However, the potential for creating change has become evident through the basic condition that the 5G network connectivity is still in its infancy. There must be some requirements that would require a proper form of addressing this (Chen and Zhao 2014). A prominent factor among these is setting up policy benchmarks that significantly reflect everything essential about the requirements that would not just pervade through the 5G networks but also the technologies that will operate upon it (O'Leary 2013). This might indicate an increase in the goals set by numerous organizations and individuals, but cybersecurity concerns on the network are most likely to affect more people in more critical ways.
The prevailing thought is that to address the cybersecurity issues, there is a need for AI routines implementation. Particularly, the machine learning aspects should play a very important role in such a significant need for detecting security threats across the different aspects of the 5G network (Jiang et al. 2017). The network will have multiple layers of both inputs and outputs and implement necessary perspectives that will speak about the continual monitoring of the different nodes that pervade all across the network at large (Dong et al. 2017). Moreover, proper machine learning should be able to “learn” about these threats, even when they might not be evident under any condition, which will inimitably identify these attacks in real‐time. Additionally, it should also indicate whether the overall conditions that pervade across the entire field should be updated (Hansen et al. 2015). This is an essential aspect of ensuring proper cybersecurity because the remedial measures become developed and implemented spontaneously and responsively.
One cannot deny the sheer advantage of having such an approach in the first place. However, some considerations need to be made. For one, Mobile Operators should be the initial purveyors of AI routines because they are responsible for managing all issues and factors that may arise within a network (Jiang et al. 2017). Another major concern is the scenario of whether the developments that the routines develop by themselves will be possible when considering the exponential increase of coverage, complexities, and domains that 5G technology will bring forth (Dong et al. 2017). This indicates that there needs to be significant effort put in to develop the operators' AI capabilities (Pagé and Dricot 2016). This will inimitably mean that the AI technologies will also undergo a critical increase in their capabilities and experience full flexibilities and versatilities in terms of the volume and type of problems they might face at large.
2.4 Intelligent Connectivity Use‐Cases
As the terminology indicates, AI will integrate and make the overall 5G network of the world “intelligent” in terms of the network's standard expectations. This has a wide range of definitions, and the difference in their operations is normal because there exist so many implementation scenarios in the first place (Hansen et al. 2015). These are all the necessary use‐cases wherein the 5G network will play a very consequential role in promoting feasibility and enhancement in daily operations. Therefore, these are an essential discussion that must be kept in mind because of all the opportunities and challenges they bring and compel the board's technologies to move forward.
2.4.1 Transportation and Logistics
Advanced Driver Assistance Systems (ADAS) have existed for some time. They are mainly utilized to highlight the necessary technological developments and implementation so that there is a definite increase in car and road safety, respectively. These systems are developed to automate, adapt, and actuate certain aspects of the vehicle so that every possible instance of accidents or any other misfortune is avoided (Dong et al. 2017). ADAS exists in many different versions, but 5G network connectivity and AI routines indicate interesting scenarios. Among the many options that have been presented, it might include enhancing the cabin area and focus on driving factors better than ever before (Mellit et al. 2009). The AI‐enhanced cameras will respond to any inconsistencies in the situation, such as intoxication, drowsiness, distraction, fatigue, etc.
2.4.2 AI‐based Driver Assistance and Monitoring
In addition to this, AI involvement will also inherently involve specifying and managing necessary tasks if something happens. There are already many different enhancements in ADAS that identify different strategies to bring about an avoidance in case accidents do happen. However, with the help of AI routines as well as IoT implementation, there would be computer vision and sensor fusion that will ensure adherence to safety precautions that inimitably help in the reduction of a great deal of damage at large (Duan and Wang 2015). Moreover, this will also involve real‐time passenger and driving movement tracking, which greatly enhances the user experience within the vehicle itself. Gesture recognition and the interaction through normative language are all essential features that lay the necessary groundwork for more intervention‐based technologies at large (Lemley, Bazrafkan, and Corcoran 2017). The electronic enhancements of “under the hood” circuity will also undergo significant enhancement when considered under the perspective of IoT in 5G developments (French and Shim 2016). All of this makes it possible that the entire field of operating the car requires minimal human input, while also ensuring that essential and required contingencies are deployed if something is wrong with the vehicle itself.
2.4.3 Self‐Driving Vehicles
However, the most consequential aspect of AI implementation in modern vehicles is self‐driving technology. It has been something that the entire automotive industry has been working towards for a very long time, and the effects delineate the exact circumstances (Hansen et al. 2015). One of the most obvious challenges is that people want AI to drive their vehicles, but they also want to be driven in the same way. This goes beyond what one would expect to be following safety rules and might consider the speed and the responsive behavior when other vehicles are on the road (Ge, Li, and Li 2017). This means that there needs to be a significant contribution to decision‐making capabilities within the AI routines in addition to sensory and cognitive functions. Moreover, self‐driving vehicles can also be looked upon as IoT devices themselves, or a series of the same working towards a highly complex human goal. It should also specifically involve consideration for communications. Under these circumstances, there should be a focus on vehicle‐to‐vehicle (V2V) or vehicle‐to‐communication (V2X) aspects (Dong et al. 2017). However, the specific processes that lead the vehicle to drive itself are pursued in many different ways.
2.4.4 Deliveries with Unmanned Vehicles
The consideration for cases of transportation would not be complete without the implementation of technologies across logistics. Specifically, with the rise of online shopping and many other smart warehouse operations, logistics, as an entire subject unto itself, has become quite a consequential topic to address (Martini et al. 2015). However, the supply chain would become fully automated by the inclusion, which has already garnered attention from many of the biggest retailers (French and Shim 2016). This involves selecting the best delivery pathway that sophisticated algorithms need to handle, which can be executed by the AI routines within the delivery vehicles themselves. Moreover, the fast and efficient processing of all the necessary data involved also indicates better service provision under any circumstance (Ge, Li, and Li 2017). However, many other nuanced challenges are apparent in handling deliveries with unmanned vehicles, which should constitute an inherent part of innovating and implementing new IoT inventions.
2.5 Industrial and Manufacturing Operations
There has been a significant initiative to automate many different aspects of industrial operations, particularly concerning manufacturing. Remote control mostly refers to the wireless connectivity of controlling operations that require minimal movement interventions (Lemley, Bazrafkan, and Corcoran 2017). The subject of control is industrial robots