**4. Results**

In this section, we have briefly discuss the results of the SLR work. We have formulated the research questions presented in Table 3 and divided the results section into seven subsections to answer them. The information about the contribution of WSN and IoT in IR 4.0, network security attacks and intruders in WSN and IoT, WSN coverage, issues in IoT and WSN framework, and limitations of existing reviews are explained in this section. The challenges section summarizes all the problems encountered in WSN and IoT usage.

### *4.1. RQ1: Contributions of WSN in IR 4.0*

The use of WSN has attracted a lot of attention in industry. Because of their prevalence and use in industry, WSN have given rise to IWSN and IWSAN, respectively. These networks enable autonomous work without human intervention. The in-network transmission characteristics are fundamental properties of WSN. Sensor nodes do not transmit raw data, but integrate it to save communication costs. Due to their unique properties and wide range of applications, they are used in many systems, such as military, surveillance, home automation, smart cities, smart buildings, and healthcare monitoring [27]. WSN- and IoT-based devices are used to create reliable, realistic, efficient, flexible, and economical smart cities and buildings in heterogeneous environments [48].

**Figure 8.** Taxonomy of existing studies.

The categories discussed in this paper and the contribution of WSN in IR 4.0 are listed in the form of a taxonomy presented in Figure 8. WSN is also used in health care managemen<sup>t</sup> systems to monitor medically ill patients, periodically check their various measurements such as blood glucose levels and pulse, and wirelessly transmit this information to a central repository for further diagnosis [49]. WSN is also used to assist elderly and disabled people. Disabled people are informed of relevant information about real-time activities using smart devices, such as wristwatches [28,50]. In recent decades, WSN have been applied in many fields, including transportation, agriculture [51], automation, manufacturing process control, and supply chain management. In addition, WSN

can be easily deployed, have low construction cost, no expenditure on wiring, and lower complexity [52,53].

WSN can be used in various manufacturing applications, such as industrial control, process automation, rescue, and defense. WSN is also used to control and automate industrial processes known as actuators. They can operate independently of a physical environment defined by predefined dimensions [54]. WSN is used to collect, track, and record data in smart factories. Data acquisition is usually done by product information in smart factories. After data collection, processing is done by intelligent machines and manufacturing systems. Nowadays, these factories are self-sufficient, cost-effective, and automated by integrating wireless communications with existing private networks and reducing labor [30].

In software, WSN takes maximum advantage of wireless technologies used to build industrial network infrastructure [55]. On the other side, Industry 4.0 is integrating big data analytics and cloud services [56], 3D printing, computer security, autonomous robotics, the Internet of Things (IoT), 5G, Augmented Reality (AR), and modeling [57,58].

### *4.2. 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 [67–69]. 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.

#### *4.3. RQ3: Type of WSN Coverage Area for IR 4.0*

WSN coverage is an important factor in sensor quality. Sensing and connectivity are key features of WSN. The former indicate how well a particular sensor behaves and monitors a particular area of interest in which it is deployed. Connectivity shows how well different nodes communicate with each other. The types of wireless sensor network coverage are as follows.
