A Review of the Transition from Industry 4.0 to Industry 5.0: Unlocking the Potential of TinyML in Industrial IoT Systems

Abstract

The integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation. Tiny Machine Learning (TinyML) is emerging as a key technology that enables the deployment of machine learning models on ultra-low-power devices. This study presents a systematic review of 110 peer-reviewed publications (2020–2025) identified from Scopus, Web of Science, and IEEE Xplore following the PRISMA protocol. Bibliometric and thematic analyses were conducted using Biblioshiny and VOSviewer to identify major trends, architectural approaches, and industrial applications of TinyML. The results reveal four principal research clusters: edge intelligence and energy efficiency, federated and explainable learning, human-centric systems, and sustainable resource management. Importantly, the surveyed industrial implementations report measurable gains—typically reducing inference latency to the millisecond range, lowering on-device energy cost to the sub-milliwatt regime, and sustaining high task accuracy, thereby substantiating the practical feasibility of TinyML in real IIoT settings. The analysis indicates a conceptual shift from engineering- and energy-focused studies toward cognitive, ethical, and security-oriented perspectives aligned with the principles of Industry 5.0. TinyML is positioned as a catalyst for the transition from automation to cognitive autonomy and as a technological foundation for building energy-efficient, ethical, and sustainable industrial ecosystems.

1. Introduction

Over the past decade, industrial systems have undergone a fundamental transformation, driven by the synergy between Industry 4.0 and the Internet of Things (IoT). The introduction of connected sensor devices, cyber-physical systems, and cloud infrastructures has created conditions for data-driven digital process management [1]. With the advent of Industry 5.0, the focus is shifting from automation and efficiency to human-centeredness, sustainability, and intelligent collaboration between humans and machines [2].

In this context, Tiny Machine Learning (TinyML) is emerging as a key catalyst for the new generation of industrial intelligent systems. TinyML is an approach for performing machine learning on extremely energy-efficient and resource-constrained devices, such as microcontrollers and edge sensors [3]. Unlike traditional cloud-based ML systems, TinyML performs inference directly at the point of data generation, minimizing latency, increasing security, and reducing dependence on network connectivity [4]. In industrial IoT (IIoT), this enables real-time sensor-level decisions that are critical for applications such as predictive maintenance, adaptive process control, quality and safety monitoring, and energy efficiency management [5].

While Industry 4.0 is characterized by digitization, automation, and mass communication between machines (M2M), Industry 5.0 builds on this paradigm by integrating human creativity and the intelligence of artificial intelligence [6]. This leads to a new level of interaction, where machines do not replace humans, but complement them, providing more flexible and sustainable production systems [7].

In this sense, it is evident that the transition from Industry 4.0 to 5.0 is primarily driven by the integration of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT), which form the basis of sustainable, human-centered, and flexible manufacturing ecosystems [6]. Their scientometric analysis outlines key thematic areas in the literature—predictive maintenance, collaborative robotics, and cyber-physical systems—but does not specifically address the role of ultra-efficient embedded models and TinyML within this transformation.

TinyML plays a strategic role in this transition—it enables the implementation of edge intelligence, where data is processed locally, close to its source. This aligns with the vision of sustainable, secure, and human-centric systems that limit the carbon footprint of cloud centers and reduce energy costs [4,8].

Despite the growing interest in TinyML, a systematic analysis of its applications, challenges, and potential in the context of Industry 5.0 is still lacking in the literature. Most studies focus on individual aspects—for example, model optimization [9], energy management [8], or hardware architectures [10], but there is no integrated perspective on the causal relationship between TinyML, IIoT, and the transformation to Industry 5.0 [5].

A similar absence of integration can be observed in review studies devoted to Industry 5.0 and smart industrial technologies. For instance, [11] provides a comprehensive bibliometric analysis of over 900 publications, tracing the thematic evolution of Industry 5.0 and its relationship with artificial intelligence, IoT, and sustainability, but without including TinyML as a separate technology category. Similarly, [12] focuses on the sustainability of IoT infrastructure through the application of lightweight cryptographic and machine learning algorithms. Meanwhile, [13] analyzes IoT applications in urban water management, demonstrating the potential of IoT and AI for sustainability and predictive analytics.

The present work is distinct from previous approaches in that it combines bibliometric, thematic, and industrial analysis, with a specific focus on TinyML as an enabling technology within IIoT and Industry 5.0. By systematically mapping publications from 2020 to 2025 and examining their interrelationships, it offers an integrated perspective that links technological miniaturization, decentralized intelligence, and human-centered sustainability. These aspects have not previously been considered together in the scientific literature.

This review addresses this gap by systematically analyzing publications from 2020 to 2025 indexed in Scopus, Web of Science, and IEEE Xplore using PRISMA. The main objectives are as follows:

• To identify the leading research directions and real-world industrial applications of TinyML in IIoT.
• To analyze the hardware and software solutions, algorithms, and architectural models employed in existing studies.
• To formulate the main technological and organizational challenges facing the implementation of TinyML in Industry 5.0.
• To outline the prospects and strategic directions for future research and implementation.

This structured approach allows for a critical synthesis of contemporary scientific output and outlines the path towards a human-centered, sustainable, and intelligent industry of the future.

The main questions that the analysis must answer are as follows:

RQ1: In which industrial applications has TinyML already been implemented in IIoT and what results have been achieved?

RQ2: What hardware and software architectures dominate in industrial applications of TinyML?

RQ3: What technological and organizational challenges arise when implementing TinyML in a real industrial environment?

RQ4: How does TinyML contribute to the implementation of Industry 5.0 principles, with a particular focus on sustainability and human-centredness?

2. Concepts and Methodology

The following section is dedicated to the presentation of the conceptual and methodological foundations of the study. Firstly, the theoretical context in which TinyML is defined as a key technology for intelligent edge computing within the IIoT and the broader Industry 5.0 paradigm is examined. The conceptual framework underpinning the study establishes a link between technological evolution, spanning data-driven automation to human-centered and sustainable intelligence, thereby providing an interpretative basis for the subsequent bibliometric and thematic analysis. The subsequent section details the methodological approach that was applied in this study, including the PRISMA protocol for systematic reviews, data extraction and pre-processing procedures, and the analytical tools Biblioshiny and VOSviewer that were utilized. In this manner, the conceptual and methodological elements are amalgamated into a comprehensive framework that ensures theoretical consistency and analytical transparency in the study of the role of TinyML in the transition from Industry 4.0 to Industry 5.0.

2.1. Conceptual Background

This subsection presents the conceptual framework of the study, examining the evolution from Industry 4.0 to Industry 5.0, the role of TinyML in the context of IIoT, and the synergy between artificial intelligence, robotics, and human-centeredness. This theoretical context outlines the basis on which the methodological model of this study is built.

The concept of Industry 4.0 is defined as a paradigm based on the integration of cyber-physical systems (CPS), the Internet of Things, and big data analytics within manufacturing processes. It strives for optimization, efficiency, and autonomy through digital interconnectivity [14]. In this context, industrial systems are becoming self-regulating ecosystems where machines communicate and make decisions without human intervention.

However, with the advent of Industry 5.0, a paradigm shift has occurred, with technological progress now being placed at the service of people, rather than the inverse. The European Commission’s 2021 concept [2] defines Industry 5.0 as a human-centered and change-resistant system that integrates not only artificial intelligence and robots, but also the cognitive and ethical dimensions of human–machine interaction.

