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Traffic accidents, road congestion and environmental pollution are persistent problems faced by both developed and developing countries, which have made people live in difficult situations. Among these, the traffic incidents are the most serious ones because they result in huge loss of life and property. For decades, we have seen governments and car manufacturers struggle for safer roads and car accident prevention. The development in wireless communications has allowed companies, researchers and institutions to design communication systems that provide new solutions for these issues. Therefore, new types of networks, such as vehicular ad hoc networks (VANETs) have been created. VANET consists of a network of vehicles in which vehicles are capable of communicating among themselves in order to deliver valuable information such as safety warnings and traffic information.
Nowadays, every car is likely to be equipped with various forms of smart sensors, wireless communication modules, storage and computational resources. The sensors will gather information about the road and environment conditions and share it with neighboring vehicles and adjacent roadside units (RSU) via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication.
As more and more smart sensors are installed on modern vehicles, massive amounts of data are generated from monitoring the on-road and in-board status. This exponential growth of generated vehicular data, together with the increasing data demands from in-vehicle users, has led to a tremendous amount of data in VANETs (Xu, et al., 2018). The traditional centralized VANET technology is no longer efficient in handling these massive amounts of traffic data, and in order to collect and process this amount of instantaneous traffic information, additional servers are required in distributed areas. Cloud computing was first considered an appropriate solution for these types of situations.
Recently, fog computing extends cloud nearer to the user. This new architecture analyzes data close to devices for minimizing latency, decision making in real time and offloading massive traffic flow from the core networks. With edge computing, resources and services of computing, networking, storage and control capabilities are distributed anywhere along the continuum from the cloud to things (Hu, Patel, Sabella, Sprecher, & Young, 2015).
By leveraging the fog/edge computing technology, a significant amount of computing power will be distributed near/to the vehicles. Therefore, most of data will be processed and stored at the fog/edge, which can minimize latency and ensure better quality of service for connected vehicles (Yuan, et al., 2018).
Although the integration of cloud, fog and edge computing in VANETs is very promising to offer many services by offering scalable access to storage, networking and computing resources, this network architecture lacks mechanisms needed for resources and connectivity management as it controls the network in a decentralized manner. The prospective solution to solve these problems is by augmenting Software Defined Networking (SDN) with this architecture. SDN provides flexibility, programmability, scalability and global knowledge by controlling the network in a centralized and programmable approach. In Figure 1, we illustrate the topology of this novel VANET architecture, which is composed of cloud computing data centers, fog servers with SDN Controllers (SDNCs), RSU Controllers (RSUCs), RSUs, Base Stations and vehicles. We also illustrate the infrastructure-to-infrastructure (I2I), V2I, and V2V communication links.
In this work, we focus on the resource management in this new architecture and propose an intelligent approach based on fuzzy logic. We present a Cloud-Fog-Edge (CFE) SDN-VANETs layered architecture which is coordinated by a fuzzy system implemented in the SDN modules.
Figure 1.
Topology architecture of SDN-VANETs using cloud, fog and edge computing
The proposed system called FSRM (Fuzzy-based System for Resource Management) decides which resources will be used by a particular vehicle based on its relative speed with the neighboring vehicles, the time-sensitivity of the application data and the size of the data to be processed. We evaluate the performance of FSRM by computer simulations.