Skip to content
2000
image of An Energy-Efficient Two-Phase Intelligent BAN Routing Algorithm Utilizing Reinforcement Learning

Abstract

Introduction

Wireless Sensor Networks (WSNs) and Body Area Networks (BANs) confront major issues with mobility, scalability, topology management, and energy consumption. Because these networks are dynamic, traditional routing techniques frequently find it difficult to adjust, which results in wasteful energy use and a shorter network lifespan.

Materials and Methods

This study introduces an energy-efficient Reinforcement Learning (RL) routing strategy for Body Area Networks (BANs). The presented approach encompasses two distinct phases. The process commences by transmitting data to a designated sink node. The subsequent phase is transmitting this data to a server for additional surveillance. In the initial phase, the optimization of data transmission from sensors affixed to the body is achieved by cycling through the ON and OFF states of each sensor. The implementation of a dynamic approach to determine the duration of the ON state, taking into account the priority of data, leads to a reduction in inefficient energy consumption. The subsequent module of the proposed model employs Q-learning, a model-free reinforcement learning technique, to determine the optimal routing policy for the transmission of dynamic data. Reinforcement Learning methods excel at adjusting to dynamic situations, making them ideal for networks with changing topologies. Reinforcement learning enables routing algorithms to adapt dynamically to node movement, improving efficiency.

Results

By taking node energy, computing power, and hop count into account, the RL-based routing technique greatly extends network lifetime. According to experimental results, energy usage is reduced by 60–65% when compared to conventional approaches.

Discussion

The suggested RL-based routing method efficiently tackles the dynamic and energy-sensitive characteristics of Body Area Networks by utilising Q-learning for adaptive path selection. The technology effectively minimises superfluous energy use by dynamically regulating sensor activity according to data priority.

Conclusion

This reinforcement learning methodology improves network durability and efficiency, attaining up to 65% energy conservation compared to conventional routing techniques. Its versatility to evolving topologies renders it a promising alternative for future energy-efficient BAN applications.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/swcc/10.2174/0122103279392128251016052512
2025-10-31
2026-02-27

