Skip to content
2000
Volume 15, Issue 3
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

Background

Wireless Medical Sensor Networks (WMSNs) are crucial for monitoring patients' health in Wireless Body Area Networks (WBANs). However, the energy consumption of medical sensors (MSs) presents a significant challenge, impacting the efficiency and longevity of these networks. Optimizing energy consumption while maintaining spectral efficiency is essential for enhancing the performance of WMSNs.

Objective

This study aims to optimize energy consumption in WMSNs by exploring the balance between energy conservation and spectral efficiency. The focus is on selecting optimal modulation schemes that minimize total energy consumption while considering various distances and the Additive White Gaussian Noise (AWGN) channel.

Methods

The research investigates energy and constellation optimization using different modulation types, particularly in the context of the Internet of Medical Things (IoMT). A coefficient is introduced to vary circuit consumption independently of power emitted. Additionally, the study derives a performance-optimized energy efficiency measure for circuits and provides a closed-form transmission efficiency measure for M-ray quadrature amplitude modulation (M-QAM), validated numerically.

Results and Discussion

The findings reveal the optimal modulation schemes for various conditions, demonstrating significant energy savings while maintaining adequate spectral efficiency. The introduced coefficient effectively decouples circuit consumption from emitted power, optimizing energy use in WMSNs.

Conclusion

This research offers a comprehensive approach to energy consumption optimization in WMSNs, contributing to more efficient WBANs. The proposed methods and findings support the development of energy-efficient, remote medical care systems, enhancing the reliability and longevity of IoMT-based healthcare solutions.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/swcc/10.2174/0122103279344006250118155240
2025-09-01
2025-12-31

