A Multi-Objective Direction of Arrival Estimation Technique Minimizing Energy Consumption in Wireless Sensor Network
DOI:
https://doi.org/10.69996/jcai.2024018Keywords:
Wireless Sensor Network (WSN), Direction of Arrival (DOA), Multi-Objective Optimization, Cramer-Rao, RankingAbstract
Wireless Sensor Network (WSN) demand for secure communication is challenging due to its limited resource constraints. Hence, this research developed an effective distributed DOA estimation model through Direction Optimization Integrated Ranking Voting (DOIRV) to improve the performance of WSNs. The developed model comprises the multi-objective optimization model with the Whale technique for the WSN. The constructed DOIRV model uses objective function estimation with branch-and-bound for effective data transmission. The developed DOIRV model uses the Artificial Intelligence-based machine learning model for the DOA routing path computation and estimation. Finally, the DOIRV estimation is evaluated for the performance analysis with the consideration of the Cramer-Rao analysis for the data transmission in the WSN model. Simulation results stated that the proposed weighted technique significantly improves the network performance such as packet delivery ratio, throughput, and network delay. Analysis of the technique expressed that the proposed DOIRV technique exhibits effective performance rather than the conventional technique in terms of network delay, throughput, and packet delivery ratio. The comparative analysis stated that the performance of the proposed DOIRV model is ~13% higher than the conventional technique in terms of packet delivery ratio and ~18% higher for the throughput.
References
[1] N. Mazloomi, M. Gholipour and A. Zaretalab, “Efficient configuration for multi-objective QoS optimization in wireless sensor network,” Ad Hoc Networks, vol.125, pp.102730, 2022.
[2] T.Sravanti, K.Ram Mohan Rao and D.Sandhya Rani, “Distance Energy-Efficient Soft Computing Model for Data Forwarding in Healthcare Sensor Network,” Journal of Sensors, IoT & Health Sciences, vol.1, no.1, pp.1-14, 2023.
[3] E. Devika and A. Saravanan, “AI-WSN: direction of arrival estimation based on Bee swarm optimization for wireless sensor networks,” Journal of Information Technology Management, vol.14, no.4, pp.69-86, 2022.
[4] WenFen Liu, Yijun Guo and Jian Li, “5G Resource Allocation between Channels with NonLinear Analysis to Construct Urban Smart Information Communication Technology (ICT),” Journal of Computer Allied Intelligence, vol.1, no.1, pp.54-65, 2023.
[5] M. Waqas and S.S. Qureshi, “Optimized wireless Sensor Nodes Placement with multi-Objective Hybrid Optimization Algorithm,” International Journal of Computational Intelligence in Control, vol.14, no.2, 2022.
[6] A. Janarthanan and V. Srinivasan, “Multi‐ objective cluster head‐ based energy aware routing using optimized auto‐ metric graph neural network for secured data aggregation in Wireless Sensor Network,” International Journal of Communication Systems, vol.37, no.3, pp.e5664, 2024.
[7] H. Mohtashami, A. Movaghar and M. Teshnehlab, “Lifetime Improvement Based on Event Occurrence Patterns for Wireless Sensor Networks Using Multi-Objective Optimization,” Wireless Personal Communications, vol.125, no.4, pp.3333-3349, 2022.
[8] B. G. Sheena and N. Snehalatha, “Multi‐ objective metaheuristic optimization‐ based clustering with network slicing technique for Internet of Things‐ enabled wireless sensor networks in 5G systems,” Transactions on Emerging Telecommunications Technologies, vol.34, no.8, pp.e4626, 2023.
[9] T. K. Mohanta and D. K. Das, “Multiple objective optimization-based DV-Hop localization for spiral deployed wireless sensor networks using Non-inertial Opposition-based Class Topper Optimization (NOCTO),” Computer Communications, vol.195, pp.173-186, 2022.
[10] C. Savithi and C. Kaewta, “Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization,” Journal of Sensor and Actuator Networks, vol.13, no.1, pp.3, 2024.
[11] G.S. Sujeetha, “A Multi-objective Fuzzy Logic based Multi-path Routing Algorithm for WSNs,” IJ Wireless and Microwave Technologies, vol.1, pp.30-40, 2022.
[12] A.D.C. Navin Dhinnesh and T. Sabapathi, “Multi-objective Grey Wolf Optimization based self configuring wireless sensor network,” Wireless Networks, pp.1-12, 2024.
[13] S. Suganthi, N. Umapathi, M. Mahdal and M. Ramachandran, “Multi swarm optimization based clustering with tabu search in wireless sensor network,” Sensors, vol.22, no.5, pp.1736, 2022.
[14] X. Li, S. Niu, H. Bao and N.Hu, “Improved adaptive multi-objective particle swarm optimization of sensor layout for shape sensing with inverse finite element method,” Sensors, vol.22, no.14, 5203, 2022.
[15] P. Xing, H. Zhang, M.E. Ghoneim and M. Shutaywi, “UAV flight path design using multiobjective grasshopper with harmony search for cluster head selection in wireless sensor networks,” Wireless Networks, vol.29, no.2, pp.955-967, 2023.
[16] M. Elshrkawey, H. Al-Mahdi and W. Atwa, “An enhanced routing algorithm based on a reposition particle swarm optimization (RA-RPSO) for wireless sensor network,” Journal of King Saud University-Computer and Information Sciences, vol.34, no.10, pp.10304-10318, 2022.
[17] S. Liang, M.Yin, G. Sun and J. Li, “Multi-Objective Optimization Approach for Reducing Hovering and Motion Energy Consumptions in UAV-Assisted Collaborative Beamforming,” IEEE Internet of Things Journal, 2023.
[18] B. Saha and A. Jain, “Managing cross-functional teams in cloud delivery excellence centers: A framework for success,” International Journal of Multidisciplinary Innovation and Research Methodology (IJMIRM), vol.1, no.1, pp.84-107, 2022.
[19] R. Muthukkumar, L.Garg, K. Maharajan, M. Jayalakshmi, N. Jhanjhi et al., “A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks,” PeerJ Computer Science, vol.8, pp.e1029, 2022.
[20] L. Kumar and A. Biswanath Saha, “Evaluating the impact of AI-driven project prioritization on program success in hybrid cloud environments,” International Journal of Research in All Subjects in Multi Languages (IJRSML), vol.7, no.1, pp.78-99, 2019
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