## 3 Key Internet of Things Scenarios
### Internet of Vehicles  
Internet of Vehicles (IoV), also referred to as Vehicle to Everything (V2X), is a scenario that includes different interaction modes. They include Vehicle to Pedestrians (V2P), allowing user devices to communicate with in-vehicle devices, Vehicle to Infrastructure (V2I), by which in-vehicle devices can communicate with roadside infrastructure, and Vehicle to Network (V2N), enabling in-vehicle devices to exchange data with edge-cloud platforms, in addition to the classic Vehicle to Vehicle (V2V) communication. This scenario is part of the 3 GPP Cellular V2X (C-V2X) service, which includes both LTE-V2X (3 GPP TR 22.885) and 5G V2X for automated driving (3 GPP TR 22.886). The latter poses significant performance challenges for realizing future transport services, such as vehicle platooning and remote driving. Furthermore, the expected future urban traffic management systems will rely on the acquisition of a comprehensive traffic picture using massive IoT devices, which will enable multi-source data analysis for decision making based on AI. Some of these \begin{table}
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**Solution** & **Resource Footprint** & **Startup Time** & **Supported Protocols** & **Application Portability** & **Security** \\ \hline VMs & Large- full operating system virtualization & Order of minutes & Any & Complete & Isolation granted, but potentially large attack surfaces \\ \hline Traditional containers * & Medium: multiple abstraction layers and context switches & Order of seconds [20] & Any & Complete & Isolation issues, caused by the use of a shared kernel (see also Ref. [21]) \\ \hline Calvin [14] & Limited memory overhead and optimized network functions & Potentially low functions & HTTP only & Only apps using HTTP and specifically built for Calvin & N/A \\ \hline Wasm [15] & Low footprint & Fast scaling, but execution can be slowed down [10] & Any & Portable code written in high-level languages & Sandboxed environment \\ \hline Unikernel [16] & Very low, designed for resource-constrained devices & Fast startup time (\(\ll\)100 ms) & Any (but required libraries may need to be rewritten) & Complete app rewrite is needed & Potentially dangerous kernel functions can be invoked from applications \\ \hline Proposed & Limited memory overhead and CPU consumption (shown in Section 5.2) & \(\leq\)100 ms (shown in Section 5.2) & Any & Complete: optimized VNFs written with NetBricks or standard containerized apps & Minimal attack surface: Kata container and microVM isolation from Firecracker \\ \hline \hline \end{tabular}  
* Container runtime technologies like Containerd, managed by KNative [11], OpenFaaS [12] or OpenWhisk [13] serverless platforms.  
\end{table} 
Table 1: Comparison of the most advanced frameworks for implementing IoT services. services can be classified as mMTC, whereas others, which have real-time requirements, are better classified as uRLLC. Performance guarantees depend on the supported vehicular functions and include a maximum end-to-end (E2E) latency for sensor information sharing of 3 ms to 1 s, 90 to 99.999% reliability, and a throughput of 10-100 Mbps over a coverage radius lower than 1000 m. These functions are related to different services, such as video streaming or C-V2X messaging [23]. Entertainment services in vehicles and traffic management applications are clearly more tolerant to latency, even if video streaming can be a component of a more complex service, such as remote driving. In general, by analysing service requirements by using the data reported in Ref. [23], the results show that most of services based on V2N communications, which are those of interest for serverless computing deployed in edge nodes, require a maximum data rate equal to 25 Mbps. Serverless computing could be a great option for implementing IoV mMTC services, especially those dedicated to information sharing, such as those involved in remote vehicle monitoring, cooperative awareness and platooning.