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Vector Based Genetic Lavrentyev Paraboloid Network Wireless Sensor Network Lifetime Improvement

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Abstract

In dynamic situations, limited processing power in Wireless Sensor Networks (WSN) makes it difficult to handle network lifetime and coverage. This work proposes a Genetic Lavrentyev Paraboloid Lagrange Support Vector Machine-based (GLPL-SVM) multiclass classification method to optimize WSN performance. The approach involves Genetic Lavrentyev Regularized Machine Learning-based Node Deployment for sensor node placement, Quadrant Count Event-based Data Aggregation for efficient data collection, and Paraboloid Lagrange Multiplier SVM-based Multiclass Classification for dynamic network coverage. The GLPL-SVM method is implemented in a Python simulator and compared with existing methods, demonstrating improvements in scheduling time, network lifetime, energy consumption, and classification accuracy.

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Data Availability

Data from WSN measurements made between May 5 and June 6, 2014, are taken from the network feature dataset [https://github.com/apanouso/wsn-indfeatdataset]. A 500-node network with a 6-s transmission window is selected. In addition, metrics from the Physical, MAC, and NWK layers are included in network monitoring traffic, together with voltage thresholds for each sensor node and sensor readings (temperature and humidity). The application layer linkages that are created between every sensor node and a sink node are connected to the recorded traffic. Consequently, the traffic is recorded by the sink node. Additionally, eighteen characteristics, including the mean and standard deviation of the received signal strength, the mean and standard deviation of the link quality indicator, the mean and standard deviation of the noise floor, and the mean value of the noise floor.

References

  1. Lakshmi, Y. V., Singh, P., Abouhawwash, M., Mahajan, S., Pandit, A. K., Ahmed, A. B. (2022). Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization. IEEE Access, Mar 2022 [ELPSO (Ensemble learning particle swarm optimization) and PSO-BPNN (Back-propagation neural network optimized by particle swarm optimization)].

  2. Mohar, S. S., Goyal, S., & Kaur, R. (2022). Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics. The Journal of Supercomputing, 78(9), 11975–12023.

    Article  Google Scholar 

  3. Saravanan, T. R. (2021). A hybrid fuzzy weighted centroid and extreme learning machine with crow-particle optimization approach for solving localization problem in wireless sensor networks. https://doi.org/10.1002/dac.4819

  4. Bhat, S. J., Santhosh, K. V. (2021). Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields, Telecommunication Systems. Springer.

  5. Bochem, A., & Zhang, H. (2022). Robustness Enhanced Sensor Assisted Monte Carlo Localization for Wireless Sensor Networks and the Internet of Things. IEEE Access. DOI:https://doi.org/10.1109/ACCESS.2022.3162288

    Article  Google Scholar 

  6. Hadir, A., Regragui, Y., & Garcia, N. M. (2021). Accurate range-free localization algorithms based on PSO for wireless sensor networks. IEEE Access 9:1-1 DOI:10.1109/ACCESS.2021.3123360

    Article  Google Scholar 

  7. Singh, A., Kotiyal, V., Sharma, S., Nagar, J., & Lee, C.-C. (2020). A machine learning approach to predict the average localization error with applications to wireless sensor networks. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3038645

    Article  Google Scholar 

  8. Seyyedabbasi, A., & Kiani, F. (2020). MAP-ACO: An efficient protocol for multi-agent pathfinding in real-time WSN and decentralized IoT systems. Microprocessors and Microsystems, 79, 103325. https://doi.org/10.1016/j.micpro.2020.103325

    Article  Google Scholar 

  9. Hajizadeh, N., Javidan, R., Shamsinejad, P., Akbari, R. (2019). Node deployment in wireless sensor networks using the new multi-objective Levy flight bee algorithm. The Institution of Engineering and Technology.

  10. Chu, S.-C., Dao, T.-K., Pan, J.-S., & Nguyen, T.-T. (2020). Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naïve Bayes classification. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-020-01671-y

    Article  Google Scholar 

  11. Abdul-Qawy, A. S. H., Alduais, N. A. M., Saad, A. M. H., Taher, M. A. A., Nasser, A. B., Saleh, S. A. M., & Khatri, N. (2023). An enhanced energy efficient protocol for large-scale IoT-based heterogeneous WSNs. Scientific African, 21, e01807. https://doi.org/10.1016/j.sciaf.2023.e01807

    Article  Google Scholar 

  12. Yarinezhad, R., & Sabaei, M. (2021). An optimal cluster-based routing algorithm for lifetime maximization of Internet of Things. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2021.05.005