  • From This Site

    /content/journals/swcc/10.2174/0122103279344006250118155240
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. SinghA. PayalA. BhartiS. A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues.J. Netw. Comput. Appl.201914311115110.1016/j.jnca.2019.06.013
     [Google Scholar]
  2. Ar-ReyouchiE.M. GhoumidK. Ar-ReyouchiD. RattalS. YahiaouiR. ElmazriaO. Protocol wireless medical sensor networks in IoT for the efficiency of healthcare.IEEE Internet Things J.202111
     [Google Scholar]
  3. Ar-ReyouchiE.M. LamraniY. BenchaibI. RattalS. GhoumidK. NCBP: Network coding based protocol for recovering lost packets in the Internet of Things.Commun. Comput. Inf. Sci.20201264384910.1007/978‑3‑030‑61143‑9_4
     [Google Scholar]
  4. WuF. WuT. YuceM.R. An Internet-of-Things (IoT) network system for connected safety and health monitoring applications.Sensors (Basel)20181912110.3390/s1901002130577646
     [Google Scholar]
  5. ZhangY. Technology framework of the Internet of Things and its application.IEEE Xplore20114109411210.1109/ICECENG.2011.6057290
     [Google Scholar]
  6. Ar-ReyouchiE.M. ChateiY. GhoumidK. HammoutiM. HajjiB. Efficient coding techniques algorithm for cluster-heads communication in wireless sensor networks.AEU Int. J. Electron. Commun.20178229430410.1016/j.aeue.2017.08.047
     [Google Scholar]
  7. ShaikhF.K. ZeadallyS. Energy harvesting in wireless sensor networks: A comprehensive review.Renew. Sustain. Energy Rev.2016551041105410.1016/j.rser.2015.11.010
     [Google Scholar]
  8. ArunkumarK. ThavaselvanP. Deepak KumarA. Wireless medical sensor network based healthcare monitoring system in narrow Band IoTInternational Conference on Computer Communication and Informatics (ICCCI)Coimbatore, India, 2023, pp.1-1110.1109/ICCCI56745.2023.10128355
     [Google Scholar]
  9. ElayanH. ShubairR.M. Wireless sensors for medical applications: Current status and future challenges.2017 11th European Conference on Antennas and Propagation (EUCAP)Paris, France, 19-24 March 2017, pp. 2478-2482
     [Google Scholar]
  10. YuS. ParkY. A robust authentication protocol for wireless medical sensor networks using blockchain and physically unclonable functions.IEEE Internet Things J.2022920202142022810.1109/JIOT.2022.3171791
     [Google Scholar]
  11. RaultT. BouabdallahA. ChallalY. Energy efficiency in wireless sensor networks: A top-down survey.Comput. Netw.20146710412210.1016/j.comnet.2014.03.027
     [Google Scholar]
  12. OgundileO. AlfaA. A survey on an energy-efficient and energy-balanced routing protocol for wireless sensor networks.Sensors2017175108410.3390/s1705108428489054
     [Google Scholar]
  13. ZehnderM. WacheH. WitschelH-F. ZanattaD. RodriguezM. Smart Cities Conf. (ISC)201516
     [Google Scholar]
  14. ZhangX. ZhangM. MengF. QiaoY. XuS. HourS. A low-power wide-area network information monitoring system by combining NB-IoT and LoRa.IEEE Internet Things J.20196159059810.1109/JIOT.2018.2847702
     [Google Scholar]
  15. SangaiahArun Kumar SadeghilalimiMehdi Energy consumption in point-coverage wireless sensor networks via bat algorithm.IEEE Access20197180258180269
     [Google Scholar]
  16. Ar-ReyouchiE.M. GhoumidK. YahiaouiR. ElmazriaO. Optimized reception sensitivity of WBAN sensors exploiting network coding and modulation techniques in an advanced NB-IoT.IEEE Access202210357843579410.1109/ACCESS.2022.3163314
     [Google Scholar]
  17. Dutt BohraD. BoraA. Bit error rate analysis in simulation of digital communication systems with different modulation schemes.Int. J. Innov. Sci.201413
     [Google Scholar]
  18. LeiX. FanP. On the error performance of M-ary modulation schemes on rician–nakagami fading channels.Wirel. Pers. Commun.201053459160210.1007/s11277‑009‑9705‑4
     [Google Scholar]
  19. MadankarM.M. AshtankarP.S. Performance analysis of BPSK modulation scheme for different channel conditionsIEEE Students Conference on Electrical, Electronics and Computer ScienceBhopal, India, 05-06 March 2016, pp. 1-510.1109/SCEECS.2016.7509290
     [Google Scholar]
  20. FarzamniaA. HlaingN.W. MariappanM. HaldarM.K. 2018
  21. PanickerN.V. SukeshA.K. BER performance evaluation of different digital modulation schemes for biomedical signal transceivers under AWGN and fading channel conditions.Int. J. Eng. Adv. Technol.201435[IJEAT].
