Energy-Efficient Hybrid Protocol with Optimization Inference Model for WBANs

Rajaram  Pichamuthu; Prabaharan  Sengodan; Saravanan  Matheswaran; Karthik  Srinivasan

doi:10.6180/jase.202503_28(3).0001

Energy-Efficient Hybrid Protocol with Optimization Inference Model for WBANs

Computer Science and Information Engineering

Rajaram Pichamuthu¹This email address is being protected from spambots. You need JavaScript enabled to view it., Prabaharan Sengodan², Saravanan Matheswaran³, and Karthik Srinivasan⁴

¹Department of CSE, GITAM University, India

²Department of CSE, Malla Reddy Institute of Engineering and Technology, India

³Department of CSE, Aurora’s Technological and Research Institute, India

⁴Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, Saudi Arabia

Received: August 29, 2023
Accepted: February 4, 2024
Publication Date: May 4, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202503_28(3).0001

The significant advances in the Wireless Body Area Networks (WBANs) and the impact of internetwork interference caused by the combination of several WBANs, the performance accuracy of WBANs may suffer significantly. In this article, a Hybrid Energy-Efficient Multi-Channel Communication Protocol (HEEMCCP) is proposed to mitigate and avoid inter-WBAN interference. The Dynamic Interference Avoidance Algorithm (DIAMA) reduces intra-WBAN interference and consumes less energy by dynamically adjusting the Superframe (SF) distance and restricting the number of channels to two. Rescheduling or channel switching is proposed when WBAN performance falls below tolerance using the neighborhood Sensor Node (SN) list set up using Optimum Interference Mitigation Algorithm (OIMA). The simulation results show that WBAN performance parameters such as Packet Delivery Ratio (PDR), Network Bandwidth Efficiency (NBE), Lower Energy Consumption (LEC), and End-to-End Delay (EED) and Energy Residual (ER) are decreased. The system throughput improved significantly up to 60% in high-interference situations.

Keywords: WBANs, Energy Consumption, PDR, Multi-channel MAC, Inference Detection and Avoidance, End-to-End Delay

