Journal Menu
By: Dr. Kazi Kutubuddin Sayyad Liyakat.
Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur (MS), India.
Internet of Things (IoT), which links billions of devices and produces enormous volumes of data, is growing quickly. Although this interconnection offers previously unheard-of potential, it also poses a serious security risk. Robust cryptographic solutions are necessary to protect sensitive IoT data from malevolent actors, and True Random Number Generator(TRNG) is a dependable source of randomness at the core of these solutions. Despite their efficiency, traditional pseudo- random number generators(PRNGs) are susceptible to assaults because they are deterministic and predictable. Conversely, TRNGs use physical processes to produce genuinely unexpected sequences. The implementation of TRNGs using Very High-Speed Integrated Circuit Hardware Description Language(VHDL) is examined in this article, emphasising how it might improve security in IoT devices with limited resources. Security is still a top priority as the Internet of Things continues to enter our daily lives. VHDL offers a strong framework for putting TRNGs into practice, which can greatly improve IoT device security. Developers may produce reliable and effective TRNG designs that address the particular difficulties of the IoT environment by utilising the flexibility and hardware optimisation capabilities of VHDL. Adopting VHDL-based TRNGs is essential to creating a future for the Internet of Things that is more reliable and secure. Future studies should focus on creating more effective post-processing methods, investigating new noise sources, and creating TRNGs that are naturally immune to certain types of attacks.
IoT, Security, VHDL, True Random Number Generator, Malicious
Citation:
Refrences:
- Ravale P. FPGA based finger vein recognition system for personal verification. Int J Eng Res Gen Sci. 2015;3(4).
- Ravale PM, et al. Retinal image decomposition using variational mode decomposition. Int Res J Eng Technol. 2018;5(6).
- Khadake S, Kawade S, Moholkar S, Pawar M. A review of 6G technologies and its advantages over 5G technology. In: Pawar PM, et al., editors. Techno-societal 2022. ICATSA 2022. Cham: Springer; 2024. doi: 10.1007/978-3-031-34644-6_107.
- Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant VA. Review of AI in power electronics and drive systems. In: 2024 3rd Int Conf Power Electron IoT Appl Renewable Energy Control (PARC). Mathura, India; 2024. p. 94-99. doi: 10.1109/PARC59193.2024.10486488.
- 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 Int Conf Mobile Comput Sustain Informatics (ICMCSI). Lalitpur, Nepal; 2024. p. 484-491. doi: 10.1109/ICMCSI61536.2024.00076.
- 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. doi: 10.48175/IJARSCT-19811.
- Khadake SB, Chounde A, Gopnarayan BB, Patil KB, Kamble SS. Human health care system: A new approach towards life. In: 15th Int Conf Adv Comput Control Telecommun Technol (ACT 2024). 2024;2:5487-5494.
- Chounde AB, Suryagan AA, Mallad HM, Khadatare MR. AI-driven IoT (AIIoT) based decision-making system for high-blood pressure patient healthcare monitoring. In: 2024 Int Conf Sustain Commun Netw Appl (ICSCNA). Theni, India; 2024. p. 96-102. doi: 10.1109/ICSCNA63714.2024.10863954.
- Sayyad. AI-powered IoT (AIIoT)-based decision-making system for BP patient’s healthcare monitoring: KSK approach for BP patient healthcare monitoring. In: Aouadni S, Aouadni I, editors. Recent Theories and Applications for Multi-Criteria Decision- Making. IGI Global; 2025. p. 205-238. doi: 10.4018/979-8-3693-6502-1.ch008.
- Sayyad. 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. IGI Global; 2025. p. 67-96. doi: 10.4018/979-8-3693-6230-3.ch003.
- Sayyad. 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. IGI Global; 2025. p. 87-120. doi: 10.4018/979-8-3693-9491-5.ch005.
- Sayyad. KK approach for IoT security: T-cell concept. In: Kumar R, Peng S-L, Elngar A, editors. Deep Learning Innovations for Securing Critical Infrastructures. IGI Global; 2025.
- Sayyad. Healthcare monitoring system driven by machine learning and Internet of Medical Things (MLIoMT). In: Kumar V, Katina P, Zhao J, editors. Convergence of Internet of Medical Things (IoMT) and Generative AI. IGI Global; 2025. p. 385-416. doi: 10.4018/979-8-3693-6180-1.ch016.
- Shinde SS, Nerkar PM, Kazi SS, Kazi VS. Machine learning for brand protection: A review of a proactive defense mechanism. In: Khan M, Amin Ul Haq M, editors. Avoiding Ad Fraud and Supporting Brand Safety: Programmatic Advertising Solutions. IGI Global; 2025. p. 175-220. doi: 10.4018/979-8-3693-7041-4.ch007.