Studies in hybrid systems: modeling, analysis, and control
Studies in hybrid systems: modeling, analysis, and control
A survey of design techniques for system-level dynamic power management
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on low-power electronics and design
Dynamic battery state aware approaches for improving battery utilization
CASES '02 Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systems
An analytical high-level battery model for use in energy management of portable electronic systems
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Dynamic Power Management in Wireless Sensor Networks
IEEE Design & Test
Battery-Driven System Design: A New Frontier in Low Power Design
ASP-DAC '02 Proceedings of the 2002 Asia and South Pacific Design Automation Conference
Dynamic Power Management in Wireless Sensor Networks: An Application-Driven Approach
WONS '05 Proceedings of the Second Annual Conference on Wireless On-demand Network Systems and Services
An Efficient Dynamic Power Management Policy on Sensor Network
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Protocol assessment issues in low duty cycle sensor networks: The switching energy
SUTC '06 Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing -Vol 1 (SUTC'06) - Volume 01
Hardware-aware communication protocols in low energy wireless sensor networks
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
IEEE Communications Magazine
Information Sciences: an International Journal
Personal and Ubiquitous Computing
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A Wireless Sensor Network (WSN) comprises many sensor nodes each one containing a processing unit, one or more sensors, a power unit, and a radio for data communication. Nodes are power constrained, because they run on batteries which usually cannot be replaced due to the nature of the applications. We present a novel dynamic power management approach, named Dynamic Power Management with Scheduled Switching Modes (DPM-SSM), derived from a more realistic analysis of the battery capacity recovery effect and the switching energy. This was only possible thanks to the application of a more realistic battery model (i.e., Rakhmatov-Vrudhula battery model). We also devised a Hybrid Differential Petri Nets formalism to evaluate our power management solution. Preliminary results showed the potential for improving the battery lifetime by taking advantage of the battery recovery effect when a node transitions to a sleeping state mostly after packet transmissions. DPM-SSM provides several DPM modes which are triggered depending on the battery remaining capacity. Simulations results show that, depending on the scheduling approach, DPM-SSM can provide real battery power recovery without compromising the timeliness of the applications running on the sensor network.