Keywords

1 Introduction

The rapid spread of digital connectivity has led to a new world where billions of devices are intertwined through the Internet of Things (IoT). This includes everyday devices such as smartphones, wearable technology, and vehicles (Kosmas, Papadopoulos, and Michalakelis 2021). This article will focus specifically on the Internet of Vehicles (IoV), a promising and transformative field within the extensive IoT landscape.

The IoV combines IoT and AI to collect and process real-time data from vehicular sensors, providing valuable insights into vehicle operations and affecting broader economic, social, and environmental aspects. IoV enables predictive analytics, enhancing transportation efficiency and safety globally and impacting urban planning and smart city initiatives. IoV has implications beyond transportation. It intersects IoT, AI, and transportation, creating opportunities for innovation in Healthcare, Robotics, Retail Supply, Pharmaceuticals, and more.

2 Artificial Intelligence and Internet of Things

2.1 AI and IoT

Both quantum computing and AI provide an excellent methodology to manage and handle big data generated from IoT devices connected to vehicles or any other tools and equipment. Applying computing everywhere concept will allow the computing and data analysis everywhere by sharing the computing ability between devices and benefiting from sharing the nearby devices’ computing sources. That required a high level of security and supporting the latest technologies of BlockChain for ease of data sharing of all devices.

In general, AI can simulate human intelligence to make the necessary actions to operate vehicles and machines, depending on the collected data from sensors connected to them using IoT/ IoV concepts (Andås, 2020).

The evolving of IoT and AI increase dramatically recently, and the benefit of evolving business come by combining IoT and AI to introduce AIoT, that using the IoT as sensors to collect certain data for a specific domain and the AI used to analyze these data and generate business value, and increase the interaction between machines and human. AIoT can be designed as a smart system that can analyze the data and make the required decision without human interaction to empower real-time actions. Also, It could be considered as a decision support system for decision-makers. Additionally, there are many other benefits for AIoT, such as increasing the efficiency and productivity of machines, excellent monitoring for business risks, human language communication gadgets, increasing systems availability, and reducing operational costs (Revathy et al. 2020).

2.2 Applications of AI and IoT

Smart logistics depend on a smart transportation system that includes many systems that work together to manage and perform specific tasks. It required to use AI, IoT,and Industrial IoT (IIoT) (Woschank, Rauch, and Zsifkovits, 2020). There are many application of AIoT, such as.

  1. 1)

    Street supervision by drones: this considers part of the smart cities concept, as drones can monitor the road conditions and determine the necessary action that needs to be taken in specific scenarios, like changing the traffic light sequence in order to facilitate police or ambulance moves with a smooth flow (Fig. 1).

Fig. 1.
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Fleet management for Autonomous Vehicles (Revathy et al., 2020).

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  1. 2)

    Fleet management and supervision: that helps maintain vehicle routes reduce fuel consumption and drive behavior. Additionally, AIoT essential for all self-driven cars as it uses sensors, cameras, and radars to provide all required data to the AI system, which considered as car brain; the AI part ensure smooth and safe driving for the driver, passengers, and road users and the road/city assets.

  2. 3)

    The futuristic of robot delivery: a specific purpose robot dedicated to delivering goods. These robots contain built-in AI systems that enable them to interact with the delivery location and environment to ensure efficient delivery with the shortest path.

IoT is used to gather information about many objects like tools, buildings, vehicles, pets, or humans automatically without human interference (AlGanem, and Abdallah, 2022). The collected data usually sent to one or more data collection stations to prepare it to be processed. One of the most critical challenges of IoT is security issues.

The main applications of IoT are:

  1. 1)

    Smart home: which include more than 500 gadgets connected to home appliances, furniture, doors locks, cameras, and many other devices that communicate with the primary system which provide knowledge about the home status and provide a better life for people, but at the same time having data security issue might lead to severe problems to home living people.

  2. 2)

    Smart Healthcare: in this domain, IoT plays for all patients and healthcare institutes, and insurance companies. Regarding patients, IoT gadgets can detect patient vital signs that can be wearable or fixed with a human body. For healthcare institutes, IoT can be connected to medical devices, Lab devices. Operations rooms and building facilities will give a better way to manage, operate, keep an eye on all changes, and get notified when needed to take the necessary action. Also, insurance companies benefit from employing IoT by making the automatic approval for an insurance health claim for patients based on IoT gadgets and IoT of connected devices in the healthcare institute.

