory & Imperial College London.
Papers
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Journal ArticleDOI
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
TL;DR: This work presents the Tiny AGgregation (TAG) service for aggregation in low-power, distributed, wireless environments, and discusses a variety of optimizations for improving the performance and fault tolerance of the basic solution.
Journal ArticleDOI
TinyDB: an acquisitional query processing system for sensor networks
TL;DR: This work evaluates issues in the context of TinyDB, a distributed query processor for smart sensor devices, and shows how acquisitional techniques can provide significant reductions in power consumption on the authors' sensor devices.
Book ChapterDOI
TinyOS: An Operating System for Sensor Networks
Philip Levis,Samuel Madden,Joseph Polastre,Robert Szewczyk,Kamin Whitehouse,Alec Woo,Jason Hill,Matt Welsh,Eric Brewer,David E. Culler +9 more
TL;DR: A qualitative and quantitative evaluation of the TinyOS system is provided, showing that it supports complex, concurrent programs with very low memory requirements and efficient, low-power operation.
Proceedings Article
TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.
Sirish Chandrasekaran,Owen Cooper,Amol Deshpande,Michael J. Franklin,Joseph M. Hellerstein,Wei Hong,Sailesh Krishnamurthy,Samuel Madden,Vijayshankar Raman,Frederick Reiss,Mehul A. Shah +10 more
TL;DR: The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams and leverages the PostgreSQL open source code base.
Book ChapterDOI
Model-driven data acquisition in sensor networks
TL;DR: This paper enrichs interactive sensor querying with statistical modeling techniques, and demonstrates that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy.