Accuracy-guided runtime adaptive profiling optimization of wireless sensor networks

Optimization of sensor networks relies on accurate profiling information collected about the state of individual nodes and the network as a whole. A single fixed profiling methodology may incur significant overheads on the sensor network or produce inaccurate profiling results due to dynamic changes in application behavior at runtime. Alternatively, reconfiguring the profiling methodology at runtime in response to such changes can help maintain the accuracy of profiling results while minimizing the associated overheads. In this paper, we present a runtime adaptive profiling methodology that can adapt to runtime behavior of the network and preserve the accuracy of profiling data. This runtime adaptive profiling strategy further allows application experts to control the profiling accuracy, thereby providing a mechanism to tradeoff accuracy and overhead. Experimental results demonstrate that network, computational time, and power consumption overheads can be reduced by more than 50% compared to using a fixed profiling methodology while only missing 2% of profiled events.