  3. 3)

    Logistics and transportation: by the increasing demand for online trading and e-commerce, the need for logistic services increase dramatically, and there is a need to followup up shipping and goods delivery, which required better management for packaging and fleet of vehicles. By employing IoT and RFID, a better service is provided, for example, for checking the road and traffic conditions, make sure the temperature for goods required cooling during shipping. The use of AI and IoT is to detect the driver’s behavior and expression and eye looking way to check the focus of the driver and notify/alert him to focus on the road, as a higher percentage of accidents on the road happened because drivers are not paying attention to the road (Uganya,and Baskar, 2020)

Besides vehicles, routing algorithms can also work for a fleet of robots to manage the logistics in general and specifically in the healthcare domain like hospitals, to deliver goods such as medicines for patients or medical tools and supplements. To enable fleet management system to manage the request and set the orders to robots within the required time. To manage all fleet operations, a set of systems need to collaborate: routing engine and traffic control, task scheduler and task assignment, supervision, and monitoring system, robots with IoT gadgets and sensors. Also, the same concept is used as shopping assistance during customers visit big shops/malls so that robots can carry the goods during the shopping journey in the process (Ortiz et al. 2021). in addition to robots, drones are also utilized better when applying the AI to it and can be used in military uses having similar components of the robots such as routing system but for three dimensions positioning, and there are task management and scheduling to distribute the tasks between a fleet of drones (Johnson, 2020).

3 Internet of Vehicles ( IoV)

Internet of Vehicles (IoV) is considered as employing smart system and IoT to vehicles using Artificial Intelligence (AI) and Machine Learning (ML), that enables the vehicles on the road to communicate with each another in order to avoid roads issues like a traffic jam, avoid accidents and self-breaking, self-driving, self-repair vehicles, fuel consumption. Additionally, communication with other objects like infrastructure, roadside, pedestrian, and the management application that communicate and coordinate between all vehicles and use AI and ML facilitates the use of the vehicles and road for all road users. IoV is considered a complex system consisting of multiple systems that integrate and collaborate in real-time and give alerts notification, decision support for vehicle drivers or road users, such as people who walk on the sidewalk or bike riders are wearing smartwatches. As the system come complex, this makes a lot of issues and challenges such as:

The storage of all the collected data from all entities, the unexpected behavior of drivers routs, network coverage in all areas and locations, the load on the network, sensors’ reliability and calibration, safety, and privacy. In general, IoV works better when using AI and ML to achieve better networking, better results, and business value (Kumar, and Singh, 2020).

Fleet management systems help manage the fleet operation to operate within all areas of fleet management, including planning, maintenance, and retirement. A framework to manage all these areas comes into two parts. The first part covers solution architecture considering future growth of the fleet, deploying, and maintaining the system. While the second part focuses on the best AI algorithms that fit each used tool within the framework, it also determines when required to retrain the AI algorithms. As managing big-size fleets for 1000 + vehicles come as mission-critical for organizations that depend on delivering their services or goods to their customers, predicting failure before it happens increases availability, efficiency, and productivity. The provided framework’s main actors are Data scientists, Sustenance engineers, Organization Data Lake, end-user/ end product (Thomas et al. 2020).

One of the main challenges for the transportation domain is the car accidents that happened due to insufficient knowledge of driving or driver gets distracted by vehicles, entertainment systems, food, or mobile usage. To overcome this issue and increase driving Welfare, some companies invented self-driving vehicles by using IoT sensors, radars, and cameras and connected them to AI systems that can analyze and take the necessary action to drive safely. This concept reduces the injuries and loss of lives and financial loss by avoiding drivers’ mistakes and destruction that lead to accidents. In general, a car can be said it is autonomous vehicles if it can do some of the human driving actions, and fully autonomous vehicles mean a vehicle can operate fully without any human interaction. There are five levels of vehicle categorization depending on the autonomous level:

  • Level 0: means there are no autonomous at all and driver action required all the time

  • Level 1: minimal help for the driver like speed control

  • Level 2: Vehicle can maintain speed control and path position

  • Level 3: self-driven car in a specific condition and notify the driver to take control when needed.

  • Level 4: fully self-driven car in a specific area like a city.

  • Level 5: fully self-driven car in all areas under all conditions. (AGENMONMEN, 2020)

Self-driven vehicles can detect and interact with the surrounded environment using sensors and cameras by applying computer vision algorithms and signal analysis. To recognize the objects and trace the path lanes, all of them become a source of data to reinforce the training for AI logic within the vehicle, so the vehicle can decide the way, speed, and the lanes to be used, and how to react to all changing conditions on the road, including other vehicles people on the sidewalk, traffic lights, accidents, traffic jam, weather conditions, and limited sight visibility.

Many challenges face autonomous vehicles, like dealing with unknown objects or environment, defect or poor working conditions for IoT Gadgets(cameras or sensors)due to many weather conditions, manufacturing defect, or tear and wear, which lead to a faulty alarm. Another challenge is to keep the evaluation of the current status of the vehicle and the surrounded objects. As a complex environment, many AI algorithm needs to work together to achieve a high level of self-driven vehicles. To simplify the complexity, a map is created to track the destination and determine the environment’s objects that surround the vehicles.

Using perception process by using a variety of AI technologies like support victors machine (SVM), Multi-view Stereo, Visual SLAM, Tracking filters, Signal processing techniques, Supervised learning networks, Unsupervised learning ( clustering, decision trees, Bayesian networks), Deep neural networks (Convolutional neural networks, RBM and deep belief networks, Generative adversarial network), Reinforcement Learning, and many other techniques.

