  In this work we present in-network techniques to improve the efficiency of
spatial aggregate queries. Such queries are very common in a sensornet setting,
demanding more targeted techniques for their handling. Our approach constructs
and maintains multi-resolution cube hierarchies inside the network, which can
be constructed in a distributed fashion. In case of failures, recovery can also
be performed with in-network decisions. In this paper we demonstrate how
in-network cube hierarchies can be used to summarize sensor data, and how they
can be exploited to improve the efficiency of spatial aggregate queries. We
show that query plans over our cube summaries can be computed in polynomial
time, and we present a PTIME algorithm that selects the minimum number of data
requests that can compute the answer to a spatial query. We further extend our
algorithm to handle optimization over multiple queries, which can also be done
in polynomial time. We discuss enriching cube hierarchies with extra summary
information, and present an algorithm for distributed cube construction.
Finally we investigate node and area failures, and algorithms to recover query
results.
