Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Authors

  • Souad Kamel College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Abeer Al Qahtani College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Abdullah Saad Musaed Al-Shahrani College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13665-13670 | April 2024 | https://doi.org/10.48084/etasr.6752

Abstract

Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.

Keywords:

scheduling, target coverage problem, non-disjoint set covers, wireless sensor networks, lifespan, particle swarm optimization

Downloads

Download data is not yet available.

References

H. T. T. Binh and N. Dey, Eds., Soft Computing in Wireless Sensor Networks, 1st ed. Boca Raton, FL, USA: CRC Press, 2018.

Y. Emami and R. Javidan, "An Energy-efficient Data Transmission Scheme in Underwater Wireless Sensor Networks," Engineering, Technology & Applied Science Research, vol. 6, no. 2, pp. 931–936, Apr. 2016.

S. P. Singh and S. C. Sharma, "A Novel Energy Efficient Clustering Algorithm for Wireless Sensor Networks," Engineering, Technology & Applied Science Research, vol. 7, no. 4, pp. 1775–1780, Aug. 2017.

A. Rajab, "Genetic Algorithm-Based Multi-Hop Routing to Improve the Lifetime of Wireless Sensor Networks," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7770–7775, Dec. 2021.

V. Kesavan, R. Kamalakannan, R. Sudhakarapandian, and P. Sivakumar, "Heuristic and meta-heuristic algorithms for solving medium and large scale sized cellular manufacturing system NP-hard problems: A comprehensive review," Materials Today: Proceedings, vol. 21, pp. 66–72, Jan. 2020.

S. Mekni, B. Chaar, and M. Ksouri, "A Novel Particle Swarm Optimization Approach for Multiobjective Flexible Job Shop Scheduling Problem," in ICINCO-ICSO 2008, 2008.

S. Mekni and B. C. Fayech, "A Modified Invasive Weed Optimization Algorithm for MultiObjective Flexible Jobe Shop Scheduling Problems," Computer Science & Information Technology, vol. 10, pp. 51–60, Nov. 2014.

S. Mekni and B. Chaar Fayech, "Multiobjective Flexible Job Shop Scheduling Using A Modified Invasive Weed Optimization," International Journal on Soft Computing, vol. 6, no. 1, pp. 25–36, Feb. 2015.

N. T. Hanh, H. T. T. Binh, N. V. Son, N. T. Trang, and P. N. Lan, "Optimizing wireless sensor network lifetime through K-coverage maximization and memetic search," Sustainable Computing: Informatics and Systems, vol. 40, Dec. 2023, Art. no. 100905.

D. Arivudainambi, R. Pavithra, and P. Kalyani, "Cuckoo search algorithm for target coverage and sensor scheduling with adjustable sensing range in wireless sensor network," Journal of Discrete Mathematical Sciences and Cryptography, vol. 24, no. 4, pp. 975–996, May 2021.

J. Li, Z. Luo, and J. Xiao, "A Hybrid Genetic Algorithm With Bidirectional Mutation for Maximizing Lifetime of Heterogeneous Wireless Sensor Networks," IEEE Access, vol. 8, pp. 72261–72274, 2020.

V. R. Ekhlas, M. Hosseini Shirvani, A. Dana, and N. Raeisi, "Discrete grey wolf optimization algorithm for solving k-coverage problem in directional sensor networks with network lifetime maximization viewpoint," Applied Soft Computing, vol. 146, Oct. 2023, Art. no. https://doi.org/10.1016/j.asoc.2023.110609.

Y. E. E. Ahmed, "Modeling, Scheduling and Optimization of Wireless Sensor Networks lifetime," Ph.D. dissertation, Université de Lorraine, Lorraine, France, 2016.

M. E. Keskin, "A column generation heuristic for optimal wireless sensor network design with mobile sinks," European Journal of Operational Research, vol. 260, no. 1, pp. 291–304, Jul. 2017.

D. K. Sah, S. Srivastava, R. Kumar, and T. Amgoth, "An energy efficient coverage aware algorithm in energy harvesting wireless sensor networks," Wireless Networks, vol. 29, no. 3, pp. 1175–1195, Apr. 2023.

M. Suresh Kumar and G. A. Sathish Kumar, "Enhanced ant colony optimization algorithm for packet delivery with improved energy efficiency in wireless sensor networks," Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7909–7917, Jan. 2023.

Y. E. E. Ahmed, K. H. Adjallah, R. Stock, I. Kacem, and S. F. Babiker, "NDSC based methods for maximizing the lifespan of randomly deployed wireless sensor networks for infrastructures monitoring," Computers & Industrial Engineering, vol. 115, pp. 17–25, Jan. 2018.

M. Clerc, Particle Swarm Optimization, 1st ed. Hoboken New Jersey, USA; London UK: Wiley-ISTE, 2006.

Downloads

How to Cite

[1]
Kamel, S., Al Qahtani, A. and Al-Shahrani, A.S.M. 2024. Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization. Engineering, Technology & Applied Science Research. 14, 2 (Apr. 2024), 13665–13670. DOI:https://doi.org/10.48084/etasr.6752.

Metrics

Abstract Views: 205
PDF Downloads: 363

Metrics Information

Most read articles by the same author(s)