1 Blockchain-based Route Selection with Allocation of Radio and Computing Resources for Connected Vehicles Marcel Voloˇ sin, Eugen ˇ Slapak, Zdenek Becvar, Senior Member, IEEE, Taras Maksymyuk Member, IEEE, Adam Petik, Madhusanka Liyanage, Senior Member, IEEE, Juraj Gazda Abstract—Most of the existing solutions for communication of connected vehicles (CVs) are focused on the optimization of resource allocation, however, not taking the driving routes of the CVs into account. In addition, no existing work considers the offloading of computing tasks from CVs to edge computing servers. In this paper, we introduce joint vehicular route selection and radio and computing resource allocation for CVs. The proposed algorithm minimizes the ratio of failed tasks along the entire vehicular route considering the availability of both radio and computing resources. To manage the allocation of resources among multiple CVs for each vehicular route, we develop a blockchain-based framework allowing resource reservation by means of nonfungible tokens (NFTs). Each NFT represents an exclusive right to the required amount of radio and computing resources for the given road segment and defined time interval. Simulation results show that the proposed vehicular route selec- tion algorithm reduces the ratio of tasks not completed before deadline by up to 69% compared to the existing state-of-the-art algorithms. Index Terms—resource allocation, vehicular edge computing, mobile networks, connected vehicles, NFT, blockchain. I. I NTRODUCTION The Internet of Things (IoT) era has shifted the paradigm of city development towards digitalization and novel information services based on artificial intelligence (AI) and blockchain. One of the areas of most active development is the automotive industry, which is rapidly transforming towards autonomous and connected vehicles (CVs). Such CVs typically contain various supplementary systems, such as sensors, lidars, or global electronic assistance systems. All these supplementary systems generate a massive amount of data and heavily rely on vehicle-to-infrastructure (V2I) communications. An additional complexity of CV scenarios, for example, autonomous driving, is that a significant percentage of the data is transferred to an external cloud or multi-access edge computing (MEC) servers [1]. Juraj Gazda, Adam Petik, Marcel Voloˇ sin and Eugen ˇ Slapak are with the Department of Computers and Informatics, Technical University of Kosice, 04200 Kosice, Slovakia. Email: {firstname.lastname}@tuke.sk Zdenek Becvar is with the Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic. Email: zdenek.becvar@fel.cvut.cz Taras Maksymyuk is with the Department of Telecommuni- cations, Lviv Polytechnic National University, Ukraine. Email: taras.maksymyuk@gmail.com Madhusanka Liyanage is with the School of Computer Science, Univer- sity College Dublin, Ireland, and the Centre for Wireless Communications, University of Oulu, Finland. Email: madhusanka@ucd.ie The existing fifth-generation (5G) mobile network standards enable ultrareliable low-latency communications and network slicing to facilitate the adoption of CVs. The network slicing functionality allows the radio resources of CVs to be separated from those of other users [2]. However, in addition to radio resource allocation, the allocation of edge computing resources is also vital to meet the low latency requirements of CVs. Thus, the optimization of radio resources and computing resources should be considered jointly [3]. CVs typically move over much larger distances than pedes- trians, and the allocated resources should be available along the entire vehicular route of the trip. Thus, the main problem studied in this paper is related to the vehicular routing strategy for CVs considering the reliability of network connectivity and the availability of computing resources to ensure low latency of computing task processing. The most basic solution for the navigation of vehicles is the selection of the fastest possible route (FPR) [4]. The FPR approach selects the fastest vehicular route between specified origin and destination points based solely on the conventional Global Positioning System (GPS). Navigation systems based on this approach often consider additional parameters, such as traffic jams, speed limits and overall road conditions. However, while FPR selection can be optimal from the perspective of driving time, it is not always good from the perspective of wireless connectivity or computing resource availability, which may be essential for the autonomous driving scenario. Under certain conditions, the ve- hicle may require essential information from the surrounding environment to ensure a safe and comfortable trip. Thus, there is a critical need to propose a scalable routing algorithm that addresses the joint optimization of radio and computing resources to ensure the seamless generation of various service requests by CVs in the MEC environment. In addition, the route selection mechanism must be designed such that the route prolongation of the CVs falls within a specific acceptable threshold. Decentralized solutions are of prominent interest considering the presence of a large number of CVs and their high mobility, which could impose high time and memory requirements on the typical centralized solutions presented in, e.g., [5], [6]. To fill this research gap, in this paper, we propose a blockchain-based framework for vehicular route selection con- sidering the end-to-end radio and computing resource avail- ability. The main motivation for the blockchain implemen- tation is the distributed ledger, which provides decentralized