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A Multi-Objective Metaheuristic Approach Based Adaptive Clustering and Path Selection in IoT Enabled Wireless Sensor Networks


Affiliations
1 Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Chhattisgarh, India
 

The application-oriented Internet of Things (IoT) systems that exhibit the use of wireless sensor networks (WSNs) have energy constraint issues. The nodes in the WSN are driven by batteries that cannot be used for a very long time and thus the network is unable to combat the energy efficiency challenge. Also, the energy of the nodes drains rapidly with time as a result of a steady sensing task. Moreover, there are several intermediate tasks performed by the wireless network from sensing to sending the data to the destination. The traditional wireless models can accomplish the task of sensing and transmitting but are unable to avoid the tradeoff between many quality-of-service matrices such as network latency and throughput. So, there is a need to employ optimization techniques with a multi-objective paradigm. In this paper, a model for both choosing the cluster head and selecting the efficient path in a WSN for IoT applications has been proposed. The cluster head selection which is a part of clustering is done using a multi-objective rider optimization algorithm (ROA) which considers 3 objectives namely energy, distance, and delay. The routing is performed by selecting efficient and optimal paths using the multi-objective sailfish optimization algorithm (SFO). The results reveal that the proposed model proves itself superior to other similar existing works when compared based on execution time, energy depletion, network delay, throughput, packet delivery ratio, alive nodes in the network, and increase in dead nodes. The experimentation is done on a dense sensor network and it is observed that the proposed work can mitigate up to 30-40% of energy utilization and 40-60% of delay when compared with similar multi-objective techniques for routing and clustering. The intensification in the network lifespan and throughput is also marked by the proposed multi-objective technique which makes it profitable to be used in various IoT applications.

Keywords

IoT Enabled WSN, Multi-Objective Optimization, Clustering, Quality-of-Service, Rider Optimization, Sailfish Optimization
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  • B. Pourghebleh, and N. J. Navimipour, “Data aggregation mechanisms in the Internet of things : A systematic review of the literature and recommendations for future research,” J. Netw. Comput. Appl., vol. 97, pp. 23–34, Nov. 2017. https://doi.org/10.1016/j.jnca.2017.08.006

  • M. Shakeri, A. Sadeghi-niaraki, S. Choi, and S. M. R. Islam, “Performance Analysis of IoT-Based Health and Environment WSN Deployment,” Sensors, vol. 20, no. 20, pp. 5923, Jan. 2020. https://doi.org/10.3390/s20205923

  • F. Javed, M. K. Afzal, M. Sharif, and B. Kim, “Internet of Things (IoT) Operating Systems Support , Networking Technologies , Applications , and Challenges : A Comparative Review,” IEEE Commun. Surv. Tutorials, vol. 20, no. 3, pp. 2062–2100, Mar. 2018. DOI:10.1109/COMST.2018.2817685

  • H. V. Chaitra, and G. K. Ravikumar, “Energy efficient clustering method for wireless sensor network,” Indones. J. Electr. Eng. Comput. Sci., vol. 14, no. 2, pp. 1039–1048, May. 2019. DOI: http://doi.org/10.11591/ijeecs.v14.i2.pp1039-1048

  • W. Asiedu, M. D. Jnr, and S. A. Gyamfi, “Reliable Scheme for Cluster Head Election in Mobile Ad Hoc Networks,” International Journal of Computer Trends and Technology (IJCTT), vol. 61, no. 2, pp. 93–99, 2018.doi: 10.14445/22312803/IJCTT-V61P116

  • S. T. Tayal, “Energy Efficient Clustering In Wireless Sensor Network : A Review,” Int J Wired Wireless Comm., 2016.

  • L. Bhasker, “Genetically derived secure cluster-based data aggregation in wireless sensor networks,” IET Inf. Secur., vol. 8, no. 1, pp. 1–7, 2013. doi: 10.1049/iet-ifs.2013.0133

  • Hasson, S. T., & Abd Al-kadhum, H. (2017, April). Developed clustering approaches to enhance the data transmissions in WSNs. In 2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT) (pp. 99-106). IEEE.

