无线传感器网络中数据再生的最大生存时间
Maximum Lifetime for Data Regeneration in Wireless Sensor Networks
摘要：
专用于无线传感器网络的鲁棒分布式存储系统利用多个节点冗余地存储传感数据，使得当一些存储节点失效时，传感数据仍然可以被重构。对于相同级别的冗余，已知基于纠删编码的方法比复制方法需要更少的数据存储空间。为了在一个存储节点发生故障时保持相同的冗余水平，可以通过使该节点从其他活动存储节点下载相应的片段来将擦除编码的数据恢复到某个其他存储节点上。以前的工作表明，使用擦除编码的好处，强大的存储复制是没有吸引力的复杂性，在重新生成丢失的数据。然而，最近的工作表明，通过提出再生编码，可以进一步减少纠删编码数据的带宽，从而使纠删码再次适合于鲁棒的数据存储。但是，这些工作的再生编码考虑这些代码将如何执行无线传感器网络中的数据再生。因此，我们提出了一个分析模型来量化再生编码在经典方案的网络寿命增益。我们还提出了一个分布式算法，TROY，确定哪些节点和路由用于数据再生。我们的分析研究表明，对于某些拓扑结构，TROY实现了最大的网络寿命。我们在真实传感器网络跟踪的评估研究表明，TROY实现了接近最佳的寿命和性能优于基线算法。
英文摘要：
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.
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