This paradigm shift necessitates the development of intelligent, adaptable, and localized solutions that guarantee autonomy without depending on cloud infrastructures. In this context, TinyML emerges as a pivotal technology, facilitating intelligence at the edge nodes [15].

Tiny Machine Learning (TinyML) represents a new paradigm in machine learning, where inference and part of the training of ML models are performed on microcontrollers and embedded devices with extremely low power consumption (below 1 mW) [3]. This is made possible by optimized frameworks such as TensorFlow Lite Micro, PyTorch Mobile, Edge Impulse, as well as hardware platforms such as ARM Cortex-M, ESP32, NVIDIA Jetson Nano, STM32, and others.

TinyML integrates three primary components:

• AI model optimization–through knowledge distillation, quantization, and pruning, models are adapted for execution on end devices with limited resources [16].
• Energy-efficient hardware–microcontrollers with built-in DSP and NPU modules, enabling efficient operation with AI models.
• Local inference–minimizing data exchange with the cloud, which increases security and reduces latency.

As posited by [17], TinyML represents not merely a technological progression, but rather a philosophical paradigm of decentralizing intelligence, thereby bringing data processing closer to the physical realm. This facilitates the development of autonomous IIoT systems that function with minimal resources, in real time, with high security and low latency.

The concept of Industrial IoT combines sensors, actuators, robotic systems, PLC controllers, and cloud/local servers into a unified cyber-physical architecture. Its main purpose is to acquire and analyze data in order to optimize production efficiency, safety, and quality [18].

The integration of TinyML transforms this architecture by changing the location of intelligence. Instead of sending data to a centralized cloud, inference is performed at the edge or even in the sensor itself—so-called on-sensor intelligence. This rearranges the hierarchy of systems.

This architecture provides several key advantages:

• Real-time response: decisions are made locally, without delays from communication channels.
• Reduced network load: data exchange is minimal, which is critical for large sensor networks;
• Increased security: sensitive data does not leave the local environment;
• Energy efficiency and sustainability: local processing reduces the need for energy-intensive cloud operations.

Industry 5.0 views the synergy between humans and machines as cooperative intelligence. Here, TinyML enables the implementation of context-aware systems that respond to human actions and working environment conditions [19]. A notable example of this is in the domain of collaborative robotics, where TinyML is employed to process visual and tactile signals in real time, thereby enabling robots to recognize human gestures without requiring access to a cloud system [20,21,22]. Within the domain of adaptive manufacturing lines, TinyML-based controllers are utilized for the analysis of vibrations and temperatures, with the objective of accident prevention [23,24,25,26]. In the domain of safety management, TinyML facilitates the deployment of energy-autonomous wearable sensors that monitor operators’ biometric indicators [27,28,29].

These mechanisms implement the concept of human-centric automation, in which machines adapt their behavior to human needs and context.

In summary, the relationship between TinyML and Industry 5.0 can be represented by a model that includes four layers:

Layer 1–Physical world (sensors, machines, operators)

Layer 2–Edge intelligence (TinyML inference, local solutions)

Layer 3–Information connectivity (IIoT communication)

Layer 4–Human-centered integration (HMI, cognitive systems, sustainability)

This model shows that TinyML is not just a component of the technological infrastructure, but a fundamental mediator between the physical and cognitive levels of Industry 5.0.

The conceptual framework presented herein summarizes the key technological and philosophical dependencies between TinyML, IIoT and Industry 5.0. The term defines the context in which intelligence is decentralized and transferred to the edge of industrial systems, where the interaction between humans, machines, and data takes place. The methodological model of the study is based on this theoretical foundation and aims to provide a quantitative and thematic mapping of the scientific field through bibliometric and network analysis.

2.2. Data Collection and PRISMA Strategy

In order to achieve a high degree of transparency, reproducibility, and scientific reliability, this review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. The methodology is applied to systematically identify, select, and synthesize peer-reviewed publications that analyze the role of TinyML in industrial IoT systems in the context of the transition from Industry 4.0 to Industry 5.0 (see

Table 1. Publication selection criteria.

Category	Criterion	Explanation
Type of publications	Included: peer-reviewed journal articles and conference papers	Ensures scientific reliability
	Excluded: books, book chapters, incomplete reports, and conference reviews	Limits the scope to validated research
Time range	2020–2025	Covers the period of active TinyML development
Language	English	Ensures consistency of terminology
Application domain	Industrial IoT, smart factories, robotics, predictive maintenance	Focuses on real industrial scenarios
Technological focus	TinyML, Edge AI, Embedded ML, Industry 5.0	Excludes cloud-centric ML approaches
Methodological quality	Clear description of architecture, algorithm, and metrics	Minimizes uncertainty in the analysis

).

The procedure consists of four main stages:

• Identification of relevant publications using predefined keywords;
• Screening–removal of duplicates and irrelevant documents;
• Evaluation of full texts against inclusion and exclusion criteria;
• Final inclusion and synthesis of data for analysis.

The screening of titles, abstracts, and full texts was performed independently by two authors, with all disagreements discussed until consensus was reached. The literature search was finalized on 31 October 2025 on the three most authoritative databases in this field: Scopus, Web of Science, and IEEE Xplore. Therefore, publications indexed after this date were not included, and 2025 trends should be interpreted as partial-year evidence constrained by database indexing cycles. Combinations of keywords and logical operators were used. The query in Scopus is in the following format, and equivalent queries including the same keywords were used for the other databases:

(“TinyML” OR “Tiny Machine Learning” OR “Tiny-ML” OR “Embedded Machine Learning” OR “Edge AI”) AND (“Industrial Internet of Things” OR “IIoT” OR “Industrial IoT”) AND (“Industry 4.0” OR “Industry 5.0” OR “Smart Factory” OR “Smart Manufacturing”) AND (PUBYEAR > 2019) AND (LANGUAGE(English))

In the initial stage of the research, 1770 publications were identified. Following a thorough screening and evaluation process against the established criteria, a total of 110 full-text studies were ultimately selected for inclusion in the analysis (see Figure 1 for a visual representation of this process).

A detailed list of excluded full-text publications and the reasons for their exclusion is not provided, as the reasons are summarized in the PRISMA diagram.

The following parameters are extracted for each publication included in the study: source (journal/conference) and institutional affiliation; application type (e.g., monitoring, control, robotics, energy management); hardware type (e.g., MCU, FPGA, ASIC, SoC); algorithm or machine learning model (e.g., decision tree, CNN, RNN, TinyTransformer); performance metrics (e.g., accuracy, latency, energy consumption, memory); reported results and limitations. The process of data extraction was carried out independently by two authors based on the predefined set of analytical parameters described above, with discrepancies resolved by reviewing the original sources.

The data has been methodically organized and synthesized through narrative analysis, with supplementary tabular and graphical representations included in the following sections. Given the bibliometric and thematic nature of this systematic review, a formal assessment of risk of bias in individual studies was not performed.

2.3. Bibliometric Methodology

The present bibliometric analysis aims to identify trends, structural dependencies, and thematic evolution in scientific research related to the application of Tiny Machine Learning (in industrial IoT). The analysis encompasses 110 peer-reviewed publications that have successfully passed the PRISMA protocol.