  • From This Site

    /content/journals/swcc/10.2174/0122103279392128251016052512
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Kumari Jyoti An energy efficient routing algorithm for wireless body area network. Int. J. Microw. Wirel. Technol. 2015 5 5 56 62 10.5815/ijwmt.2015.05.06
    [Google Scholar]
  2. Majumder B. An intelligent BAN routing mechanism for transferring remote patient monitoring data using K means clustering (). Proceedings of the International Conference on Innovative Computing & Communication (ICICC) July 11, 2021. 10.2139/ssrn.3884383
    [Google Scholar]
  3. Naik M.R.K. Samundiswary P. Wireless body area network security issues — Survey. 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) Kumaracoil, India, 2016, pp. 190-194. 10.1109/ICCICCT.2016.7987943
    [Google Scholar]
  4. Akram J. Munawar H. Kouzani A. Mahmud M. Using adaptive sensors for optimised target coverage in wireless sensor networks. Sensors 2022 22 3 1083 10.3390/s22031083 35161829
    [Google Scholar]
  5. Bouhali M. Bendjeddou A. Ghoualmi-Zine N. A routing protocol with quality of service in body area network. 2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) Oum El Bouaghi, Algeria, 2022, pp. 1-6. 10.1109/PAIS56586.2022.9946884
    [Google Scholar]
  6. Kumari R. Nand P. An optimized routing algorithm for BAN by considering hop-count, residual energy and link quality for route discovery. 2017 International Conference on Computing, Communication and Automation (ICCCA) Greater Noida, India, 2017, pp. 664-668. 10.1109/CCAA.2017.8229884
    [Google Scholar]
  7. Majumder A.B. Majumder S. Gupta S. Singh D. An intelligent, geo-replication, energy-efficient ban routing algorithm under framework of machine learning and cloud computing. Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies. Sharma N. Chakrabarti A. Balas V.E. Bruckstein A.M. Singapore Springer 2022 71 10.1007/978‑981‑16‑2937‑2_4
    [Google Scholar]
  8. Samanta A. Misra S. Energy-efficient and distributed network management cost minimization in opportunistic wireless body area networks. IEEE Trans. Mobile Comput. 2018 17 2 376 389 10.1109/TMC.2017.2708713
    [Google Scholar]
  9. Hussain S. Sami A. Thasin A. AI-enabled ant-routing protocol to secure communication in flying networks. Appl. Comput. Intell. Soft. Comput. 2022 2022 1 9 10.1155/2022/3330168
    [Google Scholar]
  10. Ahmed G. Mahmood D. Islam S. Thermal and energy aware routing in wireless body area networks. Int. J. Distrib. Sens. Netw. 2019 15 6 10.1177/1550147719854974
    [Google Scholar]
  11. Zhang M. Chen Y. Weisfeiler-Lehman Neural Machine for Link Prediction. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17) New York, NY, USA, 04 August 2017, pp. 575–583. 10.1145/3097983.3097996
    [Google Scholar]
  12. Bangotra D.K. Singh Y. Selwal A. Kumar N. Singh P.K. Hong W.C. An intelligent opportunistic routing algorithm for wireless sensor networks and its application towards e-healthcare. Sensors 2020 20 14 3887 10.3390/s20143887 32668605
    [Google Scholar]
  13. Ayatollahitafti V. Ngadi M.A. Mohamad Sharif J. Abdullahi M. An efficient next hop selection algorithm for multi-hop body area networks. PLoS One 2016 11 1 0146464 10.1371/journal.pone.0146464 26771586
    [Google Scholar]
  14. Karunanithy K. Cluster-tree based energy efficient data gathering protocol for industrial automation using WSNs and IoT. J. Ind. Inform. Integr. 2020 19 100156 10.1016/j.jii.2020.100156
    [Google Scholar]
