Measurement Sum Master (MSM-Clustering) Approach: Increasing Lifespan and Scalability for Reliable Data Delivery of WSNs

Abstract

In wireless sensor networks (WSNs), energy consumption is a critical concern, and replacing batteries at the node level is often costly and impractical. Network lifespan extension not only improves operational capacity but also minimizes maintenance overhead. As the network size increases, overall network management and scalability become challenging, especially when handling a large amount of sensor data. To address these issues, we proposed an approach known as Measurement Sum Master (MSM) clustering, which leverages particle swarm optimization to detect healthier inter-cluster routing directions. This approach introduces a master node strategy to select the average cluster size for maintaining scalability according to the dimensional factor. The proposed approach is designed to analyze such issues in clustering based on the concept of particle swarm methodology for identifying the best-suited inter-cluster routing, that is, master-based sum-measured optimization offers improvements over existing methods by maintaining the fitness of the cluster head in terms of energy and packet delivery. It is formulated with an algorithm to identify the best cluster size to handle the dimension. It is finally compared with the existing protocol in terms of energy consumption to analyze the performance of the protocols. The suggested approach introduces a new methodology of clustering for WSNs to improve efficiency and enhance operational longevity. The MSM-PSO protocol was used for the analysis, and the results were compared in terms of lifespan and scalability. The power efficiency increased by approximately 8–9% and the packet transmission cost was reduced by approximately 5% along with the improved fitness measurement. A comparative analysis with existing protocols was also presented to demonstrate that the MSM clustering approach significantly minimizes energy consumption, improves data delivery reliability, and increases network lifetime. This novel clustering approach offers an energy-efficient and scalable solution for enhancing the reliability of WSNs.

Keywords

• Wireless networks
• MSM clustering
• Efficiency
• Lifespan
• Scalability

References

1. P. Kuila and P. K. Jana, “A novel differential evolution based clustering algorithm for wireless sensor networks,” Applied Soft Computing, vol. 25, pp. 414–425, Sep. 2014, doi: 10.1016/j.asoc.2014.08.064.
2. M. Kumar, S. Mittal, and Md. A. K. Akhtar, “A NSGA-II based energy efficient routing algorithm for wireless sensor networks.,” Journal of Information Science and Engineering, vol. 36, pp. 777–794, Jan. 2020, https://dblp.uni-trier.de/db/journals/jise/jise36.html#KumarMA20
3. M. Kumar, D. Kumar, and M. A. K. Akhtar, “A modified GA-Based load balanced clustering algorithm for WSN,” International Journal of Embedded and Real-Time Communication Systems, vol. 12, no. 1, pp. 44–63, Dec. 2020, doi: 10.4018/ijertcs.20210101.oa3.
4. W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660–670, Oct. 2002, doi: 10.1109/twc.2002.804190.
5. M. Saleem, G. A. Di Caro, and M. Farooq, “Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions,” Information Sciences, vol. 181, no. 20, pp. 4597–4624, Jul. 2010, doi: 10.1016/j.ins.2010.07.005.
6. M. Kumar, A. Kumar, S. Kumar, P. Chauhan, and S. Selvarajan, “An African vulture optimization algorithm based energy efficient clustering scheme in wireless sensor networks,” Scientific Reports, vol. 14, no. 1, p. 31412, Dec. 2024, doi: 10.1038/s41598-024-83005-2.
7. H. L. Liu, Y. Wang, and Y. M. Cheung, “A Multi-Objective evolutionary algorithm using Min-Max strategy and sphere coordinate transformation,” Intelligent Automation & Soft Computing, vol. 15, no. 3, pp. 361–384, Jan. 2009, doi: 10.1080/10798587.2009.10643036.
8. L. T. Bui and S. Alam, Multi-objective optimization in computational intelligence theory and practice. 2008. doi: 10.4018/978-1-59904-498-9.