The processing and visualization of bibliometric indicators were performed using Biblioshiny (R package Bibliometrix v4.1.2) and VOSviewer v1.6.20, applying full counting and fractional counting approaches depending on the specifics of the analysis.

Thematic and conceptual relationships were assessed using Multiple Correspondence Analysis (MCA), implemented in Biblioshiny, which operates on a reduced term–document matrix generated through internal frequency-based preprocessing. Conversely, network structures were examined through the use of co-occurrence, co-citation, and co-authorship visualizations, employing VOSviewer in accordance with the algorithm proposed by Van Eck and Waltman [30].

For the network-based analyses, an iterative minimum occurrence threshold strategy was adopted. Thresholds of 5, 4, and 3 were used to progressively explore core, secondary, and emerging thematic structures, respectively. The threshold of three occurrences is the minimum required for inclusion, thereby ensuring consistency across network visualizations. This combined procedure enables a systematic examination of thematic dynamics, co-authorship patterns, and interdisciplinary interactions in TinyML research within the context of Industry 5.0.

3. Results

3.1. Bibliometric and Thematic Analysis of TinyML in IIoT

3.1.1. Scientific Output and Impact

The bibliometric analysis of the scientific output in the field of Tiny Machine Learning (TinyML) and its application within the Industrial Internet of Things (IIoT) covers the period 2020–2025 and is based on a final corpus of 110 publications selected through a PRISMA-based systematic review process. This subsection aims to identify quantitative trends, assess the impact of publications, and analyze the dynamics of scientific development, as reflected in key indicators of productivity and citation impact.

During the analyzed period (2020–2025), the scientific production related to the application of TinyML in industrial IoT systems demonstrates a clear trend of accelerated growth (Figure 2). Although the first publications appeared only in 2020, the subsequent years show an exponential increase in research activity compared to the initial stage.

The intensification of publication activity after 2022 coincides with the emergence of the first specialized frameworks and hardware platforms for TinyML, as well as with the growth of funded projects in the fields of edge AI and federated learning. This indicates that the scientific community already perceives TinyML not as a niche technology but as a strategic direction in the evolution of the Industrial Internet of Things. The trend after 2024 suggests the onset of a stabilization and thematic consolidation phase, typical for mature and rapidly developing research domains.

The analysis of the average number of citations per publication (Figure 3) reveals clearly distinguishable phases in the development of the TinyML–IIoT research domain. The highest citation impact is observed for the early publications (2020–2021), which established the methodological and conceptual foundations of the field and served as reference points for subsequent studies. These pioneering works—often published in high-impact journals such as Sensors and the IEEE Internet of Things Journal—achieved an average of over 30 citations per year, reflecting their rapid assimilation and influence within the scientific community.

After 2022, a gradual decline in average citation count is observed, which does not indicate a decrease in the relevance of the topic but rather reflects the accelerated influx of new publications within a limited time frame for citation accumulation. The increase in 2023 corresponds to the emergence of the first systematic reviews and empirical studies linking TinyML with the Industry 5.0 paradigm. The subsequent decline after 2024 is characteristic of maturing research areas, where the volume of publications grows faster than their citation cycle. Nevertheless, an average of 15.8 citations per document over the analyzed period indicates sustained scientific interest and an established significance of the field within industrial research on edge intelligence.

3.1.2. Sources and Publication Venues

The analysis of publication sources provides additional insight into the interdisciplinary profile and maturity of the research field. The distribution of articles across journals and conferences reveals which scientific platforms serve as the main channels for the development and dissemination of knowledge in the areas of TinyML and industrial IoT (Figure 4).

The studies are published across 54 different sources, demonstrating the interdisciplinary nature of the field. The distribution of publications by source shows a clear concentration of scientific output in several leading high-impact journals with a strong technological orientation. The dominant role of periodicals such as Internet of Things (Elsevier, Amsterdam, The Netherlands), IEEE Access, and Sensors reflects the highly interdisciplinary character of research combining engineering, computer science, and industrial systems. These journals serve as the primary channels for disseminating results in embedded intelligent systems, low-power computing, and the Industrial Internet of Things.

At the same time, the inclusion of journals such as Expert Systems with Applications and ACM Transactions on Embedded Computing Systems emphasizes the theoretical and algorithmic maturity of the field, focusing on the optimization of models, architectures, and edge AI applications. The presence of numerous IEEE conferences in the dataset indicates a high level of innovation activity and rapid knowledge transfer from academia to industrial practice.

3.1.3. Keyword Landscape

The analysis of keywords provides the most direct insight into the cognitive and thematic structure of the research field. It enables the identification of core concepts, dominant directions, and emerging themes that define the evolution of the scientific discourse in TinyML and industrial IoT (Figure 5).

The analysis of author keywords reveals a well-defined core of terms that structure the thematic framework of the field. The most frequent concepts—TinyML, machine learning, Internet of Things, and edge computing—form the conceptual center of research, where the technological principles of embedded machine learning intersect with the industrial applications of IoT. This combination indicates that the primary scientific focus is on implementing artificial intelligence directly within resource-constrained edge devices.

Complementary keywords such as deep learning, embedded systems, anomaly detection, and energy efficiency highlight the application of TinyML in critical industrial scenarios—including predictive maintenance, energy optimization, and real-time process monitoring. The rising frequency of terms like federated learning and data privacy reflects the gradual shift in research toward more complex architectures emphasizing security and decentralized learning.

3.1.4. Thematic Evolution and Trend Topics

The analysis of thematic evolution enables the tracing of how research priorities and conceptual foci have shifted over time. The application of trend topic analysis within Biblioshiny reveals the dynamics of key terms between 2020 and 2025, allowing for a clear differentiation of the emergence, growth, and maturity phases within the TinyML–IIoT domain.

In the initial period (2020–2021), publications were primarily focused on foundational concepts such as machine learning and Internet of Things, reflecting efforts to define the interconnection between artificial intelligence and industrial networks. After 2022, there is a marked rise in the use of the term TinyML, which gradually replaces the broader notions of machine learning and becomes the leading research direction.

In parallel, the frequency of terms such as edge computing, deep learning, and anomaly detection increases, indicating growing interest in implementing intelligent algorithms within edge computing environments and in real-time, latency-sensitive tasks. After 2023, research activity expands toward the integration of TinyML with learning systems and energy-efficient architectures, reflecting a transition from conceptual models to real-world engineering applications. The cumulative rise in all key terms during 2024–2025 signals that the field has entered a maturity phase, characterized by the interdisciplinary convergence of artificial intelligence, embedded systems, and industrial IoT solutions.

The tracking of thematic trends (Figure 6) reveals clearly distinguishable stages in the evolution of research related to the integration of TinyML into industrial IoT systems. In the early phase (2021–2022), topics related to hardware efficiency, such as low power electronics and energy efficiency, dominate, emphasizing energy optimization in peripheral devices. This corresponds to the initial efforts to adapt machine learning to resource-constrained environments.

From 2023 onward, there is a transition toward more complex concepts such as learning algorithms, TinyML, machine learning, and Internet of Things, marking the consolidation of the technological foundation of the field and its integration into industrial applications. These topics form the core of research activity, reflecting the establishment of TinyML as a key component of edge intelligence.