  15. Majumder A.B. Gupta S. An energy-efficient congestion avoidance priority-based routing algorithm for body area network. Industry Interactive Innovations in Science, Engineering and Technology. Bhattacharyya S. Sen S. Dutta M. Biswas P. Chattopadhyay H. Singapore Springer 2018 11 10.1007/978‑981‑10‑3953‑9_52
    [Google Scholar]
  16. Thermal aware routing protocols for wireless body area. Karbala Int J Mod Sci 2022 8 3 356 374 10.33640/2405‑609X.3244
    [Google Scholar]
  17. Gladkov A. Shiriaev E. Tchernykh A. Deryabin M. Babenko M. Nesmachnow S. DT-RRNS: Routing protocol design for secure and reliable distributed smart sensors communication systems. Sensors 2023 23 7 3738 10.3390/s23073738 37050798
    [Google Scholar]
  18. Ahmmad B.A. Alabady S.A. Energy‐efficient routing protocol developed for internet of things networks. IET Quantum Commun. 2023 4 1 25 38 10.1049/qtc2.12051
    [Google Scholar]
  19. Sheeja R. Mohamed Iqbal M. Sivasankar C. Multi-objective-derived energy efficient routing in wireless sensor network using adaptive black hole-tuna swarm optimization strategy. Ad Hoc Netw 2023 144 103140 10.1016/j.adhoc.2023.103140
    [Google Scholar]
  20. Pedditi R.B. Debasis K. Energy efficient routing protocol for an iot-based WSN system to detect forest fires. Appl. Sci. 2023 13 5 3026 10.3390/app13053026
    [Google Scholar]
  21. Goyal N. A new approach of location aided routing protocol using minimum bandwidth in mobile ad-hoc network. Int. J. Comput. Technol. Appl. 2013 4 4 653 659
    [Google Scholar]
  22. Choudhary M. Goyal N. Data collection routing techniques in underwater wireless sensor networks. 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Noida, India, 2021, pp. 1-6. 10.1109/ICRITO51393.2021.9596521
    [Google Scholar]
  23. Gaba A. Review over diverse location aided routing. Glob J Curr Eng Res 2013 2 2 141 144
    [Google Scholar]
  24. Gude D.K. Bandari H. Challa A.K.R. Tasneem S. Tasneem Z. Bhattacharjee S.B. Lalit M. Flores M.A.L. Goyal N. Transforming urban sanitation: Enhancing sustainability through machine learning-driven waste processing. Sustainability 2024 16 17 7626 10.3390/su16177626
    [Google Scholar]
  25. Ridwan Mohammad Azmi Mohamed Radzi Nurul Asyikin Mohd Azmi Kaiyisah Hanis Abdullah Fairuz Wan Ahmad Wan Siti Halimatul Munirah A new machine learning-based hybrid intrusion detection system and intelligent routing algorithm for MPLS network. Int. J. Adv. Comput. Sci. Appl. 2023 14 4 94 107 10.14569/IJACSA.2023.0140412
    [Google Scholar]
  26. Hussain S. Sami A. Thasin A. Saad R.M.A. AI-enabled ant routing protocol to secure communication in flying networks. Appl Comput Intell Soft Comput 2022 2022 1 9 10.1155/2022/3330168
    [Google Scholar]
  27. Almasan P Xiao S Cheng X Shi X Barlet-Ros P Cabellos-Aparicio A ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning. Comput. Netw. 2022 214 C 109166 10.1016/j.comnet.2022.109166
    [Google Scholar]
  28. Chen Y-R Rezapour A Tzeng W-G Tsai S-C RL-routing: An SDN routing algorithm based on deep reinforcement learning. IEEE Trans. Netw. Sci. Eng. 2020 7 4 3185 3199 10.1109/TNSE.2020.3017751
    [Google Scholar]
  29. Arafat M.Y. Pan S. Bak E. An adaptive reinforcement learning-based mobility-aware routing for heterogeneous wireless body area networks. IEEE Sens J 2024 24 19 31201 31214 10.1109/JSEN.2024.3440412
    [Google Scholar]
  30. Arafat M.Y. Pan S. Bak E. Distributed energy-efficient clustering and routing for wearable iot enabled wireless body area networks. IEEE Access 2023 11 5047 5061 10.1109/ACCESS.2023.3236403
    [Google Scholar]
  31. Mehmood G. Khan M.Z. Bashir A.K. Al-Otaibi Y.D. Khan S. An efficient QoS-based multi-path routing scheme for smart healthcare monitoring in wireless body area networks. Comput. Electr. Eng. 2023 109 Part A 108517 10.1016/j.compeleceng.2022.108517
