  Robust distributed storage systems dedicated to wireless sensor networks
utilize several nodes to redundantly store sensed data so that when some
storage nodes fail, the sensed data can still be reconstructed. For the same
level of redundancy, erasure coding based approaches are known to require less
data storage space than replication methods.
  To maintain the same level of redundancy when one storage node fails, erasure
coded data can be restored onto some other storage node by having this node
download respective pieces from other live storage nodes. Previous works showed
that the benefits in using erasure coding for robust storage over replication
are made unappealing by the complication in regenerating lost data. More recent
work has, however, shown that the bandwidth for erasure coded data can be
further reduced by proposing Regenerating Coding, making erasure codes again
desirable for robust data storage.
  But none of these works on regenerating coding consider how these codes will
perform for data regeneration in wireless sensor networks. We therefore propose
an analytical model to quantify the network lifetime gains of regenerating
coding over classical schemes. We also propose a distributed algorithm, TROY,
that determines which nodes and routes to use for data regeneration. Our
analytical studies show that for certain topologies, TROY achieves maximum
network lifetime. Our evaluation studies in real sensor network traces show
that TROY achieves near optimal lifetime and performs better than baseline
algorithms.