After 2024, the focus gradually shifts toward emerging directions—federated learning, machine learning models, and intrusion detection systems. These developments signify the entry of the field into a new stage oriented toward decentralized learning, enhanced security, and resilience of industrial networks. This thematic evolution illustrates the transition from engineering-oriented to cognitive and security-focused aspects of intelligent systems—a defining feature of next-generation industrial intelligence.

3.1.5. Conceptual Structure (MCA)

While the keyword analysis outlines the surface-level thematic structure of the research field, the conceptual analysis using Multiple Correspondence Analysis (MCA) enables the visualization of its internal logic and the interrelationships between individual research directions (Figure 7). This procedure reveals how terms and topics cluster into higher-order conceptual domains.

The observed thematic trends trace the evolution of research focus from hardware efficiency toward decentralized intelligence and security, while the conceptual analysis complements this picture by uncovering the underlying structure and connections among the main thematic directions within TinyML and industrial IoT research.

The MCA-based conceptual analysis of author keywords reveals an integrated and relatively consolidated thematic structure of TinyML research in the industrial context. The results outline three interrelated domains that form the logical framework of the field.

The first domain is technological–architectural, dominated by terms such as embedded systems, microcontrollers, deep learning, and convolutional neural networks. It reflects the engineering aspect of TinyML, focused on hardware optimization, model compression, and embedded computing environments.

The second domain can be defined as edge intelligence and applied analytics. It encompasses concepts such as edge computing, learning algorithms, anomaly detection, predictive maintenance, and energy efficiency. This cluster concentrates research efforts aimed at enabling local data processing in industrial environments, minimizing latency, and improving energy sustainability.

The third domain is human-centric and security-oriented, represented by terms such as human, decision making, intrusion detection, and data privacy. This cluster reflects the emerging research line linking TinyML with the cognitive and ethical dimensions of next-generation industrial intelligence—including autonomy, explainability, and trust in artificial intelligence.

The overall topology of the conceptual space indicates that TinyML functions as a connecting element between the technological infrastructure of IIoT and higher-level cognitive systems in next-generation industrial environments. The ensuing subsection is devoted to an in-depth investigation of the aforementioned conceptual relationships through the lens of co-occurrence network analysis.

3.1.6. Co-Occurrence Networks (VOSviewer)

In addition to the conceptual analysis, a co-occurrence network analysis was performed using VOSviewer, providing a quantitative visualization of thematic interdependencies and the evolution of research priorities. Two levels of network structure were examined—author keywords and all keywords (both author and indexed).

The analysis of author keywords, conducted at thresholds of 5, 4, and 3 (Figure 8), reveals the evolution of the thematic structure. Across all thresholds, TinyML remains the central unifying term, connecting machine learning, edge computing, and IoT. Lower thresholds introduce additional directions such as energy efficiency, predictive maintenance, and data privacy, indicating the growing incorporation of sustainability and ethics into the research landscape. The comparison across the three thresholds confirms the co-occurrence and structural integration of hardware efficiency, decentralized learning, and sustainability-related topics within the network.

In contrast, the analysis of all keywords (both author and indexed) at a threshold of 5 reveals five thematic clusters (Figure 9):

• Engineering and Algorithmic Architectures—encompassing machine learning, deep learning, and embedded systems, representing the technological and computational foundations of TinyML.
• Edge Intelligence and Decentralized Learning—including TinyML, federated learning, and learning systems, reflecting the evolution toward distributed and autonomous computation at the network periphery.
• Energy Efficiency and Real-Time Computing—characterized by low power electronics and real-time systems, emphasizing optimization of energy consumption and latency in industrial environments.
• Cybersecurity and Data Protection—involving network security, intrusion detection systems, and data privacy, highlighting the growing importance of trust, resilience, and secure communication in intelligent industrial networks.
• Human-Centricity and Sustainability—comprising decision making, human, and edge intelligence, signifying the alignment of TinyML research with the ethical, cognitive, and sustainable principles of Industry 5.0.

The network confirms that TinyML functions as a connecting axis between the technological and cognitive domains of industrial intelligence, forming a conceptual bridge between established and emerging paradigms of industrial intelligence.

3.2. Thematic Synthesis

After integrating the quantitative indicators and the co-occurrence maps, the thematic synthesis delineates stable research clusters that structure the development of TinyML: Edge AI and resource efficiency, Federated and privacy-preserving learning, Human-centric and Explainable systems, as well as Sustainability and energy awareness. In parallel, emerging axes are forming—Blockchain-integrated TinyML, Digital Twins, Neuromorphic/Event-Driven, and Cooperative Edge—marking the transition from an engineering to a cognitive and ethical phase. In the following subsections, we condense the empirical evidence and the shared mechanisms that connect these themes and prepare the systemic interpretation in Industrial Synthesis and the conceptual framework of the future.

3.2.1. Edge AI and Resource Efficiency

Edge AI represents the fundamental technological axis upon which the TinyML paradigm is built. It unites local data processing, compressed models, and energy effi-ciency within microcontrollers, enabling real-time analytics without dependence on cloud infrastructures [4,10]. In recent years, numerous studies have demonstrated progress in model optimization, quantization, compression, and hardware-aware architectures capable of executing artificial intelligence under severe resource constraints [31,32,33].

In the context of predictive maintenance, TinyML has proven that complex models can be executed directly on MCUs. Study [34] presents a CNN model with 8-bit quantization on an STM32 MCU, achieving high accuracy and minimal latency; similarly, ref. [35] proposes an autoencoder–LSTM architecture for estimating the remaining useful life of Li-Polymer batteries. Practical implementations using Edge Impulse and STM32Cube. AI confirm the feasibility of inference below 10 ms and energy consumption under 150 µJ in visual quality control tasks [36,37].

The engineering focus on energy profiling has led to the development of FPGA accelerators [38] and energy-efficient hardware co-design [39], as well as the introduction of DVFS (Dynamic Voltage and Frequency Scaling) and event-triggered sensing, which reduce consumption by up to 35% [33,40]. The integration between TinyML and LPWAN protocols provides energy autonomy and extended battery life for wear-able and remote edge devices [41]. Adaptive networking mechanisms such as 6TiSCH demonstrate low latency and reliable synchronization for industrial applications [42].

From an engineering perspective, Edge AI represents a balance between hardware efficiency and cognitive autonomy [43]; the implementation of such solutions in pro-duction lines enhances operational efficiency and reduces process analysis latency [44]. Consequently, two interconnected trajectories emerge: an engineering one, focused on hardware, energy, and latency optimization, and a cognitive one, oriented toward autonomous and adaptive decision-making at the edge [4,10,45]. Their intersection defines the new Edge AI paradigm, where intelligence is no longer a centralized service but an embedded feature of devices, laying the groundwork for the next thematic cluster—Federated and Privacy-Preserving Learning [43].

3.2.2. Federated and Privacy-Preserving Learning

Federated learning in the context of TinyML represents a natural extension of the Edge AI paradigm, enabling collective intelligent training without centralized exchange of raw data. The core principle is that each edge node trains a local model, after which only the parameters, not the data themselves, are aggregated into a global model through cryptographically secured communication [46,47,48]. This approach enhances both security and energy efficiency, particularly in industrial IoT systems where latency and privacy are critical [49,50,51].

