International Journal of Parallel Programming

, Volume 43, Issue 1, pp 3–23 | Cite as

Low-Power Reconfigurable Miniature Sensor Nodes for Condition Monitoring

  • Teemu Nyländen
  • Jani Boutellier
  • Karri Nikunen
  • Jari Hannuksela
  • Olli Silvén


Wireless sensor networks (WSNs) are being deployed at an escalating rate for various application fields. The ever growing number of application areas requires a diverse set of algorithms with disparate processing needs. WSNs also need to adapt to prevailing energy conditions and processing requirements. The preceding reasons rule out the use of a single fixed design. Instead, a general purpose design that can rapidly be adapted to different conditions and requirements is desired. In lieu of the traditional inflexible wireless sensor node consisting of a separate micro-controller, radio transceiver, sensor array and energy storage, we propose a unified rapidly reconfigurable miniature sensor node, implemented with a transport triggered architecture processor on a low-power Flash FPGA. To our knowledge, this is the first study of its kind. The proposed approach does not solely concentrate on energy efficiency but a high emphasis is also put on the ease of development perspective. Power consumption and silicon area usage comparison based on solutions implemented using our novel rapid design approach for wireless sensor nodes are performed. The comparison is performed between 16-bit fixed point, 16-bit floating point and 32-bit floating point implementations. The implemented processors and algorithms are intended for rolling bearing condition monitoring, but can be fully extended for other applications as well.


Wireless sensor networks Transport triggered architecture Application specific processors 



This study was carried out in the InterSync project. The project is a part of the Finnish Metals and Engineering Competence Cluster (FIMECC) research program Energy and Life Cycle Cost Efficient Machines (EFFIMA). The project is also financially supported by the Finnish Funding Agency for Technology and Innovation (TEKES) and industrial companies. Their support is gratefully acknowledged. We would also like to thank VTT Technical Research Centre of Finland for co-operation and for providing the trial hardware for a complete mote solution.


