Reducing Energy Consumption in Sensor-Based Internet of Things Networks Based on Multi-Objective Optimization Algorithms
Subject Areas : Wireless NetworkMohammad sedighimanesh 1 , Hessam Zandhessami 2 , Mahmood Alborzi 3 , Mohammadsadegh Khayyatian 4
1 - Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran
Keywords: Internet of Things (IoT) Based on Wireless Sensor, Clustering, and Routing, Type-2 Fuzzy and Genetic Algorithms, Multi-Objective Optimization Algorithms.,
Abstract :
Energy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The most important objective of the present study is to minimize the energy consumption of sensors and increase the IoT network's lifetime by applying multi-objective optimization algorithms when selecting cluster heads and routing between cluster heads for transferring data to the base station. In the present article, after distributing the sensor nodes in the network, the type-2 fuzzy algorithm has been employed to select the cluster heads and also the genetic algorithm has been used to create a tree between the cluster heads and base station. After selecting the cluster heads, the normal nodes become cluster members and send their data to the cluster head. After collecting and aggregating the data by the cluster heads, the data is transferred to the base station from the path specified by the genetic algorithm. The proposed algorithm was implemented with MATLAB simulator and compared with LEACH, MB-CBCCP, and DCABGA protocols, the simulation results indicate the better performance of the proposed algorithm in different environments compared to the mentioned protocols. Due to the limited energy in the sensor-based IoT and the fact that they cannot be recharged in most applications, the use of multi-objective optimization algorithms in the design and implementation of routing and clustering algorithms has a significant impact on the increase in the lifetime of these networks.
[1] S. Rani, R. Talwar, J. Malhotra, S. H. Ahmed, M. Sarkar, and H. Song, “A novel scheme for an energy efficient internet of things based on wireless sensor networks,” Sensors (Switzerland), 2015, doi: 10.3390/s151128603.
[2] F. K. Shaikh and S. Zeadally, “Energy harvesting in wireless sensor networks: A comprehensive review,” Renewable and Sustainable Energy Reviews. 2016, doi: 10.1016/j.rser.2015.11.010.
[3] F. Gregorio, G. González, C. Schmidt, and J. Cousseau, “Internet of Things,” in Signals and Communication Technology, 2020.
[4] P. P. Ray, “A survey on Internet of Things architectures,” Journal of King Saud University - Computer and Information Sciences. 2018, doi: 10.1016/j.jksuci.2016.10.003.
[5] P. Sethi and S. R. Sarangi, “Internet of Things: Architectures, Protocols, and Applications,” Journal of Electrical and Computer Engineering. 2017, doi: 10.1155/2017/9324035.
[6] J. Y. Chang, “A Distributed Cluster Computing Energy-Efficient Routing Scheme for Internet of Things Systems,” Wirel. Pers. Commun., vol. 82, no. 2, pp. 757–776, 2015, doi: 10.1007/s11277-014-2251-8.
[7] T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Daneshmand, and A. H. Gandomi, “Residual energy-based cluster-head selection in WSNs for IoT application,” IEEE Internet Things J., 2019, doi: 10.1109/JIOT.2019.2897119.
[8] A. Shukla and S. Tripathi, “An Effective Relay Node Selection Technique for Energy Efficient WSN-Assisted IoT,” Wirel. Pers. Commun., vol. 112, no. 4, pp. 2611–2641, 2020, doi: 10.1007/s11277-020-07167-8.
[9] M. Sedighimanesh, H. Zandhesami, and A. Sedighimanesh, “Presenting the Hybrid Algorithm of Honeybee - Harmony in Clustering and Routing of Wireless Sensor Networks,” Int. J. Sensors, Wirel. Commun. Control, 2018, doi: 10.2174/2210327908666181029094346.
[10] M. Sedighimanesh* and H. Z. H. and A. Sedighimanesh, “Routing Algorithm based on Clustering for Increasing the Lifetime of Sensor Networks by Using Meta-Heuristic Bee Algorithms,” International Journal of Sensors, Wireless Communications and Control, vol. 10, no. 1. pp. 25–36, 2020, doi: http://dx.doi.org/10.2174/2210327909666190129154802.
[11] A. Kochhar, P. Kaur, P. Singh, and S. Sharma, “Protocols for wireless sensor networks: A survey,” Journal of Telecommunications and Information Technology. 2018, doi: 10.26636/jtit.2018.117417.
[12] Z. Cui, Y. Cao, X. Cai, J. Cai, and J. Chen, “Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things,” J. Parallel Distrib. Comput., 2019, doi: 10.1016/j.jpdc.2017.12.014.
[13] N. N. Srinidhi, S. M. Dilip Kumar, and K. R. Venugopal, “Network optimizations in the Internet of Things: A review,” Engineering Science and Technology, an International Journal. 2019, doi: 10.1016/j.jestch.2018.09.003.
[14] T. Salman and R. Jain, “Networking protocols and standards for internet of things,” in Internet of Things and Data Analytics Handbook, 2017.
[15] S. Srivastava, M. Singh, and S. Gupta, “Wireless Sensor Network: A Survey,” in 2018 International Conference on Automation and Computational Engineering, ICACE 2018, 2019, doi: 10.1109/ICACE.2018.8687059.
