The terms “fog computing,” “cloud computing,” and “Internet of Vehicles” are quite important with the growing trends in the industry market. But why Fog computing is important? Along with cloud computing is it without any challenges and limitations?

• Fog computing is a paradigm extending cloud computing to the edge of the network, where devices such as routers, switches, gateways, and sensors perform data processing, storage, and communication locally rather than sending data to a remote central cloud server. It also reduces latency, bandwidth consumption, and network congestion, improving security, privacy, and reliability.
• Cloud computing is a paradigm enabling on-demand access to a shared pool of computing resources, such as servers, storage, networks, and applications, over the internet. Users access these resources without having to own or manage them and pay only for what they use. It also provides scalability, flexibility, and cost-efficiency for users and service providers.
• Internet of Vehicles (IoV) is a paradigm integrating vehicles, road infrastructure, and other devices into a networked system that can exchange information and provide intelligent transportation services. IoV also enables applications such as autonomous driving, traffic management, safety monitoring, and entertainment.

The term IoV evolved from the term VANET (Vehicular Ad Hoc Networks), a category of mobile ad hoc networks used for communication between vehicles and roadside systems. The term IoV was first coined by Eun-Kyu Lee, Mario Gerla, Giovanni Pau, Uichin Lee, and Jae-Han Lim in their paper “Internet of Vehicles: From intelligent grid to autonomous cars and vehicular fogs” published in 2016. The term IoV is expected to ultimately evolve into an “Internet of autonomous vehicles.”

IoV is enabled by various applications and benefits in smart cities and intelligent transportation systems:

• Autonomous driving: IoV supports the development and deployment of self-driving vehicles, allowing them to communicate with each other and with the infrastructure to navigate safely and efficiently.
• Traffic management: IoV helps in monitoring and controlling traffic flow, optimizing routes and schedules, reducing congestion and emissions, and improving the mobility and accessibility of users.
• Safety monitoring: IoV helps in detecting and preventing accidents, collisions, and hazards, alerting drivers and authorities, and providing emergency services and assistance.
• Entertainment: IoV provides infotainment and social networking services to drivers and passengers, such as music, video, games, and social media.

One of the important methods machine learning algorithms support in learning from data and making predictions or decisions based on the learned patterns. The algorithms are also implemented in the fog and the cloud server for providing different benefits for IoV applications.

Examples include:

• In the fog, machine learning algorithms are used for processing data locally and providing real-time feedback or control to vehicles, such as lane detection, obstacle avoidance, or traffic signal coordination. It also improves the performance, safety, and efficiency of the IoV system.
• In the cloud, machine learning algorithms are used for analyzing data globally and providing insights or recommendations to users, such as traffic prediction, route optimization, or driver behavior analysis. It also enhances the functionality, convenience, and intelligence of the IoV system.

Let’s dive into the details of the interconnection of fog computing, cloud computing, and Internet of Vehicle:

• The vehicles, road infrastructure, and other devices are connected to each other through wireless communication technologies, such as Wi-Fi, Bluetooth, cellular, or Vehicular Ad Hoc Networks (VANETs). It forms the IoV network, which can exchange data and information among the nodes.
• The fog nodes, such as routers, switches, gateways, or sensors, are deployed at the edge of the IoV network, close to the data sources and users. They also communicate with the vehicles and other devices, as well as with the cloud server, through wired or wireless links. They also communicate with each other to form a fog network.
• The cloud server is allocated at a remote data center, providing large-scale computing and storage resources, as well as various applications and services, to the IoV network. It also communicates with the fog nodes through the internet or other networks.

It has been observed that the fog nodes and the cloud server can cooperate to provide different levels of data processing, storage, and communication for the IoV network, depending on the requirements and characteristics of the data and the applications.

For example, some data need to be processed locally by the fog nodes for real-time feedback or control, while some data may need to be sent to the cloud server for global analysis or optimization. The fog nodes and the cloud server also balance the workload and resources among themselves to improve the efficiency and reliability of the system.

The major challenges faced in fog computing are:

• Security and privacy: Fog nodes are vulnerable to cyberattacks, data breaches, or unauthorized access, as they are distributed and exposed to the public network. Fog needs to be implemented with encryption, authentication, and intrusion detection mechanisms to protect the data and the network.
• Resource management: Fog nodes have limited resources, such as power, memory, or bandwidth, compared to cloud servers. Fog nodes need to balance the workload and resources among themselves with the cloud server to optimize the performance and efficiency of the system.
• Data management: Fog nodes also generate and process a large amount of data from various sources and applications. They need to manage the data quality, consistency, and synchronization, as well as the data transmission and storage, ensuring the reliability and functionality of the system.

The major applications for Fog computing enabled in various industries are:

• In Healthcare, it can be used for monitoring and analyzing the patient’s condition, alerting doctors in case of emergencies, supporting telemedicine and remote surgery, and ensuring data privacy and security.
• In Smart cities, it can be used to manage traffic flows, optimize public transportation, reduce energy consumption, improve public safety, and provide smart services to citizens.
• In Logistics and supply chains, it can be used for tracking and monitoring the location, status, and condition of goods, vehicles, and workers, optimizing routes and schedules, enhancing inventory management, and preventing theft and loss.
• In Smart grid, it is used for balancing the supply and demand of electricity, integrating renewable energy sources, detecting and preventing faults and outages, and improving the efficiency and reliability of the power system.
• In Smart factories, it can be used for controlling and coordinating the production processes, machines, and robots, monitoring and maintaining the equipment and environment, ensuring the quality and safety of the products, and supporting human-machine interaction.

The major predictions of the interconnection of fog computing, cloud computing, and IoV technology for the future of smart cities and intelligent transportation systems are:

• The fog computing market will grow rapidly and reach approximately $18 to $20 billion by 2022.
• The 5G network will enable faster and more reliable data transmission and resource allocation for the IoV architecture based on SDN and fog-cloud computing.
• Fog data analytics will provide various insights and solutions for IoT applications such as healthcare, logistics, smart grid, and smart factories.
• Fog computing will support the development and deployment of autonomous vehicles, allowing them to communicate with each other and with the infrastructure to navigate safely and efficiently.

Sources:- IEEexplore, geeksforgeeks, yourtechdiet, wseas, springerlink, wipro, easternpeak, Internet of Vehicles explanation, comparethecloud, mdpi, IoT network used in Fog