Bandwidth Optimization in 802.15.4 Networks through Evolutionary Slot Assignment
Vidya KRISHNAMURTHY, Edward SAZONOV
.
DOI: 10.4236/ijcns.2009.26057   PDF    HTML     6,383 Downloads   10,743 Views   Citations

Abstract

Traditional Wireless Sensor Networks (WSNs) based on carrier sense methods for channel access suffer from reduced bandwidth utilization, increase energy consumptions and latency problems in networks with high traffic. In this work, a novel Evolutionary Slot Assignment (ESA) algorithm has been developed to in-crease the throughput of large wireless mesh networks with no centralized controller. In the presented scheme, the sensor nodes self-adapt to the traffic patterns of the network by selecting transmission slots us-ing evolutionary learning methods. Each sensor node evolves an independent transmission schedule. Unlike traditional evolutionary methods, fitness evaluation of every node impacts fitness of every other sensor node in the network. The ESA algorithm has been simulated using Network Simulator-2 and compared with the IEEE 802.15.4 CSMA-CA, a Static Slot Assignment (SSA) and a Random Slot Assignment schemes (RSA). Results show a remarkable improvement in the network throughput using the proposed ESA method as op-posed to other compared methods.

Share and Cite:

V. KRISHNAMURTHY and E. SAZONOV, "Bandwidth Optimization in 802.15.4 Networks through Evolutionary Slot Assignment," International Journal of Communications, Network and System Sciences, Vol. 2 No. 6, 2009, pp. 518-527. doi: 10.4236/ijcns.2009.26057.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] H. Karl and A. Willig, “A short survey of wireless sensor networks,” Telecommunication Networks Group, Tech-nische Universitat Berlin, Hasso-Plattner Institute, Pots-dam.
[2] A. El-Hoiydi, “Aloha with preamble sampling for spo-radic traffic in ad hoc wireless sensor networks,” in Pro-ceedings of IEEE International Conference Communica-tions, pp. 3418–3423, 2002.
[3] I. Demirkol, C. Ersoy, and F. Alag?z, “MAC protocols for wireless sensor networks: A survey,” IEEE Commu-nications Magazine, Vol. 44, No. 4, pp. 115–121, 2006.
[4] G. Bianchi, “Performance analysis of the IEEE 802.11 distributed coordination function,” IEEE Journal on Se-lected Areas in Communications, Vol. 18, No. 3, pp. 535–547, March 2000.
[5] J. Zheng and M. J. Lee, “A comprehensive performance study of IEEE 802.15.4,” IEEE Press Book, 2004.
[6] IEEE P802.15.4/D18, Draft Standard: Low Rate Wireless Personal Area Networks, February 2003.
[7] G. Lu, B. Krishnamachari, and C. S. Raghavendra, “Per-formance evaluation of the IEEE 802.15.4 MAC for low-rate low power wireless networks,” in Proceedings of IEEE International Performance Computing and Com-munication Conference (IPCCC’04), pp. 701–706, Phoe-nix, AZ, April 2004.
[8] J. Misic, S. Shafi, and V. B. Misic, “Maintaining reliabil-ity through activity management in 802.15.4 sensor clus-ters,” IEEE Transactions on Vehicular Technology, Vol. 55, No. 3, pp. 779–788, 2006.
[9] J. Misic, S. Shafi, and V. B. Misic, “Performance of bea-con enabled IEEE 802.15.4 cluster with downlink and uplink traffic,” IEEE Transactions on Parallel and Dis-tributed Systems, Vol. 17, No. 4, pp. 361–376, 2006.
[10] K. Varadhan, “The Ns manual,” The VINT Project, Au-gust 2000.
[11] E. Sazonov, R. Jha, K. Janoyan, V. Krishnamurthy, M. Fuchs, and K. Cross, “Wireless intelligent sensor and ac-tuator network (WISAN): A scalable ultra-low-power platform for structural health monitoring,” SPIE Pro-ceedings 6177, March 2006.
[12] W. Ye, J. Heidemann, and D. Estrin, “An energy-efficient MAC protocol for wireless sensor networks,” IEEE In-focom’02, Vol. 3, pp. 1567–1576, June 2002.
[13] T. van Dam, and K. Langendoen, “An adaptive en-ergy-efficient MAC protocol for wireless sensor net-works,” ACM SenSys 2003, pp. 171–180, November 2003.
[14] C. S. Raghavendra and S. Singh, “PAMAS–power aware multi-access protocol with signaling for ad hoc net-works,” ACM Computer Communications Review, Vol. 28, No. 3, pp. 5–26, 1998.
[15] M. J. Miller and N. H. Vaidya, “On-demand TDMA scheduling for energy conservation in sensor networks,” Technical Report, University of Illinois at Urbana Champaign, 2004.
[16] M. L. Sichitiu, “Cross-layer scheduling for power effi-ciency in wireless sensor networks,” IEEE Infocom’04, March 2004.
[17] V. Rajendran, K. Obraczka, and J. J. Garcia-Luna-Aceves, “Energy-efficient, collision-free medium access control for wireless sensor networks,” ACM SenSys’03, pp. 181–192, November 2003.
[18] V. Krishnamurthy and E. Sazonov, “A reservation-based protocol for monitoring applications using IEEE 802.15.4 sensor networks,” in International Journal of Sensor Networks, Vol. 4, No. 3, pp. 155 –171, 2008.
[19] T. B?ck, “Evolutionary algorithms in theory and prac-tice,” Oxford University Press, New York, 1996.
[20] T. B?ck, D. Fogel, and Z. Michalewicz, “Handbook of evolutionary computation,” Oxford University Press, 1997.
[21] S. Baluja, “Population-based incremental learning: A method for interacting genetic search based function op-timization and coemptive learning,” Technology Repub-lic, No. CMU-CS-94-163, Carnegie Mellon University, 1994.
[22] G. Harik, F. G. Lobo, and D. E. Goldberg, “The compact genetic algorithm,” in Proceedings of the International Conference on Evolutionary Computation (ICEC 98), pp. 523–528, 1998.
[23] H. Pang, K. Hu, and Z. Hong, “Adaptive PBIL algorithm and its application to solve scheduling problems,” IEEE International Symposium on Computer-Aided Control Systems Design, pp. 784–789, October 2006.
[24] Z. He, C. Wei, B. Jin, W. Pei, and L. Yang, “A new population-based incremental learning method for the t-raveling salesman problem,” in Proceedings of the 1999 Congress on Evolutionary Computation, Vol. 2, pp. –1156, 1999.
[25] S. Yang and X. Yao, “Dual population-based incremental learning for problem optimization in dynamic environ-ments,” in M. Gen et al. (editors), Proceedings of the 7th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 49–56, 2003.
[26] S. Baluja and D. Simon, “Evolution-based methods for selecting point data for object localization: Applications to computer-assisted surgery,” International Journal of Applied Intelligence, Vol. 8, pp. 1–13, 1997.
[27] L. Chen and A. Petroianu, “Application of PBIL to the optimization of PSS tuning, power system technology,” in Proceedings of 1998 International Conference on Power System Technology, Vol. 2, pp. 834–838, 1998.
[28] B. L. Miller and D. E. Goldberg, “Genetic algorithms, tournament selection, and the effects of noise,” Complex Systems, pp. 193–212, June 1995.
[29] C. E. Perkins and E. M. Royer, “The ad hoc on-demand distance vector protocol,” Ad hoc Networking, Addi-son-Wesley, pp. 173–219, 2000.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.