  • C. Gherbi, Z. Aliouat, and M. Benmohammed, “A survey on clustering routing protocols in wireless sensor networks,” Sensor Review. vol. 37, no. 1, Jan. 2017. https://doi.org/10.1108/SR-06-2016-0104

  • P. Maurya and A. Kaur, “A Survey on Descendants of LEACH Protocol,” International Journal of Information Engineering and Electronic Business, vol. 8, no. 2, pp. 46–58, Mar. 2016. DOI: 10.5815/ijieeb.2016.02.06

  • J. Rejinaparvin and C. Vasanthanayaki, “Particle swarm optimization based clustering by preventing residual nodes in wireless sensor networks, ” IEEE Sens. J., vol. 15, no. 8, pp. 4264–4274, Mar. 2015.DOI:10.1109/JSEN.2015.2416208

  • R. Hamidouche, Z. Aliouat, A. Adamou A. Ari, and M. Gueroui, “An efficient clustering strategy avoiding buffer overflow in IoT sensors : a bio-inspired based approach,” IEEE Access, vol. 7, pp. 156733-156751, Sep. 2019. DOI:10.1109/ACCESS.2019.2943546

  • 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, Mar. 2020. https://doi.org/10.1007/s12652-019-01382-3

  • T. Wang, G. Zhang, X. Yang, A. Vajdi, “Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks,” J. Syst. Softw.,vol. 146, pp. 196-214, Dec. 2018. https://doi.org/10.1016/j.jss.2018.09.067

  • G. P. Gupta and S. Jha, “Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony Search based metaheuristic techniques,” Eng. Appl. Artif. Intell., vol. 68, pp. 101–109, Feb. 2018. https://doi.org/10.1016/j.engappai.2017.11.003

  • W. Osamy, A. A. El-sawy, and A. Salim, “CSOCA : Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks,” IEEE Access, vol. 8, pp. 60676–60688, Mar. 2020. DOI:10.1109/ACCESS.2020.2983483

  • G. Yogarajan, and T. Revathi, “Improved Cluster Based Data Gathering Using Ant Lion Optimization in Wireless Sensor Networks,” Wirel. Pers. Commun., vol. 98, no. 3, pp. 2711-2731, Feb. 2017. https://doi.org/10.1007/s11277-017-4996-3

  • N. Sirdeshpande and V. Udupi, “Fractional lion otimization for cluster head-based routing protocol in wireless sensor network,” J. Franklin Inst., vol. 354, no. 11, pp. 4457-4480, Jul. 2017. https://doi.org/10.1016/j.jfranklin.2017.04.005

  • S. B. Sasi and R. Santhosh, “Multiobjective routing protocol for wireless sensor network optimization using ant colony conveyance algorithm,” International Journal of Communication Systems, vol. 34, no. 6, pp. 1–13, Apr. 2021. https://doi.org/10.1002/dac.4270

  • R. Kumar and D. Kumar, “Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network,” Wirel. Networks, vol. 22, no. 5, pp. 1461-1474, Jul. 2016. https://doi.org/10.1007/s11276-015-1039-4

  • Antoo, A., & Mohammed, A. R. (2014, July). EEM-LEACH: Energy efficient multi-hop LEACH routing protocol for clustered WSNs. In 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT) (pp. 812-818). IEEE.

  • K. Miranda, S. Zapotecas-Martínez, A. López-Jaimes, and A. García-Nájera, “A Comparison of Bio-Inspired Approaches for the Cluster-Head Selection Problem in WSN,” In: Shandilya S., Shandilya S., Nagar A. (eds) Advances in Nature-Inspired Computing and Applications, Cham, Germany: EAI/Springer Innovations in Communication and Computing. Springer, 2018, pp.65-187.