     [Google Scholar]
  22. TaissirE. Effect of AWGN and fading (Rayleigh and Rician) channels on BER performance of free space optics (FSO) communication systems.Int. J. Res. Wirel. Syst.201322
     [Google Scholar]
  23. Sudhir BabuA. Sambasiva RaoK.V. Evaluation of BER for AWGN, rayleigh and rician fading channels under various modulation schemes.Int. J. Comput. Appl.2011269232810.5120/3132‑4317
     [Google Scholar]
  24. RajuM. Ashoka ReddyK. Evaluation of BER for AWGN, rayleigh fading channels under M-QAM modulation schemeInternational Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)Chennai, India, 03-05 March 2016, pp. 3081-308610.1109/ICEEOT.2016.7755268
     [Google Scholar]
  25. VarastehM. RassouliB. ClerckxB. Wireless information and power transfer over an AWGN channel: Nonlinearity and asymmetric Gaussian signalingIEEE Information Theory Workshop (ITW)Kaohsiung, Taiwan, 06-10 November 2017, pp. 181-18510.1109/ITW.2017.8278010
     [Google Scholar]
  26. MorinE. MamanM. GuizzettiR. DudaA. Comparison of the device lifetime in wireless networks for the Internet of Things.IEEE Access201757097711410.1109/ACCESS.2017.2688279
     [Google Scholar]
  27. XuT. DarwazehI. Non-orthogonal narrowband Internet of Things: A design for saving bandwidth and doubling the number of connected devices.IEEE Internet Things J.2018532120212910.1109/JIOT.2018.2825098
     [Google Scholar]
  28. LiuY. WangK. QianK. DuM. GuoS. Tornado: Enabling blockchain in heterogeneous Internet of Things through a space-structured approach.IEEE Internet Things J.2020721273128610.1109/JIOT.2019.2954128
     [Google Scholar]
  29. BoukrichaS. BouzidiA. GhoumidK. Ar-ReyouchiE. YahiaouiR. ElmazriaO. Performance enhancement for m-sequence and hadamard code SAC-OCDMA systems based on narrowband filters.Int. J. Wirel. Inf. Netw.202229334135310.1007/s10776‑022‑00562‑x
     [Google Scholar]
  30. BoukrichaS. GhoumidK. MazariA. Ar-ReyouchiE.M. YahiaouiR. ElmazriaO. Performance improvement of SAC-OCDMA FSO system under rain and snow conditions using different zero cross-correlation codesInternational Conference on VLSI, Communication and Signal processingSingapore2022245257
     [Google Scholar]
  31. ReyouchiE.M.A.R. GhoumidK. AmezianeK. El MrabetO. The improvement of end-to-end delays in network management systemusing networkcoding.IJCNC201356658410.5121/ijcnc.2013.5604
     [Google Scholar]
  32. AlisaZ.T. NasrullahH.A. Minimizing energy consumption in wireless sensor networks using modified genetic algorithm and an energy balance filterAl-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)Baghdad, Iraq, 09-10 May 2016, pp. 1-610.1109/AIC‑MITCSA.2016.7759947
     [Google Scholar]
  33. HammoutiM. MiloudE. GhoumidK. LichiouiA. Clustering analysis of wireless sensor network based on network coding with low-density parity check.Int. J. Adv. Comput. Sci. Appl.20167310.14569/IJACSA.2016.070319
     [Google Scholar]
  34. MasudM. GabaG.S. AlqahtaniS. MuhammadG. GuptaB.B. KumarP. GhoneimA. A lightweight and robust secure key establishment protocol for internet of medical things in COVID-19 patients care.IEEE Internet Things J.2021821156941570310.1109/JIOT.2020.304766235782176
     [Google Scholar]
  35. MahmudM. KaiserM.S. HussainA. VassanelliS. Applications of deep learning and reinforcement learning to biological data.IEEE Trans. Neural Netw. Learn. Syst.20182962063207910.1109/TNNLS.2018.279038829771663
     [Google Scholar]
  36. AlsabahM. NaserM.A. MahmmodB.M. AbdulhussainS.H. EissaM.R. Al-BaidhaniA. NoordinN.K. SaitS.M. Al-UtaibiK.A. HashimF. 6G wireless communications networks: A comprehensive survey.IEEE Access2021914819114824310.1109/ACCESS.2021.3124812
     [Google Scholar]
  37. AbeledoM.C. BruschettiF.S. GuevaraJ. GonzálezJ. MarcicanoM. MarroneL. IrisoP. Integrating IEEE 802.11 technology and Wireless sensor networks using PORME routing protocol," , 2018 Congreso Argentino de Ciencias de la Informática y Desarrollos de Investigación (CACIDI)Buenos Aires, Argentina, 28-30 November 2018, pp. 1-9
     [Google Scholar]
  38. GhoumidK. Ar-ReyouchiE.-M. Ar-ReyouchiD. BenbrikJ. BoukrichaS. ElmazriaO. Optimization analysis of average message delivery time for healthcare monitoring using a developed NB-IoT technology in a smart cityIoT202427101290
     [Google Scholar]