9. W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660–670, Oct. 2002, doi: 10.1109/twc.2002.804190.
10. W. W. Fang, J. M. Chen, L. Shu, T. S. Chu, and D. P. Qian, “Congestion avoidance, detection and alleviation in wireless sensor networks,” Journal of Zhejiang University SCIENCE C, vol. 11, no. 1, pp. 63–73, Dec. 2009, doi: 10.1631/jzus.c0910204.
11. M. M. Monowar, Md. O. Rahman, A. K. Pathan, and C. S. Hong, “Prioritized Heterogeneous Traffic-Oriented Congestion Control Protocol for WSNs,” The International Arab Journal of Information Technology, vol. 9, pp. 39–48, Jan. 2012, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.4443
12. M. C. Saxena, F. Banu, A. Shrivastava, M. Thyagaraj, and S. Upadhyay, “Comprehensive analysis of energy efficient secure routing protocol over sensor network,” Materials Today Proceedings, vol. 62, pp. 5003–5007, Jan. 2022, doi: 10.1016/j.matpr.2022.04.857.
13. W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, vol. vol.1. 2005, p. 10. doi: 10.1109/hicss.2000.926982.
14. S. Lindsey and C. S. Raghavendra, “PEGASIS: Power-efficient gathering in sensor information systems,” Proceedings - IEEE Aerospace Conference, vol. 3, pp. 3–1125, Jun. 2003, doi: 10.1109/aero.2002.1035242.
15. H. Li, J. Cao, and J. Xiong, “Constructing Optimal Clustering Architecture for Maximizing Lifetime in Large Scale Wireless Sensor Networks,” ICPADS ’09: Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems, vol. 67, pp. 182–189, Jan. 2009, doi: 10.1109/icpads.2009.10.
16. W. Naruephiphat and C. Charnsripinyo, The Clustering Algorithm for Enhancing Network Lifetime in Wireless Sensor Networks, vol. 7. 2009, pp. 240–245. doi: 10.1109/uic-atc.2009.66.
17. S. D. Muruganathan, D. C. F. Ma, R. I. Bhasin, and A. O. Fapojuwo, “A centralized energy-efficient routing protocol for wireless sensor networks,” IEEE Communications Magazine, vol. 43, no. 3, pp. S8-13, Mar. 2005, doi: 10.1109/mcom.2005.1404592.
18. C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Directed diffusion for wireless sensor networking,” IEEE/ACM Transactions on Networking, vol. 11, no. 1, pp. 2–16, Feb. 2003, doi: 10.1109/tnet.2002.808417.
19. D. Ganesan, R. Govindan, S. Shenker, and D. Estrin, “Highly-resilient, energy-efficient multipath routing in wireless sensor networks,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 5, no. 4, pp. 11–25, Oct. 2001, doi: 10.1145/509506.509514.
20. M. Maimour, Maximally radio-disjoint multipath routing for wireless multimedia sensor networks. 2008, pp. 26–31. doi: 10.1145/1454573.1454579.
21. Z. Wang, E. Bulut, and B. K. Szymanski, Energy Efficient Collision Aware Multipath Routing for Wireless Sensor Networks. 2009, pp. 1–5. doi: 10.1109/icc.2009.5198989.
22. M. Cherian and T. R. G. Nair, “Multipath routing with novel packet scheduling approach in wireless sensor networks,” International Journal of Computer Theory and Engineering, pp. 666–670, Jan. 2011, doi: 10.7763/ijcte.2011.v3.389.
23. B. Karp and H. T. Kung, “GPSR: Greedy Perimeter Stateless routing for wireless networks,” Jan. 2005. doi: 10.21236/ada440078.
24. Y. Xu, J. Heidemann, and D. Estrin, Geography-informed energy conservation for Ad Hoc routing. 2001, pp. 70–84. doi: 10.1145/381677.381685.
