Looking into how modern technology combines sensors and Artificial Intelligence

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 : 2026 | Volume : 13 | 01 | Page :
    By

    V. Basil Hans,

  1. Research Professor, Department of Commerce and Management and Humanities & Social Sciences, Srinivas University, Mangalore, Karnataka, India

Abstract

The combination of sensors and Artificial Intelligence (AI) is changing modern technology by making it possible to collect, analyse, and make decisions based on data in real time. Sensors are the main link between the real and digital worlds. They collect several types of data, like temperature, motion, pressure, and visual information. When used with AI methods like machine learning and deep learning, this data may be processed in a smart way to find patterns, make predictions, and automate difficult activities. This synergy has led to big improvements in many areas, such as healthcare, smart cities, industrial automation, environmental monitoring, and self-driving systems. With AI-enhanced sensor systems, things become more accurate, efficient, and flexible while reducing the need for human involvement. But issues like data privacy, sensor dependability, energy use, and the difficulty of integrating systems are still very important. This integration has been bolstered by the swift expansion of Internet of Things (IoT) technologies, which enable smooth communication and collaboration among interconnected devices. Trends like edge computing and real-time analytics boost system responsiveness and lower latency, improving the practicality of AI-sensor systems for time-sensitive applications. The development of intelligent and autonomous environments is expected to be accelerated by these innovations, shaping the future of smart technologies. This article talks about the basic ideas, uses, pros, and cons of combining sensors with AI. It also talks about how this could lead to new ideas and change the way smart systems work in the future.

Keywords: Artificial Intelligence (AI), Sensors, Machine Learning, Internet of Things (IoT), and Data Analytics

How to cite this article:
V. Basil Hans. Looking into how modern technology combines sensors and Artificial Intelligence. Recent Trends in Sensor Research & Technology. 2026; 13(01):-.
How to cite this URL:
V. Basil Hans. Looking into how modern technology combines sensors and Artificial Intelligence. Recent Trends in Sensor Research & Technology. 2026; 13(01):-. Available from: https://journals.stmjournals.com/rtsrt/article=2026/view=241422


