Autonomous driving paper index
Impact of IoT Technology Implementation in the Manufacturing Sector: A Systematic Literature Review
One-line summary
The rapid development of IoT research in various fields has promoted the evolution of manufacturing in the Industry 4.0 context.
Engineering notes
Key topics: autonomous driving, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
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Original abstract
The rapid development of IoT research in various fields has promoted the evolution of manufacturing in the Industry 4.0 context. However, the growing and dispersed literature makes it difficult to see the dominant trends and open challenges. The aim of the study is to synthesize the existing IoT research in the manufacturing, by analyzing the sectoral adoption, enabling technologies and implementation objectives. The review develops a systematic understanding of the links between manufacturing sectors, IoT technologies and operational priorities to identify dominant research directions and gaps for future research. A systematic literature review was conducted according to the PRISMA guidelines, screening and analysing peer-reviewed studies along three analytical dimensions: distribution by manufacturing sector, typologies of IoT technologies and strategic objectives of implementation. The analysis identified shared adoption patterns in some manufacturing sectors, common use of sensor-based and cloud-enabled technologies, and a high emphasis on productivity, monitoring and efficiency of operations. The results reveal a significant concentration of IoT research in discrete manufacturing, as well as noticeable attention in process manufacturing, healthcare and general manufacturing, while other sectors remain less explored, indicating an uneven research focus across industries. In terms of technology, Industrial IoT and smart manufacturing solutions are the most common, followed by IoT-enabled digital twin technologies, while the combination of IoT with artificial intelligence, machine learning, and computer vision indicates a growing shift towards more adaptive and intelligent systems. A smaller portion of IoT implementations are related to sensors and monitoring applications, blockchain enabled IoT solutions and distributed architectures, while middleware and system integration appear least often. Regarding implementation objectives, efficiency enhancement is the main driver, followed by predictive maintenance, quality control and productivity enhancement, and real-time monitoring, showing a strong orientation toward improving operational performance. In summary, the synthesis implies that the IoT research in manufacturing is mainly focused on discrete manufacturing applications, operational efficiency objectives, and intelligent automation technologies. The concentration indicates a continued research focus on production optimization, while broader contexts of industrial integration are relatively underexplored.
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