Millimeter Wave in Internet of Things Connectivity: A Study


Year : 2025 | Volume : 03 | Issue : 01 | Page : 13-23
    By

    Kazi Kutubuddin Sayyad Liyakat,

  1. Professor & Head, Department of E&TC Engineering, BMIT, Solapur, Maharashtra, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_175551’);});Edit Abstract & Keyword

The internet of things (IoT) is rapidly evolving, connecting billions of devices and transforming industries. However, this growth is stretching the limitations of traditional wireless communication technologies. Enter millimeter wave (mmWave), a high-frequency band promising to revolutionize IoT connectivity by offering significantly higher bandwidth and lower latency. While still in its early stages of implementation, mmWave is poised to become a cornerstone of the next generation of IoT ecosystems. Millimeter waves operate within the 30 GHz to 300 GHz frequency range, offering a significantly larger spectrum compared to the lower frequencies commonly used in Wi-Fi and cellular networks. This extensive bandwidth enables ultra-fast data transfer speeds, making mmWave technology ideal for applications that require high throughput and real-time communication. With the rapid growth of the IoT, the demand for greater bandwidth continues to rise as more connected devices generate massive amounts of data. While sub-6 GHz frequencies have been effective, the need for faster speeds and reduced latency has shifted focus toward mmWave technology. Operating in the 30 GHz to 300 GHz range, mmWave offers vast spectrum resources and the potential for multi-gigabit speeds. However, implementing mmWave in IoT presents unique design challenges. This article explores the key steps involved in navigating these challenges and harnessing the power of mmWave for enhanced IoT communication.

Keywords: Millimeter wave (mmWave), internet of things (IoT), bandwidth, spectrum, high frequency

[This article belongs to International Journal of Wireless Security and Networks (ijwsn)]

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat. Millimeter Wave in Internet of Things Connectivity: A Study. International Journal of Wireless Security and Networks. 2025; 03(01):13-23.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat. Millimeter Wave in Internet of Things Connectivity: A Study. International Journal of Wireless Security and Networks. 2025; 03(01):13-23. Available from: https://journals.stmjournals.com/ijwsn/article=2025/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_175551’);});Edit

