Abstract
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.
This work is partially supported by TI University Program, NSF EIA-0103709, Texas ARP 009741-0028-2001, NSF CCR-0309461, NSF IIS-0513669, and Microsoft, USA.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kallakuri, S., Doboli, A.: Energy conscious online architecture adaptation for varying latency constraints in sensor network applications. CODES+ISSS 2005, Jesey City, New Jersey, pp. 148–154 (September 2005)
Tongsima, S., Sha, E., Chantrapornchai, C., Surma, D., Passos, N.: Probabilistic Loop Scheduling for Applications with Uncertain Execution Time. IEEE Trans. on Computers 49, 65–80 (2000)
Weng, Y., Doboli, A.: Smart sensor architecture customized for image processing applications. IEEE Real-Time and Embedded Technology and Embedded Applications, pp. 336–403 (2004)
Hua, S., Qu, G., Bhattacharyya, S.S.: Exploring the Probabilistic Design Space of Multimedia Systems. In: IEEE International Workshop on Rapid System Prototyping, pp. 233–240. IEEE Computer Society Press, Los Alamitos (2003)
Zhang, Y., Hu, X., Chen, D.Z.: Task Scheduling and Voltage Selection for Energy Minimization. DAC 40, 183–188 (2002)
Ishihara, T., Yasuura, H.: Voltage scheduling problem for dynamically variable voltage processor. In: ISLPED, pp. 197–202 (1998)
Shin, D., Kim, J., Lee, S.: Low-Energy Intra-Task Voltage Scheduling Using Static Timing Analysis. In: DAC, pp. 438–443 (2001)
Stan, M.R., Burleson, W.P.: Bus-Invert Coding for Low-Power I/O. IEEE Trans. on VLSI Syst. 3(1), 49–58 (1995)
Saputra, H., Kandemir, M., Vijaykrishnan, N., Irwin, M.J., Hu, J.S., Hsu, C.-H., Kremer, U.: Energy-conscious compilation based on voltage scaling. In: LCTES 2002 (June 2002)
Sakurai, T., Newton, A.R.: Alpha-power law MOSFET model and its application to CMOS inverter delay and other formulas. IEEE J. Solid-State Circuits SC-25(2), 584–589 (1990)
Chandrakasan, A., Sheng, S., Brodersen, R.: Low-Power CMOS Digital Design. IEEE Journal of Solid-State Circuits 27(4), 473–484 (1992)
Semeraro, G., Albonesi, D., Dropsho, S., Magklis, G., Dwarkadas, S., Scott, M.: Dynamic Frequency and Voltage Control for a Multiple Clock Domain Microarchitecture. In: 35th Intl. Symp. on Microarchitecture (November 2002)
Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced cpu energy. In: 36th symposium on Foundations of Computer Science (FOCS), Milwankee, Wisconsin, pp. 374–382 (October 1995)
Li, M., Yao, F.: An efficient Algorithm for computing optimal discrete voltage schedules. SIAM J. Comput. 35(3), 658–671 (2005)
ITRS: International Technology Roadmap for Semiconductors. International SEMATECH, Austin, TX (2000), http://public.itrs.net/
Burd, T.B., Pering, T., Stratakos, A., Brodersen, R.: A dynamic voltage scaled microprocessor system. IEEE J. Solid-State Circuits 35(11), 1571–1580 (2000)
Intel: The Intel Xscale Microarchitecture. Technical Summary (2000)
Im, C., Kim, H., Ha, S.: Dynamic Voltage Scheduling Technique for Low-Power Multimedia Applications Using Buffers. In: Proc. of ISLPED (2001)
Rahimi, M., Pon, R., Kaiser, W., Sukhatme, G., Estrin, D., Srivastava, M.: Adaptive sampling for environmental robots. In: International Conference on Robotics and Automation (2004)
Berman, P., Calinescu, G., Shah, C., Zelikovsly, A.: Efficient energy management in sensor networks. Ad Hoc and Sensor Networks (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qiu, M., Sha, E.H.M. (2007). Energy-Aware Online Algorithm to Satisfy Sampling Rates with Guaranteed Probability for Sensor Applications. In: Perrott, R., Chapman, B.M., Subhlok, J., de Mello, R.F., Yang, L.T. (eds) High Performance Computing and Communications. HPCC 2007. Lecture Notes in Computer Science, vol 4782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75444-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-75444-2_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75443-5
Online ISBN: 978-3-540-75444-2
eBook Packages: Computer ScienceComputer Science (R0)