Research on the Integrated Development of Intelligent Technology and Embedded Systems

Research Article
Open access

Research on the Integrated Development of Intelligent Technology and Embedded Systems

Yue Fei 1*
  • 1 College of Internet of Things Engineering, Wuxi University, Wuxi, Jiangsu, China, 214105    
  • *corresponding author 18858158935@163.com
Published on 2 October 2025 | https://doi.org/10.54254/2755-2721/2025.LD27510
ACE Vol.184
ISSN (Print): 2755-2721
ISSN (Online): 2755-273X
ISBN (Print): 978-1-80590-307-9
ISBN (Online): 978-1-80590-308-6

Abstract

In the digital age. Intelligent technologies such as artificial intelligence and the Internet of Things are driving the upgrade of embedded systems. This paper focuses on the development relationship between the two. Using literature analysis and case analysis methods, it sorts out the characteristics of intelligent technology and the structure of embedded systems, explores the mechanism of mutual promotion, and summarizes application cases in the fields of smart home, smart medical care, and autonomous driving. The study found that intelligent technology promotes the innovation of embedded systems with its data processing and adaptive capabilities, and embedded systems provide hardware support for intelligent technology, but both face challenges in resources and computing power; the software research will focus on edge computing and 5G integrated development to expand application scenarios. It also offers theoretical references for enterprises to advance technology integration, and lays a foundation for solving practical problems in the industrialization of intelligent embedded products, further boosting the sustainable development of related fields.

Keywords:

Intelligent Technology, Embedded System, Technology Integration, Internet of Things

Fei,Y. (2025). Research on the Integrated Development of Intelligent Technology and Embedded Systems. Applied and Computational Engineering,184,180-185.
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1. Introduction

In the wave of global digital transformation, intelligent technology and embedded systems have become the core driving force of scientific and technological innovation. From smart factories that realize automated production to smart cities that optimize public resource allocation, the two technologies are deeply integrated into all aspects of social and economic development, breaking the limitations of traditional industrial models and promoting the leapfrog development of multiple industries. Intelligent technology is a collection of technologies that allow machines to simulate human intelligence, including artificial intelligence, machine learning, Internet of Things and other fields. Embedded systems are specialized computer systems that are integrated into the device to perform specific functions [1]. With the continuous growth of user demand for intelligent, personalized and portable devices, the single application of either technology can no longer meet the complex market needs. Therefore, the integration of intelligent technology and embedded systems is very important to promote the intelligence of terminal devices, which directly affects the performance of intelligent devices in medical, transportation, home and other fields. This paper specifically studies the key characteristics and mutual promotion mechanisms of intelligent technology and embedded systems, analyzes practical cases and discusses future development trends. Through literature analysis and case study, we hope to clarify the development relationship between the two and provide a reference for academic research and industrial practice.

2. Overview of intelligent technology and embedded systems

2.1. Definition and composition of intelligent technology and embedded systems

The core characteristics of intelligent technology include the ability to perceive independently, learn and adapt to changes, communicate in a variety of ways, and analyze and judge and make decisions. They first collect environmental information through sensors and conduct preliminary processing and analysis, then optimize model algorithms through data training to adapt to changes, make logical judgments based on perceived information and existing knowledge, and finally realize information exchange between humans and machines through voice, images, etc.

Embedded systems are application-centric special-purpose computer systems. Based on computer technology, the hardware and software can be customized to meet the strict requirements of function, reliability, cost, volume, and power consumption [2]. Its hardware part includes microprocessors, microcontrollers, memory, input and output interfaces, and peripheral devices. The microprocessor is the core responsible for executing instructions and processing data; the software part consists of embedded operating systems (such as Linux, FreeRTOS) and applications. The operating system is responsible for resource management and task scheduling, and the application completes specific functions.

2.2. The main technical characteristics and intersection of the two

The data-driven architecture of intelligent technology relies on a large amount of high-quality data and needs to implement complex functions based on advanced algorithms such as machine learning and deep learning. Moreover, the algorithm is complex and requires powerful computing power to process large-scale data, emphasizing autonomous operation and intelligence that can be unintervened.

Because of the strong real-time nature of embedded systems, they can respond to external events in a timely manner, such as industrial control tasks. At the same time, they also have high reliability, can operate stably for a long time in harsh environments, can be designed for specific scenarios, and have a single function but stable performance. However, due to functional limitations, integration in volume and power consumption, the processor performance and storage capacity are limited.

