## 6 Discussion  
Based on an analysis of research gaps in industrial maintenance, as well as knowledge of market trends regarding emerging technologies such as the Internet of Things, wireless sensor networks, AI Wearables, and advanced data analysis techniques such as big data and AI algorithms, the importance of implementing the new Industry 5.0 proposals have been found. The objectives of Industry 5.0 refer in the first place to the leading role of the human being to move from a productivity model to one of personalization, betting on the worker as an investment and not as an expense; secondly, it seeks to mitigate uncertainty by increasing resilience; and thirdly, it seeks to have an impact on society. According to the needs of the industry explored, the research question was formulated: could a human-in-the-loop based maintenance framework improve physical assets' resilience? A review of the literature was then conducted, which helped to group the keywords into three clusters: (a) Industry 4.0: predictive maintenance systems, (b) Industry 5.0: human-in-the-loop and (c) Industry 5.0: resilience and metrics. With the collected literature and complemented with related literature and the experience of the authors, a proposal was made for an intelligent maintenance reference framework inspired by the objectives of Industry 5.0, and it was called Maintenance 5.0. Maintenance 5.0 seeks to answer the research question through the following hypothesis: if physical assets maintenance had the human-in-the-loop, would be possible to improve the resilience of a manufacturing system. An important contribution is the determination of a control cycle for maintenance 5.0, within which Worker 5.0 plays a significant role, who is the person who participates in maintenance decisions and gives  
Figure 24: Resilience with simulated data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & **Symptoms Times** & **Detection Times** & **Total Mitigation Times** & **Resilience (\(J\bullet s\))** \\  & **(\(t_{1}-t_{0}\))** & **(\(t_{2}-t_{1}\))** & **(\(t_{3}-t_{2}\)) from Table 10** & **(from Equation (2))** \\  & **(\(Random\))** & **From Algorithm in Figure 16** & **Human** & **No Human** & **Human** & **No Human** \\ \hline Zigbee & 2.1 & 0.39453125 & \begin{tabular}{c} 2.456 + 1.875 \\ = 4.331 \\ \end{tabular} & \begin{tabular}{c} 2.456 + 1.9375 \\ = 4.3935 \\ \end{tabular} & 0.752 & 0.761 \\ \hline LoRa & 2.1 & 0.37109375 & \
\begin{tabular}{c} 2.332 + 1.875 \\ = 4.207 \\ \end{tabular} &
\begin{tabular}{c} 2.332 + 1.9375 \\ = 4.2695 \\ \end{tabular} & 0.728 & 0.737 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Energy resilience calculation from simulation data. feedback to control systems. Another contribution of this publication is the definition and description of the connected worker 5.0, which is the person who makes Industry 5.0 a reality, and who uses AI wearables to be connected to control systems. Due to its holistic vision, professional experience, and ability to understand the context, worker 5.0 assists artificial intelligence in decisions to detect and mitigate risks present in physical assets. The degree of human-in-the-loop involvement (independent variable) must impact the resilience (dependent variable) of the physical asset that is being maintained; the foregoing is achieved by reducing symptom reading times, the rapid application of detection and mitigation algorithms, as well as the change in the configuration of the assets. In the presented use case, the increase in resilience in an IWSN is achieved through a change in the routing protocols of the nodes belonging to the IWSN, which changes from a cooperative scheme to a collaborative one, which was demonstrated in a previous study [107] that it is an effective strategy to achieve resilience when subjected to a jamming attack.