Skip to main content

Advertisement

SpringerLink
Log in

A generic framework for energy evaluation on wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Due to reliance on batteries, energy consumption has always been of significant concern for sensor node networks. This work presents the design and implementation of a house-build experimental platform, named Energy Management System for Wireless Sensor Networks (EMrise) for the energy management and exploration on wireless sensor networks. Consisting of three parts, the SystemC-based simulation environment of EMrise enables the HW/SW co-simulation for energy evaluation on heterogeneous sensor networks. The hardware platform of EMrise is further designed to facilitate the realistic energy consumption measurement and calibration as well as accurate energy exploration. In the meantime, a generic genetic algorithm based optimization framework of EMrise is also implemented to automatically, quickly and intelligently fine tune hundreds of possible solutions for the given task to find the best suitable energy-aware tradeoffs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Lorincz, K., Chen, B., Challen, G. W., Chowdhury, A. R., Patel, S., Bonato, P., & Welsh, M. (2009). Mercury: A wearable sensor network platform for high-fidelity motion analysis. In Proceedings of the 7th ACM conference on embedded networked sensor systems (SenSys’09) (pp. 183–196)

  2. Lloret, J., Bosch, I., Sendra, S., & Serrano, A. (2011). A wireless sensor network for vineyard monitoring that uses image processing. Sensors, 11(6), 6165–6196.

    Article  Google Scholar 

  3. Yeh, L.-W., Wang, Y.-C., & Tseng, Y.-C. (2009). iPower: an energy conservation system for intelligent buildings by wireless sensor networks. International Journal of Sensor Networks, 5(1), 1–10.

    Article  Google Scholar 

  4. Mikhaylov, K., Tervonen, J., Heikkila, J., & Kansakoski, J. (2012, April). Wireless sensor networks in industrial environment: Real-life evaluation results. In 2nd Baltic congress on future internet communications (BCFIC 2012) (pp. 1–7).

  5. Durisic, M. P., Tafa, Z., Dimic, G. & Milutinovic, V. (2012, June). A survey of military applications of wireless sensor networks. In 2012 Mediterranean conference on embedded computing (MECO 2012) (pp. 196–199).

  6. Fall, K. & Varadhan, K. (2011, November) The ns manual (formerly ns notes and documentation). http://www.isi.edu/nsnam/ns/doc/ns_doc.pdf

  7. OMNeT++ network simulation framework. http://www.omnetpp.org/.

  8. Simon, G., Volgyesi, P., Maroti, M. & Ledeczi, A. (2003, March) Simulation-based optimization of communication protocols for large-scale wireless sensor networks. In Proceedings of 2003 IEEE aerospace conference (Vol. 3, pp. 1339–1346).

  9. Levis, P., Lee, N., Welsh, M. & Culler, D. (2003) TOSSIM: Accurate and scalable simulation of entire tinyos applications. In Proceedings of the 1st international conference on embedded networked sensor systems (SenSys’03) (pp. 126–137).

  10. Polley, J., Blazakis, D., McGee, J., Rusk, D., & Baras, J. S. (2004, October). ATEMU: A fine-grained sensor network simulator. In 2004 first annual IEEE communications society conference on sensor and ad hoc communications and networks (IEEE SECON’04) (pp. 145–152).

  11. Titzer, B. L., Lee, D. K., & Palsberg, J. (2006, April). Avrora: Scalable sensor network simulation with precise timing. In Processing of fourth international symposium on information sensor networks (IPSN’05) (pp. 477–482).

  12. Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., et al. (2005). Tinyos: An operating system for sensor networks. Ambient intelligence (pp. 115–148). Heidelberg: Springer.

    Google Scholar 

  13. Crossbow Technology Inc. MicaZ datasheet. http://www.openautomation.net/uploadsproductos/micaz_datasheet.pdf.

  14. Barboni, L. & Valle, M. (2008, August). Experimental analysis of wireless sensor nodes current consumption. In The second international conference on sensor technologies and applications, SENSORCOMM 2008 (pp. 401–406).

  15. Landsiedel, O., Wehrle, K., & Gotz, S. (2005). Accurate prediction of power consumption in sensor networks. In The second IEEE workshop on embedded networked sensors (pp. 37–44).

  16. Crossbow Technology Inc. MICA2 datasheet. https://www.eol.ucar.edu/rtf/facilities/isa/internal/CrossBow/DataSheets/mica2.pdf

  17. Crossbow Technology Inc. TelosB datasheet. http://www.willow.co.uk/TelosB_Datasheet.pdf.

  18. Texas Instruments Inc. (2013). ADC10664 datasheet. http://www.ti.com/lit/ds/symlink/adc10664.pdf.

  19. Hurni, P., Nyffenegger, B., Braun, T., & Hergenroeder, A. (2011). On the accuracy of software-based energy estimation techniques. Lecture notes in computer science (Vol. 6567, pp. 49–64).

