Abstract
The process of cluster head selection under the process of cluster formation in wireless sensor networks is determined to be essential for extending the lifetime of the network. In this paper, a Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm (SADSS-IABCA)-based Cluster Head Selection Scheme is proposed for prolonging the lifetime of the network with improved Quality of Service. The differential search strategies employed in the SADSS-IABCA-based Cluster Head Selection Scheme are reliable in updating the dependent variables in periodic intervals of time through the integration of mutation and crossover. This proposed SADSS-IABCA-based Cluster Head Selection Scheme incorporated diversified search strategies associated with differential evolution with the employee and onlooker bee phase in order to improve the local searching ability of ABC with the view to eliminate its limitations of delayed convergence. In addition, the appropriate selection of differential evolution strategies is computed through the probability-based self adapting process for effective selection of cluster heads. The simulation results of the proposed SADSS-IABCA-based Cluster Head Selection Scheme also confirmed a predominant improvement in percentage of alive nodes, throughput and mean residual energy compared to the benchmarked cluster head schemes used for investigation.
Similar content being viewed by others
References
Lakshmeesha, P. (2016). Dynamic cluster head selection mechanism for wireless sensor networks. International Journal of Engineering and Computer Science, 1(1), 12–24.
Palaniappan, S., & Periasamy, P. (2017). Enhanced approach for wireless sensor network based on localization, time synchronization and quality of service routing. Cluster Computing, 1(1), 67–78.
Singh, S. P., & Sharma, S. (2018). An improved cluster-based routing algorithm for energy optimisation in wireless sensor networks. International Journal of Wireless and Mobile Computing, 14(1), 82.
Narendran, M., & Prakasam, P. (2017). An energy aware competition based clustering for cluster head selection in wireless sensor network with mobility. Cluster Computing, 1(1), 56–67.
Shalini, V. B., & Vasudevan, V. (2017). Achieving energy efficient wireless sensor network by choosing effective cluster head. Cluster Computing, 1(1), 34–47.
Sampath, A. C. T., & Thampi, S. M. (2011). An ACO algorithm for effective cluster head selection. Journal of Advances in Information Technology, 2(1), 45–56.
Gupta, V., & Sharma, S. K. (2014). Cluster head selection using modified ACO. Advances in Intelligent Systems and Computing, 1(2), 11–20.
Sharma, R., Jain, G., & Gupta, S. (2015). Enhanced cluster-head selection using round robin technique in WSN. In 2015 International Conference on Communication Networks (ICCN) (pp. 23–35).
Shalini, V. B., & Vasudevan, V. (2017). Achieving energy efficient wireless sensor network by choosing effective cluster head. Cluster Computing, 1(1), 23–34.
Janakiraman, S. (2018). A hybrid ant colony and artificial bee colony optimization algorithm-based cluster head selection for IoT. Procedia Computer Science, 143(1), 360–366.
Gupta, G. P. (2018). Improved cuckoo search-based clustering protocol for wireless sensor networks. Procedia Computer Science, 125, 234–240.
Gambhir, A., Payal, A., & Arya, R. (2018). Performance analysis of artificial bee colony optimization based clustering protocol in various scenarios of WSN. Procedia Computer Science, 132, 183–188.
Sarkar, A., & Senthil Murugan, T. (2017). Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Networks, 25(1), 303–320.
Senthil Murugan, T., & Sarkar, A. (2018). Optimal cluster head selection by hybridisation of firefly and grey wolf optimisation. International Journal of Wireless and Mobile Computing, 14(3), 296.
Sengathir J., Deva Priya, M. An energy-proficient clustering-inspired routing protocol using improved Bkd-tree for enhanced node stability and network lifetime in wireless sensor networks. International Journal of Communication Systems, 33(16), 1099–1131.
Sengathir Janakiraman, Deva Priya M., Siamala Devi S., Sandhya G., Niveditha G., & Padmavathi S. A Markov process-based opportunistic trust factor estimation mechanism for efficient cluster head selection for extending lifetime of wireless sensor networks. EAI Endorsed Transactions on Energy Web.
Rambabu, B., Reddy, A. V., & Janakiraman, S. (2019). A Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm for Optimized Clustering in Wireless Sensor Network.