There is an excellent benefit for logistics companies in increase their efficiency/productivity and reduce the cost of their services. Using Google maps, historical data of the fleet of vehicles, IoV, and AI algorithms, namely Dijkstra, build a perception model that determines the best route for multi destinations depending on traffic conditions and vehicles’ s considering the current location of the vehicles and new orders come across during the journey.

Currently, autonomous vehicles are used in military services by making cheap foldable self-driving cars that imitate the shape and sound of tunks to distract the enemies and trick them. The future of vehicles, tools, and equipment will be a self-driven vehicle and self-repair and self-assembly, which will help many domains improve the availability level, especially for vehicles working away from maintenance stations like vehicles in war scenarios. All these features can be developed using self-checking sensors (IoV) and AI methods that can check the current situation of the vehicle and find the defective parts and replace it to continue its operation with minimal downtime; furthermore, these vehicles can predict the failure before it happens; so this minimizes downtime near to zero.

Additionally, in the future, there will be a swarm of self-driving flight fighters that can fly in groups and interact with each other independently to organize the attacks and distributes the targets between them based on war and flight conditions (Torossian, 2020).

4 Challenges

The future of the IoT comes as improvement of many sub-areas within the IoT like improving 1) the hardware by using a mix of a very tiny chipset, nanotechnology, and minimal electricity usage for the components. 2) sensors, by providing small sensors and lower power use and high accuracy 3)communication, besides the standard communication channels a new communication way to interact with other devices to use the concept of computing everywhere. 4) software provides specific mission software within a domain perspective. 5) data are analyzing, having a built-in data processing, analyzing, and visualizing within the same device. 6) security, provide a highly secure data transmission mechanism, and use of secure protocols (Aqeel, 2020).

IoT is used to generate new knowledge about the machine connected to it and generate a bid data pool about all actions and changes to that machine. Fleet management can benefit from the IoT and use it specifically for vehicles IoV, to better manage the maintenance for a fleet of vehicles by predicting the failure before it happens and do the required prevention action. There are challenges regarding IoT regarding data ownership, is the one who uses the sensor or device, is the data collection and analysis, or the manufacturer of the devices. Another challenge is determining the party that he/she should get the financial benefits of these data. Finally, there is no international standard for IoT devices to connect with each other (Killeen, 2020).

Recently, there have been significant improvements in IoT and IIoT concepts, including smart sensors, connectivity devices, RFID, and many other gadgets. Additional to cameras, radars, ultrasounds sensors, heat sensors that combined to gather and assembled within vehicles to produce autonomous vehicles that apply the concept of the Internet of Vehicles ( IoV) which use AI to analyze all collected data with a realtime to make a smart decision without human interaction (Vermesan, 2020).

5 Future of IoV

Transportation is considered one of the most critical domains to develop urban cities and smart cities. There are many attractive functionalities to build smart cities, such as self-driven vehicles and smart transportation in general. Regarding future cities, futuristic technology can implement connected autonomous vehicles, personal self-driven vehicles, flying vehicles, and transportation as a service; all that mentioned before come as an application of IoT, IoV, and AI algorithms such as Fuzzy logic model, Artificial immune system, Genetic Algorithms, and Artificial Neural Networks. Soon the vehicles in the city will be connected to each other through a network called an Intelligent transport system that enables all vehicles to collaborate and share their data about the traffic/roads conditions to benefit other vehicles using recent technologies of BlockChain and intelligent vehicle trust point ( IVTP) to apply the IoV concept. Currently, Google and Tesla provide vehicles with fully self-driving features that can communicate with other vehicles and road facilities and units. Many issues will be solved using blockchain technology as it provides a decentralization datastore, high security/ encryption, and privacy (Mujtaba, and Javaid, 2020).

Fig. 2.
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A futuristic expectation of high tech field (Fountas et al. 2020)

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Figure 2 above show a common vision of futuristic field that supported by IoT in many applications of Equipment, vehicles, robots, drones, satellite, and many other sensors. That all work to gather to collect data about the plants’ soil and weather, then they interact with each other to decide what is the required action need to be taken for the good of plants and soil. The task then delivered to the concerned equipment such as a tractor, robot, or drone to put more fertilizer, watering, spraying bugs, or start harvesting (Fountas et al. 2020). IoV will help build smart cities and make them more connected to vehicles and collect data from autonomous vehicles that required a new robust network like 5G or better in the future, enabling vehicles to communicate in real-time with other vehicles and roadside infrastructure. In the future flying vehicles will be available for commercial use and will be by all people, as IoV will playing a significant role in managing flying autonomous vehicles. Finally, regulations, policies, and strategies should be placed to govern the wok of autonomous vehicles.

6 Conclusion

There is a continuous improvement of IoV technology, including hardware, software, and network, and lead to better-provided services. Improving on IoV will lead to better results in the number of accidents, time and cost-saving, reducing co2 emissions and better transport experience, in addition to the contribution of the smart city. in this paper, we explorer many technologies that support IoV, such as IoT, AIot, and AI. in future we expect more advanced autonomous vehicles including transportation system and flying vehicles.