  1. 1.
    Hempstead, M., Lyons, M.J., Brooks, D., Gu-Yeon, W.: Survey of hardware systems for wireless sensor networks. J. Low Power Electron. 4(1), 11–20 (2008)CrossRefGoogle Scholar
  2. 2.
    Project: Tmote Sky. [Online].
  3. 3.
    MICAz wireless measurement system. MEMSIC Inc., Tech. RepGoogle Scholar
  4. 4.
    Raju, M., Grazier, M.: Ultra low power meets energy harvesting. Texas instruments, Tech. Rep. [Online]. (2010)
  5. 5.
    Paradiso, J.A., Starner, T.: Energy scavenging for mobile and wireless electronics. IEEE Pervasive Comput. 4(1), 18–27 (2005)CrossRefGoogle Scholar
  6. 6.
    Najafi, K.: Micro energy harvesters—an alternative source of renewable energy. [Online]. (2010)
  7. 7.
    Polastre, J., Szewczyk, R., Sharp, C., Culler, D.: The mote revolution: low power wireless sensor network devices. In: Hot Chips 16: A Symposium on High Performance Chips (2004)Google Scholar
  8. 8.
    Chen, Y., Gnawali, O., Kazandjieva, M., Levis, P., Regehr, J.: Surviving sensor network software faults. In: Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles. Ser. SOSP ’09, pp. 235–246 (2009)Google Scholar
  9. 9.
    Wan, V., Young, E.: Power management in an RF5 audio streaming application using DSP/BIOS. Texas instruments, Tech. Rep. (2005)Google Scholar
  10. 10.
    Kansal, A., Hsu, J., Zahedi, S., Srivastava M.B.: Power management in energy harvesting sensor networks. ACM Trans. Embed. Comput. Syst. 6 Sept 2007Google Scholar
  11. 11.
    Jiang, X., Polastre, J., Culler, D.: Perpetual environmentally powered sensor networks. In: Fourth International Symposium on Information Processing in Sensor Networks, 2005. IPSN 2005, pp. 463–468, April 2005Google Scholar
  12. 12.
    Simjee, F., Chou, P.H.: Everlast: long-life, supercapacitor-operated wireless sensor node. In: Proceedings of the 2006 International Symposium on Low Power Electronics and Design, 2006. ISLPED’06, pp. 197–202, Oct 2006Google Scholar
  13. 13.
    Raghunathan, V., Ganeriwal, S., Srivastava, M.: Emerging techniques for long lived wireless sensor networks. IEEE Commun. Mag. 44(4), 108–114 (2006)CrossRefGoogle Scholar
  14. 14.
    Alippi, C., Anastasi, G., Di Francesco, M., Roveri, M.: Energy management in wireless sensor networks with energy-hungry sensors. IEEE Instrument. Meas. Mag. 12(2), 16–23 (2009)CrossRefGoogle Scholar
  15. 15.
    Chou, J., Petrovic, D., Ramachandran, K.: A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks. In: INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, 2, vol. 2, pp. 1054–1062 (2003)Google Scholar
  16. 16.
    Heo, J., Hong, J., Cho, Y.: EARQ: energy aware routing for real-time and reliable communication in wireless industrial sensor networks. IEEE Trans. Indus. Inf. 5(1), 3–11 (2009)CrossRefGoogle Scholar
  17. 17.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (2000)Google Scholar
  18. 18.
    Li, F., Ye, L., Zhang, G., Meng, G.: Bearing fault detection using higher-order statistics based ARMA model. Key engineering materials, vol. Damage assessment of structures VII, pp. 271–276, Sept 2007Google Scholar
  19. 19.
    Soltani Bozchalooi, I., Liang, M.: Parameter-free bearing fault detection based on maximum likelihood estimation and differentiation. Meas. Sci. Technol. 20(6), 065102 (2009)CrossRefGoogle Scholar
  20. 20.
    Esko, O., Jääskelainen, P., Huerta, P., de la Lama, C.S., Takala, J., Martinez, J.I.: Customized exposed datapath soft-core design flow with compiler support. In: 2010 international conference on field programmable logic and applications (FPL), pp. 217–222 (2010)Google Scholar
  21. 21.
    Yifan, H., Dongrui, S., Mesman, B., Corporaal, H.: MOVE-Pro: a low power and high code density TTA architecture. In: 2011 International Conference on Embedded Computer Systems (SAMOS), pp. 294–301 (2011)Google Scholar
  22. 22.
    Pitkänen, T., Mäkinen, R., Heikkinen, J., Partanen, T., Takala, J.: Low-power, high-performance TTA processor for 1,024-point dast fourier transform. In: Embedded Computer Systems: Architectures, Modeling, and Simulation: Proceedings of the 6th International Workshop SAMOS VI, LNCS 4017, pp. 227–236 (2006)Google Scholar
  23. 23.
    Microsemo SoC Products Group. Flash FPGAs in the value-based market. MicroSemi SoC Products Group, Tech. Rep. [Online]. (2005)
  24. 24.
    MicroSemi SoC Products Group. Igloo low-power Flash FPGAs with Flash*Freeze technology. [Online]. (2011)
  25. 25.
    Tiwari, V., Malik, S., Wolfe, A., Lee, M.T.-C.: Instruction level power analysis and optimization of software. In: Proceedings of the Ninth International Conference on VLSI Design, 1996, pp. 326–328 (1996)Google Scholar
  26. 26.
    Rintaluoma, T., Silvén, O.: Energy efficiency of mobile video decoding. In: International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, IC-SAMOS 2007, pp. 103–109 (2007)Google Scholar
  27. 27.
    Janhunen, J., Pitkänen, T., Silven, O., Juntti, M.: Fixed- and floating-point processor comparison for MIMO-OFDM detector. IEEE J. Sel. Top. Signal Process. 5(8), 1588–1598 (2011)CrossRefGoogle Scholar
  28. 28.
    Lomont, C.: Fast inverse square root. Tech. Rep., (2003)Google Scholar
  29. 29.
    Bishop, D.: Floating-point package user’s guide. EDA Industry Working Groups, Tech. Rep. (2008)Google Scholar
  30. 30.
    Nyländen, T., Boutellier, J., Nikunen, K., Hannuksela, J., Silven, O.: Reconfigurable miniature sensor nodes for condition monitoring. In: Proceedings of the International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XII) (2012)Google Scholar
  31. 31.
    Ghazi, A., Boutellier, J., Hannuksela, J., Silvén, O., Janhunen, J.: Low-complexity sdr implementation of IEEE 802.15.4 (zigbee) baseband transceiver. In: Proceedings on Application Specific Processor, SDR’13 WInnComm (2013)Google Scholar
  32. 32.
    Mais, J.: Spectrum analysis: the key features of analyzing spectra. [Online]. (2002)
  33. 33.
    Wang, Q., Zhang, Y., Sun, N.X., McDaniel, J.G., Wang, M.L.: High power density energy harvester with high permeability magnetic material embedded in a rotating wheel. In: SPIE Proceedings, 8347, (2012)Google Scholar
  34. 34.
    Joyce, B.S.: Development of an electromagnetic energy harvester for monitoring wind turbine blades. Master’s thesis, Virginia Polytechnic Institute and State University (2011)Google Scholar
  35. 35.
    Wang, Y.-J., Shen, S.-C., Chen C.-D.: Wideband Electromagnetic Energy Harvesting from a Rotating Wheel. Small-Scale Energy Harvesting. 10 (2012)Google Scholar
  36. 36.
    Li, B., Chow, M.-Y., Tipsuwan, Y., Hung, J.C.: Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 47(5), 1060–1069 (2000)CrossRefGoogle Scholar
  37. 37.
    Pullin, A.: An energy audit of automotive vibration sources for energy harvesting and applied computation in wireless sensor networks. University of California, Berkeley, Tech. Rep. [Online]. (2010)
  38. 38.
    Polastre, J., Szewczyk, R., Culler, D.: Telos: enabling ultra-low power wireless research. In: Fourth International Symposium on Information Processing in Sensor Networks, 2005. IPSN 2005, pp. 364–369, April 2005Google Scholar
  39. 39.
    Bier, J.: Jeff Bier’s impulse response-a rising tide lifts all boats. [Online]., Nov 2012
  40. 40.
    Sudevalayam, S., Kulkarni, P.: Energy harvesting sensor nodes: survey and implications. IEEE Commun. Surv. Tutor. 13(3), 443–461 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Teemu Nyländen
    • 1
  • Jani Boutellier
    • 1
  • Karri Nikunen
    • 2
  • Jari Hannuksela
    • 1
  • Olli Silvén
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of OuluOuluFinland
  2. 2.Department of Electrical EngineeringUniversity of OuluOuluFinland

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