Recent research focuses on lightweight federated protocols compatible with the resource constraints of microcontrollers and sensor nodes. In [52], an adaptive gradient synchronization and compression scheme is proposed, reducing energy consumption by more than 30% without compromising the global model’s accuracy. A similar approach is implemented in [53], where event-driven federated training enables dynamic participant selection based on available energy, achieving 1.7× faster convergence. Study [54] analyzes decentralized edge federation architectures built on intelligent communication standards that eliminate the need for a central coordinator and reduce network traffic by up to 60%.

The integration of TinyML and blockchain technologies opens a new pathway for trusted federated learning. In [49], a sidechain-based TinyML-FL architecture for soil quality monitoring is demonstrated, ensuring traceability and immutability of parameters. Similar secure aggregation mechanisms using hashed records and elliptic-curve cryptography are discussed in [52,55], where the decentralized structure ensures trust among industrial nodes.

Parallel developments are observed in Explainable and Personalized Federated Learning (XFL & PFL). In [56], a lightweight personalized model is presented, adapting local weights according to the individual characteristics of each sensor, thus improving training stability under heterogeneous data conditions. Study [57] introduces the concept of self-healing FL, in which the network autonomously detects and compensates for nodes exhibiting degraded behavior using local TinyML meta-models for self-assessment.

From the perspective of energy efficiency and sustainability, refs. [8,48] demonstrate that the combined use of federated learning and energy-adaptive inference can reduce total consumption by up to 40% while maintaining accuracy above 95%. According to [58], the integration of virtualized agents and distributed edge services enables scalable and sustainable learning, where intelligence evolves collectively and continuously.

In synthesis, federated TinyML learning defines a new cognitive paradigm in which trust, security, and energy optimization converge into a unified process. This architecture transforms the traditional model of device–cloud interaction, turning each edge node into a self-learning agent that simultaneously generates, filters, and protects knowledge [50,53,54]. Thus, it lays the foundation for the next thematic cluster—Human-Centric and Explainable Systems, where local intelligence is combined with interpretability and ethical alignment.

Importantly, the thematic clusters identified in this review are not isolated; rather, they co-evolve through shared mechanisms that connect resource constraints, trust requirements, and operational feasibility. Federated learning increasingly serves as an enabling layer for explainable TinyML, where interpretability must be delivered under privacy constraints and heterogeneous device conditions. Similarly, edge intelligence and energy management form a coupled design space: latency budgets, duty-cycling strategies, and event-driven inference policies jointly determine both energy sustainability and decision timeliness. Making these intersections explicit is essential for avoiding conceptual fragmentation and for deriving implementation guidance that reflects realistic industrial tradeoffs.

3.2.3. Human-Centric and Explainable Systems

The transition from Industry 4.0 to Industry 5.0 marks the evolution of intelligent systems from automated solutions toward human-centered and explainable intelligence, where technology does not replace humans but rather extends their cognitive and sensory capabilities. TinyML plays a central role in this transformation by enabling embedded, locally interpretable artificial intelligence in devices, wearables, and collaborative robots [32,59,60,61].

Recent studies show that TinyML and Explainable AI (XAI) are converging into a new research direction—Explainable TinyML (X-TinyML)—focused on transparency and trust in decisions made at the network edge. In [38], an attention-based model is presented that visualizes the importance of input signals during industrial system diagnostics, while [62] applies local interpretability via LIME to explain TinyML classifier outputs in energy-related applications. A similar methodology is integrated in [63], where federated explainable learning ensures interpretability without compromising data privacy.

The human-centered focus becomes particularly evident in industrial and ergonomic applications. Studies [59,61] demonstrate wearable devices that monitor physiological signals and operator workload using TinyML models consuming less than 2 mW. These solutions enable real-time adaptation of the work environment based on human conditions, contributing to reduced incidents and lower musculoskeletal strain [64]. In [65], a vision-based system employing TinyML detects improper movements, enhancing error prevention and improving safety in manufacturing environments.

From the perspective of human–machine interaction, refs. [66,67] explore emotion-aware and context-adaptive models that allow systems to interpret the operator’s mood and cognitive state. Study [57] investigates the self-aware cobotics approach, where TinyML agents embedded in collaborative robots adapt their behavior according to human feedback. Similar principles are found in [58], where virtualized edge agents enable cognitive cooperation between humans and autonomous machines.

Explainability and ethical alignment are increasingly recognized as structural attributes of industrial intelligence. Article [54] emphasizes that standards for intelligent communication systems should include transparency and trust as certification criteria for IoT solutions. Similarly, ref. [68] highlights the role of 5G infrastructure in supporting interpretable, low-latency interaction between operators and intelligent devices.

In summary, human-centered and explainable systems define a new layer of the cognitive industry, where local autonomy is integrated with transparency, ethics, and trust. TinyML is no longer merely an optimization tool, but an ethical-technological mediator between the human operator and the autonomous industrial ecosystem [32,57,60,67,69]. This synthesis paves the logical transition to the next cluster—Sustainability and Energy Awareness, where intelligence becomes linked to the energy and environmental responsibility of systems.

3.2.4. Sustainability and Energy Awareness

Sustainability in the context of TinyML is defined as a synergy between energy efficiency, environmental footprint, and intelligent resource management, where computation itself becomes a conscious process. In the Industry 5.0 era, TinyML transforms the paradigm of “smaller models–lower consumption” into a systemic approach, in which optimization spans across hardware, software, communication networks, and the learning process itself [33,48,53].

Numerous studies demonstrate that on-device inference on microcontrollers operating below 10 mW can achieve over 95% accuracy with latency under 10 ms, reducing emissions associated with cloud-based data transfer and processing [51,70]. In [70], an evolutionary algorithm for multivariate time-series compression is proposed, achieving over 40% reduction in communication volume without loss of relevant information—a direct contribution to the “green autonomy” of industrial IoT systems.

A fundamental role is played by energy-aware hardware architectures. Study [41] describes a hierarchical inference model for mobile equipment in heavy industry, achieving a 60% reduction in energy consumption through the combination of quantized models and Dynamic Voltage and Frequency Scaling (DVFS). Similar approaches are extended in [8,52], where event-driven TinyML mechanisms activate computations only upon detection of significant events, resulting in over 80% energy savings compared to periodic sampling methods.

Energy sustainability is directly related to network protocols. In [71], it is demonstrated that adaptive 6TiSCH scheduling enables low-latency communication with up to 35% lower energy consumption in industrial environments. Studies [33,70] emphasize the importance of compression and meta-learning for optimizing network traffic and dynamically balancing the workload between edge and cloud layers.

The field of energy-harvesting TinyML is also gaining momentum. Research in [51,53] shows that integrating photovoltaic micro-sources with energy-adaptive models can extend device autonomy to several months without recharging—a critical factor for environmental monitoring and intelligent agricultural systems. Concurrently, refs. [48,52] analyze the accuracy–power trade-off in Intrusion Detection System (IDS) models, demonstrating an energy efficiency improvement by a factor of five to six compared to traditional cloud-based solutions.

From the perspective of environmental sustainability, refs. [50,54] position TinyML within the framework of Green AI, highlighting that combining proprietary and open ecosystems accelerates the adoption of energy-responsible innovations in manufacturing and logistics. In the industrial sector, local TinyML-based consumption analysis has been reported to reduce idle time by 20–30%, resulting in significant CO₂ emission reductions [41,51].

