Energy Scavenging for Mobile and Wireless Electronics
IEEE Pervasive Computing
Harvesting aware power management for sensor networks
Proceedings of the 43rd annual Design Automation Conference
Power management in energy harvesting embedded systems with discrete service levels
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
Adapting task utility in externally triggered energy harvesting wireless sensing systems
INSS'09 Proceedings of the 6th international conference on Networked sensing systems
Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design
Harvesting-aware power management for real-time systems with renewable energy
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Adaptive scheduling of real-time systems cosupplied by renewable and nonrenewable energy sources
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on ESTIMedia'10
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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.