Maximum Lifetime for Data Regeneration in Wireless Sensor Networks (1012.5834v5)

Abstract: 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.