In synthesis, sustainability and energy awareness are becoming central characteristics of intelligent systems in Industry 5.0. TinyML is no longer merely a tool for local computation but an integrated strategy for green cognitive autonomy, where every device continuously balances accuracy, resource usage, and environmental impact [8,33,51,53,72]. This establishes the technological foundation for the next thematic axis—Emerging Directions, where the focus shifts toward the synergy between sustainability, trust, and adaptive cognition.

3.2.5. Emerging Directions

The emerging era of Industry 5.0 shifts the focus from engineering optimization to cognitive autonomy, trust, and ethical alignment. In this context, the research dynamics of TinyML unfold across several emerging directions that combine security, explainability, and symbiotic human–machine interaction. These themes not only build upon mature research clusters but also define the architecture of future intelligent ecosystems, where knowledge is distributed and self-improving at the local level [50,53,58].

The first direction, Blockchain-assisted TinyML, focuses on trusted decentralized learning. In [49], a sidechain-based architecture for agricultural monitoring is developed, where TinyML model parameters are recorded in an immutable ledger for verification and traceability. A similar concept is extended in [66,67] through the use of lightweight cryptographic schemes and zero-knowledge proofs, providing authentication without compromising inference speed. These studies demonstrate that blockchain can serve not only as a security infrastructure but also as a trust mechanism among intelligent edge nodes.

The second line of research, Explainable TinyML (X-TinyML), integrates transparency and interpretability directly into the model. Study [38] introduces an attention-based visualization of feature importance in diagnostics, while [73] presents the concept of semantic distillation, where each TinyML agent generates metadata explaining its own decisions. Research such as [62,63] highlights that explainability increases operator trust and is a prerequisite for adopting autonomous decision-making in critical industrial environments.

The third direction, Digital Twin–TinyML Integration, envisions digital twins not as passive simulators but as active cognitive agents. Studies [60,65] demonstrate the deployment of lightweight models in digital twins for defect prediction and energy optimization. More recent work, such as [74], proposes self-synchronizing twins that combine sensor data and TinyML inference for autonomous real-time calibration—a key step toward the sustainable factories of the future.

The fourth direction, Neuromorphic and Event-Driven TinyML, aims to emulate biological efficiency in computation. Papers [63,69] show the use of spiking neural networks on event-based sensors, achieving up to 90% energy savings while maintaining accuracy. In [75], a memristor-based TinyML architecture is presented, supporting continuous learning with minimal power consumption, thus forming the foundation for ultra-low-power cognitive devices.

The final axis, Cooperative Edge Intelligence, explores systems where multiple TinyML nodes exchange intermediate representations rather than raw data. Studies [32,61] describe architectures for collaborative learning among robots and wearable devices, while [58,76] advance the concept of multi-agent edge learning, where each device acts simultaneously as a learner and a coordinator. This cooperation marks a crucial step toward self-regulating and self-explaining industrial ecosystems.

In synthesis, the emerging directions delineate a vision for TinyML 2.0—an ecosystem where intelligence is distributed, trusted, explainable, and energy-sustainable. The convergence of blockchain, explainability, digital twins, and neuromorphic computing forms the technological foundation for the cognitive evolution of Industry 5.0, where autonomy is measured not only by computational power but by the capacity for ethical and sustainable interaction among humans, machines, and data [49,53,58,67,74].

3.3. Industrial Synthesis

The industrial synthesis of TinyML demonstrates its evolution from a collection of application-specific prototypes to a unifying edge-native infrastructure aligned with the principles of Industry 5.0. Across manufacturing, energy, safety, and cybersecurity domains, the reviewed studies consistently emphasize three feasibility criteria: real-time inference under strict latency constraints, ultra-low energy consumption compatible with long-term or energy-harvested operation, and task accuracy sufficient for industrial reliability. To ground this synthesis in quantitative evidence and to enable cross-domain and cross-hardware comparison, representative industrial TinyML implementations extracted from the surveyed literature are summarized in

Table 2. Comparative industrial TinyML implementations across applications and hardware platforms.

Industrial Application	Hardware Platform	TinyML Model/Approach	Inference Latency	Energy Consumption	Reported Accuracy	Industrial Relevance	References
Predictive maintenance (vibration analysis)	STM32 (ARM Cortex-M)	Quantized CNN (8-bit)	<10 ms	<150 µJ per inference	>95%	Real-time fault detection directly at machine level, enabling early intervention and reduced downtime	[34,37,52,57]
Battery health monitoring	ESP32	Autoencoder–LSTM (compressed)	~10–15 ms	Sub-mW regime	≈94–96%	Enables on-device estimation of remaining useful life with minimal communication overhead	[35,41]
Visual quality inspection	ESP32 + camera module	Lightweight CNN (Edge Impulse)	<10 ms	<1 mW	>96%	Supports in-line inspection in production environments without cloud dependency	[36,37]
Wearable operator safety monitoring	nRF52	Shallow NN/feature-based classifier	<5 ms	<2 mW	≈93–97%	Continuous privacy-preserving monitoring of physiological and ergonomic indicators	[59,61,64]
Human and motion detection	ESP32	Quantized CNN/temporal NN	~5–10 ms	Low-mW range	≈95%	Improves ergonomic safety and reduces operator injury risk in collaborative environments	[50,65,66,67]
Edge intrusion detection (network telemetry)	ARM Cortex-M MCU‘s	Lightweight IDS classifier	<1 ms	≈0.01–0.1 mW	>94%	Ultra-low-latency anomaly detection directly at the edge, reducing exposure to network attacks	[46,47,55]
Federated industrial sensing (distributed nodes)	Heterogeneous MCUs (STM32, ESP32)	Federated TinyML with compressed updates	Dependent on synchronization cycle	30–40% lower than centralized ML	Comparable to centralized ML	Enables collaborative learning while preserving data privacy and reducing communication load	[53,54,55,68,72]
Energy-aware environmental monitoring	MCU + energy harvesting	Event-driven TinyML	Event-triggered (<ms)	Up to 80% energy savings	>90%	Extends autonomous operation in remote or resource-constrained industrial settings	[8,33,51,53]

.

As summarized in Table 2, industrial TinyML deployments achieve millisecond-level inference latency and sub-milliwatt energy consumption across heterogeneous hardware platforms, while maintaining accuracy comparable to centralized machine learning approaches. This evidence confirms that TinyML is not merely a conceptual enabler, but a practically viable foundation for distributed industrial intelligence. Importantly, the convergence of low-latency operation, energy-aware design, and privacy-preserving inference directly supports the human-centric and sustainability-oriented objectives of Industry 5.0.

3.4. Conceptual Framework of the Future

The conceptual framework of the future outlines the transformation of Industry 5.0 as an integration between cognitive technologies, human-centric intelligence, and energy sustainability. While Industry 4.0 focused on automation and connectivity, the new paradigm emphasizes awareness, adaptability, and trust. TinyML occupies a central place in this transformation, becoming the “neural tissue” of industrial intelligence [10,45,77]. By enabling training and inference on ultra-low-power peripherals, TinyML breaks the cloud dependency and provides cognitive autonomy–a key feature of industrial evolution after 2025 [8,51,78,79]. This autonomy enables not just automation, but self-adjusting and ethically regulated intelligence that functions in synergy with humans [50,67,80].

