## 4 Results
### RQ2: Contributions of IoT in IR 4.0  
An integrated digital system would introduce a new intelligent and economical manufacturing process using cutting-edge technology for a variety of existing items and processes [59]. The data collected from production process warehouses and consumer information can be critically analyzed to make a decision in real time under Industry 4.0. The real-time decision-making capability of each small and medium organization enables them to efficiently accept new technologies [60; 61]. Industrial IoT delivers solutions and services that provide insights into an organization's ability to monitor and control its operations and assets. IIoT software and tools provide important solutions for better process, layout scheduling, organization, and administration.  
In addition, IIoT enables real-time and decision-making features among numerous networked devices that can communicate and interact with each other [62]. Because of the rapid communication and data transfer, attackers can attack data and cause harm to an organization, resulting in cyber attacks. Cyber attacks have become a major challenge for the industrial Internet of Things (IIoT). Therefore, integrating IoT with Industry 4.0 plays a critical role in securing IoT devices from attacks. Unique security objectives and challenges of IIoT have been introduced to overcome industrial-level issues. IIoT challenges and objectives relate to IoT being used by consumers and its capabilities leading to longer life of IoT devices and sensor nodes. In [63], the authors analyzed security challenges and attacks at three levels of the network (perception, network, and application). They considered cryptographic challenges, authentication, network monitoring, and access control mechanisms. The IIoT also addresses local network connectivity and protection from attackers inside. Cyber attacks have become a serious challenge for the IIoT. Hackers attack infrastructure/devices through intrusion and hiding, resulting in poor performance. A bidirectional long and short term memory network with a multi-feature layer has been developed to avoid temporal attacks. Machine learning-based networks that learn temporal attacks from historical data and make associations with test data can effectively identify and detect different attacks within different intervals [64].  
DL-IIoT has enormous potential to improve data processing and contribute to IR 4.0. Similarly, machine learning algorithms, such as logistic regression, are widely used for malware detection and security threat protection [65]. Deep learning algorithms are also used for intelligent analysis and processing. Deep learning [46]algorithms such as CNN, auto-encoders, and recurrent neural networks have applications such as intelligent assembly and manufacturing, network monitoring, and accident prevention. The application of deep learning algorithms in IIoT has also enabled various smart applications such as manufacturing, active attack detection and prevention systems, smart meters, and smart agriculture [66]. DL-IIoT relies heavily on data collection, which affects the privacy of the organization's data. Therefore, differentiated privacy is used to protect user privacy, reduce privacy risk, and achieve high performance in IIoT.  
On the other hand, IoT and IIoT must provide \"differentiated privacy\" for individuals and industrial data [67; 68; 69]. The contribution of IoT in Industry 4.0 has improved the average availability and sustainability of the enterprise by knowing market trends and decreasing unanticipated downturns [70]. The taxonomy of existing studies and the contribution of IoT in IR 4.0 is shown in Figure 8.