    Article  Google Scholar 

  13. Guleria, K., Verma, A. K., Goyal, N., Sharma, A. K., Benslimane, A., & Singh, A. (2021). An enhanced energy proficient clustering (EEPC) algorithm for relay selection in heterogeneous WSNs. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2021.102473

    Article  Google Scholar 

  14. Kiyani, F., Chalangari, H., & Yari, S. (2010). DCSE: A dynamic clustering for saving energy in wireless sensor network. In 2010 Second international conference on communication software and networks. IEEE. https://doi.org/10.1109/ICCSN.2010.98

  15. Mohamed, R. E., Saleh, A. I., Abdelrazzak, M., & Samra, A. S. (2018). Survey on wireless sensor network applications and energy efficient routing protocols. Wireless Personal Communications, 101, 1019–1055.

    Article  Google Scholar 

  16. Kiani, F., Seyyedabbasi, A., & Nematzadeh, S. (2021). Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: Efficient cluster head selection. Sensor Review. https://doi.org/10.1108/SR-03-2021-0094

    Article  Google Scholar 

  17. Tang, L., Lu, Z., & Fan, B. (2020). Energy efficient and reliable routing algorithm for wireless sensors networks. Applied Sciences, 10(5), 1885. https://doi.org/10.3390/app10051885

    Article  Google Scholar 

  18. Del-Valle-Soto, C., Rodríguez, A., & Ascencio-Piña, C. R. (2023). A survey of energy-efficient clustering routing protocols for wireless sensor networks based on metaheuristic approaches. Artificial Intelligence Review, 56(9), 9699–9770. https://doi.org/10.1007/s10462-023-10402-w

    Article  Google Scholar 

  19. Anastasi, G., Conti, M., Di Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad hoc Networks, 7(3), 537–568.

    Article  Google Scholar 

  20. Seyyedabbasi, A., Kiani, F., Allahviranloo, T., Fernandez-Gamiz, U., & Noeiaghdam, S. (2023). Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms. Alexandria Engineering Journal, 63, 339–357. https://doi.org/10.1016/j.aej.2022.08.009

    Article  Google Scholar 

  21. Fang, W., Zhang, W., Chen, W., Liu, J., Ni, Y., & Yang, Y. (2021). MSCR: multidimensional secure clustered routing scheme in hierarchical wireless sensor networks. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-020-01884-1

    Article  Google Scholar 

  22. Kundaliya, A., & Lobiyal, D. K. (2021). Q-learning based routing protocol to enhance network lifetime in WSNs. International Journal of Computer Networks & Communications (IJCNC). https://doi.org/10.5121/ijcnc.2021.13204

    Article  Google Scholar 

  23. Fernando Jurado-Lasso, F., Marchegiani, L., Jurado, J. F., Abu-Mahfouz, A. M., Fafoutis, X. (2022). A survey on machine learning software-defined wireless sensor networks (ML-SDWSNs): Current status and major challenges. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3153521

  24. Tam, N. T., Binh, H. T. T., Dung, D. A., Lan, P. N., Vinh, L. T., Yuan, B., & Yao, X. (2019). A hybrid clustering and evolutionary approach for wireless underground sensor network lifetime maximization. Information Sciences. https://doi.org/10.1016/j.ins.2019.07.060

    Article  Google Scholar 

  25. Bhat, S. J., & Santhosh, K. V. (2020). Is localization of wireless sensor networks in irregular fields a challenge? Wireless Personal Communications. https://doi.org/10.1007/s11277-020-07460-6

    Article  Google Scholar 

  26. George, A. M., Kulkarni, S. Y., & Kurian, C. P. (2022). Gaussian regression models for evaluation of network lifetime and cluster-head selection in wireless sensor devices. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3152804

    Article  Google Scholar 

  27. Wang, M., Wang, S., & Zhang, B. (2020). APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2020.102138

    Article  Google Scholar 

  28. Tam, N. T., Hai, D. T., Son, L. H., & Vinh, L. T. (2016). Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wireless Network. https://doi.org/10.1007/s11276-016-1412-y

    Article  Google Scholar 

  29. Castano, F., Rossi, A., Sevaux, M., & Velasco, N. (2017). An exact approach to extend network lifetime in a general class of wireless sensor networks. Information Sciences. https://doi.org/10.1016/j.ins.2017.12.028

    Article  Google Scholar 

  30. Nematzadeh, S., Torkamanian-Afshar, M., Seyyedabbasi, A., & Kiani, F. (2023). Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deployment. Neural Computing and Applications, 35(1), 611–641. https://doi.org/10.1007/s00521-022-07786-1

    Article  Google Scholar 

  31. Kiani, F., & Seyyedabbasi, A. (2022) Metaheuristic algorithms in IoT: Optimized edge node localization. In Engineering applications of modern metaheuristics (pp. 19–39). Cham: Springer International Publishing.

