Improving the Efficiency of Wireless Sensor Networks using Fountain codes | ||
| Journal of Communication Engineering | ||
| مقاله 12، دوره 9، شماره 1 - شماره پیاپی 18، فروردین 2020، صفحه 168-183 اصل مقاله (977.29 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22070/jce.2021.5334.1156 | ||
| نویسنده | ||
| Seyed Masoud Mirrezaei* | ||
| Faulty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran | ||
| چکیده | ||
| Fountain codes are erasure codes that are characterized by their rateless property and their global acknowledgment. The larger the network size, the more efficient the fountain codes are degraded because of multi-hops causing an overflow. The optimization of wireless communication is also a focus of study exciting and an important issue always to maximize performance, the lifetime of the sensor nodes, and to reduce the consumption of energy. Estimation becomes one of the attractive topics in wireless sensor networks nowadays. In this paper, I consider a distributed estimation scheme composing of a sensor member and a fusion center, which is the cluster head. To minimize the number of transmissions as well as the impact of overflow, I determine the optimal minimal number of encoded packets needed for successful decoding. Sensor observations are encoded using fountain codes, and then messages are collected at the cluster head where a final estimation is provided with a classification based on Bayes rule. The main goal of this paper is to estimate the total number of received packets using the Bayes rule so that it is possible to minimize the overflow and extend the network lifetime. | ||
| کلیدواژهها | ||
| Wireless Sensor Networks؛ Fountain codes؛ Estimation؛ Bayes rule؛ Naive Bayes | ||
| مراجع | ||
|
1- Rashid and M. H. Rehmani, “Applications of wireless sensor networks for urban areas: A survey,” Journal of network and computer applications, vol. 60, pp. 192-219, Jan. 2016.
2- Kumar, F. Zhao, and D. Shepherd, “Collaborative signal and information processing in microsensor networks,” IEEE Signal Processing Magazine, vol. 19, no. 2, pp. 13-14, March 2002.
3-Hossain, “First quarter 2016 IEEE communications surveys and tutorials,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 1-7, 1st Quarter 2016.
4- Tubaishat and S. Madria, “Sensor networks: an overview,” IEEE potentials, vol. 22, no. 2, pp. 20-23, April-May 2003.
5- Al-Anbagi, M. Erol-Kantarci, and H. T. Mouftah, “A survey on cross-layer quality of service approaches in WSNs for delay and reliability-aware applications,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 525-552, 1st quarter 2014.
6- H. Mohajerzadeh, M. H. Yaghmaee, and V. Fakoor, “Total data collection algorithm based on estimation model for wireless sensor network,” Wireless Personal Communications, vol. 81, no. 2, pp. 745-778, 2015.
7- C. Aysal and K. E. Barner, “Constrained decentralized estimation over noisy channels for sensor networks,” IEEE Transactions on Signal Processing, vol. 56, no. 4, pp. 1398-1410, April 2008.
8- Huang and Y. Hua, “On energy for progressive and consensus estimation in multihop sensor networks,” IEEE Transactions on Signal Processing, vol. 59, no. 8, pp. 3863-3875, Aug. 2011.
9- Lam and A. Reibman, “Quantizer design for decentralized systems with communication constraints,” IEEE Trans. Communications, vol. 41, pp. 1602-1605, 1993.
10- Q. Luo, “Universal decentralized estimation in a bandwidth constrained sensor network,” IEEE Trans. Information Theory, vol. 51, no. 6, pp. 2210-2219, June 2005.
11- Belabed and R. Bouallegue, “A comparative analysis of machine learning classification approaches for fountain data estimation in wireless sensor networks,” in IEEE 2019 15th Intern. Wireless Communications & Mobile Computing Conference (IWCMC), 2019, pp. 1251-1254.
12- Argyriou and O. Alay, “Distributed estimation in wireless sensor networks with an interference canceling fusion center,” IEEE Trans. Wireless Communications, vol. 15, no. 3, pp. 2205-2214, March 2015.
13- Sun, X. Yuan, J. Wang, Q. Li, L. Chen, and D. Mu, “End-to-end data delivery reliability model for estimating and optimizing the link quality of industrial WSNs,” IEEE Trans. Automation Science and Engineering, vol. 15, no. 3, pp. 1127-1137, Aug. 2017.
14- Miranda and V. Ramos, “Improving data aggregation in wireless sensor networks with time series estimation,” IEEE Latin America Transactions, vol. 14, no. 5, pp. 2425-2432, May 2016.
