A Cooperative Power-Saving Technique Using DVS and DMS Based on Load Prediction in Sensor Networks

In wireless sensor networks (WSN), energy saving is the main issue of research, and many energy saving techniques have been proposed. Dynamic Voltage Scaling (DVS) and Dynamic Modulation Scaling (DMS) are techniques reduce energy consumption by using the idle time of the sensor node. When we use both techniques at the same time, an appropriate mechanism of cooperation is required because of the limitation of available idle time of the sensor node. Therefore, we propose a cooperative power. saving technique that applies DVS and DMS to sensor nodes for minimizing energy consumption. It uses a prediction mechanism that estimates the load of the processor and the radio communication device based on the log data to realize proper cooperation. We observed that the adaptation of the proposed technique is able to achieve the energy reduction up to 40 percent compared to without any energy saving techniques.

[1]  B. Hohlt,et al.  Flexible power scheduling for sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[2]  Dimitrios D. Vergados,et al.  A survey on power control issues in wireless sensor networks , 2007, IEEE Communications Surveys & Tutorials.

[3]  Anantha P. Chandrakasan,et al.  Dynamic voltage scaling techniques for distributed microsensor networks , 2000, Proceedings IEEE Computer Society Workshop on VLSI 2000. System Design for a System-on-Chip Era.

[4]  P. Ranjan,et al.  Co-Ordinated Adaptive Power (CAP) Managemet for Wireless Sensor Networks , 2007, 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007).

[5]  Mani B. Srivastava,et al.  Modulation scaling for Energy Aware Communication Systems , 2001, ISLPED '01.

[6]  Thomas D. Burd,et al.  Energy efficient CMOS microprocessor design , 1995, Proceedings of the Twenty-Eighth Annual Hawaii International Conference on System Sciences.

[7]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.