The basis of this conceptual framework is the three-layer cognitive architecture, built from a physical, information, and cognitive layer. The physical layer includes microcontrollers and sensors with embedded quantized models capable of performing local processing with a minimal energy footprint [33,49,53]. The information layer implements intermediate knowledge management through Edge computing and decentralized aggregation [77,78], while the cognitive layer connects the human, who is not an external observer, but an active element of the decision-making network. This three-layer dynamic realizes the idea of a “cognitive feedback loop”, in which data becomes context, and context into predictive intelligence [57,58] (see Figure 10). New research on federated and neurosymbolic TinyML systems shows that by combining local learning and symbolic interpretability, explainability, and verifiability of solutions are achieved, which is critical for high-risk industrial environments [43,81,82].

Building upon this multi-layer cognitive architecture, this study introduces the concept of the Sustainable Cognitive Industry (SCI)—a novel paradigm that extends the principles of Industry 5.0 toward a deeper integration between sustainability, cognition, and human-centered intelligence. The SCI framework envisions industrial ecosystems where energy awareness, ethical intelligence, and technological adaptability operate as mutually reinforcing dimensions of the same cognitive process. In this paradigm, TinyML functions as the technological substrate that unites these domains—embedding intelligence into the physical environment while maintaining transparency, interpretability, and self-regulation. The SCI model therefore provides a unifying ontology in which sustainability is not only an environmental or economic goal, but a property of cognition itself, transforming industrial systems into ethically aware, self-adaptive, and human-aligned networks.

To clarify the conceptual novelty of the proposed Sustainable Cognitive Industry (SCI) and to position it relative to established industrial paradigms,

Table 3. Paradigmatic comparison between Industry 4.0, Industry 5.0, and the proposed Sustainable Cognitive Industry.

Dimension	Industry 4.0	Industry 5.0	Sustainable Cognitive Industry
Primary focus	Automation, efficiency, connectivity	Human–machine collaboration, resilience	Cognitive autonomy, sustainability, ethical intelligence
Location of intelligence	Centralized cloud and CPS	Hybrid cloud–edge	Edge-native, distributed cognition (TinyML)
Role of AI	Optimization and automation	Decision support and collaboration	Embedded cognitive agent with local autonomy
Energy perspective	Secondary, system-level	Integrated at process level	Intrinsic to cognition (energy-aware intelligence)
Sustainability	Operational efficiency	Environmental and social alignment	Sustainability as a property of intelligence itself
Human role	Operator/supervisor	Partner and collaborator	Co-agent in a cognitive feedback loop
Trust & transparency	Implicit, system-centric	Emerging concern	Explicit, explainable, and verifiable by design
Adaptability	Predefined automation rules	Adaptive collaboration	Self-adaptive, learning-oriented edge systems
Typical technologies	CPS, PLCs, Cloud AI	Cobots, Edge AI, IoT	TinyML, Federated Learning, XAI, Energy-aware Edge

compares Industry 4.0, Industry 5.0, and SCI across key structural dimensions.

The comparison highlights fundamental shifts introduced by SCI. Intelligence is relocated from centralized or hybrid architectures to edge-native cognitive agents, where TinyML enables local inference and learning under strict energy constraints. Sustainability is no longer treated as an external optimization objective, but as an intrinsic attribute of cognition itself—systems are designed to reason within their own energetic and operational limits.

TinyML-based systems can reduce energy consumption by 70–90% while maintaining accuracy through event-based inference and compressed models [8,33,53]. Combined with energy harvesting technologies, this creates the prerequisites for sustainable and self-sustaining industrial networks in which energy and intelligence are recycled [49,83]. In this context, energy balance is not just an engineering metric, but an ethical category–intelligence that knows its own limits. At the same time, multi-model fallback and adaptive redundancy systems provide cognitive resilience by allowing networks to reassign tasks and resources in the event of failure or overload [32,84]. This turns industrial processes into “living systems” that do not just function, but evolve according to their environment and goals [57].

At the level of communication and security, the conceptual framework envisages federated ecosystems where knowledge exchange takes place without transferring raw data. These architectures combine federated learning, differential privacy, and blockchain verification, guaranteeing both security and traceability [46,47,55,83]. TinyML in this context is not just a computational mechanism, but a trusted intermediary–an intelligent agent that stores local knowledge and participates in collective learning [56,70,71]. This gives rise to a “cooperative cognitive infrastructure”, where security is based not on centralized monitoring, but on mutual trust between nodes [67,78]. In the latest developments, TinyML is even combined with quantum-inspired optimization and semantic management systems, which opens up the prospect of self-regulating industrial networks with high explainability and predictability [10,43,82].

The philosophical meaning of the conceptual framework is expressed in the transition from a data-driven industry to a consciousness-driven industry. Intelligence is no longer perceived as a tool for efficiency, but as a mediator between energy, knowledge and ethics. Industry 5.0 brings humans back to the center of the technological ecosystem–not as operators, but as partners interacting with autonomous systems that are responsible and transparent [50,67,80]. This paradigm transforms industrial processes into adaptive, self-regulating and socially conscious systems. In it, TinyML plays the role of a cognitive mediator between the physical and the digital, between machine and human, between energy and knowledge [58,68,78]. As a result, the conceptual framework of the future does not simply describe technological developments, but proposes a new ontology of industrial intelligence–one that is self-learning, self-limiting, and collaborative. It integrates technological efficiency, ethical awareness, and adaptive intelligence as coexisting elements of the future industrial ecosystem.

4. Discussion

4.1. Synthesis of Main Results

This systematic review, conducted using the PRISMA methodology, identifies and analyzes 110 key publications (2020–2025) addressing the integration of TinyML into industrial IoT systems in the context of the transition from Industry 4.0 to Industry 5.0. The results show that TinyML is not just a tool for miniaturization of machine learning, but a transformational technology that changes the way intelligence is distributed in the industrial ecosystem.

The certainty of the evidence was assessed qualitatively through narrative synthesis, without applying a formalized framework such as GRADE, which is consistent with the review and non-quantitative nature of the study.

The main conclusions can be summarized in the following areas:

• Technological contribution. TinyML allows for the execution of machine learning models with high accuracy and minimal latency directly on microcontrollers and embedded devices. This provides real-time autonomy at the edge and lays the foundation for intelligent, energy-efficient IoT systems, confirming the observations of [53,85] that TinyML acts as a key enabling technology for achieving ultra-low-power, locally adaptive computing architectures. A similar trend is also highlighted in the analysis of [86], who summarize that the integration of Edge AI and TinyML in industrial environments leads to a fundamental change in the way intelligence is distributed between the cloud and edge nodes.
• Architectural transformation. The decentralized architecture built on TinyML reduces dependence on cloud infrastructures, increases data security and optimizes energy management, which is in line with the analysis of [87], emphasizing the role of TinyML as a key catalyst for energy-efficient edge-based solutions. This achieves a more sustainable and reliable industrial ecosystem.
• Industrial applications. The most common practical implementations of TinyML are related to predictive maintenance, visual inspection of production, energy optimization and monitoring of operator safety. These applications prove the ability of TinyML to provide real-time intelligence even in resource-constrained environments.
• Socio-ethical aspect. TinyML supports the implementation of the human-centric model of Industry 5.0 by facilitating collaborative interaction between people and machines. This enhances safety, transparency, and trust in autonomous systems.
• Scientific Perspective. The development of TinyML 2.0, federated and quantum architectures outlines a new generation of intelligent systems that combine local adaptability, decentralized learning, and ethical autonomy. These trends herald the era of cognitively integrated and sustainable industry.