    [Google Scholar]
  32. Li J. Xiao J. Yuan J. Two-tier cooperation based high-reliable and lightweight forwarding strategy in heterogeneous WBAN. Sustainability 2023 15 6 5588 10.3390/su15065588
    [Google Scholar]
  33. Tang Y. Cheng N. Wu W. Wang M. Dai Y. Shen X. Delay-minimization routing for heterogeneous vanets with machine learning based mobility prediction. IEEE Trans. Vehicular Technol. 2019 68 4 3967 3979 10.1109/TVT.2019.2899627
    [Google Scholar]
  34. Singh P. Raw R.S. Khan S.A. Link risk degree aided routing protocol based on weight gradient for health monitoring applications in vehicular ad-hoc networks. J. Ambient Intell. Humaniz. Comput. 2022 13 12 5779 5801 10.1007/s12652‑021‑03264‑z
    [Google Scholar]
  35. Savaglio C. Pace P. Aloi G. Liotta A. Fortino G. Lightweight reinforcement learning for energy efficient communications in wireless sensor networks. IEEE Access 2019 7 29355 29364 10.1109/ACCESS.2019.2902371
    [Google Scholar]
  36. Xiao L. Hong S. Xu S. Yang H. Ji X. IRS-aided energy-efficient secure wban transmission based on deep reinforcement learning. IEEE Trans. Commun. 2022 70 6 4162 4174 10.1109/TCOMM.2022.3169813
    [Google Scholar]
  37. Guo W. Wang Y. Gan Y. Lu T. Energy efficient and reliable routing in wireless body area networks based on reinforcement learning and fuzzy logic. Wirel. Netw. 2022 28 6 2669 2693 10.1007/s11276‑022‑02997‑9
    [Google Scholar]
  38. Muthu Ganesh V. Nithiyanantham J. Heuristic-based channel selection with enhanced deep learning for heart disease prediction under WBAN. Comput. Methods Biomech. Biomed. Engin. 2022 25 13 1429 1448 10.1080/10255842.2021.2013828 35156487
    [Google Scholar]
  39. Ashraf M. Hassan S. Rubab S. Khan M.A. Tariq U. Kadry S. Energy-efficient dynamic channel allocation algorithm in wireless body area network. Environ. Dev. Sustain. 2022 1 5 10.1007/s10668‑021‑02037‑0
    [Google Scholar]
  40. Sefati S. Abdi M. Ghaffari A. Cluster‐based data transmission scheme in wireless sensor networks using black hole and ant colony algorithms. Int. J. Commun. Syst. 2021 34 9 4768 10.1002/dac.4768
    [Google Scholar]
  41. Bhardwaj T. Sharma S.C. Cloud-WBAN: An experimental framework for Cloud-enabled wireless body area network with efficient virtual resource utilization. Sustain. Comput. Inform. Syst. 2018 20 14 33 10.1016/j.suscom.2018.08.008
    [Google Scholar]
  42. Saba T. Haseeb K. Ahmed I. Rehman A. Secure and energy-efficient framework using internet of medical things for e-healthcare. J. Infect. Public Health. 2020 13 10 1567 1575 10.1016/j.jiph.2020.06.027
    [Google Scholar]
  43. Cremonezi B.M. Vieira A.B. Nacif J.A. Nogueira M. A dynamic channel allocation protocol for medical environment. Ann. Telecommun. 2021 76 7-8 483 497 10.1007/s12243‑020‑00826‑8
    [Google Scholar]
  44. Kour K. An energy efficient routing algorithm forwban. Turkish J. Comp. Mathe. Educat. 2021 12 10 7174 7180
    [Google Scholar]
  45. Ilyas M. Ullah Z. Khan F.A. Chaudary M.H. Malik M.S.A. Zaheer Z. Durrani H.U.R. Trust-based energy-efficient routing protocol for Internet of things–based sensor networks. Int. J. Distrib. Sens. Netw. 2020 16 10 10.1177/1550147720964358
    [Google Scholar]
  46. Caballero E. Ferreira V.C. Robson A. LimaCélio Albuquerque, Débora C. MuchaluatSaade, “LATOR: Link-Quality Aware and Thermal Aware On-Demand Routing Protocol for WBAN”, 2020. International Conference on Systems, Signals and Image Processing (IWSSIP) 2020.
    [Google Scholar]
