Internet of Battlefield Things (IoBT): An IoBT-inspired Battlefield of Tomorrow

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2024 | Volume :11 | Issue : 03 | Page : 18-25
By
vector

Kazi Kutubuddin Sayyad Liyakat,

  1. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology (BMIT), Solapur, Maharashtra, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_130732’);});Edit Abstract & Keyword

The battlefield of tomorrow will be radically different from the ones we know today. The internet of battlefield things (IoBT) is a game-changer in modern warfare. By providing enhanced situational awareness, improved decision-making, and new capabilities for soldiers, IoBT empowers military forces to operate more effectively and efficiently. However, it is crucial to address challenges related to cybersecurity, interoperability, and implementation to fully harness the potential of this revolutionary technology. Sensors are the backbone of the IoBT, providing crucial data for informed decision-making, enhanced situational awareness, and improved battlefield outcomes. Despite challenges, the ongoing development and integration of these technologies will continue to revolutionize warfare, shaping future battlefields and defining the future of combat. It will be a symphony of sensors, continuously feeding real-time data to intelligent systems, enabling commanders to make informed decisions at lightning speed. The IoBT holds immense potential to revolutionize warfare, offering unprecedented situational awareness, targeted operations, and autonomous capabilities. However, the challenges of security, interoperability, data management, and ethical considerations are significant obstacles that need to be addressed. As technology advances, research and development must prioritize these challenges to ensure the IoBT fulfills its promise of a safer, more efficient, and responsible battlefield of the future.

Keywords: Internet of battlefield things (IoBT), sensors, security, decision making, Internet of Things (IoT), Kobetsu Kaizen approach

[This article belongs to Journal of Telecommunication, Switching Systems and Networks (jotssn)]

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat. Internet of Battlefield Things (IoBT): An IoBT-inspired Battlefield of Tomorrow. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(03):18-25.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat. Internet of Battlefield Things (IoBT): An IoBT-inspired Battlefield of Tomorrow. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(03):18-25. Available from: https://journals.stmjournals.com/jotssn/article=2024/view=0

