## 1 Introduction  
The capability to process, store, and transmit the immense volume of data produced by sensors, controllers, and various interconnected devices is essential for the optimal operation of the IoT. The IoT comprises a network of interlinked objects and devices that exchange data and communicate across diverse and decentralized networks, including both local area networks (LANs) and wide area networks (WANs) [1]. This interconnected-ness facilitates an unprecedented level of automation, as devices, sensors, and connected entities provide crucial data that can be utilized to make informed decisions and improve efficiency [2]. The extensive amount of data produced by IoT devices presents a significant challenge, underscoring the need for adequate storage and organization to ensure effective use. Data analytics is fundamental in this context, as it allows for the identification of trends and patterns within these data. Utilizing these insights can lead to the automation of processes and more informed decision-making, thereby enhancing accuracy and diminishing the reliance on manual checks. A prominent instance of this was observed during the recent COVID-19 pandemic, where IoT devices were extensively utilized for monitoring purposes. These devices enabled the remote measurement of patients' temperatures through real-time data transmission over the internet, thus significantly aiding healthcare professionals in their response efforts [3]. According to a prediction by the International Telecommunication Union (ITU), the quantity of devices linked to internet protocol (IP) networks is expected to exceed three times the global population. By 2023, it is expected that machine-to-machine (M2M) connections will constitute almost half of all worldwide connected devices and connections. Furthermore, a significant surge in global mobile data traffic is forecasted over the next decade. An increasing share of this traffic is predicted to be allocated to machine-to-machine communications, signaling the imminent advent of an IoT revolution. Figure 1 presents a visual representation of the forecasted global mobile data traffic as projected by the International Telecommunication Union (ITU), illustrating the expected growth in exabytes per month from 2020 to 2030 [4]. Fifth-generation (5G) networks are recognized as a key element of IoT systems, owing to their capability to provide the high speed and low latency essential for real-time communication between IoT devices and the internet. Furthermore, 5G technology facilitates the connection of up to one million devices per square kilometer, making it vital for extensive IoT deployments. Beyond scalability, 5G also enhances data rate efficiency, leading to reduced energy usage and longer battery lifespan in IoT sensors. These technological advancements amplify the future prospects of IoT, positioning 5G as a central force in realizing its utmost potential [5]. Moreover, 5G signals operate across three distinct frequency spectrums: millimeter waves, mid-band, and low-band, each with a specific bandwidth range. Millimeter waves deliver a downlink bitrate between 1 Gbps and 2 Gbps, operating at carrier frequencies ranging from 24 GHz to 72 GHz. Mid-band signals, on the other hand, offer downlink bitrates between 100 Mbps and 400 Mbps and utilize carrier frequencies between 2.4 GHz and 4.2 GHz. Lastly, the low-band spectrum functions within the same bandwidth range as 4G networks, providing the broadest coverage, albeit with a slower downlink bitrate [6; 7]. The capacity of IoT solutions to collect, process at the edge, and transmit human biometric data via 5G networks is fundamental for the efficacy of the human-machine interface. This survey explores the diverse biometric authentication methods implemented in IoT frameworks, particularly emphasizing EEG biometric authentication.