[16] B. Bhushan and G. Sahoo, “Routing protocols in wireless sensor networks,” in Studies in Computational Intelligence, 2019.
[17] G. Xie and F. Pan, “Cluster-Based Routing for the Mobile Sink in Wireless Sensor Networks with Obstacles,” IEEE Access, 2016, doi: 10.1109/ACCESS.2016.2558196.
[18] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” in System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on, 2000, p. 10 pp. vol. 2.
[19] M. Sedighimanesh and A. Sedighimanesh, “Reducing Energy Consumption of the SEECH Algorithm in Wireless Sensor Networks Using a Mobile Sink and Honey Bee Colony Algorithm,” Law, State Telecommun. Rev. / Rev. Direito, Estado e Telecomunicações; Vol 10 No 1, 2018.
[20] R. I. Tandel, “Leach Protocol in Wireless Sensor Network: A Survey,” Int. J. Comput. Sci. Inf. Technol., 2016.
[21] K. M. sedighimanesh mohammad, Zandhessami Hessam, Alborzi Mahmood, “Energy Efficient Routing-Based Clustering Protocol Using Computational Intelligence Algorithms in Sensor-Based IoT,” J. Inf. Syst. Telecommun., vol. 33, no. 9, 2021, doi: 10.52547/jist.9.33.55.
[22] V. Chauhan and S. Soni, “Mobile sink-based energy efficient cluster head selection strategy for wireless sensor networks,” J. Ambient Intell. Humaniz. Comput., 2019, doi: 10.1007/s12652-019-01509-6.
[23] J. Bhola, S. Soni, and G. K. Cheema, “Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 3, pp. 1281–1288, 2020.
[24] S. Rani, S. H. Ahmed, and R. Rastogi, “Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications,” Wirel. Networks, 2020, doi: 10.1007/s11276-019-02083-7.
[25] F. Fanian and M. Kuchaki Rafsanjani, “Cluster-based routing protocols in wireless sensor networks: A survey based on methodology,” J. Netw. Comput. Appl., vol. 142, pp. 111–142, Sep. 2019, doi: 10.1016/J.JNCA.2019.04.021.
[26] S. A. Sert, H. Bagci, and A. Yazici, “MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks,” Appl. Soft Comput., vol. 30, pp. 151–165, 2015.
[27] K. Sundaran, V. Ganapathy, and P. Sudhakara, “Fuzzy logic based Unequal Clustering in wireless sensor network for minimizing Energy consumption,” in 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), 2017, pp. 304–309, doi: 10.1109/ICCCT2.2017.7972283.
[28] A. Vatankhah and S. Babaie, “An optimized Bidding-based coverage improvement algorithm for hybrid wireless sensor networks,” Comput. Electr. Eng., vol. 65, pp. 1–17, 2018, doi: https://doi.org/10.1016/j.compeleceng.2017.12.031.
[29] S. Babaie, N. Zekrizadeh, and S. Nobahary, “NHEEP: A New Hybrid Energy Efficient PartitioningApproach for Wireless Sensor Network Clustering,” Int. J. Inf. Electron. Eng., vol. 2, no. 3, p. 323, 2012.
[30] A. Kousar, N. Mittal, and P. Singh, “An Improved Hierarchical Clustering Method for Mobile Wireless Sensor Network Using Type-2 Fuzzy Logic,” in Lecture Notes in Electrical Engineering, 2020, doi: 10.1007/978-3-030-30577-2_11.
[31] D. Wu, “On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers,” IEEE Trans. Fuzzy Syst., vol. 20, no. 5, pp. 832–848, 2012, doi: 10.1109/TFUZZ.2012.2186818.
[32] W. W. Tan and T. W. Chua, “Uncertain rule-based fuzzy logic systems: introduction and new directions (Mendel, JM; 2001)[book review],” IEEE Comput. Intell. Mag., vol. 2, no. 1, pp. 72–73, 2007.
[33] H. Hagras, “Type-2 FLCs: A new generation of fuzzy controllers,” IEEE Comput. Intell. Mag., vol. 2, no. 1, pp. 30–43, 2007.
[34] D. W. W. W. Tan, “A simplified type-2 fuzzy logic controller for real-time control,” ISA Trans., vol. 45, no. 4, pp. 503–516, 2006.
[35] D. Wu and W. W. Tan, “Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers,” Eng. Appl. Artif. Intell., vol. 19, no. 8, pp. 829–841, 2006.
[36] N. N. Karnik, J. M. Mendel, and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Trans. Fuzzy Syst., 1999, doi: 10.1109/91.811231.
[37] D. Wu, “An interval type-2 fuzzy logic system cannot be implemented by traditional type-1 fuzzy logic systems,” in Proc. World Conference on Soft Computing, 2011.
[38] X. Wang, L. Wang, and Y. Wu, “An Optimal Algorithm for Prufer Codes.,” J. Softw. Eng. Appl., vol. 2, no. 2, pp. 111–115, 2009.
[39] S. Mirjalili, “Genetic algorithm,” in Studies in Computational Intelligence, 2019.