4.2. Strategic Guidelines for Industrial Implementation

Based on the conducted analyses, several strategic guidelines can be formulated for industrial enterprises planning to integrate TinyML into their production systems.

First, the integration should be grounded in the principle of decentralized intelligence, in which management and data processing are distributed across multiple edge nodes. This approach minimizes latency, reduces the risk of connectivity loss, and enhances system resilience. As noted in [85], effective TinyML deployment requires a careful balance between architectural decentralization and hardware constraints—a challenge that remains central to industrial applications. Additionally, Adlakha and Kabbar [88] emphasize that these technical limitations are often compounded by organizational factors such as the lack of standardization, insufficient collaboration between academia and industry, and a shortage of expertise in resource-constrained machine learning.

Second, a key factor for successful implementation is the establishment of clear standards and interoperability frameworks among heterogeneous hardware and software platforms. The development of common reference architectures and open communication protocols is essential to ensure the reliability, security, and scalability of TinyML-based industrial solutions. Active collaboration between academia, industry, and standardization bodies will accelerate the creation of unified methodologies for benchmarking, certification, and long-term maintenance of TinyML deployments.

Third, training and reskilling of personnel represent a critical element of the process. Within the human-centric paradigm of Industry 5.0, engineers and operators need to acquire competencies for working with tools such as TensorFlow Lite Micro and Edge Impulse, as well as the ability to interpret and manage local ML models.

Another strategic direction involves the integration between TinyML and digital twins, providing predictive resilience through the synergy of edge and cloud intelligence. This creates a closed loop of monitoring, forecasting, and real-time optimization.

Furthermore, ethical and trustworthy deployment must be prioritized. Industrial systems involving wearable or collaborative devices should adhere to the principles of Explainable AI, data privacy protection, and algorithmic transparency to ensure trust and continuity.

In the long term, open innovation should be promoted through the establishment of joint laboratories between universities and industrial organizations. Such experimental centers for TinyML and robotic systems will accelerate the development of adaptive, resilient, and energy-efficient solutions, laying the foundation for the next generation of cognitive industrial technologies.

4.3. Research Priorities (2025–2030)

Based on the identified trends, the main scientific priorities can be classified according to

Table 4. Main scientific priorities, based on the identified trends.

Scientific Direction	Short Description	Expected Effect
Adaptive TinyML	Local self-learning and autonomous model reconfiguration	Increased autonomy and resilience
Federated TinyML	Collaborative learning among devices without data sharing	Privacy and scalability
Explainable TinyML	Development of models with transparent logic	Ethical and trustworthy deployment
Quantum TinyML	Utilization of quantum circuits for inference	Drastically reduced latency
TinyML Digital Twins	Integration with digital twins of processes	Real-time prediction and adaptation

.

4.4. Political and Economic Aspects

The European Industry 5.0 Framework [2], published by the European Commission in 2021, outlines a strategic transition from an efficiency paradigm to one oriented towards sustainability, human-centricity and social responsibility. In this context, technologies are seen not only as a means of automation, but also as a tool for strengthening human capital and creating a host industrial ecosystem.

TinyML fits perfectly into this philosophy, as it is a secured, decentralized and energy-efficient intelligence applicable both in large industrial corporations and in small and medium-sized enterprises (SMEs). It is this decentralized nature of the technology that is in line with the European priorities for the democratization of artificial intelligence, digital independence and strategic autonomy [89,90].

From an economic perspective, TinyML offers significant potential for optimizing production costs, reducing energy consumption and increasing operational sustainability. Its integration into industrial processes supports the transition to a green economy by minimizing the carbon footprint and by enabling the implementation of predictive maintenance systems with minimal infrastructure. This is particularly important in sectors with limited investment opportunities, where classic AI solutions are economically unaffordable.

In a regional context, TinyML provides new opportunities for accelerated digital transformation in countries with a predominant share of small and medium-sized manufacturing enterprises, such as Bulgaria. Through its low barrier to entry and its flexibility, the technology can become a catalyst for smart industrial clusters, where local companies develop their own end-to-end solutions without depending on global cloud service providers.

More broadly, TinyML has the potential to be included in the key elements of the European industrial policy for sustainability and digital security, contributing to a balanced development between technological innovation, social impact and environmental responsibility.

4.5. Limitations and Future Considerations

Despite the broad scope and systematic nature of the study, there are some limitations that should be taken into account when interpreting the results. A notable limitation is the absence of an assessment of bias related to unpublished or selectively reported results (reporting bias), which may affect the comprehensiveness of the evidence base considered. Another limitation is that the bibliometric corpus is based on publications indexed in major bibliographic databases (Scopus, Web of Science, and IEEE Xplore), which may lead to the partial exclusion of relevant works published in other sources or to incomplete coverage for 2025. This may limit the completeness of the presented picture, although without significantly changing the main trends.

Furthermore, although the Industrial Synthesis section includes a representative qualitative analysis of key industrial applications, its scope does not cover all potential sectors and real pilot implementations, which leaves room for future expansion through more in-depth empirical research.

Last but not least, the high dynamics of the TinyML field and the continuous development of hardware and software solutions suggest that some of the conclusions drawn may undergo evolution in the near future.

Despite these limitations, this review provides a reliable, structured, and up-to-date foundation for understanding the thematic, industrial, and conceptual evolution of TinyML, outlining specific directions for future research and practical implementations in the context of the evolving and still-forming field of Industry 5.0.

5. Conclusions

The transition from Industry 4.0 to Industry 5.0 is not just a technological evolution, but a conscious transformation in the way industrial systems integrate intelligence, human participation, and sustainability. TinyML plays a central role in this transformation, shifting intelligence from the cloud to the edge, where real interaction between humans and machines occurs.

Based on the analyses performed, this review builds a generalized conceptual framework that connects the technical, industrial, and cognitive dimensions of the new industrial era. This framework proposes a vision for a Sustainable Cognitive Industry (SCI)–an integrative model in which energy awareness, cognitive intelligence, and human-centric collaboration develop as interconnected levels of a single adaptive ecosystem. In this context, TinyML serves as an intellectual infrastructure that connects efficiency, ethics, and sustainability, transforming automation into self-regulating, transparently managed, and socially responsible intelligence.

The convergence of TinyML with digital twins, artificial intelligence, and collaborative robotics is shaping a new type of industrial environment—adaptive, self-learning, and ethically governed systems that not only produce but also perceive, reason, and collaborate. This evolution defines the foundations of the cognitive industry of the future, where sustainability, human participation, and artificial intelligence are intertwined into a single, self-sustaining structure. By combining bibliometric, thematic, and industry analysis, the study demonstrates that future industrial intelligence is not about technological efficiency, but about a new type of conscious cognition. In this context, knowledge, energy, and human values form a common ethical core. Thus, the concept of a sustainable cognitive industry is established not as an abstraction, but as an analytical framework for understanding and modeling the next generation of industrial systems.