This paper presents a system architecture to address the traffic density and the network coverage open research challenges in vehicular networks. This architecture adopts mobile edge computing (MEC) and software‐defined networking (SDN) technologies to increase the overall network reliability and scalability under high traffic density conditions. Then, a device‐to‐device (D2D) clustering method is described to provide network coverage for orphan nodes. The proposed architecture offers a reliable structure to support ultra‐low latency applications. Evaluation results using realistic conditions for various network scenarios show that the proposed MEC/SDN‐enabled vehicular network architecture achieves performance gains of up to 74% in terms of task blocking compared to its baseline implementation.

The study has the method of random multiple wireless access in the networks of the Internet of Things and the control architecture, similar to a software-configured network, examined. The models of collision description, network parameters selection and their initial values, which provide target values for the probability of the delivery in the considered networks, are analyzed. The paper proposes a method the novelty of which lies in the usage of cognitive control of network parameters, taking into account the heterogeneity of conditions for different users with a given probability of data delivery. The proposed method and model of random multiple access and cognitive selection of network parameters are relevant and can be applied when building the access level of the Internet of Things in networks with allowable losses when managing large volumes of heterogeneous traffic and ensuring the required quality of service.

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