15- Gong, X. Wang, J. Guo, A. Wang, D. Xu, N. An, X. Chen, D. Fang, and X. Zheng, “Dedv: A data collection method for mobile sink based on dynamic estimation of data value in WSN,” in IEEE 2016 Intern. Conf. Networking and Network Applications (NaNA), 2016, pp. 77-83.
16- Yarinezhad, and A. Sarabi, “MLCA: A Multi-Level Clustering Algorithm for Routing in Wireless Sensor Networks,” Journal of Communication Engineering vol. 8, no. 2, pp. 249-265, Summer-Autumn 2019.
17- Vahabi, M. Lahabi, M. Eslaminejad, and S. E. Dashti, “Geographic and Clustering Routing for Energy Saving in Wireless Sensor Network with Pair of Node Groups,” Journal of Communication Engineering vol. 8, no.1, pp. 146-157, Winter-Spring 2019.
18- M. Mirrezaei, “Towards systematic Luby transform codes: optimisation design over binary erasure channel,” Electronics Letters, vol. 56, no. 11, pp. 550-553, May 2020.
19- M. Mirrezaei, K. Faez, and S. Yousefi, “Towards Fountain Codes. Part II: Belief Propagation Decoding” Wireless personal communications, vol. 77, no. 2, pp. 1563-1584, 2014.
20- Apavatjrut, C. Goursaud, K. Ja_res-Runser, C. Comaniciu, and J.-M. Gorce, “Toward increasing packet diversity for relaying LT fountain codes in wireless sensor networks,” IEEE Communications Letters, vol. 15, no. 1, pp. 52-54, Jan. 2010.
21- Luby, “Lt codes,” in the 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002, Proceedings IEEE, 2002, pp. 271-280.
22- Zhang, W. Lei, and X. Xie, “Combined degree distribution: A simple method to design the degree distribution of fountain codes,” in 2013 IEEE Third Intern. Conf. on Information Science and Technology (ICIST), pp. 1089-1092.
23- K. Yen, Y.-C. Liao, C.-L. Chen, and H.-C. Chang, “Modified robust soliton distribution (MRSD) with improved ripple size for LT codes,” IEEE Communications Letters, vol. 17, no. 5, pp. 976-979, May 2013.
24- T. Chen, L. Cao, F. Zhao, H.-f. Zheng, and M. Pan, “Analysis of robust soliton distribution for LT code,” in 2012 IEEE 11th International Conference on Signal Processing, vol. 2, pp. 1546-1549.
25- J. MacKay and D. J. Mac Kay, Information theory, inference and learning algorithms. Cambridge university press, 2003.
26- Chen, “Performance-energy tradeoffs for decentralized estimation in a multihop sensor network,” IEEE Sensors Journal, vol. 10, no. 8, pp. 1304-1310, 2010.
27- Wesolowski, N. Klco, R. Furnstahl, D. Phillips, and A. Thapaliya, “Bayesian parameter estimation for effective field theories,” Journal of Physics G: Nuclear and Particle Physics, vol. 43, no. 7, p. 074001, 2016.
28- Cai, N. W. Schuck, J. W. Pillow, and Y. Niv, “A bayesian method for reducing bias in neural representational similarity analysis,” in Advances in Neural Information Processing Systems, 2016, pp. 4951-4959.
29- R. Solow, “A simple bayesian method of inferring extinction: comment,” Ecology, vol. 97, no. 3, pp. 796-798, 2016.
30- J. Gordon, D. J. Salmond, and A. F. Smith, “Novel approach to nonlinear/nongaussian bayesian state estimation,” IEE proceedings F (radar and signal processing), vol. 140, no. 2, pp. 107-113, April 1993,
31- Suo, G. Zhang, J. Lv, and X. Tian, “Performance analysis for finite length LT codes via classical probability evaluation,” IEEE Communications Letters, vol. 21, no. 9, pp. 1957-1960, Sept. 2017.
32- Enciso, P. Quezada, J. Fernandez, B. Figueroa, and V. Espinoza, “Analysis of performance of the routing protocols ad hoc using random waypoint mobility model applied to an urban environment,” 12th Intern. Conf. on Web Information Systems and Technologie, WEBIST 2016, pp. 208-213.
33- Pramanik, B. Choudhury, T. S. Choudhury, W. Arif, and J. Mehedi, “Behavioral study of random waypoint mobility model based energy aware manet,” in 2016 IEEE 3rd Intern. Conf. on Signal Processing and Integrated Networks (SPIN), 2016, pp. 624-629.
34- Belabed and R. Boouallegue, “Clustering approach using node mobility in wireless sensor networks,” in 2017 IEEE 13th Intern. Wireless Communications and Mobile Computing Conf. (IWCMC), 2017, pp. 987-992. | ||
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