  47. Ahmed Omar Ren Fuji Hawbani Ammar Al-Sharabi Yaser Energy optimized congestion control-based temperature aware routing algorithm for software defined wireless body area networks. IEEE Access. 2020 8 41085 41099 10.1109/ACCESS.2020.2976819
    [Google Scholar]
  48. Liu Q. Mkongwa K.G. Zhang C. Performance issues in wireless body area networks for the healthcare application: A survey and future prospects. SN Appl. Sci. 2021 3 2 155 10.1007/s42452‑020‑04058‑2
    [Google Scholar]
  49. Soderi S. Särestöniemi M. Fuada S. Hämäläinen M. Katz M. Iinatti J. Securing hybrid wireless body area networks (HyWBAN): Advancements in semantic communications and jamming techniques. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science. Särestöniemi M. Cham Springer 2024 Vol. 2084 10.1007/978‑3‑031‑59091‑7_24
    [Google Scholar]
  50. Batista E. Lopez-Aguilar P. Solanas A. Smart health in the 6G Era: Bringing security to future smart health services. IEEE Commun. Mag. 2023 ••• 1 7
    [Google Scholar]
  51. Kamalakis T. Ghassemlooy Z. Zvanovec S. Nero Alves L. Analysis and simulation of a hybrid visible-light/infrared optical wireless network for IoT applications. J. Opt. Commun. Netw. 2022 14 3 69 78 10.1364/JOCN.442787
    [Google Scholar]
  52. Khan R.A. Xin Q. Roshan N. RK-energy efficient routing protocol for wireless body area sensor networks. Wirel. Pers. Commun. 2020 1 7 10.1007/s11277‑020‑07734‑z
    [Google Scholar]
  53. Huang R Ma L Zhai G He J Chu X Yan H Resilient routing mechanism for wireless sensor networks with deep learning link reliability prediction. IEEE Access. 2020 8 64857 64872 10.1109/ACCESS.2020.2984593
    [Google Scholar]
  54. Verma S. Kaur S. Sharma A.K. Kathuria A. Piran M.J. Dual sink-based optimized sensing for intelligent transportation systems. IEEE Sens. J. 2021 21 14 15867 15874 10.1109/JSEN.2020.3012478
    [Google Scholar]
  55. Menon V.G. Verma S. Kaur S. Sehdev P.S. Internet of things-based optimized routing and big data gathering system for landslide detection. Big Data 2021 9 4 289 302 10.1089/big.2020.0279 34085838
    [Google Scholar]
  56. Khan R.A. Xin Q. Roshan N. RK-energy efficient routing protocol for wireless body area sensor networks. Wirel. Pers. Commun. 2021 116 1 709 721 10.1007/s11277‑020‑07734‑z
    [Google Scholar]
  57. Xie Z. Huang G. A novel nest-based scheduling method for mobile wireless body area networks. Digi. Commun. Netw. 2020 Jul 6 4 10.1016/j.dcan.2020.06.006
    [Google Scholar]
  58. Saboor A. Ahmad R. Ahmed W. Kiani A.K. Alam M.M. Kuusik A. Le Moullec Y. Dynamic slot allocation using non overlapping backoff algorithm in ieee 802.15. 6 WBAN. IEEE Sens. J. 2020 20 18 10862 10875 10.1109/JSEN.2020.2993795
    [Google Scholar]
  59. Sun G. Luo L. Wang K. Yu H. Toward improving QsS and energy efficiency in wireless body area networks. IEEE Syst. J. 2021 15 1 865 876 10.1109/JSYST.2020.2999670
    [Google Scholar]
  60. Rajawat A.S. Goyal S.B. Bedi P. Verma C. Safirescu C.O. Mihaltan T.C. Sensors energy optimization for renewable energy-based WBANS on sporadic elder movements. Sensors 2022 22 15 5654 10.3390/s22155654 35957210
    [Google Scholar]
  61. Fathima Shemim K. Witkowski U. 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. Proceedings of the 32nd International Conference on Microelectronics (ICM) 2020, pp. 1-5. 10.1109/ICM50269.2020.9331768
    [Google Scholar]
  62. HajilooVakil Mahdieh Khani Mohammadjavad Shirmohammadi Zahra An efficient compression method to improve energy consumption in WBANs. Proceedings of the 7th International Conference on Web Research (ICWR) 2021, pp. 301-305. 305
    [Google Scholar]
