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With the rapid advancement of smart city infrastructure, vehicle-to-network(V2N) communication has emerged as a crucial technology to enable intelligenttransportation systems (ITS). The investigation of new methods to improve V2Ncommunications is sparked by the growing need for high-speed and dependablecommunications in vehicular networks. To achieve ultra-reliable low latencycommunication (URLLC) for V2N scenarios, we propose a smart meter (SM)-basedcognitive network (CN) architecture for V2N communications. Our scheme makesuse of SMs' available underutilized time resources to let them serve asdistributed access points (APs) for V2N communications to increase reliabilityand decrease latency. We propose and investigate two algorithms for efficientlyassociating vehicles with the appropriate SMs. Extensive simulations arecarried out for comprehensive performance evaluation of our proposedarchitecture and algorithms under diverse system scenarios. Performance isinvestigated with particular emphasis on communication latency and reliability,which are also compared with the conventional base station (BS)-based V2Narchitecture for further validation. The results highlight the value ofincorporating SMs into the current infrastructure and open the door for futureITSs to utilize more effective and dependable V2N communications.

A Cognitive Network Architecture for Vehicle-to-Network (V2N) Communications over Smart Meters for URLLC