Browse Figures

References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_130732’);});Edit

  1. Liyakat KK Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Udgata SK, Sethi S, Gao XZ, editors. Intelligent Systems. ICMIB 2023. Lecture Notes in Networks and Systems, Vol. 728. Singapore: Springer; 2024.pp. 123–134. doi: 10.1007/978-981-99-3932-9_12.
  2. Pradeepa M, Jamberi K, Sajith S, Rama Bai M, Prakash A, Kazi KSL. Student health detection using a machine learning approach and IoT, 2022. In: IEEE 2nd Mysore Subsection International Conference (MysuruCon), Mysuru, India, October 16–17, pp. 1–5.
  3. 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. 1–5. doi: 10.1109/ESCI56872.2023.10099544.
  4. Kasat K, Shaikh N, Rayabharapu VK, Nayak M, Kutubuddin K. 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. 1661–1665. doi: 10.1109/ICAISS58487.2023.10250690.
  5. Liyakat KK Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Shukla PK, Mittal H, Engelbrecht A, editors. Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Singapore: Springer; 2023. pp. 27–38. doi: 10.1007/978-981-99-4577-1_3
  6. Kazi AI-driven IoT (AIIoT) in healthcare monitoring. In: Nguyen T, Vo N, editors. Using Traditional Design Methods to Enhance AI-Driven Decision Making. Hershey, PA, USA: IGI Global; 2024. pp. 77–101. doi: 10.4018/979-8-3693-0639-0.ch003.
  7. Kazi 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, Varthanan GV, Siano P, editors. E-Mobility in Electrical Energy Systems for Sustainability. Hershey, PA, USA: IGI Global; 2024. pp. 295–320. doi: 10.4018/979-8-3693-2611-4.ch014.
  8. Kazi K Computer-aided diagnosis in ophthalmology: a technical review of deep learning applications. In Garcia M, de Almeida R, editors. Transformative Approaches to Patient Literacy and Healthcare Innovation. Hershey, PSA, USA: IGI Global; 2024. pp. 112–135. doi: 10.4018/979-8-3693-3661-8.ch006.
  9. Magadum Machine learning for predicting wind turbine output power in wind energy conversion systems. Grenze Int J Eng Technol. 2024; 10 (1): 2074–2080.
  10. Nerkar PM, Dhaware BU. Predictive data analytics framework based on heart healthcare system (HHS) using machine learning. J Adv Zool. 2023; 44 (Special Issue 2): 3673–
  11. Neeraja P, Kumar RG, Kumar MS, Liyakat KKS, 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. 589–594. doi: 10.1109/IC2PCT60090.2024.10486714.
  12. Liyakat Explainable AI in healthcare. In: Kamaraj AA, Acharjya DP, editors. Explainable Artificial Intelligence in Healthcare Systems. Hauppauge, NY, USA: Nova Science Publishers; 2024. Chapter 16. doi: 10.52305/GOMR8163.
  13. Liyakat KK ChatGPT: an automated teacher’s guide to learning. In: Bansal R, Chakir A, Hafaz Ngah A, Rabby F, Jain A, editors. AI Algorithms and ChatGPT for Student Engagement in Online Learning. Hershey, PA, USA: IGI Global; 2024. pp. 1–20. doi: 10.4018/979-8-3693-4268-8.ch001.
  14. Veena C, Sridevi M, Liyakat KKS, Saha B, Reddy SR, Shirisha 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. doi: 10.1109/ICIIP61524.2023.10537627.
  15. Rajendra Prasad K, Karanam SR, Ganesh D, Liyakat KKS, Talasila V, Purushotham P. AI in public-private partnership for IT infrastructure development. J High Technol Manage Res. 2024; 35 (1): doi: 10.1016/j.hitech.2024.100496.
  16. Nagrale M, Pol RS, Birajadar GB, Mulani Internet of robotic things in cardiac surgery: an innovative approach. Afr J Biol Sci. 2024; 6 (6): 709–725. doi: 10.33472/AFJBS.6.6.2024.709-725.
  17. Kazi K IoT driven by machine learning (MLIoT) for the retail apparel sector. In: Tarnanidis T, Papachristou E, Karypidis M, Ismyrlis V, editors. Driving Green Marketing in Fashion and Retail. Hershey, PA, USA: IGI Global; 2024. pp. 63–81. doi: 10.4018/979-8-3693-3049-4.ch004.
  18. Kazi 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. doi: 10.4018/979-8-3693-3033-3.ch002.
  19. Kazi K 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. doi: 10.4018/979-8-3693-3583-3.ch005.
  20. Khadake SB, Patil 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. doi: 10.1109/SMART
    GENCON60755.2023.10442455.
  21. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM Sawant 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. doi: 10.1109/PARC59193.2024.10486488.
  22. Khadake SB. Detecting salient objects of natural scene in a video using spatio-temporal saliency and colour map. JournalNX. 2021; 2 (8): 30–35.
  23. Khadake SB, Dolli SP, Rathod KS, Waghmare OP, Deshpande AV. An overview of intelligent traffic control system using PLC and use of current data of vehicle travels. JournalNX. 2021; VESCCOMM 2016: 1–4.
  24. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant 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. doi: 10.1109/ICMCSI61536.2024.00076.
  25. Khadake S, Kawade S, Moholkar S, Pawar A review of 6G technologies and its advantages over 5G technology. In: Pawar PM, et al., editors. Techno-Societal 2022. ICATSA 2022. Cham, Switzerland: Springer; 2022. pp. 1043–1051. doi: 10.1007/978-3-031-34644-6_107.
  26. Dudgikar B, Ingalgi AAA, Jamadar AG, et al. 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. doi: 10.48175/IJARSCT-7867.
  27. 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.
  28. Khadake SB. Detecting salient objects in a video by using spatio-temporal saliency & colour map. Int J Innov Eng Res Technol.2021; 3 (8): 1–
  29. Khadake SB, Kashid PJ, Kawade AM, Khedekar SV, Mallad SM. Electric vehicle technology battery management – review. Int J Adv Res Sci Commun Technol. 2023; 3 (2): 319–325. doi: 10.48175/ijarsct-13048.
  30. KutubuddinVehicle health monitoring system (VHMS) by employing IoT and sensors. Grenze Int J Eng Technol.2024; 10 (2): 5367–5374.

31.   Kutubuddin K. A novel approach on ML based palmistry. Grenze Int J Eng Technol. 2024; 10 (2): 5186–5193.

32.   Kutubuddin K. IoT based boiler health monitoring for sugar industries. Grenze Int J Eng Technol. 2024; Vol 10 (2): 5178–5185.

33.   Liyakat KKS. Explainable AI in healthcare. In: Kamaraj AA, Acharjya DP, editors. Explainable Artificial Intelligence in Healthcare Systems. Hauppauge, NY, USA: Nova Science Publishers; 2024. pp. 271–284.

  1. Kazi K 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. doi: 10.4018/979-8-3693-3061-6.ch019.
  2. Kazi K IoT technologies for the intelligent dairy industry: a new challenge. In: Thandekkattu S, Vajjhala N, editors. Designing Sustainable Internet of Things Solutions for Smart Industries. Hershey, PA, USA: IGI Global; 2025. pp. 321–350. doi: 10.4018/979-8-3693-5498-8.ch012.
  3. Kazi Machine learning-driven-internet of things (MLIoT) based healthcare monitoring system. In. Wickramasinghe N, editor. Impact of Digital Solutions for Improved Healthcare Delivery. Hershey, PA, USA: IGI Global; 2025.
  4. Kazi Moonlighting in Career. In: Tunio MN, editor. Applications of Career Transitions and Entrepreneurship. Hershey, PA, USA: IGI Global; 2025.
  5. Liyakat K 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. doi: 10.4018/979-8-3693-3212-2.ch010.
  6. Kazi 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. Hershey, PA, USA: IGI Global; 2025.
  7. Kazi 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.
Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 11/07/2024
Accepted 09/09/2024
Published 18/09/2024
165