A linear space algorithm for computing maximal common subsequences
Communications of the ACM
Maté: a tiny virtual machine for sensor networks
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Efficient code distribution in wireless sensor networks
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
The dynamic behavior of a data dissemination protocol for network programming at scale
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Simulating the power consumption of large-scale sensor network applications
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
CFLRU: a replacement algorithm for flash memory
CASES '06 Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems
Wireless sensor networks for structural health monitoring
Proceedings of the 4th international conference on Embedded networked sensor systems
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Fidelity and yield in a volcano monitoring sensor network
OSDI '06 Proceedings of the 7th symposium on Operating systems design and implementation
Spatial query processing in wireless sensor network for disaster management
WD'09 Proceedings of the 2nd IFIP conference on Wireless days
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
SFC: a simple flow control protocol for enabling reliable embedded network systems reprogramming
Proceedings of the 50th Annual Southeast Regional Conference
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We present an incremental code update strategy used to efficiently reprogram wireless sensor nodes. We adapt a linear space and quadratic time algorithm (Hirschberg's algorithm) for computing maximal common subsequences to build an edit map specifying an edit sequence, required to transform the code running in a sensor network to a new code image. We then present a heuristic-based optimization strategy for efficient edit script encoding to reduce the edit map size. Finally, we present experimental results to demonstrate the reduction in data size to reprogram a network using this mechanism. The approach achieves reductions of 99.987% for simple changes, and between 86.95% and 94.58% for more complex changes, compared to full image transmissions --- leading to significantly lower energy costs for wireless sensor network reprogramming. We compare the results with reductions achieved by other incremental update strategies described in prior work.