The above two have obvious technical intersections. At the technical level, both involve data processing and control functions. Intelligent systems require embedded hardware to support data acquisition and execution, and embedded systems require intelligent algorithms to improve their intelligence level. At the same time, at the application level, many devices have the characteristics of both types of systems. For example, smart cameras require embedded systems to realize image acquisition and transmission, and rely on intelligent algorithms to complete face recognition. Such integration has given rise to the emerging field of intelligent embedded systems.

3. Application of intelligent technology in embedded systems

3.1. Artificial intelligence and the internet of things in embedded systems

Artificial intelligence technology enables embedded devices to have the ability to make intelligent decisions and learn autonomously. In fault diagnosis, through machine learning algorithms, device operation data is analyzed, prediction models are established, and possible faults are warned in advance, which greatly improves the reliability of the equipment [1]. For instance, in industrial machinery like CNC lathes, this tech can spot subtle anomalies in vibration or temperature data that human inspectors might miss, cutting unplanned downtime by 30% on average. Natural language processing technology is applied to devices such as smart speakers, allowing devices to understand and respond to user voice commands, optimizing the human-computer interaction experience. It can even recognize dialects or context-aware requests—like adjusting music volume based on a user’s tone—to make interactions more natural. The combination of the Internet of Things and embedded systems has expanded the ability of device interconnection [3]. In smart homes, embedded sensors collect environmental data such as temperature, humidity, and light intensity, transmitting it to controllers via the IoT to enable automatic control of air conditioning and lighting. This not only saves energy but also adapts to residents’ habits, like lowering lights at bedtime. In the Industrial Internet of Things, embedded devices on production equipment collect operating parameters in real time and transmit them to monitoring centers via the network for remote management. Engineers can then adjust settings or troubleshoot from anywhere, boosting production efficiency significantly.

3.2. Intelligent perception, data processing, and adaptive control

Intelligent perception is the foundation of embedded device intelligence. Through various sensors, such as temperature, pressure, image, and sound, physical quantities are converted into electrical signals, providing raw data for subsequent processing. In smart wearable devices, accelerometers sense motion and heart rate sensors monitor physiological changes, enabling real-time monitoring of health. In industrial scenarios, vibration sensors capture abnormal vibrations in equipment, providing a basis for fault diagnosis.

Data processing utilizes edge computing technology for local analysis, reducing data traffic and latency to the cloud. In intelligent transportation systems, embedded devices on roads process traffic flow data locally, determining congestion conditions and adjusting traffic lights in real time. Agricultural monitoring equipment analyzes soil moisture data locally, avoiding ineffective transmission and wasting resources.

Adaptive control uses intelligent algorithms to adjust control strategies in real time. Industrial control systems automatically adjust valve openings and motor speeds based on temperature and pressure fluctuations to maintain stable production. Robot control systems adjust their motion based on road conditions to ensure safe operation. These applications demonstrate the dynamic optimization capabilities of embedded systems.

3.3. Practical application examples

In the smart home sector, intelligent temperature control systems collect data through embedded temperature sensors. Controllers automatically adjust air conditioning operation based on user settings and actual temperature to maintain a comfortable environment. Smart door locks use embedded biometric recognition technology (fingerprint and facial recognition) for secure and convenient door opening control. Devices are interconnected through the Internet of Things to create scenario-based linkages, improving daily convenience.

In the smart healthcare sector, embedded sensors in portable monitors monitor heart rate, blood pressure, and other parameters in real time, wirelessly transmitting them to hospital monitoring centers, allowing doctors to remotely monitor patients' conditions. Smart blood glucose meters collect samples through embedded sensors, and intelligent algorithms quickly detect blood glucose concentrations and display the results, assisting patients in self-management. Rehabilitation robots combine intelligent sensing and embedded control to adjust motion parameters based on rehabilitation progress and optimize training plans.

Autonomous driving, which has been very popular in the past two years, is a typical example of the integration of the two. By equipping the vehicle with embedded sensors such as lidar and cameras, it can sense environmental information such as road markings and obstacles. The processor quickly integrates and analyzes the data, completes path planning and decision control through intelligent algorithms, and adjusts acceleration, deceleration and steering in real time [4]. The high reliability and real-time performance of the system ensure driving safety, reflecting the high level of technology integration.

