1. ACPS
  2. All Volumes and Issues
  3. Volume 9, Number 2, 2024
  4. Optimization Algorithms for Wireless Sensor Networks to Solve Maximization Problems

Optimization Algorithms for Wireless Sensor Networks to Solve Maximization Problems

ACPS.
2024;
Volume 9, Number 2
: pp. 181 - 186
Authors:
  1. Tinatin Mshvidobadze
1
Gori State University

The paper describes a constant time clustering algorithm that can be applied on wireless sensor networks. The scheme for rate control, scheduling, routing, and power control protocol for wireless sensor networks based on compressive sensing has been shown. Using network utility maximization formulations, cross-optimization solutions using Lagrangian multipliers in network access control and physical layers have been presented. The optimization solutions have been developed by solving the optimization model of network utility maximization. The paper presents a cross-sectional design problem that jointly maximizes network utility and lifetime. The solution to the problem leads to the optimal source rate as well as the optimal routes between each source and sink in the network. The presence of a common sink node in the network has been formulated to develop a distributed algorithm that minimizes the energy overhead in its implementation.

wireless sensor networks
cross layer
optimization
network utility maximization
algorithm
  1. Yang, X. S. (2020). Nature-inspired optimization algorithms: Challenges and open problems.  Journal of Computational     Science,     vol.     46,     pp.     101104. DOI:10.1016/j.jocs.2020.101104
  2. Alabadi, M., Habbal, A., & Wei, X. (2022). Industrial internet of things: Requirements, architecture, challenges, and future research directions. IEEE Access, vol. 10, pp. 66374-66400. DOI:10.1109/ACCESS.2022.3185049
  3. Ahmad, H., Khan, T. A., Stanimirović, P. S., Chu, Y. M., & Ahmad, I. (2020). Modified Variational Iteration Algorithm‐II: Convergence and Applications to Diffusion Models. Complexity, vol.  2020(1),  pp. 8841718. DOI:10.1155/2020/8841718
  4. Lv, Z., Wu, J., Li, Y., & Song, H. (2022). Cross-layer optimization for industrial Internet of Things in real scene digital twins. IEEE Internet of Things Journal, vol. 9(17),    pp.    15618-15629.    DOI:10.1109/TMM.2023.3331946
  5. Venkatachalam, K., Prabu, P., Balaji, B. S., Kang, B. G., Nam, Y., & Abouhawwash, M. (2021). Cross-layer hidden Markov analysis for intrusion detection. CMC- Computers, Materials Continua, vol. 70(1), pp. 3685- 3700. DOI:10.32604/cmc.2022.019502
  6. Sankar, A., & Liu, Z. (2004). Maximum lifetime routing in wireless ad-hoc networks. IEEE INFOCOM, vol. 2, pp. 1089-1097. DOI:10.1109/INFCOM.2004.1356995
  7. Cong, S., & Zhou, Y. (2023). A review of convolutional neural network architectures and their optimizations. Artificial    Intelligence    Review,    56(3),    1905-1969.DOI:10.1007/s10462-022-10213-5
  8. Pham, K. D. (2020). Risk-Sensitive Rate Correcting for Dynamic Heterogeneous Networks: Autonomy and Resilience. 2020 IEEE Aerospace Conference, pp. 1-10. DOI:10.1109/AERO47225.2020.9172717
  9. Richtárik, P., & Takác, M. (2020). Stochastic reform- lations of linear systems: algorithms and convergence theory. SIAM Journal on Matrix Analysis and Applicati- ons, vol. 41(2),pp. 487-524. DOI:10.1137/18M1179249
  10. Ji, K., & Ying, L. (2023). Network utility maximization with unknown utility functions: A distributed, data- driven bilevel optimization approach. Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, pp. 131-140. DOI:10.1145/3565287.361026
  11. Aljubayri, M., Yang, Z., & Shikh‐Bahaei, M. (2021). Cross-layer multipath congestion control, routing and scheduling design in ad hoc wireless networks. IET Communications,      vol.      15(8),      pp.      1096-1108. DOI:10.1049/cmu2.12145
  12. Rajendran, A., Balakrishnan, N., & Ajay, P. (2022). Deep embedded median clustering for routing  misbehaviour and attacks detection in ad-hoc networks. Ad Hoc Networks,   vol.          126,           pp.           102757. DOI:10.1016/j.adhoc.2021.10275
  13. Gheisari,  M.,  Abbasi,  A.  A.,  Sayari,  Z.,  Rizvi,  Q., Asheralieva, A., Banu, S., ... & Raza, K. A. (2020). A survey on clustering algorithms in wireless sensor networks: challenges, research, and trends. 2020 International Computer Symposium (ICS), pp. 294-299. DOI:10.1109/ICS51289.2020.00065
  14. Díez-González, J., Alvarez, R., Prieto-Fernandez, N., & Perez, H. (2020). Local wireless sensor networks positioning reliability under sensor failure. Sensors, vol. 20(5), pp. 1426.  DOI:10.3390/s20051426
  15. Sun, Y., Wang, L., Sun, J., Wang, B., & Yuan, Y. (2023). An Implementable Augmented Lagrangian Method for Solving Second-Order Cone Constrained Variational Inequalities. Asia-Pacific Journal of Operational Research,         vol.         40(03),         pp.         2250030. DOI:10.1142/S0217595922500300
  16. Ramakrishnan, S., & Ramaiyan, V. (2019). Completely uncoupled algorithms for network utility maximization. IEEE/ACM Transactions on Networking, vol. 27(2), pp. 607-620. DOI:10.1109/TNET.2019.2892801
  17.  Dogan, M. S., Lund, J. R., & Medellin-Azuara, J. (2021). Hybrid linear and nonlinear programming model for hydropower reservoir optimization. Journal of Water Resources Planning and Management, vol. 147(3),  pp. 06021001.  DOI:10.1061/(ASCE)WR.1943-5452.000135