Skip to main content

Advertisement

Log in

Development of Energy Efficient Image/Video Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Image/Video Sensor Networks are emerging applications for sensor network technologies. The relatively high energy consuming image capturing process and the large size of the data collected by image/video sensors presents new challenges for the sensor network in terms of energy consumption and network capacity. We propose to address these issues through the use of a high density network deployment. A high density network allows network nodes to conserve power by reducing their transmission power and simultaneously increases the potential for spatially concurrent transmissions within the network, resulting in improved network throughput. Furthermore, with the use of additional relay nodes, we allow a communication density that differs from the sensing density. A higher communication density has the potential to further increase the spatially concurrent transmission. Moreover, this reduces the relay burden of the sensor node, thus conserving sensor energy. In this work, we show analytically how a high density network design effectively improves energy consumption and network capacity. Furthermore, we discuss the constraints placed on a high density sensor network deployment due to application latency requirements, sensor coverage requirements, connectivity requirements, and node costs.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Abrams, Z., Goel, A., & Plotkin, S. (2004). Set k-cover algorithms for energy efficient monitoring in wireless sensor networks. In IPSN ’04: Proceedings of the third international symposium on Information processing in sensor networks (pp. 424–432). New York, NY, USA: ACM Press.

  2. Bahi, J., Mostefaoui, A., & Salomon, M. (2006). Increasing lifetime of wireless ad hoc networks using a decentralized algorithmic approach. Networks, 2006. ICON ’06. 14th IEEE International Conference on, 2, 1–6.

  3. Chen, B., Jamieson, K., Balakrishnan, H., & Morris, R. (2001). Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. In MobiCom ’01: Proceedings of the 7th annual international conference on Mobile computing and networking (pp. 85–96). New York, NY, USA: ACM Press.

  4. Correia, L. H. A., Macedo, D. F., Silva, D. A. C., dos Santos, A. L., Loureiro, A. A. F., & Nogueira, J. M. S. (2005). Transmission power control in mac protocols for wireless sensor networks. In MSWiM ’05: Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems (pp. 282–289). New York, NY, USA: ACM.

  5. Crossbow micaz mote data sheet. http://www.xbow.com.

  6. Cyclops camera. http://www.cyclopscamera.com/.

  7. Dong, Q. (2005). Maximizing system lifetime in wireless sensor networks. In IPSN ’05: Proceedings of the 4th international symposium on Information processing in sensor networks (p. 3). Piscataway, NJ, USA: IEEE Press.

  8. Gupta, H., Navda, V., Das, S. R., & Chowdhary, V. (2005). Efficient gathering of correlated data in sensor networks. In MobiHoc ’05: Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing (pp. 402–413). New York, NY, USA: ACM Press.

  9. Gupta P., Kumar P.R. (2000) The capacity of wireless networks. IEEE Transactions on Information Theory 46(2): 388–404

    Article  MATH  MathSciNet  Google Scholar 

  10. Hekmat R., Mieghem P.V. (2004) Interference in wireless multi-hop ad-hoc networks and its effect on network capacity. Wirelless Network 10(4): 389–399

    Article  Google Scholar 

  11. Iranli, A., Maleki, M., & Pedram, M. (2005). Energy efficient strategies for deployment of a two-level wireless sensor network. In ISLPED ’05: Proceedings of the 2005 international symposium on Low power electronics and design (pp. 233–238). New York, NY, USA: ACM Press.

  12. Li, J., Blake, C., Couto, D. S. D., Lee, H. I., & Morris, R. (2001). Capacity of ad hoc wireless networks. In MobiCom ’01: Proceedings of the 7th annual international conference on Mobile computing and networking (pp. 61–69). New York, NY, USA: ACM Press.

  13. Lin, S., Zhang, J., Zhou, G., Gu, L., Stankovic, J. A., & He, T. (2006). Atpc: Adaptive transmission power control for wireless sensor networks. In SenSys ’06: Proceedings of the 4th international conference on Embedded networked sensor systems (pp. 223–236). New York, NY, USA: ACM Press.

  14. Pemmaraju, S. V., & Pirwani, I. A. (2006). Energy conservation via domatic partitions. In MobiHoc ’06: Proceedings of the seventh ACM international symposium on Mobile ad hoc networking and computing (pp. 143–154). New York, NY, USA: ACM Press.

  15. Ramanathan, R., & Hain, R. (2000). Topology control of multihop wireless networks using transmit power adjustment. In INFOCOM (2), pp. 404–413.

  16. Rappaport T.S. (2002) Wireless Communications, Principles and Practice (2nd ed.). Pretince Hall PTR, Upper Saddle River, NJ

    Google Scholar 

  17. Schurgers C., Tsiatsis V., Ganeriwal S., Srivastava M. (2002) Optimizing sensor networks in the energy-latency-density design space. IEEE Transactions on Mobile Computing 1(1): 70–80

    Article  Google Scholar 

  18. Szewczyk R., Osterweil E., Polastre J., Hamilton M., Mainwaring A., Estrin D. (2004) Habitat monitoring with sensor networks. Communications of the ACM 47(6): 34–40

    Article  Google Scholar 

  19. Thorstensen, B., Syversen, T., Bjornvold, T.-A., & Walseth, T. (2004). Electronic shepherd - a low-cost, low-bandwidth, wireless network system. In MobiSys ’04: Proceedings of the 2nd international conference on Mobile systems, applications, and services (pp. 245–255). New York, NY, USA: ACM Press.

  20. Xing G., Lu C., Zhang Y., Huang Q., Pless R. (2007) Minimum power configuration for wireless communication in sensor networks. ACM Trans. Sen. Netw. 3(2): 11

    Article  Google Scholar 

  21. Xu, Y., Heidemann, J., & Estrin, D. (2000). Adaptive energy-conserving routing for multihop ad hoc networks. Research Report 527, USC/ISI.

  22. Xu, Y., Heidemann, J., & Estrin, D. (2001). Geography-informed energy conservation for ad hoc routing. In MobiCom ’01: Proceedings of the 7th annual international conference on Mobile computing and networking (pp. 70–84). New York, NY, USA: ACM Press.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong Pei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bender, P., Pei, Y. Development of Energy Efficient Image/Video Sensor Networks. Wireless Pers Commun 51, 283–301 (2009). https://doi.org/10.1007/s11277-008-9643-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-008-9643-6

Keywords