  20. Hergenroder, A., Wilke, J., & Meier, D. (2010). Distributed energy measurements in WSN testbeds with a sensor node management device (SNMD). In International conference on architecture of computing systems (ARCS) (pp. 1–7).

  21. Texas Instruments Inc. (2011). MSP430 datasheet. www.ti.com/lit/ds/symlink/msp430f1611.pdf.

  22. Mackensen, E., Lai, M. & Wendt, T. M. (2012, October) Bluetooth low energy (ble) based wireless sensors. In 2012 IEEE Sensors (pp. 1–4).

  23. Zhang, J., Orlik, P. V., Sahinoglu, Z., Molisch, A. F., & Kinney, P. (2009). UWB systems for wireless sensor networks. Proceedings of the IEEE, 97(2), 313–331.

    Article  Google Scholar 

  24. Buratti, C., Conti, A., Dardari, D., & Verdone, R. (2009). An overview on wireless sensor networks technology and evolution. Sensors, 9(9), 6869–6896.

    Article  Google Scholar 

  25. Nanhao, Z. H. U., & O’Connor, I. (2013, July) Performance evaluations of unslotted CSMA/CA algorithm at high data rate WSNs scenario. In The 9th IEEE international wireless communications and mobile computing conference (IWCMC 2013) (pp. 406–411).

  26. Castagnetti, A., Pegatoquet, A., Belleudy, C., & Auguin, M. (2012). A framework for modeling and simulating energy harvesting WSN nodes with efficient power management policies. EURASIP Journal on Embedded Systems 2012, 1, 8.

    Article  Google Scholar 

  27. Ye, W., Heidemann, J., & Estrin, D. (2004). Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Transactions on Networking, 12(3), 493–506.

    Article  Google Scholar 

  28. Van Dam, T., & Langendoen, K. (2003). An adaptive energy-efficient mac protocol for wireless sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (SenSys’03) (pp. 171–180).

  29. IEEE Computer Society. (2006). Part 15.4: Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (WPANS). In IEEE Std 802.15.4-2006.

  30. Accellera Systems Initiative. About SystemC. http://www.accellera.org/downloads/standards/systemc/about_systemc/

  31. Prayati, A., Antonopoulos, Ch., Stoyanova, T., Koulamas, C., & Papadopoulos, G. (2010). A modeling approach on the TelosB WSN platform power consumption. Journal of Systems and Software, 83(8), 1355–1363.

    Article  Google Scholar 

  32. Agarwal, R., Martinez-Catala, R. V., Harte, S., Segard, C., & O’Flynn, B. (2008, August). Modeling power in multi-functionality sensor network applications. In The second international conference on sensor technologies and applications, SENSORCOMM 2008, (pp. 507–512).

  33. Sivanandam, S. N., & Deepa, S. N. (2007). Introduction to genetic algorithms, chapter 2 genetic algorithms (pp. 15–36). Heidelberg: Springer Publishing Company, Incorporated.

    Google Scholar 

  34. Moteiv Corporation. (2006). Tmote sky datasheet. http://www.eecs.harvard.edu/konrad/projects/shimmer/references/tmote-sky-datasheet.pdf

  35. Shimmer Research. Shimmer—wireless sensor platform for wearable applications. http://www.shimmer-research.com/

  36. Zhu, N. (2013). Simulation and optimization of energy consumption on wireless sensor networks. Ph.D. Thesis, EEA Dept., Ecole Centrale de Lyon.

  37. Zhu, N., & O’connor, I. (2013 April). Energy measurements and evaluations on high data rate and ultra low power WSN node. In IEEE international conference on networking, sensing and control (ICNSC’13) (pp. 232–236).

  38. Nordic Semiconductor Inc. (2008). nRF24l01+ product specification. http://www.nordicsemi.com/eng/content/download/2726/34069/file/nRF24L01P_Product_Specification_1_0.pdf.

  39. Fummi, F., Quaglia, D., & Stefanni, F. (2008, September). A SystemC-based framework for modeling and simulation of networked embedded systems. In Forum on specification, verification and design languages (FDL’08) (pp. 49–54).

  40. Hiner, J., Shenoy, A., Lysecky, R., Lysecky, S., & Ross, A. G. (2010, June). Transaction-level modeling for sensor networks using SystemC. In 2010 IEEE international conference on sensor networks, ubiquitous, and trustworthy computing (SUTC’10) (pp. 197–204).

  41. Microchip Technology Inc. (2010). Microchip MIWI wireless networking protocol stack. Application Note. http://ww1.microchip.com/downloads/en/AppNotes/AN1066%20%20MiWi%20App%20Note.pdf.

  42. Hauer, J.-H. (2009). Tkn15.4: An IEEE 802.15.4 MAC implementation for tinyos2. TKN technical report TKN-08-003. http://www.tkn.tuberlin.de/fileadmin/fg112/Papers/TKN154.pdf.

  43. Nordic Semiconductor Inc. 2.4 GHz RF ultra low power 2.4 GHz RF ICS/solutions. http://www.nordicsemi.com/eng/Products/2.4GHz-RF.

