HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks

Senan Ali Abd; Ahmed Mahdi Jubair; Seddiq Q. Abd Al-rahman

doi:10.35377/saucis...1745051

HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) represent a rapidly advancing technology with applications in diverse fields, including surveillance, smart environment development, and target tracking. Despite their versatility, WSNs continue to face persistent challenges in optimizing energy consumption and network longevity, particularly for demanding tasks like dynamic target tracking, often due to inefficient node deployment. This study introduces the Efficient Node Placement and Target Tracking Using Machine Learning (ENTML) framework, a novel method designed to address these constraints through the integration of machine learning techniques. The Hybrid Bird-Inspired Algorithm (HBIA) is utilized to compute optimal, energy-efficient node placements for establishing an efficient network topology. Meanwhile, an adaptive Deep Neural Network (DNN) model supports real-time adaptive tracking of targets by processing sensor data and dynamically adjusting parameters in real-time. This combination approach optimizes both network structure and operational responsiveness. Comprehensive simulations were conducted to evaluate ENTML against existing methods in terms of various performance metrics, including energy consumption, network lifetime, end-to-end network delay, packet delivery ratio, and packet loss, for diverse target mobility scenarios. The experimental results demonstrate the superiority of the proposed framework over the existing state-of-the-art approaches, achieving a significant 24% reduction in overall energy consumption, a 31% decrease in end-to-end delay, and a 9% extension of network lifetime. Furthermore, ENTML was also shown to provide better packet delivery ratios along with less packet loss compared to baseline methods. The outcome of these experiments highlights the remarkable advantages of utilizing combined machine learning methods, such as HBIA and DNN, to create more robust, energy-efficient sensor nodes along with adaptive WSNs tailored for challenging practical scenarios in dynamic environments. This research provides a valuable contribution towards the development of intelligent and sustainable WSN solutions.

Keywords

References

S. Hudda, and K. Haribabu, "A review on WSN based resource constrained smart IoT systems," Discov. Internet Things, vol. 5, no. 1, Art. no. 56, May 2025. doi: 10.1007/s43926-025-00152-2
N. S. Ahmad, "Recent advances in WSN-based indoor localization: A systematic review of emerging technologies, methods, challenges, and trends," IEEE Access, vol. 12, pp. 180674–180714, Nov 2024.
S. H. Nengroo, H. Jin, and S. Lee, "Management of distributed renewable energy resources with the help of a wireless sensor network," Appl. Sci., vol. 12, no. 14, Art. no. 6908, Jul. 2022. doi: 10.3390/app12146908
D. W. Wajgi, and J. V. Tembhurne, "Localization in wireless sensor networks and wireless multimedia sensor networks using clustering techniques," Multimedia Tools Appl., vol. 83, no. 3, pp. 6829–6879, Jan. 2024. doi: 10.1007/s11042-023-15956-z
V. K. Krishnamoorthy et al., "Energy saving optimization technique-based routing protocol in mobile ad-hoc network with IoT environment," Energies, vol. 16, no. 3, Art. no. 1385, Jan. 2023. doi: 10.3390/en16031385
M. Z. Iskandarani, "Effect of intelligent reflecting surface on WSN communication with access points configuration," IEEE Access, vol. 13, pp. 13380–13394, Jan. 2025. doi: 10.1109/ACCESS.2025.3531637
C. M. Chen, Z. Chen, A. K. Das, and S. A. Chaudhry, "A security-enhanced and ultralightweight communication protocol for internet of medical things," IEEE Internet Things J., vol. 11, no. 6, pp. 10168–10182, Mar. 2024. doi: 10.1109/JIOT.2023.3327322
G. Liu, C. Wang, S. Tang, and T. Jiang, "Security in wireless weak-link sensor networks: Directions, recent advances, and challenges," IEEE Netw., vol. 40, no.1, pp. 322 - 329, Jun. 2025. doi: 10.1109/MNET.2025.3580136

