Instruction-Level Power Estimator for Sensor Networks

In sensor networks, analyzing power consumption before actual deployment is crucial for maximizing service lifetime. This paper proposes an instruction-level power estimator (IPEN) for sensor networks. IPEN is an accurate and fine grain power estimation tool, using an instructionlevel simulator. It is independent of the operating system, so many different kinds of sensor node software can be simulated for estimation. We have developed the power model of a Micaz-compatible mote. The power consumption of the ATmega128L microcontroller is modeled with the base energy cost and the instruction overheads. The CC2420 communication component and other peripherals are modeled according to their operation states. The energy consumption estimation module profiles peripheral accesses and function calls while an application is running. IPEN has shown excellent power estimation accuracy, with less than 5% estimation error compared to real sensor network implementation. With IPEN’s high precision instruction-level energy prediction, users can accurately estimate a sensor network’s energy consumption and achieve fine-grained optimization of their software.

[1]  K. Wehrle,et al.  Accurate prediction of power consumption in sensor networks , 2005, The Second IEEE Workshop on Embedded Networked Sensors, 2005. EmNetS-II..

[2]  Mahmut T. Kandemir,et al.  The design and use of simplePower: a cycle-accurate energy estimation tool , 2000, Proceedings 37th Design Automation Conference.

[3]  Mahadev Satyanarayanan,et al.  PowerScope: a tool for profiling the energy usage of mobile applications , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[4]  Jens Palsberg,et al.  Avrora: scalable sensor network simulation with precise timing , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[5]  Matt Welsh,et al.  Simulating the power consumption of large-scale sensor network applications , 2004, SenSys '04.

[6]  Margaret Martonosi,et al.  Wattch: a framework for architectural-level power analysis and optimizations , 2000, Proceedings of 27th International Symposium on Computer Architecture (IEEE Cat. No.RS00201).

[7]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.

[8]  John S. Baras,et al.  ATEMU: a fine-grained sensor network simulator , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[9]  Naehyuck Chang,et al.  Energy-Monitoring Tool for Low-Power Embedded Programs , 2002, IEEE Des. Test Comput..

[10]  Jin Won Kim,et al.  Embedded sensor networked operating system , 2006, Ninth IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC'06).

[11]  A. Sinha,et al.  JouleTrack-a Web based tool for software energy profiling , 2001, Proceedings of the 38th Design Automation Conference (IEEE Cat. No.01CH37232).

[12]  Tae-Kyu Kim,et al.  Development of Sensor Network Simulator for Estimating Power Consumption and Execution Time , 2006 .

[13]  Gul A. Agha,et al.  SENS: a sensor, environment and network simulator , 2004, 37th Annual Simulation Symposium, 2004. Proceedings..

[14]  M. H. MacDougall.  Simulating computer systems , 1987 .