Jun 2026· American Journal of Science, Engineering and Technology· Vol 11, pp. 81-91· 0 citations· 23 references
TL;DR
This review critically examines the primary technical, architectural, and operational challenges inherent in deploying WSNs for smart manufacturing, and uncovers critical gaps including cross-vendor interoperability, fault-tolerance under dynamic production conditions, and lifecycle co-design of sensor modules with digital twin models.
Abstract
The convergence of Cyber-Physical Systems (CPS), smart automation, and digital manufacturing marks a pivotal evolution toward Industry-4.0, characterized by intelligent, adaptive, and data-driven production systems. Core to this transformation are IoT?based Wireless Sensor Networks (WSNs), which serve as the sensory backbone enabling real-time monitoring, predictive control, and autonomous decision?making within digitally integrated manufacturing environments. This review critically examines the primary technical, architectural, and operational challenges inherent in deploying WSNs for smart manufacturing. Key issues include energy-efficient design to sustain long?term sensor operation, large-scale network scalability amid heterogeneous device ecosystems, and stringent requirements for reliability and latency in time-sensitive control loops. Security and privacy concerns are analyzed, with emphasis on lightweight cryptographic protocols and intrusion detection tailored for constrained sensor nodes. The integration of CPS introduces additional complexity in data interoperability and standardization across manufacturing tiers, prompting exploration into middleware platforms and semantic ontologies. Moreover, the potential of edge?intelligent WSNs for data aggregation, anomaly detection, and real?time feedback control is investigated, with attention to balancing computational load and network resource usage. By synthesizing recent academic and industrial research, this review identifies prevailing trends, such as AI-augmented sensor architectures and blockchain?enhanced trust frameworks, and uncovers critical gaps including cross-vendor interoperability, fault-tolerance under dynamic production conditions, and lifecycle co?design of sensor modules with digital twin models. The insights derived provide a roadmap for future innovation, guiding researchers and practitioners toward resilient, efficient, and secure CPS?enabled smart manufacturing solutions in Industry-4.0.
Smart sensors, which integrate sensing, processing, communication, and self-calibration capabilities, are essential for modern engineering systems such as industrial automation, healthcare, aerospace, autonomous vehicles, and smart infrastructure. This paper reviews their integration into engineering architectures, highlighting their role in real-time data acquisition, edge computing, IoT connectivity, and AI-driven analytics. Smart sensors improve accuracy, reduce latency, and support predictive maintenance and autonomous control. Advances in MEMS technology have enabled miniaturization and cost-effective large-scale deployment. The study also discusses challenges including energy constraints, interoperability, security, calibration drift, and environmental interference, while proposing solutions such as edge-cloud architectures, energy harvesting, and secure communication methods. Results demonstrate that smart sensor-based systems outperform conventional sensing systems in efficiency, fault detection, responsiveness, energy savings, and data accuracy, making them a key enabler of Industry 4.0 and future autonomous engineering applications.
V. Sethi· International Journal of Mod...· 0 citations
Smart Manufacturing Systems (SMS) is the paradigm shift in the contemporary industrial manufacturing that can unite Internet of Things (IoT) technologies, automation, cyber-physical systems, and data-driven intelligence to improve efficiency, flexibility, qualities, and sustainability. Conventional manufacturing systems tend to be inhibited by fixed production lines, reduced real-time visibility, and fixed manual decision making systems. With the advent of Industry 4.0, manufacturers can now use the interconnected and intelligent systems that can operate autonomously, preventive maintenance, adaptive control and optimize the use of the resources. The current paper is a detailed analysis of smart manufacturing systems that utilize the IoT and automation technology. It examines the architectural solutions, it is allowing technology, communication protocol, data analytics, and automation solutions that all constitute smart factories. Through an extensive literature review, recent developments, issues, and research gaps in the context of IoT based manufacturing setting are pointed at. The methodology suggests an integrated smart manufacturing model that will integrate sensor networks, edge and cloud computing, industrial automation, and machine intelligence. Performance evaluation measures are addressed in detail like production efficiency, downtime, reduction, system optimization in energy and scalability of the system. The findings indicate a high improvement in operational performance, predictive accuracy, and decision-making ability when compared to the traditional manufacturing system. The paper ends with a statement of future research directions, which are autonomous manufacturing with artificial intelligence, digital twins, and secure industrial IoT ecosystems.