25. R. Alasem, A. M. Reda, and M. Mansour, “Location based energy-efficient reliable routing protocol for wireless sensor networks,” International Conference on Electronics, Hardware, Wireless and Optical Communications, pp. 180–185, Feb. 2011, https://www.researchgate.net/profile/Ahmed_Mahlous/publication/228415354_Location_based_energy-efficient_reliable_routing_protocol_for_wireless_sensor_networks/links/558d572c08ae634309f3102d.pdf
26. R. Ding and L. Yang, “A Reactive Geographic Routing Protocol for wireless sensor networks,” 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 31–36, Dec. 2010, doi: 10.1109/issnip.2010.5706734.
27. T. Y. Chen, H. W. Wei, C. R. Lee, F. N. Huang, T. S. Hsu, and W. K. Shih, “Energy Efficient Geographic Routing Algorithms In Wireless Sensor Network,” Journal of Interconnection Networks, vol. 14, no. 01, p. 1350001, Mar. 2013, doi: 10.1142/s0219265913500011.
28. A. Kumari, S. Upadhyay, and N. Gandotra, “Impact of Mobility on the Performance of Wireless Network Scenario using Distance Vector Routing Protocol,” International Journal of Computer Applications, vol. 57, no. 2, pp. 14–20, 2012, doi: 10.5120/9085-2644.
29. L. Xie, Y. Shi, Y. T. Hou, W. Lou, H. D. Sherali, and S. F. Midkiff, “Multi-Node wireless energy charging in sensor networks,” IEEE/ACM Transactions on Networking, vol. 23, no. 2, pp. 437–450, Feb. 2014, doi: 10.1109/tnet.2014.2303979.
30. P. Rawat, P. Kumar, and S. Chauhan, “Fuzzy logic and particle swarm optimization-based clustering protocol in wireless sensor network,” Soft Computing, vol. 27, no. 9, pp. 5177–5193, Feb. 2023, doi: 10.1007/s00500-023-07833-6.
31. P. Bekal, P. Kumar, and P. R. Mane, “A metaheuristic approach for hierarchical wireless sensor networks using particle swarm optimisation‐based Enhanced LEACH protocol,” IET Wireless Sensor Systems, vol. 14, no. 6, pp. 410–426, Aug. 2024, doi: 10.1049/wss2.12091.
32. T. K. Jain, D. S. Saini, and S. V. Bhooshan, “Lifetime Optimization of a Multiple Sink Wireless Sensor Network through Energy Balancing,” Journal of Sensors, vol. 2015, pp. 1–6, Jan. 2015, doi: 10.1155/2015/921250.
33. Md. Azharuddin and P. K. Jana, “A distributed algorithm for energy efficient and fault tolerant routing in wireless sensor networks,” Wireless Networks, vol. 21, no. 1, pp. 251–267, Aug. 2014, doi: 10.1007/s11276-014-0782-2.
34. M. Gholipour, A. T. Haghighat, and M. R. Meybodi, “Hop-by-hop traffic-aware routing to congestion control in wireless sensor networks,” EURASIP Journal on Wireless Communications and Networking, vol. 2015, no. 1, Jan. 2015, doi: 10.1186/s13638-015-0241-5.
35. O. Zytoune, Y. Fakhri, and D. Aboutajdine, “A novel energy aware clustering technique for routing in wireless sensor networks,” Wireless Sensor Network, vol. 02, no. 03, pp. 233–238, Jan. 2010, doi: 10.4236/wsn.2010.23031.
36. C. Wang, K. Sohraby, Y. Hu, B. Li, and W. Tang, Issues of transport control protocols for wireless sensor networks, vol. 1. 2005, pp. 422–426. doi: 10.1109/icccas.2005.1493441.
37. L. Buttyan and L. Csik, “Security analysis of reliable transport layer protocols for wireless sensor networks,” Proceedings of the 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops ’10), pp. 419–424, Mar. 2010, doi: 10.1109/percomw.2010.5470633.
38. M. H. Yaghmaee and D. Adjeroh, “A reliable transport protocol for Wireless Sensor Networks,” 2008 International Symposium on Telecommunications (IST ’08), pp. 440–445, Aug. 2008, doi: 10.1109/istel.2008.4651343.