References

  1.  Monteiro AC, França RP, Arthur R, Iano Y. An overview of artificial intelligence technology directed at smart sensors and devices from a modern perspective. Smart Sensor Networks: Analytics, Sharing and Control. 2021 Sep 2:3-26.
  2. Sloane M, Moss E, Kennedy S, Stewart M, Warden P, Plancher B, Reddi VJ. Materiality and risk in the age of pervasive AI sensors. Nature Machine Intelligence. 2025 Mar;7(3):334- 45.
  3. Chen L, Xia C, Zhao Z, Fu H, Chen Y. AI-driven sensing technology. Sensors. 2024 May 7;24(10):2958.
  4. Deng S, Zhao H, Fang W, Yin J, Dustdar S, Zomaya AY. Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal. 2020 Apr 1;7(8):7457-69.
  5. Wan Z, Lele AS, Raychowdhury A. Circuit and system technologies for energy-efficient edge robotics. In2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC) 2022 Jan 17 (pp. 275-280). IEEE.
  6. Kumar A, Braud T, Tarkoma S, Hui P. Trustworthy AI in the age of pervasive computing and big data. In2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) 2020 Mar 23 (pp. 1-6). IEEE.
  7. Mohammed AS, Amamou A, Ayevide FK, Kelouwani S, Agbossou K, Zioui N. The perception system of intelligent ground vehicles in all weather conditions: A systematic literature review. Sensors. 2020 Nov 15;20(22):6532.
  8. Weng YH, Hirata Y. Design‐Centered HRI Governance for Healthcare Robots. Journal of Healthcare Engineering. 2022;2022(1):3935316.
  9. Malik S, Khan MA, El-Sayed H, Khan J, Ullah O. How do autonomous vehicles decide?. Sensors. 2022 Dec 28;23(1):317.
  10. Dhanaselvam PS, Karthikeyan B, Kumar P, Salama H, Nasri F, Kavitha K. Artificial intelligence in sensor technology. InAdaptive AI in Sensor Informatics 2026 Jan 1 (pp. 1-23). Elsevier.
  11. Ding H, Tian J, Yu W, Wilson DI, Young BR, Cui X, Xin X, Wang Z, Li W. The application of artificial intelligence and big data in the food industry. Foods. 2023 Dec 18;12(24):4511.
  12. Khan HU, Nazir S. Assessing the role of AI-based smart sensors in smart cities using AHP and MOORA. Sensors. 2023 Jan 2;23(1):494.
  13. Varshney A, Garg N, Nagla KS, Nair TS, Jaiswal SK, Yadav S, Aswal DK. Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications: A. Varshney et al. Mapan. 2021 Jun;36(2):215-26.
  14. Maqbool S, Bajwa IS, Maqbool S, Ramzan S, Chishty MJ. A smart sensing technologies- based intelligent healthcare system for diabetes patients. Sensors. 2023 Dec 1;23(23):9558.
  15. Yogev D, Goldberg T, Arami A, Tejman-Yarden S, Winkler TE, Maoz BM. Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges. APL bioengineering. 2023 Sep 1;7(3).
  16. Hancke GP, Hancke Jr GP. The role of advanced sensing in smart cities. Sensors. 2013 Jan;13(1):393-425.
  17. Rinchi O, Alsharoa A, Shatnawi I, Arora A. The role of intelligent transportation systems and artificial intelligence in energy efficiency and emission reduction. arXiv preprint arXiv:2401.14560. 2024 Jan 25.
  18. Mohammed S, Budach L, Feuerpfeil M, Ihde N, Nathansen A, Noack N, Patzlaff H, Naumann F, Harmouch H. The effects of data quality on machine learning performance on tabular data. Information Systems. 2025 Jul 1;132:102549.
  19. Mujica G, Rodriguez-Zurrunero R, Wilby MR, Portilla J, Rodríguez González AB, Araujo A, Riesgo T, Vinagre Díaz JJ. Edge and fog computing platform for data fusion of complex heterogeneous sensors. Sensors. 2018 Oct 25;18(11):3630.
  20. Hernández-Orallo J. Evaluation in artificial intelligence: from task-oriented to ability- oriented measurement. Artificial Intelligence Review. 2017 Oct;48(3):397-447.
  21. Zoghlami F, Kaden M, Villmann T, Schneider G, Heinrich H. AI-based multi sensor fusion for smart decision making: A bi-functional system for single sensor evaluation in a classification task. Sensors. 2021 Jun 27;21(13):4405.
  22. Meltebrink C, Komesker M, Kelsch C, König D, Jenz M, Strotdresch M, Wegmann B, Weltzien C, Ruckelshausen A. REDA: a new methodology to validate sensor systems for person detection under variable environmental conditions. Sensors. 2022 Aug 1;22(15):5745.
  23. Sagl G, Resch B, Blaschke T. Contextual sensing: Integrating contextual information with human and technical geo-sensor information for smart cities. Sensors. 2015 Jul 14;15(7):17013-35.
  24. Farhad AH, Sorokos I, Akram MN, Aslansefat K, Schneider D. Scope compliance uncertainty estimate. arXiv preprint arXiv:2312.10801. 2023 Dec 17.
  25. Jenihhin M, Reorda MS, Balakrishnan A, Alexandrescu D. Challenges of reliability assessment and enhancement in autonomous systems. In2019 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) 2019 Oct 2 (pp. 1-6). ieee.
  26. Borg M, Bronson J, Christensson L, Olsson F, Lennartsson O, Sonnsjö E, Ebabi H, Karsberg M. Exploring the assessment list for trustworthy ai in the context of advanced driver-assistance systems. In2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice (SEthics) 2021 Jun 4 (pp. 5-12). IEEE.
  27. Blasch E, Pham T, Chong CY, Koch W, Leung H, Braines D, Abdelzaher T. Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges. IEEE aerospace and electronic systems magazine. 2021 Jul 7;36(7):80-93.
Ahead of Print Subscription Review Article
Volume 13
01
Received 22/04/2026
Accepted 25/04/2026
Published 29/04/2026
Publication Time 7 Days
1

Login


My IP

PlumX Metrics