KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Lifetime Maximization of Wireless Video Sensor Network Node by Dynamically Resizing Communication Buffer

  • Choi, Kang-Woo (Samsung Electronics) ;
  • Yi, Kang (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Kyung, Chong Min (School of Electrical Engineering, KAIST)
  • Received : 2017.04.13
  • Accepted : 2017.10.08
  • Published : 2017.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Reducing energy consumption in a wireless video sensor network (WVSN) is a crucial problem because of the high video data volume and severe energy constraints of battery-powered WVSN nodes. In this paper, we present an adaptive dynamic resizing approach for a SRAM communication buffer in a WVSN node in order to reduce the energy consumption and thereby, to maximize the lifetime of the WVSN nodes. To reduce the power consumption of the communication part, which is typically the most energy-consuming component in the WVSN nodes, the radio needs to remain turned off during the data buffer-filling period as well as idle period. As the radio ON/OFF transition incurs extra energy consumption, we need to reduce the ON/OFF transition frequency, which requires a large-sized buffer. However, a large-sized SRAM buffer results in more energy consumption because SRAM power consumption is proportional to the memory size. We can dynamically adjust any active buffer memory size by utilizing a power-gating technique to reflect the optimal control on the buffer size. This paper aims at finding the optimal buffer size, based on the trade-off between the respective energy consumption ratios of the communication buffer and the radio part, respectively. We derive a formula showing the relationship between control variables, including active buffer size and total energy consumption, to mathematically determine the optimal buffer size for any given conditions to minimize total energy consumption. Simulation results show that the overall energy reduction, using our approach, is up to 40.48% (26.96% on average) compared to the conventional wireless communication scheme. In addition, the lifetime of the WVSN node has been extended by 22.17% on average, compared to the existing approaches.

Keywords

References

  1. S. Soro and W. Heinzelman, "A survey of visual sensor networks." Advances in Multimedia, pp. 1-21, 2009.
  2. H. Lakdawala, et al., "32nm x86 OS-compliant PC on-chip with dual-core Atom$^{(R)}$ processor and RF WiFi transceiver," in Proc. of IEEE Int'l Solid-State Circuits Conf. (ISSCC), pp.62-64, Feb. 2012.
  3. S. Na, et al., "Lifetime maximization of video blackbox surveillance camera," in Proc. of IEEE Conf. on Multimedia and Expo (ICME), pp.1-6, July 2011.
  4. A. Zainaldin, et al., "Adaptive Rate Control Low bit-rate Video Transmission over Wireless ZigBee networks," in Proc. of IEEE International Conference on Communications, pp. 52-58, May, 2008.
  5. Schurgers, et al., "Energy efficient routing in wireless sensor networks," MILCOM 2001 Communications for Network-Centric Operations: Creating the Information Force, Oct. 2001.
  6. Krashinsky and Balakrishnan, "Minimizing energy for wireless web access with bounded slowdown," in Proc. of the 8th ACM annual international conference on Mobile computing and networking, pp. 119-130, 2002.
  7. T. Enhua, et al., "PSM-throttling: Minimizing Energy Consumption for Bulk Data Communications in WLANs," IEEE International Conference on Network Protocols, ICNP 2007, pp.123-132, Oct. 2007.
  8. Lee, Y., Kim, J., Kyung, C.-M., "Energy-Aware Video Encoding for Image Quality Improvement in Battery-Operated Surveillance Camera," IEEE Trans. on VSLI Systems, vol. 20, no. 2, pp. 310-318, 2012. https://doi.org/10.1109/TVLSI.2010.2102055
  9. S. Drago, et al., "A 2.4GHz 830pJ/bit duty-cycled wake-up receiver with -82dBm sensitivity for crys-tal-less wireless sensor nodes," in Proc. Of IEEE Int'l Solid-State Circuits Conference (ISSCC), pp.224-225, Feb. 2010.
  10. CACTI http://quid.hpl.hp.com:9081/cacti/sram.y?new.
  11. Kyung, C.M., Yoo, S., Energy-Aware System Design, (Springer, 2011, 1st ed.)
  12. Yong He, Ruixi Yuan, "A Novel Scheduled Power Saving Mechanism for 802.11 Wireless LANs," IEEE Trans. on Mobile Computing, vol. 8, no. 6, pp. 1368-1383, Oct. 2009. https://doi.org/10.1109/TMC.2009.53
  13. Kang, K., Kim, J., Yoo,S., Kyung, C.M., "Temperature-Aware Integrated DVFS and Power gating for Executing tasks with Runtime Distribution," IEEE Trans. on CAD, vol. 29, no. 9, pp. 1381-1394, 2010. https://doi.org/10.1109/TCAD.2010.2059290
  14. J. Choi, et al., "A $1.36{\mu}W $ adaptive CMOS image sensor with reconfigurable modes of operation from available energy/illumination for distributed wireless sensor network," in Proc. of IEEE Int'l Solid-State Circuits Conf. (ISSCC), pp.112-114, Feb. 2012.
  15. C. Tsai, et al., "PIR-sensor-based lighting device with ultra-low standby power consumption," in Proc . of IEEE In-strumentation and Measurement Technology Conf., (I2MTC), pp.1-6, May 2011.
  16. T. Winkler, B. Rinner "Power aware communication in wireless pervasive smart camera networks," in Proc. of Int'l Conf. on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp.283-288, Dec. 2009.