  • D. R. Prasad, P. V Naganjaneyulu, and K. S. Prasad, “Energy Efficient Clustering in Multi-hop Wireless Sensor Networks Using Differential Evolutionary MOPSO,” Brazilian Archives of Biology and Technology, vol. 59, pp.1-15, Jan 2017. https://doi.org/10.1590/1678-4324-2016161011

  • M. Micheletti, L. Mostarda, and A. Navarra, “CER-CH : Combining Election and Routing Amongst Cluster Heads in Heterogeneous WSNs,” IEEE Access, vol. 7, pp. 125481–125493, Aug. 2019. DOI:10.1109/ACCESS.2019.2938619

  • D. Kumar, T. C. Aseri, and R. B. Patel, “EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks,” Comput. Commun., vol. 32, no. 4, pp. 662–667, Mar. 2009. https://doi.org/10.1016/j.comcom.2008.11.025

  • M. Alazab, K. Lakshmanna, T. Reddy G, Q. V. Pham, and P. K. R. Maddikunta, “Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities,” Sustain. Energy Technol. Assessments, vol. 43, pp. 100973, Feb. 2021. https://doi.org/10.1016/j.seta.2020.100973

  • T. Han, L. Zhang, S. Pirbhulal, W. Wu, and V. H. C. de Albuquerque, “A novel cluster head selection technique for edge-computing based IoMT systems,” Computer Networks, vol. 158, pp. 114–122, Jul. 2019. https://doi.org/10.1016/j.comnet.2019.04.021

  • V. Jafarizadeh, A. Keshavarzi, and T. Derikvand, “Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks,” Wirel. Networks, vol. 23, no. 3, pp. 779-785, Apr. 2017. https://doi.org/10.1007/s11276-015-1169-8

  • D. Agrawal and S. Pandey, “FUCA: Fuzzy-based unequal clustering algorithm to prolong the lifetime of wireless sensor networks,” Int. J. Commun. Syst., vol. 31, no. 2, pp. 1–18, Jan. 2018. https://doi.org/10.1002/dac.3448

  • D. Chandirasekaran, and T. Jayabarathi, “Cat swarm algorithm in wireless sensor networks for optimized cluster head selection : a real time approach,” Cluster Comput., vol. 22, no. 5, pp. 11351–11361, Sep. 2019. https://doi.org/10.1007/s10586-017-1392-4

  • 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., vol. 6, no. 3, pp. 5132-5139, Feb. 2019. 10.1109/JIOT.2019.2897119

  • A. Sarkar and T. S. Murugan, “Cluster head selection for energy efficient and delay-less routing in wireless sensor network,” Wirel. Networks, vol. 25, no. 1, pp. 303-320, Jan. 2019. https://doi.org/10.1007/s11276-017-1558-2

  • N. Lavanya and T. Shankar, “Energy Efficient Cluster Head Selection using Hybrid Squirrel Harmony Search Algorithm in WSN,” Energy, vol. 10, no. 12, pp. 477–487, 2019.DOI: 10.14569/IJACSA.2019.0101265

  • B. Singh and D. K. Lobiyal, “A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks,” Human-Centric Computing and Information Sciences, vol. 2, no. 1, pp. 1–18, Dec. 2012. https://doi.org/10.1186/2192-1962-2-13

  • K. Vijayalakshmi, and P. Anandan, “A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN,” Cluster Comput., vol. 22, no. 5, pp. 12275-12282, Sep. 2019. https://doi.org/10.1007/s10586-017-1608-7

  • P. T. Karthick, and C. Palanisamy, “Optimized cluster head selection using krill herd algorithm for wireless sensor network,” Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, vol. 60, no. 3, pp. 340–348, Jul. 2019. https://doi.org/10.1080/00051144.2019.1637174

  • S. Hamrioui, C. Adam, M. Hamrioui, J. Lioret, and P. Lorenz, “Smart and self-organised routing algorithm for efficient IoT communications in smart cities,” IET Wireless Sensor Systems, vol. 8, no. 6, pp. 305–312, Nov. 2018.doi:10.1049/iet-wss.2018.5022.

