## 1 Introduction  
Before diving into the specifics of hardware module design and synthesis, it is essential to understand the internet of things (IoT)-enabled devices' hardware requirements and challenges. An IoT-enabled device can be anything that consists of a processing unit and multiple other units to support wired or wireless communication with the internet. In this design scenario, we assume that the designated device is a futuristic, intelligent, hub-style unit. In this reference, the hub is similar to the hub module used in internetworking, with additional features that enable smooth and bright data transactions between various IoT devices in a discrete locality. This device might sound similar to an IP-router, but it is different, as it handles most of the data transactions in the medium access control (MAC) layer. It is more closely related to a switch with an advanced set of protocols that the user describes depending on its utility. On a grander scale, these devices can handle deep learning algorithms, performing edge computing for applications, requiring a timeless response from other devices. Such applications might include vehicle-to-everything (V2X) communications in autonomous cars, where these devices could be deployed as roadside units to support advanced communications involving machine learning algorithms [1]. Machine learning in 5G and beyond communications has become a new research domain, as they try to predict the channel. The channel prediction becomes more relevant when the communications involve fast-moving vehicles. Here, the deployment of roadside units will become a powerful tool for efficient and effective communications. The IoT-based hubs and other similar devices might satisfy their hardware and timely needs. In an intelligent farm, it is inevitable to witness a tremendous number of sensors and other automation-related components. In a typical intelligent farming setup, we can expect the data collected at these sensors to be sent to the cloud before processing or at least to a central unit for further processing and future action generation. In the experiment conducted and stated in [2], we can see that some locations have higher download rates and lower upload rates and vice versa; these anomalies are far too common in a farm situation, thereby making it unreliable for large-scale deployment. These approaches are proven effective but pose many challenges when scaling because they are vulnerable to cyber threats and dependent on internet linkage factors. Suppose there exists an intelligent hub system by the ideology of distributed computing. In that case, the hub can only transfer data to the required external hubs and distribute computation among themselves, which keeps the security threats away from physical access by a good factor and creates a distributed processing environment. As mentioned in [3], the relative locations of IoT-enabled devices become a significant factor when dealing with edge computing, along with factors such as network address translation (NAT) overload, and internet protocol (IP) address hindrance. To tackle these issues, edge computing might have done a significant job, but with supportable hardware at our hands, this could be scaled quickly. Summing up the above-stated applications and their challenges, it is clear that many other unstated applications require smart hub-like devices to perform computation on data at the source end and transmit only limited content at high rates. It becomes evident that the hardware used must become more specific and complex to achieve this criterion. It is not contemporary to see specialized hardware on the go; many data centers already use this approach in manufacturing specialized hardware for their data center requirement. The solution we present here integrates networking as part of the computational challenge to enable a reliable infrastructure for IoT applications.