Distributed Techniques for Energy Conservation in Wireless Sensor Networks

Mohamed Abdelaal

Abstract

References

  1. (2014). Intel Berkeley Research Lab.
  2. Abdelaal, M., Kuka, C., Theel, O., and Nicklas, D. (2015). Reliable Virtual Sensing for Wireless Sensor Networks. In 2015 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). (under review).
  3. Abdelaal, M. and Theel, O. (2013a). An efficient and adaptive data compression technique for energy conservation in wireless sensor networks. The IEEE Conference on Wireless Sensors (ICWiSe 2013), pages 124- 129.
  4. Abdelaal, M. and Theel, O. (2013b). Power management in wireless sensor networks: Challenges and solutions. In 2013 International Conference in Centeral Asia on Internet ((ICI 2013)).
  5. Abdelaal, M. and Theel, O. E. (2014). Recent Energypreservation Endeavours for Long-life Wireless Sensor Networks: A Concise Survey. In Eleventh International Conference on Wireless and Optical Communications Networks, WOCN 2014, Vijayawada, Guntur District, Andhra Pradesh, India, September 11-13, 2014, pages 1-7. An extension of the article: Power Management in Wireless Sensor Networks: Challenges and Solutions.
  6. Abdelaal, M., Yang, G., Fränzle, M., and Theel, O. (2014). Eavs: Energy aware virtual sensing for wireless sensor networks. In 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).
  7. Adinya, O. J. and Daoliang, L. (2012). Low power transceiver design parameters for wireless sensor networks. Wireless Sensor Network, 4(10):243-249.
  8. Akyildiz, I., Pompili, D., and Melodia, T. (2005). Underwater Acoustic Sensor Networks: Research Challenges. Ad Hoc Networks Journal, 3(3):257-279.
  9. Akyildiz, I. F., W. Su, Y. S., and Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4):393-422.
  10. Anaya, I., Simko, B., Bourcier, J., Plouzeau, N., and Jézéquel, J. (2014). A prediction-driven adaptation approach for self-adaptive sensor networks. In Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2014, pages 145-154, New York, NY, USA. ACM.
  11. Bashlovkina, V., Abdelaal, M., and Theel, O. (2015). Fuzzycat: a lightweight fuzzy compression adaptive transform for wireless sensor networks. In The 14th International Conference on Information Processing in Sensor Networks (IPSN 7815). (under review).
  12. Chelius, G., Fraboulet, A., and Hamida, E. Wsnet: an Event-driven Simulator for Large Scale Wireless Networks. [accessed May 2014].
  13. Dargie, W. and Poellabauer, C. (2010). Fundamental of Wireless Sensor Networks Theory and Practice. John Wiley & Sons Ltd.
  14. Kim, H. J. (2009). A New Lossless Data Compression Method. In IEEE International Conference on Multimedia and Expo (ICME), pages 1740-1743.
  15. Kozma, R., Wang, L., Iftekharuddin, K., and et al. (2012). A Radar-enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking Sensors. Sensors, 12(2):1336-1351.
  16. Li-zhong, W., Hong-bo, L., Gang, Z., and Tao, H. (2011). The Network Nodes Design of Gas Wireless Sensor Monitor. The 2nd International Conference on Mechanic Automation and Control Engineering (MACE).
  17. Muller, M. (2007). Information Retrieval for Music and Motion, chapter Dynamic Time Warping. Springer.
  18. Oliveira, L. and Rodrigues, J. (2011). Wireless Sensor Networks: a Survey on Environmental Monitoring. Journal of Communications, 6(2).
  19. Perfilieva, I. (2004). Fuzzy transforms. Transactions on Rough Sets II, pages 63-81.
  20. Raza, U., Camerra, A., Murphy, A., and et al. (2012). What Does Model-driven Data Acquisition Really Achieve in Wireless Sensor Networks? In Proc. of The 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 85-94.
  21. Somov, A., Baranov, A., Savkin, A., Spirjakin, D., Spirjakin, A., and Passerone, R. (2011). Development of Wireless Sensor Network for Combustible Gas Monitoring. A: Physical Sensors and Actuators, 171(2):398-405.
  22. Wehn, N. and Mnch, M. (1999). Minimizing power consumption in digital circuits and systems: An overview. Technical report, Kaiserslautern University.
  23. Zhang, X. and Shin, K. G. (2012). E-mili: Energyminimizing idle listening in wireless networks. IEEE Transactions on Mobile Computing, 11(9):1441- 1454.
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Paper Citation


in Harvard Style

Abdelaal M. (2015). Distributed Techniques for Energy Conservation in Wireless Sensor Networks . In Doctoral Consortium - DCSENSORNETS, (SENSORNETS 2015) ISBN Not Available, pages 9-20


in Bibtex Style

@conference{dcsensornets15,
author={Mohamed Abdelaal},
title={Distributed Techniques for Energy Conservation in Wireless Sensor Networks},
booktitle={Doctoral Consortium - DCSENSORNETS, (SENSORNETS 2015)},
year={2015},
pages={9-20},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCSENSORNETS, (SENSORNETS 2015)
TI - Distributed Techniques for Energy Conservation in Wireless Sensor Networks
SN - Not Available
AU - Abdelaal M.
PY - 2015
SP - 9
EP - 20
DO -