  • Y. Chang, H. Tang, B. Li, and X. Yuan, “Distributed Joint Optimization Routing Algorithm Based on the Analytic Hierarchy Process for Wireless Sensor Networks,” IEEE Communications Letters, vol. 21, no. 12, pp. 2718–2721, Sep. 2017. DOI:10.1109/LCOMM.2017.2756035

  • S. S. Preeth, R. Dhanalakshmi, R. Kumar, and P. M. Shakeel, “An adaptive fuzzy rule based energy efficient clustering and immune- inspired routing protocol for WSN-assisted IoT system,” J. Ambient Intell. Humaniz. Comput., pp. 1-13, Dec. 2018. https://doi.org/10.1007/s12652-018-1154-z

  • H. Salarian, K. W. Chin, and F. Naghdy, “An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks,” IEEE Trans. Veh. Technol., vol. 63, no. 5, pp. 2407–2419, Nov. 2014.DOI:10.1109/TVT.2013.2291811

  • A. Kaswan, K. Nitesh, and P. K. Jana, “International Journal of Electronics and Communications ( AEÜ ) Energy efficient path selection for mobile sink and data gathering in wireless sensor networks,” AEUE - Int. J. Electron. Commun., vol. 73, pp. 110–118, Mar. 2017. https://doi.org/10.1016/j.aeue.2016.12.005

  • T. A. Alghamdi, “Secure and Energy Efficient Path Optimization Technique in Wireless Sensor Networks Using DH Method,” IEEE Access, vol. 6, pp. 53576-53582, Aug. 2018. DOI:10.1109/ACCESS.2018.2865909

  • K. N. Vhatkar and G. P. Bhole, “Improved rider optimization for optimal container resource allocation in cloud with security assurance,” International Journal of Pervasive Computing and Communications, vol. 16, no. 3, pp. 235–258, Jun. 2020. https://doi.org/10.1108/IJPCC-12-2019-0094

  • C. Hua and T. S. P. Yum, “Optimal routing and data aggregation for maximizing lifetime of wireless sensor networks,” IEEE/ACM Trans. Netw., vol. 16, no. 4, pp. 892–903, Mar. 2008. DOI:10.1109/TNET.2007.901082

  • J. Wang, Y. Gao, W. Liu, A. K. Sangaiah, and H. J. Kim, “An Improved Routing Schema with Special Clustering Using PSO Algorithm for heterogeneous wireless sensor network,” Sensors, vol. 19, no. 3, pp. 671, Jan. 2019. https://doi.org/10.3390/s19030671

  • X. Li, B. Keegan, F. Mtenzi, T. Weise, and M. Tan, “Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks,” IEEE Access, vol. 7, pp. 113182–113196, Aug. 2019.DOI:10.1109/ACCESS.2019.2934889

  • M. Fattoum and Z. Jellali, “A Joint Clustering and Routing Algorithm based on GA for Multi Objective Optimization in WSN,” In2020 IEEE Eighth International Conference on Communications and Networking (ComNet), Oct. 2020, pp. 1-5.

  • M. Li, C. Wang, W. Wang, C. Qin, and X. Li. Multi-Objective Clustering and Routing for Maximizing Lifetime of Wireless Sensor Networks. presented at 2017 9th Int. Conf. Adv. Infocomm Technol.(ICAIT), Chengdu, China., Nov. 2017.

  • L. Xu, R. Collier, and G. M. P. O. Hare, “A Survey of Clustering Techniques in WSNs and Consideration of the Challenges of Applying Such to 5G IoT Scenarios,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1229–1249, Jul. 2017.DOI: 10.1109/JIOT.2017.2726014

  • B. Ramakrishnan, R. B. Nishanth, M.M. Joe, M. Selvi, “Cluster based emergency message broadcasting technique for vehicular ad hoc network,” Wireless Networks., vol. 23, no. 1, pp. 233-48, Jan. 2017. https://doi.org/10.1007/s11276-015-1134-6

  • N. Maslekar, M. Boussedjra, and J. Mouzna, L. Houda, “Direction based clustering algorithm for data dissemination in vehicular networks,” In2009 IEEE Vehicular Networking Conference (VNC), Oct 2009 (pp. 1-6). IEEE.

  • D. C. Hoang, P. Yadav, R. Kumar, and S.K Panda, “Real-Time Implementation of a Harmony Search Algorithm-Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks,” IEEE transactions on industrial informatics, vol. 10, no. 1, pp. 774–783, Jul. 2013.DOI:10.1109/TII.2013.2273739

  • M. M. Ahmed, E. H. Houssein, A. E. Hassanien, A. Taha, and E. Hassanien, “Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm,” Telecommun. Syst., vol. 72, no. 2, pp. 243-259, Oct. 2019. https://doi.org/10.1007/s11235-019-00559-7

  • 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,Dec.2020.https://doi.org/10.1016/j.suscom.2020.100406.

