Machine Learning-Based Energy-Aware Routing for Wireless Body Area Networks
Received: 25 March 2025 | Revised: 6 May 2025 and 11 May 2025 | Accepted: 15 May 2025 | Online: 2 August 2025
Corresponding author: Mohammed Hicham Hachemi
Abstract
Wireless Body Area Networks (WBANs) consist of small, low-power sensors placed on or inside the human body for real-time health monitoring. One of the primary challenges in WBANs is ensuring energy efficiency, given the limited power capacity of biosensor nodes. Recent advancements in the field, including sophisticated techniques such as clustering, Particle Swarm Optimization (PSO), reinforcement learning, and hybrid Machine Learning (ML) approaches, have demonstrated significant improvements over traditional routing methods. This paper investigates energy-aware routing in WBANs using ML models, specifically Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Linear Regression (LinReg). The objective of this study is to predict energy consumption per transmission and analyze the resulting impact on network lifetime. The study visualizes results through energy depletion curves and alive node count plots. The findings demonstrate that ML models can effectively predict energy consumption, enabling optimized packet transmission and extended network lifespan. Moreover, the analysis identifies the most efficient ML-based routing strategy for WBANs and demonstrates that ML approaches outperform existing methods in the literature.
Keywords:
Wireless Body Area Network (WBAN), network lifetime, ML, DT, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), linear regressionReferences
D. S. Bhatti et al., "A Survey on Wireless Wearable Body Area Networks: A Perspective of Technology and Economy," Sensors, vol. 22, no. 20, Oct. 2022, Art. no. 7722. DOI: https://doi.org/10.3390/s22207722
J. Yick, B. Mukherjee, and D. Ghosal, "Wireless sensor network survey," Computer Networks, vol. 52, no. 12, pp. 2292–2330, Aug. 2008. DOI: https://doi.org/10.1016/j.comnet.2008.04.002
A. A. Ibrahim, O. Bayat, O. N. Ucan, and S. Salisu, "Weighted Energy and QoS based Multi-hop Transmission Routing Algorithm for WBAN," in 2020 6th International Engineering Conference "Sustainable Technology and Development", Erbil, Iraq, Feb. 2020, pp. 191–195. DOI: https://doi.org/10.1109/IEC49899.2020.9122909
A. Arghavani, H. Zhang, Z. Huang, and Y. Chen, "Power-Adaptive Communication With Channel-Aware Transmission Scheduling in WBANs," IEEE Internet of Things Journal, vol. 11, no. 9, pp. 16087–16102, May 2024. DOI: https://doi.org/10.1109/JIOT.2024.3355702
B. Shunmugapriya and B. Paramasivan, "Fuzzy Based Relay Node Selection for Achieving Efficient Energy and Reliability in Wireless Body Area Network," Wireless Personal Communications, vol. 122, no. 3, pp. 2723–2743, Feb. 2022. DOI: https://doi.org/10.1007/s11277-021-09027-5
Q. Tang, N. Tummala, S. K. S. Gupta, and L. Schwiebert, "TARA: thermal-aware routing algorithm for implanted sensor networks," in Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems, Berlin, Heidelberg, 2005, pp. 206–217. DOI: https://doi.org/10.1007/11502593_17
A. Bag and M. A. Bassiouni, "Hotspot Preventing Routing algorithm for delay-sensitive applications of in vivo biomedical sensor networks," Information Fusion, vol. 9, no. 3, pp. 389–398, Jul. 2008. DOI: https://doi.org/10.1016/j.inffus.2007.02.001
S. Ahmed, N. Javaid, M. Akbar, A. Iqbal, Z. A. Khan, and U. Qasim, "LAEEBA: Link Aware and Energy Efficient Scheme for Body Area Networks," in 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, Victoria, Canada, 2014, pp. 435–440. DOI: https://doi.org/10.1109/AINA.2014.54
N. Javaid, Z. Abbas, M. S. Fareed, Z. A. Khan, and N. Alrajeh, "M-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless Body Area Sensor Networks," Procedia Computer Science, vol. 19, pp. 224–231, Jan. 2013. DOI: https://doi.org/10.1016/j.procs.2013.06.033