  32. Dao, T. K., Chu, S. C., Nguyen, T. T., Nguyen, T. D., & Nguyen, V. T. (2022). An optimal WSN node coverage based on enhanced archimedes optimization algorithm. Entropy, 24(8), 1018. https://doi.org/10.3390/e24081018

    Article  MathSciNet  Google Scholar 

  33. Yaghoubi, F., Abbasfar, A.-A., & Maham, B. (2014). Energy-efficient RSSI-based localization for wireless sensor networks. IEEE Communications Letters. https://doi.org/10.1109/LCOMM.2014.2320939

    Article  Google Scholar 

  34. Kiani, F., Nematzadehmiandoab, S., & Seyyedabbasi, A. (2019). Designing a dynamic protocol for real-time Industrial Internet of Things-based applications by efficient management of system resources. Advances in Mechanical Engineering, 11(10), 1687814019866062. https://doi.org/10.1177/16878140198660

    Article  Google Scholar 

  35. Singh, M., & Soni, S. K. (2021). Network lifetime enhancement of WSNs using correlation model and node selection algorithm. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2021.102441

    Article  Google Scholar 

  36. Kiani, F., Nematzadeh, S., Anka, F. A., & Findikli, M. A. (2023). Chaotic sand cat swar optimization. Mathematics, 11(10), 2340. https://doi.org/10.3390/app13063597

    Article  Google Scholar 

  37. Zhao, Q., Xu, Z., & Yang, L. (2023). An improvement of DV-hop localization algorithm based on cyclotomic method in wireless sensor networks. Applied Sciences, 13(6), 3597.

    Article  Google Scholar 

  38. Gamal, M., Mekky, N. E., Soliman, H. H., & Hikal, N. A. (2022). Enhancing the lifetime of wireless sensor networks using fuzzy logic LEACH Technique-based particle swarm optimization. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3163254

    Article  Google Scholar 

  39. Krishna, N., & Naveen Sundar, G. (2022). In dynamic environments, how to extend the life of a wireless sensor network-a review. IEEE Access. https://doi.org/10.1109/IC3SIS54991.2022.9885727

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

    Neethu Krishna, G. Naveen Sundar & D. Narmadha

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  1. Neethu Krishna
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Contributions

NK-Node Deployment and Overfitting Mitigation. NK focuses on the initial stages of WSN optimization, which involve node deployment and addressing overfitting. Their contributions include: Node Deployment Model (Genetic Lavrentyev Regularized Machine Learning-based Node Deployment): NK is responsible for developing this model. They utilize the Centroid Cartesian Co-ordinate function for obtaining initial sensor node placements, considering the network's coverage requirements. Overfitting Mitigation: NK applies machine learning techniques, specifically Probabilistic Lavrentyev Regularization, to tackle overfitting issues that may arise during sensor node selection. They ensure that the deployed nodes are well-suited to the dynamic environment. NGS Event-Based Data Aggregation Model. NGS focuses on the data aggregation aspect of the WSN optimization. Their contributions include: Quadrant Count Event-based Data Aggregation Model: NGS is responsible for designing this model. They develop the algorithm that allows the sink node to perform event-based data aggregation using Quadrant Count. This step is critical for efficient data gathering in the network. DN Multiclass Classification and Validation. DN deals with the final stages of the proposed method, which involve dynamic network coverage and classification accuracy. Their contributions include: Multiclass Classification Model (Paraboloid Lagrange Multiplier SVM-based Multiclass Classification): DN designs and implements this supervised machine learning model. They employ the paraboloid quadric surface to perform Multiclass Classification of continuous data, thereby enhancing classification accuracy in the dynamic WSN. Validation and Performance Evaluation: DN is responsible for validating the entire GLPL-SVM method in a Python simulator. They compare the proposed method with existing state-of-the-art methods. Their contributions involve evaluating performance metrics such as scheduling time, network lifetime, energy consumption, and classification accuracy to demonstrate the improvements achieved through the inclusion of machine learning techniques.

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Correspondence to Neethu Krishna.

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Krishna, N., Sundar, G.N. & Narmadha, D. Vector Based Genetic Lavrentyev Paraboloid Network Wireless Sensor Network Lifetime Improvement. Wireless Pers Commun 134, 1917–1944 (2024). https://doi.org/10.1007/s11277-024-10906-w

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