Data Aggregation Tree Structure in Wireless Sensor Networks Using Cuckoo Optimization Algorithm
الموضوعات :Elham mohsenifard 1 , Behnam Talebi 2
1 - Islamic Azad Tabriz University
2 - Tabriz Branch, Islamic Azad University
الکلمات المفتاحية: Wireless Sensor Networks (WSNs) , Data Aggregation Technique , Data Aggregation Tree , Cuckoo Optimization Algorithm (COA) , Network Lifetime Enhancement,
ملخص المقالة :
Wireless sensor networks (WSNs) consist of numerous tiny sensors which can be regarded as a robust tool for collecting and aggregating data in different data environments. The energy of these small sensors is supplied by a battery with limited power which cannot be recharged. Certain approaches are needed so that the power of the sensors can be efficiently and optimally utilized. One of the notable approaches for reducing energy consumption in WSNs is to decrease the number of packets to be transmitted in the network. Using data aggregation method, the mass of data which should be transmitted can be remarkably reduced. One of the related methods in this approach is the data aggregation tree. However, it should be noted that finding the optimization tree for data aggregation in networks with one working-station is an NP-Hard problem. In this paper, using cuckoo optimization algorithm (COA), a data aggregation tree was proposed which can optimize energy consumption in the network. The proposed method in this study was compared with genetic algorithm (GA), Power Efficient Data gathering and Aggregation Protocol- Power Aware (PEDAPPA) and energy efficient spanning tree (EESR). The results of simulations which were conducted in matlab indicated that the proposed method had better performance than GA, PEDAPPA and EESR algorithm in terms of energy consumption. Consequently, the proposed method was able to enhance network lifetime.
[1] J. Yick, B. Mukherjee, and D. Ghosal. “Wireless sensor network survey.” Computer networks, vol. 52, pp. 2292-2330, 2008.#
[2] A. Ghaffari. “Congestion control mechanisms in wireless sensor networks: A survey. ” Journal of Network and Computer Applications, vol. 52, pp. 101-115, 2015.#
[3] A. Ghaffari. “An energy efficient routing protocol for wireless sensor networks using A-star algorithm.” Journal of applied research and technology, vol. 12, pp. 815-822, 2014.#
[4] Z. Mottaghinia and A. Ghaffari. “A Unicast Tree-Based Data Gathering Protocol for Delay Tolerant Mobile Sensor Networks.” Information Systems & Telecommunication, pp. 59, 2016.#
[5] X. Zhu, G. Chen, S. Tang, X. Wu, and B. Chen. “Fast Approximation Algorithm for Maximum Lifetime Aggregation Trees in Wireless Sensor Networks.” INFORMS Journal on Computing, vol. 28, pp. 417-431, 2016.#
[6] A. Ghaffari, L. Darougaran, and A. Shiri. “Comparing data aggregation methods in wireless sensor networks.” presented at the Third national symposium on computer engineering and information technology, 2010.#
[7] S. Upadhyayula and S. K. Gupta. “ Spanning tree based algorithms for low latency and energy efficient data aggregation enhanced convergecast (dac) in wireless sensor networks. Ad Hoc Networks, vol. 5, pp. 626-648, 2007.#
[8] A. jhumka, M. Bradbury, and S. Saginbeko. “Efficient fault-tolerant collision-free data aggregation scheduling for wireless sensor networks.” Parallel Distributed Computing, vol. 74, pp. 1789-1801, 2014.#
[9] R. R. Rout and S. Ghosh. “Adaptive data aggregation and energy efficiency using network coding in a clustered wireless sensor network: An analytical approach.” Computer Communications pp. 65-75, 2014.#
[10] SH. Niu, C. Wang, Z. Yu, and S. Cao. “Lossy data aggregation integrity scheme in wireless sensor networks.” Computers and Electrical Engineering, vol. 39, pp. 1726-1735, 2013.#
[11] S. park. “ performance Analysis of Data Aggregation Schemes For wirless sensor network, 2006.#
[12] S. Sicari, L. Grieco, G. Boggia, and A. Coen-Porisini. “DyDAP: A dynamic data aggregation scheme for privacy aware wireless sensor networks.” The Journal of Systems and Software, vol. 85, pp. 152-166, 2012.#