[1] B. Großwindhager, M. Rath, M. S. Bakr, P. Greiner, C. A. Boano, K. Witrisal, F. Gentili, J. Grosinger, W. Bösch, and K. Römer, (2019) “Dependable Wireless Communication and Localization in the Internet of Things" Mission-Oriented Sensor Networks and Systems: Art and Science: Volume 2: Advances: 209– 256. DOI: 10.1007/978-3-319-92384-0_7.
[2] T. T. T. Le and S. Moh, (2015) “Interference mitigation schemes for wireless body area sensor networks: A comparative survey" Sensors 15: 13805–13838. DOI: 10.3390/s150613805.
[3] Z. Xie, B. Wang, J. Yu, H. Wu, G. Huang, R. Zarei, and J. He, (2020) “An optimal backoff time-based internetwork interference mitigation method in wireless body area network" Journal of Sensors: 1–13. DOI: 10.1155/2020/4365191.
[4] T. T. T. Le and S. Moh, (2016) “An interference-aware traffic-priority-based link scheduling algorithm for interference mitigation in multiple wireless body area networks" Sensors 16: 2190.
[5] H. B. Elhadj, S. Boudjit, L. Chaari, and L. Kamoun. “IEEE 802.15. 6 based node and hub architectures for healthcare applications”. In: IEEE, 2014, 1–3. DOI: 10.1109/WD.2014.7020848.
[6] F. N. Khan, R. Ahmad, W. Ahmed, M. M. Alam, and M. Drieber. “Performance Analysis of Interference and Priority aware Coexistence in IEEE 802.15. 6 based WBANs”. In: IEEE, 2020, 1–5. DOI: 10.1109/ BEC49624.2020.9277066.
[7] F. Ullah, A. H. Abdullah, M. Q. Jan, and K. N. Qureshi, (2016) “Patient data prioritization in the crosslayer designs of wireless body area network" Journal of Computer Networks and Communications: 5. DOI: 10.1155/2015/516838.
[8] H. Li, B. Yang, W. Yu, X. Guan, X. Gong, and G. Yu. “Joint sleep scheduling and opportunistic transmission in wireless body area networks”. In: IEEE, 2014, 1886–1891.
[9] J. A. Paradiso and T. Starner, (2005) “Energy scavenging for mobile and wireless electronics" IEEE Pervasive computing 4: 18–27. DOI: 10.1109/MPRV.2005.9.
[10] J. Ferreira, I. Pau, K. Lindecrantz, and F. Seoane, (2016) “A handheld and textile-enabled bioimpedance system for ubiquitous body composition analysis. An initial functional validation" IEEE journal of biomedical and health informatics 21: 1224–1232.
[11] J. O. Ha, S. H. Jung, M. C. Park, K. H. Lee, and Y. S. Eo. “A fully integrated 3–5 GHz UWB RF transceiver for WBAN applications”. In: IEEE, 2013, 1–3.
[12] K.-J. Wu, Y.-W. P. Hong, and J.-P. Sheu, (2020) “Coloring-based channel allocation for multiple coexisting wireless body area networks: A game-theoretic approach" IEEE Transactions on Mobile Computing 21: 63–75.
[13] X. Nan, X. Wang, T. Kang, J. Zhang, L. Dong, J. Dong, P. Xia, and D. Wei, (2022) “Review of flexible wearable sensor devices for biomedical application" Micromachines 13: 1395.
[14] A. Lay-Ekuakille, G. Griffo, P. Vergallo, A. Massaro, F. Spano, and G. Gigli, (2015) “Implantable neurorecording sensing system: Wireless transmission of measurements" IEEE Sensors Journal 15: 2603–2613.
[15] T. T. T. Le and S. Moh, (2018) “Hybrid multi-channel MAC protocol for WBANs with inter-WBAN interference mitigation" Sensors 18: 1373.
[16] M. M. Alam and E. B. Hamida, (2014) “Surveying wearable human assistive technology for life and safety critical applications: Standards, challenges and opportunities" Sensors 14: 9153–9209.
[17] M. Sahraoui and A. Bilami. “A new Reinforcement Learning based for Energy-efficient Multi-channel Data Gathering in Wireless Sensor Networks”. In: IEEE, 2020, 1–7.
[18] Q. Wu, Y. Xu, L. Shen, and J. Wang, (2012) “Investigation on GADIA algorithms for interference avoidance: A game-theoretic perspective" IEEE Communications Letters 16: 1041–1043.
[19] M. Thachayani. “Interference mitigation scheme to support WBANs Co-existing with WLANs”. In: IEEE, 2019, 41–44.
[20] N. Naji, M. R. Abid, N. Krami, and D. Benhaddou, (2021) “Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review" Journal of Sensor and Actuator Networks 10: 67.
[21] S. Asaithambi, L. Ravi, H. Kotb, A. H. Milyani, A. A. Azhari, S. Nallusamy, V. Varadarajan, and S. Vairavasundaram, (2022) “An Energy-Efficient and BlockchainIntegrated Software Defined Network for the Industrial Internet of Things" Sensors 22: 7917.
[22] S. Movassaghi, M. Abolhasan, J. Lipman, D. Smith, and A. Jamalipour, (2014) “Wireless body area networks: A survey" IEEE Communications surveys & tutorials 16: 1658–1686.
[23] S. Movassaghi, A. Majidi, A. Jamalipour, D. Smith, and M. Abolhasan, (2016) “Enabling interference-aware and energy-efficient coexistence of multiple wireless body area networks with unknown dynamics" IEEE Access 4: 2935–2951.
[24] T. Benmansour, T. Ahmed, and S. Moussaoui. “Performance evaluation of IEEE 802.15. 6 MAC in monitoring of a cardiac patient”. In: IEEE, 2016, 241–247.
[25] T. Kang, S. Kim, K.-I. Oh, J.-H. Hwang, J. Lee, H. Park, K. Byun, and W. Lee, (2020) “Evaluation of human body characteristics for electric signal transmission based on measured body impulse response" IEEE transactions on instrumentation and measurement 69: 6399–6411.
[26] T. Wu, F. Wu, J.-M. Redoute, and M. R. Yuce, (2017) “An autonomous wireless body area network implementation towards IoT connected healthcare applications" IEEE access 5: 11413–11422.
[27] V. Cionca, T. Newe, and V. Dadârlat. “TDMA protocol requirements for wireless sensor networks”. In: IEEE, 2008, 30–35.
[28] W. Ye, J. Heidemann, and D. Estrin, (2004) “Medium access control with coordinated adaptive sleeping for wireless sensor networks" IEEE/ACM Transactions on networking 12: 493–506.
[29] Y. Zatout, E. Campo, and J.-F. Llibre. “T-tmac: Energy aware sensor mac protocol for health-care monitoring”. In: IEEE, 2012, 1–5.
[30] Z. Askari, J. Abouei, M. Jaseemuddin, and A. Anpalagan, (2021) “Energy-efficient and real-time NOMA scheduling in IoMT-based three-tier WBANs" IEEE Internet of Things Journal 8: 13975–13990.
[31] Z. Ullah, I. Ahmed, K. Razzaq, M. K. Naseer, and N. Ahmed, (2019) “DSCB: Dual sink approach using clustering in body area network" Peer-to-Peer Networking and Applications 12: 357–370.
[32] S. Essafi and T. Ezzedine, (2018) “Adaptive parameters adjustment in WBAN to mitigate Wi-Fi interferences" Journal of computing and information technology 26: 69–83.