# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 1 Introduction  
In 2001, Sir [PERSON], founder of the World Wide Web Consortium (W3C), published his famous article introducing the Semantic Web [1] and his depiction of the future of Internet. He envisioned a world where Web servers publish the data in a format that is comprehensible not only to people but also to machines. Almost two decades later, the Web has diverged from his vision, but the original concepts and the related research are now more relevant than ever in other communication fields. Internet of Things (IoT) devices are said to be one of the main beneficiaries of the application of semantics, as depicted by [PERSON] et al. in [2], but the generally limited capabilities and scope of IoT devices do not provide the most favorable playground for developing an autonomous semantic network, instead requiring the help of external processing. However, there is a particular class of autonomous intelligent and connected devices that offers substantial processing capacity and strong connectivity options, and can afford the extra layer of complexity required to work with semantic data. These are the vehicles and road-side units (RSUs) that form parts of vehicular networks. This new field of application opens up multiple options to vehicular networks and brings a new dimension to [PERSON]'s vision. Vehicles and RSUs benefit from a wider variety of sensors as compared with a typical IoT device. Parts of these readings are used as direct inputs to take immediate decisions, such as starting emergency braking or turning on the windshield wipers. Sharing raw sensor data, such as vehicle speed orcurrent position, with the rest of nodes of the network is viable but inefficient and unproductive. However, layered over these basic measurements, intelligent vehicles have the capacity to generate derived complex data obtained after processing and combining the basic data from their sensors. For example, computerized vehicles have the ability to detect other vehicles and estimate their trajectories. The processing and optimization of sharing digested data becomes a powerful tool that can be used for multiple functions without requiring immense quantities of real-time data. As an exemplary use case, traffic congestion could be reduced by using route estimations, detection of road hazards, the adjustment of traffic lights, or the exchange of trajectories in cross junctions. Sharing all this complex sensor data without any clear organization would force vehicles to implement overly complicated processing algorithms. The application of the existing semantic data research and W3C recommendations to the vehicular networks has the potential for reducing the computational burden of data processing, and it helps in painting an accurate real-world representation of the status of the vehicles and the roads, simplifying the developing of advanced autonomous systems capable of interacting with people in a natural way. When the Semantic Web was introduced by the W3C, their main tasks were to create open data standards for Web contents and design intelligent agents. Semantic agents are autonomous software entities that can analyze and perform tasks using the semantic data [3]. A semantic agent can perform a simple search task, such as \"Where is the closest gas station?\" and more complex tasks, such as \"Tell my wife when I will arrive home [based on the current location and traffic state]\". Agents perform these tasks by collecting data from multiple sources. In this paper, we propose a system that enables the usage of intelligent agents in vehicles and RSUs. With this new solution, agents would have access to all the semantic data published by vehicles and RSUs in an efficient and transparent way. The system is based on the Inter-Planetary File System (IPFS) [4], an open source peer-to-peer (P2P) hypermedia solution that includes native support for the storage and distribution of semantic data.