S. B. N. Premakumari, G. Sundaram, M. Rivera, P. Wheeler, and R. E. P. Guzmán, "Reinforcement Q-learning-based adaptive encryption model for cyberthreat mitigation in wireless sensor networks," Sensors, vol. 25, no. 7, Art. no. 2056, Mar. 2025. doi: 10.3390/s25072056
A. N. Rashid, and A. M. Jubair, "Energy assessment based smart sustainable production in wireless environment using Internet of Agricultural Things (IoAT)," J. Intell. Syst. Internet Things, vol. 14, no. 1, pp. 168–184, 2025. doi: 10.54216/JISIoT.140113
Y. Y. Ghadi et al., "Machine learning solutions for the security of wireless sensor networks: A review," IEEE Access, vol. 12, pp. 12699–12719, Jan. 2024. doi: 10.1109/ACCESS.2024.3355312
N. A. Hamad, K. A. A. Bakar, F. Qamar, A. M. Jubair, R. R. Mohamed, and M. A. Mohamed, "Systematic analysis of federated learning approaches for intrusion detection in the internet of things environment," IEEE Access, vol. 13, pp. 95410–95444, May 2025. doi: 10.1109/ACCESS.2025.3574672
S. Shamshirband, J. H. Joloudari, M. GhasemiGol, H. Saadatfar, A. Mosavi, and N. Nabipour, "FCS-MBFLEACH: Designing an energy-aware fault detection system for mobile wireless sensor networks," Mathematics, vol. 8, no. 1, Art. no. 28, Dec. 2019. doi: 10.3390/math8010028
X. Gao, X. Zhu, and L. Zhai, "AoI-sensitive data collection in multi-UAV-assisted wireless sensor networks," IEEE Trans. Wireless Commun., vol. 22, no. 8, pp. 5185–5197, Aug. 2023. doi: 10.1109/TWC.2022.3232366
L. Galluccio, J. S. Mertens, and G. Morabito, "Clustered distributed learning exploiting node centrality and residual energy (CINE) in WSNs," Network, vol. 3, no. 2, pp. 253–268, Apr. 2023. doi: 10.3390/network3020013
A. M. Jubair et al., "Optimization of clustering in wireless sensor networks: techniques and protocols," Appl. Sci., vol. 11, no. 23, Art. no. 11448, Dec. 2021. doi: 10.3390/app112311448
M. Altuwairiqi, "An optimized multi-hop routing protocol for wireless sensor network using improved honey badger optimization algorithm for efficient and secure QoS," Comput. Commun., vol. 214, pp. 244–259, Jan. 2024. doi: 10.1016/j.comcom.2023.08.011
A. Javaid et al., "Machine learning algorithms and fault detection for improved belief function based decision fusion in wireless sensor networks," Sensors, vol. 19, no. 6, Art. no. 1334, Mar. 2019. doi: 10.3390/s19061334
Z. Ullah, "A survey on hybrid, energy efficient and distributed (HEED) based energy efficient clustering protocols for wireless sensor networks," Wireless Pers. Commun., vol. 112, no. 4, pp. 2685–2713, Jun. 2020. doi: 10.1007/s11277-020-07170-z
J. Wang, Y. Gao, K. Wang, A. K. Sangaiah, and S.-J. Lim, "An affinity propagation-based self-adaptive clustering method for wireless sensor networks," Sensors, vol. 19, no. 11, Art. no. 2579, Jun. 2019. doi: 10.3390/s19112579
D. K. Bangotra, Y. Singh, A. Selwal, N. Kumar, P. K. Singh, and W.-C. Hong, "An intelligent opportunistic routing algorithm for wireless sensor networks and its application towards e-healthcare," Sensors, vol. 20, no. 14, Art. no. 3887, Jul. 2020. doi: 10.3390/s20143887
B. Poggi, C. Babatounde, E. Vittori, and T. Antoine-Santoni, "Efficient WSN node placement by coupling KNN machine learning for signal estimations and I-HBIA metaheuristic algorithm for node position optimization," Sensors, vol. 22, no. 24, Art. no. 9927, Dec. 2022. doi: 10.3390/s22249927
R. Amin, E. Rojas, A. Aqdus, S. Ramzan, D. Casillas-Perez, and J. M. Arco, "A survey on machine learning techniques for routing optimization in SDN," IEEE Access, vol. 9, pp. 104582–104611, Jul. 2021. doi: 10.1109/ACCESS.2021.3099092
M. Alrizq et al., "Optimization of sensor node location utilizing artificial intelligence for mobile wireless sensor network," Wireless Netw., vol. 30, no. 7, pp. 6619–6631, Oct. 2024. doi: 10.1007/s11276-023-03469-4
