DVFS based task scheduling in a harvesting WSN for Structural Health Monitoring

The task scheduler of an energy harvesting wireless sensor node (WSN) must adapt the task complexity and maximize the accuracy of the tasks within the constraint of limited energy reserves. Structural Health Monitoring (SHM) represents a great example of such an application comprising of both steady state operations and sporadic externally triggered events. To this end, we propose a task scheduler based on a Linear Regression Model embedded with Dynamic Voltage and Frequency Scaling (DVFS) functionality. Our results show an improvement in the average accuracy of a SHM measurement, setting it at 80% of the maximum achievable accuracy. There is also an increase of 50% in the number of SHM measurements.