[13] Q. Liua, Y.Changa, and X. Jiab. “A hybrid metod of CSM/CA and TDMA for real-time data aggregation in wireless sensor networks.” Computer Communications, vol. 39, pp. 269-278, 2013.#
[14] W. Wu, J. Cao, H. Wu, and J.Li. “Robust and dynamic data aggregation in wireless sensor networks: A cross-layer approach.” Computer Networks, vol. 57, pp. 3929-3940, 2013.#
[15] H. Yousefi, M. Yeganeh, N.Alinaghipour, and A. Movaghar. “Structure-free real-time data aggregation in wireless sensor networks.” Computer Communications, vol. 35, pp. 1132-1140, 2012.#
[16] H.Li, CH.Wua, Q. Huab, and F. Lau. “Latency-minimizing data aggregation in wireless sensor networks under physical interference mode.” Ad Hoc Networks, vol. 12, pp. 52-68, 2014.#
[17] S. Ozdemir and Y.Xiao. “Integrity protecting hierarchical concealed data aggregation for wireless sensor networks.” Computer Networks, vol. 55, pp. 1735-1746, 2011.#
[18] R. Rajabioun. “Cuckoo Optimization Algorithm.” Applied soft computing, vol. 11, pp. 5508- 5518, 2011.#
[19] H. &214;. Tan and I. K&246;rpeoǧlu. “Power efficient data gathering and aggregation in wireless sensor networks.” ACM Sigmod Record, vol. 32, pp. 66-71, 2003.#
[20] S. Hussain and O. Islam. “An energy efficient spanning tree based multi-hop routing in wireless sensor networks.” in 2007 IEEE Wireless Communications and Networking Conference, 2007, pp. 4383-4388.#
[21] P.Wang, Y. He, and L.Huang. “Near optimal scheduling of data aggregation in wireless sensor networks.” Ad Hoc Networks, vol. 11, pp. 1287- 1296, 2013.#
[22] CH.Chaon and T. Hsiao. “Design of structure-free and energy-balanced data aggregation in wireless sensor networks.” vol. 37, pp. 229-239, 2014.#
[23] H.Liu, Z.Liu, D.Li, X.Lub, and H.Due. “Approximation algorithms for minimum latency data aggregation in wireless sensor networks with directional antenna.” Theoretical Computer Science, vol. 497, pp. 139-153, 2013.#
[24] W. -H. Liao, Y. Kao, and C.-M. Fan. “An ant colony algorithm for data aggregation in wireless sensor networks.” in Sensor Technologies and Applications, 2007. SensorComm 2007. International Conference on, 2007, pp. 101-106.#
[25] O. Islam, S. Hussain, and H. Zhang. “Genetic Algorithm for Data Aggregation Trees in Wireless Sensor Networks.” 2007.#
[26] S. Lai and B.Ravindran. “Achieving Max–Min lifetime and fairness with rate allocation for data aggregation in sensor networks.” Ad Hoc Networks, vol. 9, pp. 821-834, 2011.#
[27] S. Kwond, J. H. Ko, and J.Kim. “Dynamic timeout for data aggregation in wireless sensor networks.” Computer Network, vol. 55, pp. 650-664, 2011.#
[28] J.AL-Karaki, R.Mustafa, and A.Kamal. “Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms.” Computer Network, vol. 53, pp. 954-960, 2009.#
[29] Y. Zhang, J. Pu, X. Liu, and Z. Chen. “An adaptive spanning tree-based data collection scheme in wireless sensor networks.” International Journal of Distributed Sensor Networks, vol. 2015, p. 2, 2015.#
[30] X. Xu, R. Ansari, A. Khokhar, and A. V. Vasilakos. “Hierarchical data aggregation using compressive sensing (HDACS) in WSNs.” ACM Transactions on Sensor Networks (TOSN), vol. 11, p. 45, 2015.#
[31] N.-T. Nguyen, B.-H. Liu, V.-T. Pham, and Y.-S. Luo. “On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees.” Computer Networks, vol. 105, pp. 99-110, 2016.#
[32] I. Atoui, A. Ahmad, M. Medlej, A. Makhoul, S. Tawbe, and A. Hijazi. “Tree-based data aggregation approach in wireless sensor network using fitting functions.” in 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC), 2016, pp. 146-150.#
[33] S. Abbasi-Daresari and J. Abouei. “Toward cluster-based weighted compressive data aggregation in wireless sensor networks.” Ad Hoc Networks, vol. 36, pp. 368-385, 2016.#
[34] J. Kennedy and R. Eberhart. “Particle swarm optimization.” in Neural Networks, 1995. Proceedings., IEEE International Conference on, 1995, pp. 1942-1948 vol.4.#
[35] E. Atashpaz-Gargari and C. Lucas. “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition.” in 2007 IEEE Congress on Evolutionary Computation, 2007, pp. 4661-4667.#
[36] R. Masoudi and A. Ghaffari. “Software defined networks: A survey.” Journal of Network and Computer Applications, vol. 67, pp. 1-25, 2016.#