Aiko Yamamoto· International Journal of Mod...· 0 citations
Industry 4.0 has transformed traditional manufacturing into smart, data-driven, and highly connected production environments by integrating the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), Machine Learning (ML), Cloud and Edge Computing, Cyber-Physical Systems (CPS), and Big Data Analytics. Smart Manufacturing Analytics (SMA) continuously collects and analyzes real-time data from sensors, machines, robots, programmable logic controllers (PLCs), and enterprise systems to enable intelligent decision-making. Unlike conventional manufacturing, SMA supports descriptive, diagnostic, predictive, and prescriptive analytics for applications such as predictive maintenance, fault diagnosis, quality inspection, production forecasting, energy optimization, and adaptive process control. Emerging technologies including digital twins, intelligent robotics, and Explainable AI (XAI) further enhance manufacturing resilience, transparency, and automation. Despite significant advancements, challenges such as interoperability, real-time data integration, network scalability, cybersecurity, device reliability, and decision-making under uncertainty remain. A multi-tier smart manufacturing framework combining IIoT, machine learning, cloud-edge computing, and optimization algorithms enables real-time asset monitoring, anomaly detection, predictive maintenance, resource allocation, and production optimization. Overall, Smart Manufacturing Analytics improves productivity, equipment health, product quality, energy efficiency, and operational resilience while reducing downtime and manufacturing costs, providing a strong foundation for next-generation intelligent and sustainable manufacturing ecosystems.
Alan Turing, Donald Davies· International Journal of Int...· 0 citations
The digital transformation of industrial systems under the Industry 4.0 paradigm has introduced cyber-physical systems (CPS) as a core enabler of vertical integration and data-driven production environments. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has accelerated this transformation, fostering the development of the Industrial Internet of Things (IIoT) and creating smart industries characterized by real-time monitoring, automation, and human–robot collaboration (HRC). While these advancements establish a digital ecosystem capable of optimizing production through intelligent data analysis and decision-making, their practical implementation remains constrained by unresolved challenges and gaps in validation. With the emergence of Industry 5.0, the focus shifts toward human-centric, sustainable, and resilient industrial ecosystems, where AI-driven cognitive computing further enhances interaction between humans and machines. This review examines the application of AI–IoT integrated technologies across multiple industrial domains to identify their strengths, limitations, and recurring challenges. By categorizing existing literature into key application areas, the study highlights both the opportunities and risks inherent in current approaches, bridging the conceptual design of smart industries with their real-world realizations. The findings underscore the importance of scalable, secure, and efficient frameworks to ensure the safe and reliable adoption of AI–IoT in the industrial ecosystem.
Asmarani Ahmad Puzi, Ahmad Anwar Zainuddin, Muhammad Afham Anuar et al.· International Journal of Inn...· 0 citations
Abstract. CPMS have become a major facilitator of smart factories through a combination of physical operations and the computational intelligence and real-time communication. Combination of Internet of Things (IoT) technologies and machine intelligence offers novel possibilities to realize adaptive, efficient and autonomous manufacturing processes. Nevertheless, the co-ordination of the physical and cyber layer is a serious issue because of the dynamic characteristic of industrial set ups and the high amount of heterogeneous sensor data. This paper proposes a cyber-physical architecture based on the IoT, which uses machine intelligence to monitor, analyze, and control manufacturing systems in real-time. The approach that is proposed is based on sensor-driven data acquisition, intelligent processing, and adaptive decision-making to optimize system performance. To reduce the production inefficiency, energy usage as well as fault occurrence, a multi-objective formulation is created in the context of keeping operational constraints. The structure is tested on a realistic manufacturing case based on real-time data input. Findings indicate the improvement of operational efficiency, the capability of fault detection and the response time is reduced, compared to the traditional methods. The methodology suggested can be both scaled and powerful to serve next-generation intelligent manufacturing systems.
Pankaj Mudholkar· Materials Research Proceedin...· 0 citations
Manufacturing systems increasingly require real-time performance monitoring and data-driven optimization to reduce downtime, stabilize quality, and support flexible production. Although Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies have been widely discussed in smart manufacturing, existing studies often treat sensing, key performance indicators (KPIs), analytics, and decision support as separate concerns. This paper presents a structured literature review and conceptual synthesis of IoT-enabled performance monitoring and optimization in manufacturing systems, with emphasis on recent work in IIoT architectures, edge and cloud analytics, digital twins, predictive maintenance, and manufacturing KPIs. The main contribution is an integrated five-layer conceptual framework that connects physical sensing and data acquisition, edge computing and connectivity, data management and integration, analytics and intelligence, and application-level decision support. The framework clarifies how shop-floor data can be transformed into KPI-oriented insights and optimization actions while accounting for cybersecurity, interoperability, data governance, scalability, and human-in-the-loop decision-making. An illustrative automotive parts/CNC manufacturing scenario demonstrates the framework's potential application; however, no simulation, pilot deployment, or quantitative validation is claimed. The review concludes by outlining implementation considerations and a validation roadmap for future empirical studies, including digital-twin simulation, pilot testing, baseline KPI comparison after implementation, and cost-benefit assessment.
Sami Gazem Abdullah Thabet, M. Amrani· 2026 6th International Confe...· 0 citations