References

  1. Liyakat KKS. Microwave communication in the internet of things: a study. J RF Microw Commun Technol. 2024; 1 (3): 38–49.
  2. Liyakat KKS. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Shukla PK, Mittal H, Engelbrecht A, eds. International Conference on Machine Learning, IoT and Big Data 2023. Singapore: Springer Nature; 2023. pp. 123–134.
  3. Pradeepa M, Jamberi K, Sajith S, Bai MR, Prakash A. Student health detection using a machine learning approach and IoT. In: 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, October 16–17, 2022. pp. 1–5.
  4. Liyakat KKS. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. In: 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, March 1–3, 2023. pp. 1–5.
  5. Kasat K, Shaikh N, Rayabharapu VK, Nayak M, Liyakat KKS. Implementation and recognition of waste management system with mobility solution in smart cities using internet of things. In: 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, August 23–25, 2023. pp. 1661–1665.
  6. Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens. 2019;11(2):196. DOI: 10.3390/rs11020196.
  7. Kazi K. AI-driven IoT (AIIoT) in healthcare monitoring. In: Nguyen TVT, Vo NTM, editors. Using Traditional Design Methods to Enhance AI-Driven Decision Making. Hershey, PA, USA: IGI Global; 2024. pp. 77–101.
  8. Khadake S, Kawade S, Moholkar S, Pawar M. A review of 6G technologies and its advantages over 5G technology. In: Pawar PM, Ronge BP, Gidde RR, editors. Techno-Societal 2022 International Conference on Advanced Technologies for Societal Applications 2022. Cham, Switzerland: Springer International Publishing; 222. pp. 1043–1051.
  9. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant VA. Review of AI in power electronics and drive systems. In: 2024 3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control (PARC), Mathura, India, February 23–24, 2024. pp. 94–99.
  10. Kazi K. Modelling and simulation of electric vehicle for performance analysis: BEV and HEV electrical vehicle implementation using Simulink for e-mobility ecosystems. In: Lakshmi D, Nagpal N, Kassarwani N, Vishnu Varthanan G, Siano P, editors. E-Mobility in Electrical Energy Systems for Sustainability. Hershey, PA, USA: IGI Global; 2024. pp. 295–320.
  11. Kazi KS. Computer-aided diagnosis in ophthalmology: a technical review of deep learning applications. In: Garcia MB, de Almeida RPP, editors. Transformative Approaches to Patient Literacy and Healthcare Innovation. Hershey, PA, USA: IGI Global; 2024. pp. 112–135.
  12. Liyakat KKS, Magadum PK. Machine learning for predicting wind turbine output power in wind energy conversion systems. Grenze Int J Eng Technol. 2024; 10: 2074–2080.
  13. Nerkar PM, Liyakat KK, Dhaware BU, Liyakat KS. Predictive data analytics framework based on heart healthcare system (HHS) using machine learning. J Adv Zool. 2023; 44: 3673–3686.
  14. Neeraja P, Kumar RG, Kumar MS, Liyakat KK, Vani MS. DL-based somnolence detection for improved driver safety and alertness monitoring. In: 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), Greater Noida, India, February 9–10, 2024. Vol. 5, pp. 589–594.
  15. Liyakat KK. Explainable AI in healthcare. In Kamaraj AA, Acharjya DP, editors. Explainable Artificial Intelligence in Healthcare Systems. Hauppauge, NY, USA: Nova Publishers; 2024. pp. 271–284.
  16. Kazi KS. ChatGPT: an automated teacher’s guide to learning. In: Bansal R, Chakir A, Ngah AH, Rabby F, Jain A, editors. AI Algorithms and ChatGPT for Student Engagement in Online Learning. Hershey, PA, USA: IGI Global; 2024. pp. 1–20.
  17. Veena C, Sridevi M, Liyakat KK, Saha B, Reddy SR, Shirisha N. HEECCNB: an efficient IoT-cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. In: 2023 Seventh International Conference on Image Information Processing (ICIIP), Solan, India, November 22–24, 2023. pp. 407–410.
  18. Prasad KR, Karanam SR, Ganesh D, Liyakat KK, Talasila V, Purushotham P. AI in public-private partnership for IT infrastructure development. J High Technol Manage Res. 2024; 35 (1): 100496.
  19. Kazi KS. IoT driven by machine learning (MLIoT) for the retail apparel sector. In: Tarnanidis TK, Papachristou E, Karypidis M, Ismyrlis M, editors. Driving Green Marketing in Fashion and Retail. Hershey, PA, USA: IGI Global; 2024. pp. 63–81.
