Optimal Clustering-based Routing Protocol Using Self-Adaptive Multi-Objective TLBO For Wireless Sensor Network
Subject Areas :
Ali Sedighimanesh
1
,
Hessam Zandhessami
2
*
,
Mahmood Alborzi
3
,
Mohammadsadegh Khayyatian
4
1 - Science and Research branch, Islamic Azad University
2 - Science and Research branch, Islamic Azad University
3 - Science and Research branch, Islamic Azad University
4 - shahid Beheshti university
Keywords: Clustering-based routing, Hybrid algorithms, Energy efficiency, Gateway zone.,
Abstract :
Wireless sensor networks consist of many fixed or mobile, non-rechargeable, low-cost, and low-consumption nodes. Energy consumption is one of the most important challenges due to the non-rechargeability or high cost of sensor nodes. Hence, it is of great importance to apply some methods to reduce the energy consumption of sensors. The use of clustering-based routing is a method that reduces the energy consumption of sensors. In the present article, the Self-Adaptive Multi-objective TLBO (SAMTLBO) algorithm is applied to select the optimal cluster headers. After this process, the sensors become the closest components to cluster headers and send the data to their cluster headers. Cluster headers receive, aggregate, and send data to the sink in multiple steps using the TLBO-TS hybrid algorithm that reduces the energy consumption of the cluster heads when sending data to the sink and, ultimately, an increase in the wireless sensor network’s lifetime. The simulation results indicate that our proposed protocol (OCRP) show better performance by 35%, 17%, and 12% compared to ALSPR, CRPD, and COARP algorithms, respectively. Conclusion: Due to the limited energy of sensors, the use of meta-heuristic methods in clustering and routing improves network performance and increases the wireless sensor network's lifetime.
[1] N. R. Roy and P. Chandra, “Energy dissipation model for wireless sensor networks: a survey,” Int. J. Inf. Technol., vol. 12, no. 4, pp. 1343–1353, 2020.
[2] 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.
[3] A. Belfkih, C. Duvallet, and B. Sadeg, “A survey on wireless sensor network databases,” Wirel. Networks, vol. 25, no. 8, pp. 4921–4946, 2019.
[4] A. Shahraki, A. Taherkordi, Ø. Haugen, and F. Eliassen, “Clustering objectives in wireless sensor networks: A survey and research direction analysis,” Comput. Networks, vol. 180, p. 107376, 2020.
[5] V. Parashar, B. Mishra, and G. S. Tomar, “Energy Aware Communication in Wireless Sensor Network: A Survey,” Mater. Today Proc., vol. 29, pp. 512–523, 2020.
[6] D. K. Sah and T. Amgoth, “Renewable energy harvesting schemes in wireless sensor networks: A Survey,” Inf. Fusion, vol. 63, pp. 223–247, 2020.
[7] M. Sedighimanesh* and H. Z. and A. Sedighimanesh, “Presenting the Hybrid Algorithm of Honeybee - Harmony in Clustering and Routing of Wireless Sensor Networks,” International Journal of Sensors, Wireless Communications and Control, vol. 9, no. 3. pp. 357–371, 2019.
[8] A. Kochhar, P. Kaur, P. Singh, and S. Sharma, “Protocols for wireless sensor networks: A survey,” Journal of Telecommunications and Information Technology. 2018.
[9] M. Shokouhifar and A. Jalali, “A new evolutionary based application specific routing protocol for clustered wireless sensor networks,” AEU - Int. J. Electron. Commun., vol. 69, no. 1, pp. 432–441, Jan. 2015.
[10] S. Wang, J. Yu, M. Atiquzzaman, H. Chen, and L. Ni, “CRPD: a novel clustering routing protocol for dynamic wireless sensor networks,” Pers. Ubiquitous Comput., vol. 22, no. 3, pp. 545–559, 2018.
[11] M. Khabiri and A. Ghaffari, “Energy-Aware Clustering-Based Routing in Wireless Sensor Networks Using Cuckoo Optimization Algorithm,” Wirel. Pers. Commun., vol. 98, no. 3, pp. 2473–2495, 2018.