Journal Menu
By: Ayesha Khalil Mulani and Dr. Kazi Kutubuddin Sayyad Liyakat.
UG Student, Department of E&TC Engineering, BMIT, Solapur, Maharashtra, India.
Professor & Head, 1,2 Department of E& TC Engineering, BMIT, Solapur, Maharashtra, India.
As it connects billions of devices and changes sectors, the Internet of Things (IoT) is developing quickly. But the limits of conventional wireless communication technology are being pushed by this expansion. Presenting millimetre wave (mmWave), a high-frequency band that promises to transform Internet of Things communication by providing noticeably more capacity and reduced latency. Even while mmWave is still in its infancy, it has the potential to be a key component of the IoT ecosystems of the future. The frequency range between 30 GHz and 300 GHz is referred to as millimetre waves. mmWave frequencies provide a far wider range than the lower frequencies utilized in cellular networks and Wi-Fi. Because of its enormous capacity, applications that need high-throughput and real-time communication can transmit data at much faster speeds. In order to support the vast number of connected devices and their data-intensive applications, the Internet of Things (IoT) is growing quickly and requiring ever-increasing bandwidth. Although conventional sub-6 GHz frequencies have proven useful, millimetre wave (mmWave) technology has gained attention due to the need for higher data speeds and reduced latency. With the potential for multi-gigabit speeds and extensive spectrum resources, mmWave operates in the 30 GHz to 300 GHz range. Nevertheless, there are particular design difficulties when mmWave is used in IoT. The main procedures for overcoming these obstacles and utilizing mmWave’ s potential for improved IoT connectivity are examined in this article.
mmWave, Bandwidth, Internet of Things, Spectrum, millimetre wave, High-frequency
Citation:
Refrences:
[1]. Kazi Kutubuddin Sayyad Liyakat. (2024). Microwave Communication in the Internet of
Things: A Study. Journal of RF and Microwave Communication Technologies, 38–49.
[2]. Liyakat, K.K.S. (2024). Machine Learning Approach Using Artificial Neural Networks to
Detect Malicious Nodes in IoT Networks. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent
Systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Springer, Singapore.
[3]. Pradeepa M, Jamberi K, Sajith S, Bai MR, Prakash A. (2022). Student health detection using
a machine learning approach and IoT. In 2022 IEEE 2nd Mysore sub section International
Conference (MysuruCon) (pp. 1-5). IEEE.
[4]. K. K. S. Liyakat. (2023). 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, pp. 1-5.
[5]. K. Kasat, N. Shaikh, V. K. Rayabharapu, M. Nayak. (2023). 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, pp. 1661-1665.
[6]. Kazi, K. (2024a). AI-Driven IoT (AIIoT) in Healthcare Monitoring. In T. Nguyen & N. Vo
(Eds.), Using Traditional Design Methods to Enhance AI-Driven Decision Making (pp. 77-101).
IGI Global.
[7]. Khadake, S., Kawade, S., Moholkar, S., Pawar, M. (2024). A Review of 6G Technologies
and Its Advantages Over 5G Technology. In: Pawar, P.M., et al. Techno-societal 2022. ICATSA
2022. Springer, Cham.
[8]. V. J. Patil, S. B. Khadake, D. A. Tamboli, H. M. Mallad, S. M. Takpere and V. A. Sawant.
(2024). 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, pp. 94-99.
[9]. Kazi, K. (2024b). Modelling and Simulation of Electric Vehicle for Performance Analysis:
BEV and HEV Electrical Vehicle Implementation Using Simulink for E-Mobility Ecosystems. In
L. D., N. Nagpal, N. Kassarwani, V. Varthanan G., & P. Siano (Eds.), E-Mobility in Electrical
Energy Systems for Sustainability (pp. 295-320). IGI Global.
[10]. Kazi, K. S. (2024a). Computer-Aided Diagnosis in Ophthalmology: A Technical Review of
Deep Learning Applications. In M. Garcia & R. de Almeida (Eds.), Transformative Approaches
to Patient Literacy and Healthcare Innovation (pp. 112-135). IGI Global.
[11]. Prashant K Magadum. (2024). Machine Learning for Predicting Wind Turbine Output
Power in Wind Energy Conversion Systems. Grenze International Journal of Engineering and
Technology, Vol 10, Issue 1, pp. 2074-2080.
[12]. Priya Mangesh Nerkar, Bhagyarekha U. Dhaware. (2023). Predictive Data Analytics
Framework Based on Heart Healthcare System (HHS) Using Machine Learning. Journal of
Advanced Zoology, Vol 44, Special Issue 2, pp. 3673–3686.
[13]. P. Neeraja, R. G. Kumar, M. S. Kumar, K. K. S. Liyakat and M. S. Vani. (2024). 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, pp. 589-594.
[14]. Khadake, S. B., Padavale, P. V., Dhere, P. M., & Lingade, B. M. (2021). Automatic hand
dispenser and temperature scanner for Covid-19 prevention. International Journal of Advanced
Research in Science, Communication and Technology, 3(2), 362-367.
[15]. V. J. Patil, S. B. Khadake, D. A. Tamboli, H. M. Mallad, S. M. Takpere and V. A. Sawant.
(2024). A Comprehensive Analysis of Artificial Intelligence Integration in Electrical
Engineering. In 2024 5th International Conference on Mobile Computing and Sustainable
Informatics (ICMCSI), Lalitpur, Nepal, pp. 484-491.
[16]. Suhas B. Khadake, Sudarshan P. Dolli, K.S. Rathod, O.P. Waghmare and A.V. Deshpande.
(2021). An Overview Of Intelligent Traffic Control System Using PLC And Use Of Current
Data Of Vehicle Travels. JournalNX, pp. 1-4.
[17]. K. Rajendra Prasad, Santoshachandra Rao Karanam. (2024). AI in public-private
partnership for IT infrastructure development. Journal of High Technology Management
Research, 35(1), 100496.
[18]. Altaf O. Mulani, Arti V. Bang, Ganesh B. Birajadar, Amar B. Deshmukh, Hemlata M.
Jadhav. (2024). IoT Based Air, Water, and Soil Monitoring System for Pomegranate Farming.
Annals of Agri-Bio Research, 29(2), 71-86.
[19]. Shraddha S. Magar, Archana S. Sugandhi, Shweta H. Pawar, Suhas B. Khadake, H. M.
Mallad. (2024). Harnessing Wind Vibration, a Novel Approach towards Electric Energy
Generation – Review. IJARSCT, 4(2), 73-82.
[20]. Suhas B. Khadake, Vijay J. Patil, H. M. Mallad, Buddhapriy B. Gopnarayan, Karan Babaso
Patil. (2024). Maximize Farming Productivity through Agriculture 4.0 Based Intelligence with
Agri Tech Sense. In 15th International Conference on Advances in Computing, Control, and
Telecommunication Technologies (ACT 2024), pp. 5127-5134.