4. The relationship and development of intelligent technology and embedded systems

4.1. The driving role of intelligent technology in the innovation of embedded systems

Intelligent technology not only expands the functional scope of embedded systems, but also transforms them from traditional simple control to intelligent perception and learning decision-making. After the introduction of machine learning, industrial embedded systems have upgraded from simple status monitoring to fault prediction and diagnosis. For example, predictive maintenance in manufacturing now reduces repair costs by anticipating issues before failures occur. Intelligent technology can also promote the performance improvement of embedded systems. To meet the requirements of algorithms, processors have evolved from 8-bit and 16-bit to 32-bit and 64-bit high-performance chips. Dedicated AI chips (such as NPU) have greatly improved computing power [5]. Intelligent optimization algorithms are applied to resource management and task scheduling to improve operating efficiency and reduce power consumption, thus balancing performance and energy consumption. In addition, intelligent technology promotes the expansion of embedded system application areas, from traditional industrial control and consumer electronics to emerging fields such as smart home, smart medical care, and autonomous driving, reflecting the value of cross-domain applications. These advancements enable more responsive and adaptive solutions across diverse real-world scenarios.

4.2. The hardware support of embedded systems for intelligent technology functions

Embedded systems provide a stable hardware platform, microprocessors guarantee the computing power of algorithm execution, sensors provide data input, memory stores programs and data, and communication interfaces enable information exchange. The emergence of dedicated AI chips further meets the needs of intelligent algorithms, allowing complex reasoning calculations to be implemented on terminal devices [6]. These chips, optimized for neural network operations, reduce latency compared to relying on cloud computing, critical for time-sensitive tasks. Its miniaturization and low power consumption characteristics support the portable application of intelligent technology. For example, smart watches use low-power embedded processors and sensors to achieve long-term battery life and meet health monitoring needs; portable medical devices use compact hardware design to enable intelligent diagnostic technology to be close to user scenarios and expand the scope of application. Such portability also benefits fieldwork, like environmental monitors tracking air quality in remote areas. The high reliability and real-time performance of embedded systems ensure the stable operation of intelligent technologies. For example, the real-time operating system for autonomous driving can be scheduled in a timely manner to avoid safety risks, demonstrating the fundamental support role of hardware in the implementation of technology. Even in industrial control, this reliability prevents production line halts caused by system delays.

4.3. Challenges and opportunities

In terms of resource constraints, the processor performance and storage capacity of embedded systems cannot meet the high demands of algorithms such as deep learning, making algorithm light-weighting a key issue. Techniques like model pruning—removing redundant neurons—and quantization—reducing data precision—have become common solutions, enabling complex AI tasks to run smoothly on low-power embedded hardware. In terms of data security, embedded devices are vulnerable to attacks during data collection and transmission, and the risk of privacy leakage is prominent, requiring enhanced security protection. For example, end-to-end encryption and lightweight authentication protocols are now widely adopted to shield sensitive data like user health metrics from unauthorized access. The continuous improvement of the technical integration of the two has also brought many development opportunities to people to a certain extent. For example, the advancement of chip technology has brought about heterogeneous computing architecture and dedicated AI chips, which have improved the computing power of embedded systems; 5G technology provides high-speed and low-latency communication support, promoting the interconnection and data sharing of smart devices; edge computing technology realizes local data processing, reduces transmission pressure and improves response speed [5]. This is particularly critical for time-sensitive scenarios like autonomous driving, where real-time data analysis is essential. Market demand and policy support form the driving force for development. Consumption upgrades in areas such as smart homes and smart cars promote technology research and development; governments of various countries have issued policies related to artificial intelligence and the Internet of Things, provided funding and talent guarantees, standardized data security and privacy protection, and guided the healthy development of the industry [7]. For instance, the EU’s AI Act sets clear safety standards, while China’s "14th Five-Year Plan" for the IoT industry allocates funds to support core technology breakthroughs.

5. Conclusion

This paper explores the relationship between intelligent technologies and embedded systems development, focusing on their characteristics, mutually reinforcing mechanisms, practical applications, and future trends. It takes into account the deepening integration context of global digital technologies, aiming to clarify the internal connection between the two from a multi-dimensional perspective. Research shows that intelligent technologies enhance the intelligence of embedded systems through data processing and adaptive capabilities, while embedded systems provide hardware and real-time support for the implementation of intelligent technologies. This two-way support has become a key link in promoting the upgrading of intelligent terminal industries. However, resource constraints and computing power limitations remain core challenges, especially in complex scenarios such as high-precision industrial control and real-time intelligent monitoring. A limitation of this article is the lack of in-depth analysis of specific technology integration cases. Future research could focus on optimizing embedded system resource allocation algorithms and exploring the integration of 5G and edge computing to expand application scenarios in areas such as smart cities and the Industrial Internet. As technology advances, the integration of intelligent technologies and embedded systems will drive further innovation in the field of intelligent terminals.


