Optimization Algorithms for Wireless Sensor Networks to Solve Maximization Problems
| dc.citation.epage | 185 | |
| dc.citation.issue | 2 | |
| dc.citation.journalTitle | Досягнення у кіберфізичних системах | |
| dc.citation.spage | 181 | |
| dc.citation.volume | 9 | |
| dc.contributor.affiliation | Gori State University | |
| dc.contributor.author | Mshvidobadze, Tinatin | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2025-11-06T08:48:09Z | |
| dc.date.created | 2024-02-27 | |
| dc.date.issued | 2024-02-27 | |
| dc.description.abstract | 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. | |
| dc.format.extent | 181-185 | |
| dc.format.pages | 5 | |
| dc.identifier.citation | Mshvidobadze T. Optimization Algorithms for Wireless Sensor Networks to Solve Maximization Problems / Tinatin Mshvidobadze // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 2. — P. 181–185. | |
| dc.identifier.citationen | Mshvidobadze T. Optimization Algorithms for Wireless Sensor Networks to Solve Maximization Problems / Tinatin Mshvidobadze // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 2. — P. 181–185. | |
| dc.identifier.doi | doi.org/10.23939/acps2024.02.181 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/117379 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Досягнення у кіберфізичних системах, 2 (9), 2024 | |
| dc.relation.ispartof | Advances in Cyber-Physical Systems, 2 (9), 2024 | |
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| dc.relation.references | [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 | |
| dc.relation.references | [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 | |
| dc.relation.references | [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 | |
| dc.relation.references | [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 | |
| dc.relation.references | [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 | |
| dc.relation.references | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [9] Richtárik, P., & Takác, M. (2020). Stochastic reformlations of linear systems: algorithms and convergence theory. SIAM Journal on Matrix Analysis and Applications, vol. 41(2),pp. 487-524. DOI:10.1137/18M1179249 | |
| dc.relation.referencesen | [10] Ji, K., & Ying, L. (2023). Network utility maximization with unknown utility functions: A distributed, data-driven bilevel optimization approach. Proceedings of the Twentyfourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, pp. 131-140. DOI:10.1145/3565287.361026 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [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 | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2024 | |
| dc.rights.holder | © Mshvidobadze T., 2024 | |
| dc.subject | Wireless sensor networks | |
| dc.subject | cross layer | |
| dc.subject | optimization | |
| dc.subject | network utility maximization | |
| dc.subject | algorithm | |
| dc.title | Optimization Algorithms for Wireless Sensor Networks to Solve Maximization Problems | |
| dc.type | Article |
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