Sengathir, J., & Manoharan, R. (2014). Reliability factor-based mathematical model for isolating selfish nodes in MANETs. International Journal of Information and Communication Technology, 6(3-4), 403–421.
Rambabu, B., & Janakiraman, S. (2021). Improved symbiosis organism search algorithm-based clustering scheme for enhancing longevity in wireless sensor networks (WSNs). Journal of 8th International Conference on Recent Trends in Computing (ICRTC- 2021), 2(1), 56–67.
Janakiraman, S., & Godi, R. K. (2020). Memetic particle gravitation optimization algorithm-based optimal cluster head selection in wireless sensor networks (WSNs). CVR Journal of Science and Technology, 19(1), 90–96.
Pour, S. E., & Javidan, R. (2021). A new energy aware cluster head selection for LEACH in wireless sensor networks. IET Wireless Sensor Systems, 11(1), 45–53.
Sharma, R., Vashisht, V., & Singh, U. (2020). eeTMFO/GA: A secure and energy efficient cluster head selection in wireless sensor networks. Telecommunication Systems, 74, 253–268.
Vimalarani, C., Subramanian, R., & Sivanandam, S. N. (2016). An enhanced PSO-based clustering energy optimization algorithm for wireless sensor network. The Scientific World Journal, 2016(1), 1–11.
Shankar, T., Shanmugavel, S., & Rajesh, A. (2016). Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm and Evolutionary Computation, 30(2), 1–10.
Baskaran, M., & Sadagopan, C. (2015). synchronous firefly algorithm for cluster head selection in WSN. The Scientific World Journal, 2015(1), 1–7.
Rao, P. C., Jana, P. K., & Banka, H. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005–2020.
Yadav, A., & Kumar, S. (2017). A teaching learning based optimization algorithm for cluster head selection in wireless sensor networks. International Journal of Future Generation Communication and Networking, 10(1), 111–122.
Chandirasekaran, D., & Jayabarathi, T. (2017). Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: A real time approach. Cluster Computing, 1(1), 45–56.
Lalwani, P., Banka, H., & Kumar, C. (2017). GSA-CHSR: Gravitational search algorithm for cluster head selection and routing in wireless sensor networks. Applications of Soft Computing for the Web, 1(1), 225–252.
Potthuri, S., Shankar, T., & Rajesh, A. (2018). Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing (DESA). Ain Shams Engineering Journal, 9(4), 655–663.
Vijayalakshmi, K., & Anandan, P. (2018). A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN. Cluster Computing, 1(1), 23–35.
Rambabu, B., Venugopal Reddy, A., & Janakiraman, S. (2019). Hybrid artificial bee colony and monarchy butterfly optimization algorithm (HABC-MBOA)-based cluster head selection for WSNs. Journal of King Saud University - Computer and Information Sciences, 3(2), 67–79.
Nagarajan, L., & Thangavelu, S. (2020). Hybrid grey wolf sunflower optimisation algorithm for energy-efficient cluster head selection in wireless sensor networks for lifetime enhancement. IET Communications, 15(3), 384–396.
Mehta, D., & Saxena, S. (2020). MCH-EOR: Multi-objective cluster head based energy-aware optimized routing algorithm in wireless sensor networks. Sustainable Computing Informatics and Systems, 28(2), 100406.
Balamurugan, A., Priya, M. D., Janakiraman, S., & Malar, A. C. (2021). Hybrid stochastic ranking and opposite differential evolution-based enhanced firefly optimization algorithm for extending network lifetime through efficient clustering in WSNs. Journal of Network and Systems Management, 29(3), 1–31.
Tamilarasan, N., Lenin, S., Jayapandian, N., & Subramanian, P. (2021). Hybrid shuffled frog leaping and improved biogeography-based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks. International Journal of Communication Systems, 34(4), e4722.
Funding
There is no funding received for this research work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no competing interest.
Data Availability
Data sharing not applicable—no new data generated, Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bandi, R., Ananthula, V.R. & Janakiraman, S. Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm-Based Cluster Head Selection Scheme for WSNs. Wireless Pers Commun 121, 2251–2272 (2021). https://doi.org/10.1007/s11277-021-08821-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-021-08821-5