energy constraints / energy listening / energy load / sensor network applications / local energy consumption / energy bucket fills / energy aggregating / advertising prices / active network / lowest-possible energy state / greater energy efficiency / energy use / energy-aware scheduling scheme / multihop routing protocol / lowcapability devices / energy budget constraints / energy availability / energy usage / energy / it difficult to tune network / energy consumption rates / energy reserves / typical device / energy costs / energy resources / computational systems / energy consumption / actual sensor network / greedy geographic routing protocol / sensor networks / energy depletion / energy cost / controlled-flooding protocols / energy requirements / real-time resource allocation / energy-demanding actions / energy budgets / energy management / energy budget / communication protocols / software layers / data dissemination algorithm / local energy budget / attractive solution / lowest-energy state / individual hardware devices / information-directed query processing / fixed energy budget / wireless communication / point solution / learning algorithms / sensor network / /
OperatingSystem
TinyOS / /
Organization
Harvard University / UC Berkeley / Matt Welsh Division of Engineering / /
Person
Matt Welsh / David C. Parkes / Geoffrey Mainland / /
Position
system designer / token bucket model for the budget / general data collection problem / external controller / programmer / /
Technology
controlled-flooding protocols / alternative learning algorithms / operating system / 7.3 MHz ATmega128L processor / artificial intelligence / greedy geographic routing protocol / simulation / communication protocols / data dissemination algorithm / GUI / multihop routing protocol / /
Decentralized, Adaptive Resource Allocation for Sensor Networks Geoffrey Mainland, David C. Parkes, and Matt Welsh Division of Engineering and Applied Sciences Harvard University {mainland,parkes,mdw}@eecs.harvard.edu