  44. Henderson, T. Free space model. http://www.isi.edu/nsnam/ns/doc/node217.html

  45. Jovanov, E., Milenkovic, A., Otto, C., & de Groen, P. C. (2005). A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2(6), 1–10.

    Google Scholar 

  46. Ge, Y., Liang, L., Ni, W., Wai, A. A. P. & Feng, G. (2012). A measurement study and implication for architecture design in wireless body area networks. In 2012 IEEE international conference on pervasive computing and communications workshops (PERCOM’12) (pp. 799–804).

  47. Microchip Technology Inc. (2005) Pic16f87/88 data sheet. http://ww1.microchip.com/downloads/en/devicedoc/30487c.pdf

  48. Microchip Technology Inc. PIC18F2525/2620/4525/4620 datasheet. http://ww1.microchip.com/downloads/en/devicedoc/39626b.pdf.

  49. Future Technology Devices International Limited. (2010). FT232R USB UART IC. http://www.ftdichip.com/Support/Documents/DataSheets/ICs/DS_FT232R.pdf.

  50. Hergenroder, A., Wilke, J., & Meier, D. (2010, February). Distributed energy measurements in WSN testbeds with a sensor node management device (SNMD). In 23rd international conference on architecture of computing systems (ARCS’10) (pp. 1–7).

  51. Hergenroder, A., Horneber, J., Meier, D., Armbruster, P., & Zitterbart, M. (2009) Distributed energy measurements in wireless sensor networks. In Proceedings of the 7th ACM conference on embedded networked sensor systems (SenSys’09) (pp. 299–300).

  52. Selavo, L., Zhou, G., & Stankovic, J. A. (2006, October). Seemote: In-situ visualization and logging device for wireless sensor networks. In 3rd international conference on broadband communications, networks and systems (BROADNETS’06) (pp. 1–9).

  53. Spekreijse, R. A communication class for serial port. http://www.codeguru.com/cpp/in/network/serialcommunications/article.php/c2483/Acommunication-class-for-serial-port.htm.

  54. Morton, G. (2004). MSP430 competitive benchmarking. Application report. http://www.gaw.ru/pdf/TI/app/msp430/slaa205.pdf

  55. Zhu, N., & O’Connor, I. (2013). iMASKO: A genetic algorithm based optimization framework for wireless sensor networks. Journal of Sensor and Actuator Networks., 2(4), 675–699.

    Article  Google Scholar 

  56. Sivanandam, S. N., & Deepa, S. N. (2007). Introduction to genetic algorithms, chapter 10 applications of genetic algorithm (pp. 317–402). Heidelberg: Springer Publishing Company, Incorporated.

    Google Scholar 

  57. Chipperfield, A., Fleming, P., Pohlheim, H., & Fonseca, C. Genetic algorithm toolbox user’s guide. http://crystalgate.shef.ac.uk/code/manual.pdf.

  58. Ferreira, F. F. (2012). Architectural exploration methods and tools for heterogeneous 3D-IC. Ph.D. Thesis, EEA Dept., Ecole Centrale de Lyon.

  59. Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for hierarchical wireless sensor networks. Journal of Network, 2(5), 87–97.

    Article  Google Scholar 

  60. Sudha, N., Valarmathi, M. L., & Neyandar, T. C. (2011). Optimizing energy in WSN using evolutionary algorithm. In IJCA proceedings on international conference on VLSI, communications and instrumentation (ICVCI’11) (Vol. 12, pp. 26–29).

  61. Liu, J.-L., & Ravishankar, C. V. (2011). LEACH-GA: Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks. International Journal of Machine Learning and Computing, 1(1), 79–85.

    Article  Google Scholar 

  62. Bhatti, M. S., Kapoor, D., Kalia, R. K., Reddy, A. S., & Thukral, A. K. (2011). Rsm and ann modeling for electrocoagulation of copper from simulated wastewater: Multi objective optimization using genetic algorithm approach. Desalination, 274(1), 74–80.

    Article  Google Scholar 

  63. Frantz, F., Labrak, L., & O’Connor, I. (2012). 3D IC floorplanning: Automating optimization settings and exploring new thermal-aware management techniques. Microelectronics Journal, 43(6), 423–432.

    Article  Google Scholar 

  64. Latre, B., Braem, B., Moerman, I., Blondia, C., & Demeester, P. (2011). A survey on wireless body area networks. Wireless Networks, 17(1), 1–18.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CETC Group, GCI Science and Technology Co., Ltd, No. 381, Xingangzhong Road, Haizhu District, 510310, Canton, China

    Nanhao Zhu

  2. Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187, Lulea, Sweden

    Athanasios V. Vasilakos

Authors
  1. Nanhao Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Athanasios V. Vasilakos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nanhao Zhu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, N., Vasilakos, A.V. A generic framework for energy evaluation on wireless sensor networks. Wireless Netw 22, 1199–1220 (2016). https://doi.org/10.1007/s11276-015-1033-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1033-x

Keywords

Search

Navigation