IJCNS  Vol.5 No.2 , February 2012
Increasing Throughput and Reducing Delay in Wireless Sensor Networks Using Interference Alignment
Abstract: With the advent of sensor nodes with higher communication and sensing capabilities, the challenge arises in forming a data gathering network to maximize the network capacity. The channel sharing for higher data transmission leads to interfering problems. The effects of interferences become increasingly important when simultaneous transmissions are done in order to increase wireless network capacity. In such cases, achieving a high throughput and low delay is difficult. We propose a new method that uses interference alignment (IA) technique to mitigate interference effects in Wireless Sensor Networks (WSNs). In IA technique, multiple transmitters jointly encode their signals to intended receivers such that interfering signals are separated and eliminated. Simulation results demonstrate that compared to TDMA algorithms, the proposed method significantly increases the performance of the network delay and throughput by reducing the delay and increasing throughput.
Cite this paper: V. Zibakalam and M. HosseinKahaei, "Increasing Throughput and Reducing Delay in Wireless Sensor Networks Using Interference Alignment," International Journal of Communications, Network and System Sciences, Vol. 5 No. 2, 2012, pp. 90-97. doi: 10.4236/ijcns.2012.52012.

[1]   H. Choi, J. Wang and E. A. Hughes, “Scheduling for Information Gathering on Sensor Network,” Wireless Networks, Vol. 15, No. 1, 2009, pp. 127-140. doi:10.1007/s11276-007-0050-9

[2]   J. Zheng and A. Jamalipour, “Wireless Sensor Networks: A Networking Perspective,” John Wiley & Sons, Inc., Hoboken, 2009.

[3]   L. Paradis and Q. Han, “TIGRA: Timely Sensor Data Collection Using Distributed Graph Coloring,” Proceedings of the 6th Annual International Conference on Pervasive Computing and Communications, Hong Kong, 17-21 March 2008, pp. 264-268.

[4]   G. Lu, B. Krishnamachari and C. S. Raghavendra, “An Adaptive Energy-Efficient and Low-Latency MAC for Data Gathering in Wireless Sensor Networks,” Proceedings of the 18th International Conference of the IEEE IPDPS, Santa Fe, 26-30 April 2004, pp. 224-231.

[5]   S. C. Ergen and P. Varaiya, “Tdma Scheduling Algorithms for Wireless Sensor Networks,” Wireless Networks, Vol. 16, No. 4, 2010, pp. 985-997. doi:10.1007/s11276-009-0183-0

[6]   W. Wang, Y. Wang, X. Y. Li, W. Z. Song and O. Frieder, “Efficient Interference-Aware TDMA Link Scheduling for Static Wireless Networks,” Proceedings of the 12th Annual International Conference of the ACM Mobile Computing and Networking, Los Angeles, 23-26 September 2006, pp. 262-273.

[7]   P. Gupta and P. Kumar, “The Capacity of Wireless Networks,” IEEE Transactions on Information Theory, Vol. 46, No. 2, 2000, pp. 388-404. doi:10.1109/18.825799

[8]   V. Cadambe and S. Jafar, “Interference Alignment and the Degrees of Freedom of the K User Interference Channel,” IEEE Transactions on Information Theory, Vol. 54, No. 8, 2008, pp. 3425-3441. doi:10.1109/TIT.2008.926344

[9]   D. Tse and P. Viswanath, “Fundamentals of Wireless Communication,” Cambridge University Press, Cambridge, 2005.

[10]   L.-E. Li, et al., “A General Algorithm for Interference Alignment and Cancellation in Wireless Networks,” Proceedings of the 29th International Conference of the IEEE INFOCOM, San Diego, 14-19 March 2010, pp. 1-9.