M. A. Razzaque, C. S. Hong, and S. Lee, "Data-Centric Multiobjective QoS-Aware Routing Protocol for Body Sensor Networks," Sensors, vol. 11, no. 1, pp. 917–937, Jan. 2011. DOI: https://doi.org/10.3390/s110100917
T. Watteyne, I. Augé-Blum, M. Dohler, and D. Barthel, "AnyBody: a self-organization protocol for body area networks," presented at the 2nd International ICST Conference on Body Area Networks, Florence, Italy, 2007. DOI: https://doi.org/10.4108/bodynets.2007.186
A. Ahmad, N. Javaid, U. Qasim, M. Ishfaq, Z. A. Khan, and T. A. Alghamdi, "RE-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless Body Area Sensor Networks," International Journal of Distributed Sensor Networks, vol. 10, no. 4, Apr. 2014, Art. no. 464010. DOI: https://doi.org/10.1155/2014/464010
V. Manfredi, A. P. Wolfe, B. Wang, and X. Zhang, "Relational Deep Reinforcement Learning for Routing in Wireless Networks," in 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Pisa, Italy, Jun. 2021, pp. 159–168. DOI: https://doi.org/10.1109/WoWMoM51794.2021.00029
Y. Lu et al., "GTD3-NET: A deep reinforcement learning-based routing optimization algorithm for wireless networks," Peer-to-Peer Networking and Applications, vol. 18, no. 1, Nov. 2024, Art. no. 23. DOI: https://doi.org/10.1007/s12083-024-01851-3
Y. Huang, "Deep Q-Networks," in Deep Reinforcement Learning: Fundamentals, Research and Applications, H. Dong, Z. Ding, and S. Zhang, Eds. Singapore: Springer, 2020, pp. 135–160. DOI: https://doi.org/10.1007/978-981-15-4095-0_4
Y. Gu, Y. Cheng, C. L. P. Chen, and X. Wang, "Proximal Policy Optimization With Policy Feedback," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 7, pp. 4600–4610, Jul. 2022. DOI: https://doi.org/10.1109/TSMC.2021.3098451
S. Goel, K. Guleria, S. N. Panda, F. S. Alharithi, A. Singh, and A. Ali, "An improved routing technique for energy optimization and delay reduction for Wireless body area networks," Egyptian Informatics Journal, vol. 29, Mar. 2025, Art. no. 100630. DOI: https://doi.org/10.1016/j.eij.2025.100630
L. Sidhoum, M. Hadjila, and R. Merzougui, "Cluster Head Election Algorithm Based on Fuzzy Logic to Improve Lifespan in WBAN," in 2024 2nd International Conference on Electrical Engineering and Automatic Control, Setif, Algeria, 2024, pp. 1–6. DOI: https://doi.org/10.1109/ICEEAC61226.2024.10576349
L. Sidhoum, M. Hadjila, and R. Merzougui, "Chain based routing approach to improve lifespan in wireless body area networks," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 22, no. 2, pp. 427–434, Apr. 2024. DOI: https://doi.org/10.12928/telkomnika.v22i2.25745
S. M. H. Irid, M. Hadjila, M. H. Hachemi, S. Souiki, R. Mosteghanemi, and C. Mostefai, "Node Localization based on Anchor Placement using Fuzzy C-Means in a Wireless Sensor Network," International Journal of Electronics and Telecommunications, vol. 69, no. 1, pp. 99–104, Feb. 2023. DOI: https://doi.org/10.24425/ijet.2023.144337
S. Vyas and S. Gupta, "WBAN-based remote monitoring system utilising machine learning for healthcare services," International Journal of System of Systems Engineering, vol. 13, no. 1, pp. 100–108, Feb. 2023. DOI: https://doi.org/10.1504/IJSSE.2023.129054
S. Akbar et al., "Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring," Healthcare, vol. 10, no. 11, Nov. 2022, Art. no. 2297. DOI: https://doi.org/10.3390/healthcare10112297
B. S. Liya and S. Arun, "Energy Efficient Data Aggregation in Wireless Ban Area Network Using Bee Swarm Optimisation," in 2022 IEEE 7th International conference for Convergence in Technology, Mumbai, India, 2022, pp. 1–6. DOI: https://doi.org/10.1109/I2CT54291.2022.9824770
