Energy-aware routing in wireless sensor networks using MLP and simulated annealing algorithms

Document Type : Original Article

Authors

1 Associate Professor, Department of Computer Engineering, Islamic Azad University, Tabriz Branch, Tabriz, Iran

2 Master student, Department of Computer Engineering, Islamic Azad University, Tabriz Branch, Tabriz, Iran

Abstract

The main purpose of wireless sensor networks (WSNs) is to monitor, record and announce specific conditions from different locations and different applications to the well node or end user. Wireless sensor networks have many applications such as patient status monitoring, target tracking, forest and rangeland monitoring, battlefield, and so on. In these networks, energy is one of the inherent limitations. Because the energy consumed is supplied by a battery, which has a limited lifespan. Clustering is one of the best ways to save energy due to data aggregation, and selecting the right clusters increases the lifespan of wireless sensor networks. Since clustering is one of the NP-hard problems, metaheuristic algorithms are suitable for this problem. In this paper, an energy-aware and cluster-based routing method for WSNs with a combination of multilayer perceptron (MLP)neural network algorithm and simulated annealing (SA) is presented. In the proposed method, the simulated annealing metaheuristic algorithm is simulated to determine the cluster head (CH) and multilayer perceptron neural networks are used to determine the members of each cluster. After the clustering process, data is sent from the source node to the well by creating appropriate routing tables among the headers. The simulation results of the proposed method show that this method improves the parameters of energy consumption, package delivery rate and throughput.

Keywords

References

 
[1]   S. Alizadeh and A. Ghaffari, “An Energy-efficient hirerchical Clustering protocole in wireless sensor networks,” in 2010 3rd International Conference on Computer Science and Information Technology,
pp.413-418, 2010.##
 
[3]   A. Beheshtiasl and A. Ghaffari, “Secure and trust-aware routing scheme in wireless sensor networks,” Wireless Personal Communications, vol. 107, pp. 1799-1814, 2019.##
[4]   Z. Heidary Ghiri and G. Mirjalily, “Energy-Harvesting Aware Multi-Hop Routing in Wireless Sensor Networks for Defense Applications,” Electronic and Cyber Defense, vol. 8, pp. 63-73, 2020.##
[5]   M. Dibaei and A. Ghaffari, “TSIS: A Trust-Based Scheme for Increasing Security in Wireless Sensor Networks,” Majlesi Journal of Electrical Engineering, vol. 11, pp. 45-52, 2017.##
[6]   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.##
[7]   A. Ghaffari, “Congestion control mechanisms in wireless sensor networks: A survey,” Journal of network and computer applications, vol. 52, pp. 101-115, 2015.##
[8]   A. Ghaffari and S. Nobahary, “FDMG: Fault detection method by using genetic algorithm in clustered wireless sensor networks,” Journal of AI and Data Mining, vol. 3, pp. 47-57, 2015.##
[9]   A. Ghaffari and A. Rahmani, “Fault tolerant model for data dissemination in wireless sensor networks,” in 2008 International Symposium on Information Technology, pp. 1-8, 2008.##
[10] A. Ghaffari, A. Rahmani, and A. Khademzadeh, “Energy-efficient and QoS-aware geographic routing protocol for wireless sensor networks,” IEICE Electronics Express, vol. 8, pp. 582-588, 2011.##
[11] A. Ghaffari and V. A. Takanloo, “QoS-based routing protocol with load balancing for wireless multimedia sensor networks using genetic algorithm,” World Applied Sciences Journal, vol. 15, pp. 1659-1666, 2011.##
 [12]  D. KeyKhosravi, A. Ghaffari, A. Hosseinalipour, and B. A. Khasragi, “New Clustering Protocol to Decrease Probability Failure Nodes and Increasing the Lifetime in WSNs,” Int. J. Adv. Comp. Techn., vol. 2, pp. 117-121, 2010.##
[13] M. Khabiri and A. Ghaffari, “Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm,” Wireless Personal Communications, vol. 98, pp. 2473-2495, 2018.##
[14] E. Mohsenifard and A. Ghaffari, “Data aggregation tree structure in wireless sensor networks using cuckoo optimization algorithm,” Information Systems & Telecommunication, vol. 4, pp. 182-190, 2016.##
[15] I. Mosavvar and A. Ghaffari, “Data aggregation in wireless sensor networks using firefly algorithm,” Wireless Personal Communications, vol. 104, pp. 307-324, 2019.##
[16] S. Pattnaik and P. K. Sahu, “Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN,” International Journal of Communication Systems, vol. 33, p. e4354, 2020.##
[17] A. Barzin, A. Sadegheih, H. K. Zare, and M. Honarvar, “A hybrid swarm intelligence algorithm for clustering-based routing in wireless sensor networks,” Journal of Circuits, Systems and Computers, vol. 29, p. 2050163, 2020.##
[18] M. Selvi, S. S. Kumar, S. Ganapathy, A. Ayyanar, H. K. Nehemiah, and A. Kannan, “An energy efficient clustered gravitational and fuzzy based routing algorithm in WSNs,” Wireless Personal Communications, pp. 1-30, 2020.##
[19] S. Tabibi and A. Ghaffari, “Energy-efficient routing mechanism for mobile sink in wireless sensor networks using particle swarm optimization algorithm,” Wireless Personal Communications, vol. 104, pp. 199-216, 2019.##
[20] K. Thangaramya, K. Kulothungan, S. Indira Gandhi, M. Selvi, S. Santhosh Kumar, and K. Arputharaj, “Intelligent fuzzy rule-based approach with outlier detection for secured routing in WSN,” Soft Computing, pp. 1-15, 2020.##
[21] D. Mehta and S. Saxena, “Hierarchical WSN protocol with fuzzy multi-criteria clustering and bio-inspired energy-efficient routing (FMCB-ER),” Multimedia Tools and Applications, pp. 1-34, 2020.##
[22] D. Mehta and S. Saxena, “MCH-EOR: Multi-objective cluster head based energy-aware optimized routing algorithm in wireless sensor networks,” Sustainable Computing: Informatics and Systems, vol. 28, p. 100406, 2020.##
[23] V. Rajaram and N. Kumaratharan, “Multi-hop optimized routing algorithm and load balanced fuzzy clustering in wireless sensor networks,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-9, 2020.##
[24] O. Deepa and J. Suguna, “An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks,” Journal of King Saud University-Computer and Information Sciences, 2017.##
[25]  Z. Heidary Ghiri, Gh. Mirjalily, “Energy-Harvesting Aware Multi-Hop Routing in Wireless Sensor Networks for Defense Applications,” Journal of  Electronical & Cyber Defence, vol. 8, no. 4,  Serial no. 32, 2021. (In Persian)##
Electronic and Cyber Defense
Volume 9, Issue 3 - Serial Number 35
Serial No. 35, Autumn Quarterly
December 2021
Pages 133-142

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History

  • Receive Date: 18 January 2021
  • Revise Date: 15 March 2021
  • Accept Date: 02 April 2021
  • Publish Date: 22 November 2021

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