  • G. Hacioglu, V. Faryad, A. Kand, and E. Sesli, “Multi objective clustering for wireless sensor networks,” Expert Syst. Appl., vol. 59, pp. 86-100, Oct. 2016. https://doi.org/10.1016/j.eswa.2016.04.016

  • R. Elhabyan, W. Shi, and M. St-hilaire, “A Pareto optimization-based approach to clustering and routing in Wireless Sensor Networks,” J. Netw. Comput. Appl., vol. 114, pp. 57-69, Jul. 2018. https://doi.org/10.1016/j.jnca.2018.04.005

  • S. A. Sert, H. Bagci, and A. Yazici, “MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks,” Appl. Soft Comput. J., vol. 30, pp. 151-165, May. 2015. https://doi.org/10.1016/j.asoc.2014.11.063

  • A. Kaswan, V. Singh, and P. K. Jana, “A novel multi-objective particle swarm optimization based energy efficient path design for mobile sink in wireless sensor networks,” Pervasive Mob. Comput., vol. 46, pp. 122-136, Jun. 2018. https://doi.org/10.1016/j.pmcj.2018.02.003

  • K. Guleria and A. Kumar, “Meta ‑ heuristic Ant Colony Optimization Based Unequal Clustering for Wireless Sensor Network,” Wirel. Pers. Commun.,vol. 105, no. 3, pp. 891-911, Apr. 2019. https://doi.org/10.1007/s11277-019-06127-1

  • W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Micro-sensor Networks,” In Proceedings of the 33rd annual Hawaii international conference on system sciences, Maui, HI, USA, 2000, pp. 10. DOI:10.1109/HICSS.2000.926982

  • D. Binu and B. S. Kariyappa, “RideNN : A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 1, pp. 2-26, May. 2018. DOI:10.1109/TIM.2018.2836058

  • S. Shadravan, H. R. Naji, and V. K. Bardsiri, “The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems,” Eng. Appl. Artif. Intell., vol. 80, pp. 20–34, Apr. 2019. https://doi.org/10.1016/j.engappai.2019.01.001.


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  • A Multi-Objective Metaheuristic Approach Based Adaptive Clustering and Path Selection in IoT Enabled Wireless Sensor Networks

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Authors

Pallavi Joshi
Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Chhattisgarh, India
Ajay Singh Raghuvanshi
Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Chhattisgarh, India

Abstract


The application-oriented Internet of Things (IoT) systems that exhibit the use of wireless sensor networks (WSNs) have energy constraint issues. The nodes in the WSN are driven by batteries that cannot be used for a very long time and thus the network is unable to combat the energy efficiency challenge. Also, the energy of the nodes drains rapidly with time as a result of a steady sensing task. Moreover, there are several intermediate tasks performed by the wireless network from sensing to sending the data to the destination. The traditional wireless models can accomplish the task of sensing and transmitting but are unable to avoid the tradeoff between many quality-of-service matrices such as network latency and throughput. So, there is a need to employ optimization techniques with a multi-objective paradigm. In this paper, a model for both choosing the cluster head and selecting the efficient path in a WSN for IoT applications has been proposed. The cluster head selection which is a part of clustering is done using a multi-objective rider optimization algorithm (ROA) which considers 3 objectives namely energy, distance, and delay. The routing is performed by selecting efficient and optimal paths using the multi-objective sailfish optimization algorithm (SFO). The results reveal that the proposed model proves itself superior to other similar existing works when compared based on execution time, energy depletion, network delay, throughput, packet delivery ratio, alive nodes in the network, and increase in dead nodes. The experimentation is done on a dense sensor network and it is observed that the proposed work can mitigate up to 30-40% of energy utilization and 40-60% of delay when compared with similar multi-objective techniques for routing and clustering. The intensification in the network lifespan and throughput is also marked by the proposed multi-objective technique which makes it profitable to be used in various IoT applications.

Keywords


IoT Enabled WSN, Multi-Objective Optimization, Clustering, Quality-of-Service, Rider Optimization, Sailfish Optimization

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F209988