N. Samarji and M. Salamah, "ERQTM: Energy-Efficient Routing and QoS-Supported Traffic Management Scheme for SDWBANs," IEEE Sensors Journal, vol. 21, no. 14, pp. 16328–16339, Jul. 2021. DOI: https://doi.org/10.1109/JSEN.2021.3075241
M. HajilooVakil, M. Javad Khani, and Z. Shirmohammadi, "An Efficient Compression Method to Improve Energy Consumption in WBANs," in 2021 7th International Conference on Web Research, Tehran, Iran, 2021, pp. 301–305. DOI: https://doi.org/10.1109/ICWR51868.2021.9443125
K. Fathima Shemim and U. Witkowski, "Energy Efficient Clustering Protocols for WSN: Performance Analysis of FL-EE-NC with LEACH, K Means-LEACH, LEACH-FL and FL-EE/D using NS-2," in 2020 32nd International Conference on Microelectronics, Aqaba, Jordan, 2020, pp. 1–5. DOI: https://doi.org/10.1109/ICM50269.2020.9331768
G. Sun, L. Luo, K. Wang, and H. Yu, "Toward Improving QoS and Energy Efficiency in Wireless Body Area Networks," IEEE Systems Journal, vol. 15, no. 1, pp. 865–876, Mar. 2021. DOI: https://doi.org/10.1109/JSYST.2020.2999670
M. Usman, M. Qaraqe, M. R. Asghar, and I. S. Ansari, "Energy Efficient Wireless Body Area Networks: Proximity-based Clustering in Medical Implants," in 2020 IEEE Eighth International Conference on Communications and Networking, Hammamet, Tunisia, 2020, pp. 1–5. DOI: https://doi.org/10.1109/ComNet47917.2020.9306075
N. Bilandi, H. K. Verma, and R. Dhir, "PSOBAN: a novel particle swarm optimization based protocol for wireless body area networks," SN Applied Sciences, vol. 1, no. 11, Oct. 2019, Art. no. 1492. DOI: https://doi.org/10.1007/s42452-019-1514-0
A. Khanna, V. Chaudhary, and S. H. Gupta, "Design and Analysis of Energy Efficient Wireless Body Area Network (WBAN) for Health Monitoring," in Transactions on Computational Science XXXIII, M. L. Gavrilova and C. J. K. Tan, Eds. Berlin, Heidelberg, Germany: Springer, 2018, pp. 25–39. DOI: https://doi.org/10.1007/978-3-662-58039-4_2
Z. A. Khan, S. Sivakumar, W. Phillips, and B. Robertson, "ZEQoS: A New Energy and QoS-Aware Routing Protocol for Communication of Sensor Devices in Healthcare System," International Journal of Distributed Sensor Networks, vol. 10, no. 6, Jun. 2014, Art. no. 627689. DOI: https://doi.org/10.1155/2014/627689
S. Ahmed et al., "Co-LAEEBA: Cooperative link aware and energy efficient protocol for wireless body area networks," Computers in Human Behavior, vol. 51, no. B, pp. 1205–1215, Oct. 2015. DOI: https://doi.org/10.1016/j.chb.2014.12.051
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: a survey," Computer Networks, vol. 38, no. 4, pp. 393–422, Mar. 2002. DOI: https://doi.org/10.1016/S1389-1286(01)00302-4
A. Navada, A. N. Ansari, S. Patil, and B. A. Sonkamble, "Overview of use of decision tree algorithms in machine learning," in 2011 IEEE Control and System Graduate Research Colloquium, Shah Alam, Malaysia, 2011, pp. 37–42. DOI: https://doi.org/10.1109/ICSGRC.2011.5991826
K. Taunk, S. De, S. Verma, and A. Swetapadma, "A Brief Review of Nearest Neighbor Algorithm for Learning and Classification," in 2019 International Conference on Intelligent Computing and Control Systems, Madurai, India, 2019, pp. 1255–1260. DOI: https://doi.org/10.1109/ICCS45141.2019.9065747
S. Suthaharan, Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, Boston, MA, USA: Springer US, 2016. DOI: https://doi.org/10.1007/978-1-4899-7641-3
D. Maulud and A. M. Abdulazeez, "A Review on Linear Regression Comprehensive in Machine Learning," Journal of Applied Science and Technology Trends, vol. 1, no. 2, pp. 140–147, Dec. 2020. DOI: https://doi.org/10.38094/jastt1457
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Copyright (c) 2025 Lamia Benlaldj, Mohammed Hicham Hachemi, Mohammed Mhamedi, Mourad Hadjila, Amina Bekkouche
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