A. M. Jubair, R. Hassan, A. H. M. Aman, and H. Sallehudin, "Social class particle swarm optimization for variable-length wireless sensor network deployment," Appl. Soft Comput., vol. 113, Art. no. 107926, Dec. 2021. doi: 10.1016/j.asoc.2021.107926
D. Sowmyadevi, and I. Shanmugapriya, "Unsupervised machine learning based key management in wireless sensor networks," Meas. Sens., vol. 28, Art. no. 100847, Aug. 2023. doi: 10.1016/j.measen.2023.100847
S. Anand, and S. Sinha, "Path generation protocol to improve lifetime of WSN," Procedia Comput. Sci., vol. 218, pp. 1091–1101, Jan. 2023. doi: 10.1016/j.procs.2023.01.088
I. Surenther, K. P. Sridhar, and M. K. Roberts, "Maximizing energy efficiency in wireless sensor networks for data transmission: A deep learning-based grouping model approach," Alexandria Eng. J., vol. 83, pp. 53–65, Oct. 2023. doi: 10.1016/j.aej.2023.10.016
S. S. Saleh et al., "I-OPC: An intelligent optimal path computation system using critical path prediction and deep learning for a time-sensitive network," Alexandria Eng. J., vol. 84, pp. 138–152, Dec. 2023. doi: 10.1016/j.aej.2023.10.025
E. Volnes, S. Kristiansen, and T. Plagemann, "Improving the accuracy of timing in scalable WSN simulations with communication software execution models," Comput. Netw., vol. 188, Art. no. 107855, Apr. 2021. doi: 10.1016/j.comnet.2021.107855
E. Sediyono, F. W. Wibowo, and H. D. Purnomo, "Leader election of dynamic wireless intelligent control machine in sensor network distributed processing," J. King Saud Univ.-Comput. Inf. Sci., vol. 34, no. 10, pp. 9146–9162, Nov. 2022. doi: 10.1016/j.jksuci.2022.08.037
A. Singh, J. Amutha, J. Nagar, and S. Sharma, "A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks," Expert Syst. Appl., vol. 211, Art. no. 118588, Jan. 2023. doi: 10.1016/j.eswa.2022.118588
S. Kumar, R. Sharma, and E. Vans, "Localization for wireless sensor networks: A neural network approach," International Journal of Computer Networks & Communications (IJCNC), vol. 8, no. 1, pp. 61-71, Jan. 2016. doi: 10.5121/ijcnc.2016.8105
A. Saini, A. Kansal, and N. S. Randhawa, "Minimization of energy consumption in WSN using hybrid WECRA approach," Procedia Comput. Sci., vol. 155, pp. 803–808, Aug. 2019. doi: 10.1016/j.procs.2019.08.118
R. Joon, and P. Tomar, "Energy aware Q-learning AODV (EAQ-AODV) routing for cognitive radio sensor networks," J. King Saud Univ.-Comput. Inf. Sci., vol. 34, no. 9, pp. 6989–7000, Oct. 2022. doi: 10.1016/j.jksuci.2022.03.021
P. Sivakumar and M. Radhika, "Performance analysis of LEACH-GA over LEACH and LEACH-C in WSN," Procedia Comput. Sci., vol. 125, pp. 248–256, Jan. 2018. doi: 10.1016/j.procs.2017.12.034
F. Hajjej, M. Hamdi, R. Ejbali, and M. Zaied, "A distributed coverage hole recovery approach based on reinforcement learning for wireless sensor networks," Ad Hoc Netw., vol. 101, Art. no. 102082, Feb. 2020. doi: 10.1016/j.adhoc.2020.102082
S. Phoemphon, C. So-In, and D. T. Niyato, "A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization," Appl. Soft Comput., vol. 65, pp. 101–120, Apr. 2018. doi: 10.1016/j.asoc.2018.01.004
P. Srividya, and L. N. Devi, "An optimal cluster & trusted path for routing formation and classification of intrusion using the machine learning classification approach in WSN," Glob. Transit. Proc., vol. 3, no. 1, pp. 317–325, Jun. 2022. doi: 10.1016/j.gltp.2022.03.018
P. Pandiyaraju, S. Ganapathy, N. Mohith, and A. Kannan, "An optimal energy utilization model for precision agriculture in WSNs using multi-objective clustering and deep learning," J. King Saud Univ.-Comput. Inf. Sci., vol. 35, no. 10, Art. no. 101803, Nov. 2023. doi: 10.1016/j.jksuci.2023.101803
S. Singh, V. Anand, and P. K. Bera, "A delay-tolerant low-duty cycle scheme in wireless sensor networks for IoT applications," Int. J. Cogn. Comput. Eng., vol. 4, pp. 194–204, Jun. 2023. doi: 10.1016/j.ijcce.2023.04.005

Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Senan Ali Abd
0009-0005-3415-9188
Iraq

Ahmed Mahdi Jubair ^*
0000-0003-3668-0119
Iraq

Seddiq Q. Abd Al-rahman
0000-0002-6917-7352
Iraq

Early Pub Date

March 15, 2026

Publication Date

March 15, 2026

Submission Date

July 19, 2025

Acceptance Date

October 11, 2025

Published in Issue

Year 2026 Volume: 9 Number: 1

DOI

https://doi.org/10.35377/saucis...1745051

IZ

https://izlik.org/JA65CB57FX

Cite

RIS / Bibtex

APA

Abd, S. A., Jubair, A. M., & Abd Al-rahman, S. Q. (2026). HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks. Sakarya University Journal of Computer and Information Sciences, 9(1), 57-75. https://doi.org/10.35377/saucis...1745051

AMA

1.Abd SA, Jubair AM, Abd Al-rahman SQ. HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks. SAUCIS. 2026;9(1):57-75. doi:10.35377/saucis.1745051

Chicago

Abd, Senan Ali, Ahmed Mahdi Jubair, and Seddiq Q. Abd Al-rahman. 2026. “HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks”. Sakarya University Journal of Computer and Information Sciences 9 (1): 57-75. https://doi.org/10.35377/saucis. 1745051.

EndNote

Abd SA, Jubair AM, Abd Al-rahman SQ (March 1, 2026) HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks. Sakarya University Journal of Computer and Information Sciences 9 1 57–75.

IEEE

[1]S. A. Abd, A. M. Jubair, and S. Q. Abd Al-rahman, “HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks”, SAUCIS, vol. 9, no. 1, pp. 57–75, Mar. 2026, doi: 10.35377/saucis...1745051.

ISNAD

Abd, Senan Ali - Jubair, Ahmed Mahdi - Abd Al-rahman, Seddiq Q. “HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks”. Sakarya University Journal of Computer and Information Sciences 9/1 (March 1, 2026): 57-75. https://doi.org/10.35377/saucis. 1745051.

JAMA

1.Abd SA, Jubair AM, Abd Al-rahman SQ. HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks. SAUCIS. 2026;9:57–75.

MLA

Abd, Senan Ali, et al. “HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 1, Mar. 2026, pp. 57-75, doi:10.35377/saucis. 1745051.

Vancouver

1.Senan Ali Abd, Ahmed Mahdi Jubair, Seddiq Q. Abd Al-rahman. HBIA-DNN Based Framework for Efficient Node Placement and Target Tracking in Wireless Sensor Networks. SAUCIS. 2026 Mar. 1;9(1):57-75. doi:10.35377/saucis. 1745051