  63. Samarji N. Salamah M. ERQTM: Energy-efficient routing and qos-supported traffic management scheme for SDWBANs. IEEE Sens. J. 2021 21 14 16328 16339 10.1109/JSEN.2021.3075241
    [Google Scholar]
  64. Dua N. Singh S.N. Semwal V.B. Challa S.K. Inception inspired CNN-GRU hybrid network for human activity recognition. Multimedia Tools Appl. 2023 82 4 5369 5403 10.1007/s11042‑021‑11885‑x
    [Google Scholar]
  65. Jalal A. Batool M. Kim K. Stochastic recognition of physical activity and healthcare using tri-axial inertial wearable sensors. Appl. Sci. 2020 10 20 7122 10.3390/app10207122
    [Google Scholar]
  66. Huan R. Zhan Z. Ge L. Chi K. Chen P. Liang R. A hybrid CNN and BLSTM network for human complex activity recognition with multi-feature fusion. Multimedia Tools Appl. 2021 80 30 36159 36182 10.1007/s11042‑021‑11363‑4
    [Google Scholar]
  67. Fan Y.C. Tseng Y.H. Wen C.Y. A novel deep neural network method for har-based team training using body-worn inertial sensors. Sensors 2022 22 21 8507 10.3390/s22218507 36366202
    [Google Scholar]
  68. Mishra C.S. Sampson J. Kandemir M.T. Narayanan V. Origin: Enabling on-device intelligence for human activity recognition using energy harvesting wireless sensor networks. Proceedings of the 2021 Design, Automation and Test in Europe, DATE 2021 New York City Institute of Electrical and Electronics Engineers Inc. 2021 1414 1419 10.23919/DATE51398.2021.9474017
    [Google Scholar]
  69. Abhishek K. Badar ud din Tahir S. Human verification over activity analysis via deep data mining. Comput. Mater. Continua 2023 75 1 1391 1409 10.32604/cmc.2023.035894
    [Google Scholar]
  70. Jordao A. Nazare A.C. Sena J. Schwartz W.R. Human activity recognition based on wearable sensor data: A standardization of the state-of-the-art. arXiv:1806.05226 2019 1 5 10.48550/arXiv.1806.05226
    [Google Scholar]
  71. Rajendra Prasad C. Bojja P. A hybrid energy-efficient routing protocol for wireless body area networks using ultra-low-power transceivers for eHealth care systems. SN Appl. Sci. 2020 2 12 2114 10.1007/s42452‑020‑03900‑x
    [Google Scholar]
  72. Caldas S. Chen J. Dubrawski A. Using machine learning to support transfer of best practices in healthcare. AMIA Annu. Symp. Proc. 2022 2021 265 274 35308933
    [Google Scholar]
  73. Nainamalai V. Qair H.A. Pelanis E. Jenssen H.B. Fretland Å.A. Edwin B. Elle O.J. Balasingham I. Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation. Eur. J. Radiol. Open. 2024 13 100582 10.1016/j.ejro.2024.100582
    [Google Scholar]
  74. G J. R S. H L G. Ravi V. Almeshari M. Alzamil Y. Electronic health record (EHR) system development for study on EHR data-based early prediction of diabetes using machine learning algorithms. Open Bioinform. J. 2023 16 1 187503622309010 10.2174/18750362‑v16‑e230906‑2023‑15
    [Google Scholar]
  75. Qayyum A. Qadir J. Bilal M. Al-Fuqaha A. Secure and robust machine learning for healthcare: A survey. IEEE Rev. Biomed. Eng. 2021 14 156 180 10.1109/RBME.2020.3013489 32746371
    [Google Scholar]
/content/journals/swcc/10.2174/0122103279392128251016052512
Loading
An Energy-Efficient Two-Phase Intelligent BAN Routing Algorithm Utilizing Reinforcement Learning
Bentham Science Publishers ; https://doi.org/10.2174/0122103279392128251016052512
/content/journals/swcc/10.2174/0122103279392128251016052512
/content/journals/swcc/10.2174/0122103279392128251016052512
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: reinforcement routing ; wireless sensor network ; Body area network ; Q learning ; energy efficient

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test