NSDI '05 Abstract
Decentralized, Adaptive Resource Allocation for Sensor Networks
Geoff Mainland, David C. Parkes, and Matt Welsh, Harvard University
This paper addresses the problem of resource allocation in sensor
networks. We are concerned with how to allocate limited energy,
radio bandwidth, and other resources to maximize the value of each
node's contribution to the network. Sensor networks present a novel
resource allocation challenge: given extremely limited resources,
varying node capabilities, and changing network conditions, how can
one achieve efficient global behavior? Currently, this is accomplished
by carefully tuning the behavior of the low-level sensor program
to accomplish some global task, such as distributed event detection
or in-network data aggregation. This manual tuning is difficult,
error-prone, and typically does not consider network dynamics such
as energy depletion caused by bursty communication patterns.
We present Self-Organizing Resource Allocation (SORA), a
new approach for achieving efficient resource allocation in sensor
networks. Rather than manually tuning sensor resource usage, SORA defines a
virtual market in which nodes sell goods (such as sensor readings
or data aggregates) in response to prices that are established by
the programmer. Nodes take actions to maximize their profit, subject
to energy budget constraints. Nodes individually adapt their
operation over time in response to feedback from payments, using
reinforcement learning. The behavior of the network is determined
by the price for each good, rather than by directly specifying
local node programs.
SORA provides a useful set of primitives for controlling the
aggregate behavior of sensor networks despite variance of
individual nodes. We present the SORA paradigm and a sensor network
vehicle tracking application based on this design, as well as
an extensive evaluation demonstrating that SORA realizes an efficient
allocation of network resources that adapts to changing network
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