Bus-invert coding for low-power I/O
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Voltage scheduling problem for dynamically variable voltage processors
ISLPED '98 Proceedings of the 1998 international symposium on Low power electronics and design
Probabilistic Loop Scheduling for Applications with Uncertain Execution Time
IEEE Transactions on Computers
Low-energy intra-task voltage scheduling using static timing analysis
Proceedings of the 38th annual Design Automation Conference
Dynamic voltage scheduling technique for low-power multimedia applications using buffers
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Energy-conscious compilation based on voltage scaling
Proceedings of the joint conference on Languages, compilers and tools for embedded systems: software and compilers for embedded systems
Task scheduling and voltage selection for energy minimization
Proceedings of the 39th annual Design Automation Conference
Dynamic frequency and voltage control for a multiple clock domain microarchitecture
Proceedings of the 35th annual ACM/IEEE international symposium on Microarchitecture
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Exploring the Probabilistic Design Space of Multimedia Systems
RSP '03 Proceedings of the 14th IEEE International Workshop on Rapid System Prototyping (RSP'03)
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
An Efficient Algorithm for Computing Optimal Discrete Voltage Schedules
SIAM Journal on Computing
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Energy consumption is a major factor that limits the performance of sensor applications. Sensor nodes have varying sampling rates since they face continuously changing environments. In this paper, the sampling rate is modeled as a random variable, which is estimated over a finite time window. We presents an online algorithm to minimize the total energy consumption while satisfying sampling rate with guaranteed probability. An efficient algorithm, EOSP (Energy-aware Online algorithm to satisfy Sampling rates with guaranteed Probability), is proposed. Our approach can adapt the architecture accordingly to save energy. Experimental results demonstrate the effectiveness of our approach.