  39. BeyeneY.D. JanttiR. TirkkonenO. RuttikK. IrajiS. LarmoA. TirronenT. TorsnerJ. NB-IoT technology overview and experience from cloud-RAN implementation.IEEE Wirel. Commun.2017243263210.1109/MWC.2017.1600418
     [Google Scholar]
  40. ChenM. MiaoY. HaoY. HwangK. Narrow band Internet of Things.IEEE Access20175205572057710.1109/ACCESS.2017.2751586
     [Google Scholar]
  41. QadriY.A. NaumanA. ZikriaY.B. VasilakosA.V. KimS.W. 2020
  42. GhubaishA. SalmanT. ZolanvariM. UnalD. Al-AliA. JainR. Recent advances in the Internet-of-Medical-Things (IoMT) systems security.IEEE Internet Things J.20218118707871810.1109/JIOT.2020.3045653
     [Google Scholar]
  43. Abdel-GawadM. UsamaM. HeshamH. IbrahimO. AbdellatifM.M. Remote healthcare monitoring using wearable IoT devices and cloud services5th Conference on Cloud and Internet of Things (CIoT)202210811310.1109/CIoT53061.2022.9766591
     [Google Scholar]
  44. KailasA. Power allocation strategies to minimize energy consumption in wireless body area networks.Annu Int Conf IEEE Eng Med Biol Soc2011: 2204220710.1109/IEMBS.2011.6090416
     [Google Scholar]
  45. HuangX. ShanH. ShenX. On the energy efficiency of cooperative communications in wireless body area networks.Proc. of IEEE Conference on Wireless Communications and Networking ConferenceCancun, Mexico, 28-31 March 2011, pp. 1097-1101
     [Google Scholar]
  46. UsmanM. QaraqeM. AsgharM.R. AnsariI.S. Energy efficient wireless body area networks: Proximity-based clustering in medical implants2020 IEEE Eighth International Conference on Communications and Networking (ComNet)Hammamet, Tunisia, 27-30 October 2020, pp. 1-510.1109/ComNet47917.2020.9306075
     [Google Scholar]
  47. Ar-ReyouchiE.M. SadekA.H. GhoumidK. Q-learning-enhanced random channel access for efficient energy harvesting in IoT networks4 th IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0 / 5.0 (ARCI’ 2024)INNSBRUCK, Austria, Feb 2024, pp.98-1032024
     [Google Scholar]
  48. BenmansourT. AhmedT. MoussaouiS. DoukhaZ. Performance analyses of the IEEE 802.15.6 Wireless Body Area Network with heterogeneous traffic.J. Netw. Comput. Appl.202016310265110.1016/j.jnca.2020.102651
     [Google Scholar]
  49. ArroboG.E. GitlinR.D. Improving the reliability of wireless body area networksProc. of IEEE conference on Engineering inMedicine and Biology SocietyBoston, MA, USA, 30 August 2011 - 03 September 2011, pp. 2192-219510.1109/IEMBS.2011.6090413
     [Google Scholar]
  50. RattalS. GhoumidK. Ar-ReyouchiE.M. A Flexible protocol for a robust hospitals network Based on Iot, computer networks, big data and IoTProceedings of ICCBISingapore, 2021, pp. 931-943
     [Google Scholar]
  51. Jung-Yeol Oh Jae-Hwan Kim Hyung-Soo Lee Jae-Young Kim PSSK modulation scheme for high-data rate implantable medical devices.IEEE Trans. Inf. Technol. Biomed.201014363464010.1109/TITB.2009.203625519906593
     [Google Scholar]
  52. BenchaibI. RattalS. GhoumidK. Ar-ReyouchiE.M. Polling cycle analysis using different modulation types for IoT-based health control in a smart cityProceedings of ICCBI2021Singapore327338
     [Google Scholar]
  53. EliasJ. Optimal design of energy-efficient and cost-effective wireless body area networks.Ad Hoc Netw.20141356057410.1016/j.adhoc.2013.10.010
     [Google Scholar]
  54. ZhouX. ZhangT. SongL. ZhangQ. Proc. of IEEE VTCvol. 5201416
     [Google Scholar]
  55. HammoutiM. Ar-reyouchiE.M. GhoumidK. Power Quality command and control systems in wireless renewable energy networksInternational Renewable and Sustainable Energy Conference (IRSEC)Marrakech, Morocco201676376910.1109/IRSEC.2016.7983989
     [Google Scholar]
  56. SinghV. SinghK.K. SinghS. A Secure and energy-efficient framework for air quality prediction using smart sensors and ISHO-DCNN.Int. J. Sensors Wirel. Commun. Control202313313114410.2174/2210327913666230504122805
     [Google Scholar]
  57. SultanI. BandayM.T. Sub-1 GHz RF-based energy-efficient sensor node for secure communication in low-power IoT and embedded applications.Int. J. Sensors Wirel. Commun. Control202414426527810.2174/0122103279287156240218044819
     [Google Scholar]
  58. SevuganA. KarthikeyanP. SarveshwaranV. ManoharanR. Optimized navigation of mobile robots based on faster r-cnn in wireless sensor network.Int. J. Sensors Wirel. Commun. Control202212644044810.2174/2210327912666220714091426