  20. Dudgikar AB, Ingalgi AA, Jamadar AG, Swami OR, Khadake SB, Moholkar SV. Intelligent battery swapping system for electric vehicles with charging stations locator on IoT and cloud platform. Int J Adv Res Sci Commun Technol. 2023; 3 (1): 204–208.
  21. Khadake SB, Patil VJ. Prototype design & development of solar based electric vehicle. In: 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, December 29–31, 2023. pp. 1–7.
  22. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant VA. A comprehensive analysis of artificial intelligence integration in electrical engineering. In: 2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), Lalitpur, Nepal, January 18–19, 2024. pp. 484–491.
  23. Kazi KS. Machine learning (ML)-based Braille Lippi characters and numbers detection and announcement system for blind children in learning. In: Sart G, editor. Social Reflections of Human-Computer Interaction in Education, Management, and Economics. Hershey, PA, USA: IGI Global; 2024. pp. 16–39.
  24. Kazi KS. Artificial intelligence (AI)-driven IoT (AIIoT)-based agriculture automation. In: Satapathy S, Muduli K, editors. Advanced Computational Methods for Agri-Business Sustainability. Hershey, PA, USA: IGI Global; 2024. pp. 72–94.
  25. Khadake SB, Dolli SP, Rathod KS, Waghmare MO, Deshpande MA. An overview of intelligent traffic control system using PLC and use of current data of vehicle travels. JournalNX – A Multidiscipl Peer Reviewed J. 2016; Feb 12: VESCOMM 12.
  26. Sayyad Liyakat KK, Konnur RG. Vehicle health monitoring system (VHMS) by employing IoT and sensors. Grenze Int J Eng Technol. 2024; 10: 5367–5374.
  27. Magar SS, Sugandhi AS, Pawar SH, Khadake SB, Mallad HM. Harnessing wind vibration, a novel approach towards electric energy generation – review. Int J Adv Res Sci Commun Technol. 2024; 4 (2): 73–82.
  28. Khadake SB, Padavale PV, Dhere PM, Lingade BM. Automatic hand dispenser and temperature scanner for Covid-19 prevention. Int J Adv Res Sci Commun Technol. 2023; 3 (2): 362–367. doi: 10.48175/IJARSCT-11364.
  29. Biradar PK, Pardeshi YS, Bagwan IA, Kazi TI, Ganji SS. Remotely operated video enhanced receiver. Int J Creative Res Thoughts. 2024; 12 (3): h446–h454.
  30. Landage SS, Chavan SR, Kokate PA, Lohar SP, Pawar MK, Khadake SB. Solar outdoor air purifier with air quality monitoring system. Synergies of Innovation: Proceedings of NCSTEM, Pandharpur, India, September 2024. pp. 260–266.
  31. Khadake SB. Detecting salient objects of natural scene in a video’s using spatio-temporal saliency & colour map. JournalNX – A Multidiscipl Peer Reviewed J. 2016; 2 (8): 30–35.
  32. Liyakat KK, Kutubuddin K. IoT based boiler health monitoring for sugar industries. In: 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024, Hyderabad, India, June 21–22, 2024. Vol. 2, pp. 5178–5185.
  33. Kazi KS. Machine learning-based pomegranate disease detection and treatment. In: Zia Ul Haq M, Ali I, editors. Revolutionizing Pest Management for Sustainable Agriculture. Hershey, PA, USA: IGI Global; 2024. pp. 469–498.
  34. Aquino AI, Calautit JK, Hughes BR. Integration of aero-elastic belt into the built environment for low-energy wind harnessing: Current status and a case study. Energy Convers Manag. 2017;149:830-50. DOI: 10.1016/j.enconman.2017.03.030.
  35. Khadake SB, Khedekar SV, Kawade AM, Vyavahare SS, Kashid PJ, Chounde Amol B, Mallad HM. Solar based electric vehicle charging system. Int J Adv Res Sci Commun Technol. 2024; 4 (2): 42–57.
  36. Kazi KS. IoT technologies for the intelligent dairy industry: A new challenge. In: Thandekkattu SG, Vajjhala NR, editors. Designing Sustainable Internet of Things Solutions for Smart Industries. Hershey, PA, USA: IGI Global; 2025. pp. 321–350.
  37. Khan S, Ahmad A, Ahmad F, Shafaati Shemami M, Saad Alam M, Khateeb S. A comprehensive review on solar powered electric vehicle charging system. Smart Sci. 2018;6(1):54-79. DOI: 10.1080/23080477.2017.1419054.
  38. Khadake SB, Kashid PJ, Kawade AM, Khedekar SV, Mallad HM. Electric vehicle technology battery management – review. Int J Adv Res Sci Commun Technol. 2023; 3 (2): 319–325.
  39. Liyakat KK. Heart health monitoring using IoT and machine learning methods. In: Shaik A, editor. AI-Powered Advances in Pharmacology. Hershey, PA, USA: IGI Global; 2025. pp. 257–282.
  40. Chounde A, Gopnarayan BB, Patil KB, Kamble SS. Human health care system: a new approach towards life. Grenze Int J Eng Technol. 2024; 10 (2): 5487–5494.