References

[1]. Li Lei, Gao Xue, Qin Huiping, et al. Reform and exploration of experimental courses in embedded artificial intelligence systems [J]. Laboratory Research and Exploration, 2025, 44 (3): 147-152. DOI: 10.19927 /j.cnki.syyt.2025.03.028.

[2]. Han Dongru, Liu Yongjun, Ma Qingsong. Performance analysis of industrial wireless sensor networks based on embedded multi-intelligent systems [J]. Information Recording Materials, 2022, 23 (08): 29-31. DOI: 10.16009 /j.cnki.cn13 - 1295.tq.2022.08.022.

[3]. Xu Yingzhao. Research on the development trend of the integration of embedded systems and the Internet of Things [J]. Wireless Internet Technology, 2019, 16 (4): 13-14.

[4]. S.G.Gong , Z.Q.He , Q.Liu , et al.Rapid detection system for pesticide residue based on embedded technology [C]//IETP Associaiton.Abstract of the 2015 International Conference on Advanced Materials and Engineering Structural Technology(ICAMEST 2015).School of Electrical Engineering, Guizhou University; , 2015: 16.

[5]. Shi Weisong, Sun Hui, Cao Jie, et al. Edge computing: a new computing model in the era of the Internet of Things [J]. Computer Research and Development, 2017, 54 (5): 907-924.

[6]. Xia Weiwei, Shen Lianfeng, Xiao Jie, et al. Analysis and Development of Key Technologies for Embedded Systems [J]. Microcontrollers and Embedded Systems Applications, 2003(2): 5-9.

[7]. P.M. G , A.R. M .Intelligent techniques for electronic component and system alignment [J].Electronics & communication engineering journal, 1989, 1(1): 23-32.


Cite this article

Fei,Y. (2025). Research on the Integrated Development of Intelligent Technology and Embedded Systems. Applied and Computational Engineering,184,180-185.

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About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN:978-1-80590-307-9(Print) / 978-1-80590-308-6(Online)
Editor:Hisham AbouGrad
Conference website: https://www.confmla.org/
Conference date: 17 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.184
ISSN:2755-2721(Print) / 2755-273X(Online)

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References

[1]. Li Lei, Gao Xue, Qin Huiping, et al. Reform and exploration of experimental courses in embedded artificial intelligence systems [J]. Laboratory Research and Exploration, 2025, 44 (3): 147-152. DOI: 10.19927 /j.cnki.syyt.2025.03.028.

[2]. Han Dongru, Liu Yongjun, Ma Qingsong. Performance analysis of industrial wireless sensor networks based on embedded multi-intelligent systems [J]. Information Recording Materials, 2022, 23 (08): 29-31. DOI: 10.16009 /j.cnki.cn13 - 1295.tq.2022.08.022.

[3]. Xu Yingzhao. Research on the development trend of the integration of embedded systems and the Internet of Things [J]. Wireless Internet Technology, 2019, 16 (4): 13-14.

[4]. S.G.Gong , Z.Q.He , Q.Liu , et al.Rapid detection system for pesticide residue based on embedded technology [C]//IETP Associaiton.Abstract of the 2015 International Conference on Advanced Materials and Engineering Structural Technology(ICAMEST 2015).School of Electrical Engineering, Guizhou University; , 2015: 16.

[5]. Shi Weisong, Sun Hui, Cao Jie, et al. Edge computing: a new computing model in the era of the Internet of Things [J]. Computer Research and Development, 2017, 54 (5): 907-924.

[6]. Xia Weiwei, Shen Lianfeng, Xiao Jie, et al. Analysis and Development of Key Technologies for Embedded Systems [J]. Microcontrollers and Embedded Systems Applications, 2003(2): 5-9.

[7]. P.M. G , A.R. M .Intelligent techniques for electronic component and system alignment [J].Electronics & communication engineering journal, 1989, 1(1): 23-32.