     [Google Scholar]
  59. NigamG.K. A comprehensive review on successors of leach protocols in wireless sensor networks.Int. J. Sensors Wirel. Commun. Control202212646347710.2174/2210327912666220615115331
     [Google Scholar]
  60. AroraS. NijhawanG. VermaG. A solar, thermal, and piezoelectric based hybrid energy harvesting for IoT and underwater WSN applications.Int. J. Sensors Wirel. Commun. Control202212965166010.2174/2210327913666221222145019
     [Google Scholar]
  61. GillR. DubeyT.K. Study of different techniques used in wsn for smart mobility.Int. J. Sensors Wirel. Commun. Control202212644946210.2174/2210327912666220805124234
     [Google Scholar]
  62. AlsulamiM.H. AtkinsA.S. AlaboudiA.A. KhanN.A. ZigBee technology to provide elderly people with well-being at home.Int. J. Sensors Wirel. Commun. Control202111992192710.2174/2210327911666210201105206
     [Google Scholar]
  63. SinghP. SinghM.K. SinghN. ChakravertiA. IoT and AI-based intelligent agriculture framework for crop prediction.Int. J. Sensors Wirel. Commun. Control202313314515410.2174/2210327913666230509144225
     [Google Scholar]
  64. AdM. KhelilA. Outage performance of underlay CR-NOMA-based D2D communications under imperfect CSI and SIC.Int. J. Sensors Wirel. Commun. Control202414210411210.2174/0122103279272325231130112108
     [Google Scholar]
  65. LoneF.R. VermaH.K. SharmaK.P. ETSI ITS: A comprehensive overview of the architecture, challenges and issues.Int. J. Sensors Wirel. Commun. Control20241428510310.2174/0122103279287823231207072006
     [Google Scholar]
  66. ChateiY. GhoumidK. Ar-reyouchiE.M. Narrow-band channel spacing frequencies metric in one-hop wireless mesh networks2nd International Conference on Communication and Electronics Systems (ICCES)Coimbatore, India, 19-20 October 2017, pp. 296-301Coimbatore, India10.1109/CESYS.2017.8321284
     [Google Scholar]
  67. Ar-ReyouchiE.M. HammoutiM. MaslouhiI. GhoumidK. The Internet of Things: Network delay improvement using network coding.Proc. 2nd Int. Conf. Internet Things Data Cloud Comput.New York, NY, USA, 2017, pp. 1-710.1145/3018896.3018902
     [Google Scholar]
  68. Ar-ReyouchiE.M. GhoumidK. Ar-ReyouchiD. RattalS. YahiaouiR. ElmazriaO. An accelerated end-to-end probing protocol for narrowband IoT medical devices.IEEE Access20219341313414110.1109/ACCESS.2021.3061257
     [Google Scholar]
  69. ParkS. RaghunathanV. SchurgersC. SrivastavaM.B. Energy-aware wireless microsensor networks.IEEE Signal Process. Mag.20024050
     [Google Scholar]
  70. ProakisJ. Digital communication.5th edNew York2008
     [Google Scholar]
  71. ChateiY. HammoutiM. MiloudE. GhoumidK. Downlink and uplink message size impact on round trip time metric in multi-hop wireless mesh networks.Int. J. Adv. Comput. Sci. Appl.20178310.14569/IJACSA.2017.080332
     [Google Scholar]
  72. GoldsmithA. Wireless Communications.Cambridge University Press200510.1017/CBO9780511841224
     [Google Scholar]
  73. KaragiannidisG. LioumpasA. An improved approximation for the Gaussian q-function.IEEE Commun. Lett.200711864464610.1109/LCOMM.2007.070470
     [Google Scholar]
  74. MezghaniA. NossekJ.A. Power efficiency in communication systems from a circuit perspectiveIEEE International Symposium of Circuits and Systems (ISCAS)Rio de Janeiro, Brazil, 15-18 May 2011, pp. 1896-189910.1109/ISCAS.2011.5937958
     [Google Scholar]
  75. KanhereO. PoddarH. XingY. ShakyaD. JuS. RappaportT.S. A power efficiency metric for comparing energy consumption in future wireless networks in the millimeter-wave and terahertz bands.IEEE Wirel. Commun.2022296566310.1109/MWC.005.2200083
     [Google Scholar]
/content/journals/swcc/10.2174/0122103279344006250118155240
Loading
Smart Energy Solutions for Wireless Medical Sensor Networks: Modulation Optimization in IoMT for Medical Applications
Int J Sensors Wirel Commun Control 15, 289 (2025); https://doi.org/10.2174/0122103279344006250118155240
/content/journals/swcc/10.2174/0122103279344006250118155240
/content/journals/swcc/10.2174/0122103279344006250118155240
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Internet of Medical Things; Internet of Things; Medical Sensors; modulation types; Power Consumption; WMSN

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