  41. Khadake SB, Patil VJ, Mallad HM, Gopnarayan BB, Patil KB. Maximize farming productivity through agriculture 4.0 based intelligence, with use of agri tech sense advanced crop monitoring system. Grenze Int J Eng Technol. 2024; 10 (2): 5127–5134.
  42. Kazi KS. AI-driven-IoT (AIIoT)-based decision making in kidney diseases patient healthcare monitoring: KSK approach for kidney monitoring. In: Polat LÖ, Polat O, editors. AI-Driven Innovation in Healthcare Data Analytics. Hershey, PA, USA: IGI Global; 2025. pp. 277–306.
  43. Kazi KS. AI-driven-IoT (AIIoT)-based decision making in drones for climate change: KSK approach. In Aouadni S, Aouadni I, editors. Recent Theories and Applications for Multi-Criteria Decision-Making. Hershey, PA, USA: IGi Global; 2025. pp. 311–340. doi: 10.4018/979-8-3693-6502-1.ch011.
  44. Yuan Q, Zhang H, Deng T, Tang S, Yuan X, Tang W, et al. Role of artificial intelligence in kidney disease. Int J Med Sci. 2020;17(7):970-84. DOI: 10.7150/ijms.42078, PubMed: 32308551.
  45. Mahant MA. Machine learning-driven internet of things (MLIoT)-based healthcare monitoring system. In Wickramasinghe N, editor. Digitalization and the Transformation of the Healthcare Sector. Hershey, PA, USA: IGi Global; 2025. pp. 205–236. doi: 10.4018/979-8-3693-9641-4.ch007.
  46. Nerkar P, Kazi S. IoT-based skin health monitoring system. Int J Biol Pharm All Sci. 2024; 13 (11): 5937–5950. doi: 10.31032/IJBPAS/2024/13.11.8488.
  47. Kazi KSL. AI-powered IoT (AI IoT) for decision-making in smart agriculture: KSK approach for smart agriculture. In Hai-Jew S, editor. Enhancing Automated Decision-Making Through AI. Hershey, PA, USA: IGI Global; 2025. pp. 67–96. doi: 10.4018/979-8-3693-6230-3.ch003.
  48. Kazi KSL. KK approach to increase resilience in internet of things: a T-cell security concept. In: Darwish D, Charan K, editors. Analyzing Privacy and Security Difficulties in Social Media: New Challenges and Solutions. Hershey, PA, USA: IGIG Global; 2025. pp. 87–120. doi: 10.4018/979-8-3693-9491-5.ch005.
  49. Kazi KS. Machine learning-driven internet of medical things (ML-IoMT)-based healthcare monitoring system. In Soufiene B, Chakraborty C, editors. Responsible AI for Digital Health and Medical Analytics. Hershey, PA, USA: IGI Global; 2025. pp. 49–86. doi: 10.4018/979-8-3693-6294-5.ch003.
  50. Ullo SL, Sinha GR. Advances in IoT and smart sensors for remote sensing and agriculture applications. Remote Sens. 2021;13(13):2585. DOI: 10.3390/rs13132585.
  51. Chandra Mouli GR, Bauer P, Zeman M. System design for a solar powered electric vehicle charging station for workplaces. Appl Energy. 2016;168:434-43. DOI: 10.1016/j.apenergy.2016.01.110.
  52. Randive AB, Gaikwad SK, Khadake SB, Mallad HM. Biodiesel: a renewable source of fuel. Int J Adv Res Sci Commun Technol. 4 (3): 225–240. doi: 10.48175/IJARSCT-22836.
  53. Kazi K. A novel approach on ML based palmistry. Grenze Int J Eng Technol. 2024; 10 (2): 5186–5193.
  54. Kazi K. IoT based boiler health monitoring for sugar industries. Grenze Int J Eng Technol. 2024; 10 (2): 5178–5185.
  55. Bilal B, Ndongo M, Adjallah KH, Sava A, Kébé CMF, Ndiaye PA, et al. Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data. 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France. 2018. pp.1085-92. DOI: 10.1109/ICIT.2018.8352329.
  56. Mulani AP, Bang AV, Birajadar GB, Deshmukh AB, Jadhav HM. IoT based air, water, and soil monitoring system for pomegranate farming. Ann Agri-Bio Res. 2024; 29 (2): 71–86.
  57. Kazi KS. Transformation of agriculture effectuated by artificial intelligence-driven internet of things (AIIoT). In: Garwi J, Dzingirai M, Masengu R, editors. Integrating Agriculture, Green Marketing Strategies, and Artificial Intelligence. Hershey, PA, USA: IGI Global; 2025. pp. 449–484. doi: 10.4018/979-8-3693-6468-0.ch015.

Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 07/02/2025
Accepted 09/02/2025
Published 24/02/2025
Publication Time 17 Days

async function fetchCitationCount(doi) {
let apiUrl = `https://api.crossref.org/works/${doi}`;
try {
let response = await fetch(apiUrl);
let data = await response.json();
let citationCount = data.message[“is-referenced-by-count”];
document.getElementById(“citation-count”).innerText = `Citations: ${citationCount}`;
} catch (error) {
console.error(“Error fetching citation count:”, error);
document.getElementById(“citation-count”).innerText = “Citations: Data unavailable”;
}
}
fetchCitationCount(“10.37591/IJWSN.v03i01.0”);

Loading citations…