VEHICULAR IOT

Introduction

• In this chapter, we discuss the application of IoT in connected vehicular systems. The use of connected vehicles is increasing rapidly across the globe.
• Consequently, the number of on-road accidents and mismanagement of traffic is also increasing.
• The increasing number of vehicles gives rise to the problem of parking.
• However, the evolution of IoT helps to form a connected vehicular environment to manage the transportation systems efficiently.
• Vehicular IoT systems have penetrated different aspects of the transportation ecosystem, including on-road to off-road traffic management, driver safety for heavy to small vehicles, and security in public transportation.
• In a connected vehicular environment, vehicles are capable of communicating and sharing their information.

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• Moreover, IoT enables a vehicle to sense its internal and external environments to make certain autonomous decisions.
• With the help of modern-day IoT infrastructure, a vehicle owner residing in Earth’s northern hemisphere can very easily track his vehicular asset remotely, even if it is in the southern hemisphere.
• In this chapter, we discuss the importance and applications of IoT in the vehicular systems.
• Figure 13.1 represents a simple architecture of a vehicular IoT system.
• The architecture of the vehicular IoT is divided into three sublayers: device, fog, and cloud.

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Architecture of vehicular IoT

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• Device: The device layer is the bottom-most layer, which consists of the basic infrastructure of the scenario of the connected vehicle. This layer includes the vehicles and road side units (RSU).
• These vehicles contain certain sensors which gather the internal information of the vehicles. On the other hand, the RSU works as a local centralized unit that manages the data from the vehicles.

• Fog: In vehicular IoT systems, fast decision making is pertinent to avoid

accidents and traffic mismanagement.

• In such situations, fog computing plays a crucial role by providing decisions in real-time, much near to the devices.
• on sequently, the fog layer helps to minimize data transmission time in a vehicular IoT system.

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• Cloud: Fog computing handles the data processing near the devices to take decisions instantaneously. However, for the processing of huge data, fog computing is not enough.
• Therefore, in such a situation, cloud computing is used. In a vehicular IoT system, cloud computing helps to handle processes that involve a huge amount of data.
• Further, for long-term storage, cloud computing is used as a scalable resource in vehicular IoT systems.

Components of vehicular IoT

• Modern cars come equipped with different types of sensors and electronic components. These sensors sense the internal environment of the car and transmit the sensed data to a processor.
• The on-road deployed sensors sense the external environment and transmit the sensed data to the centralized processor.
• Thereafter, based on requirements, the processor delivers these sensed data to fog or cloud to perform necessary functions.
• These processes seem to be simple, but practically, several components, along with their challenges, are involved in a vehicular IoT system.

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• Sensors: We have already discussed how sensors play a crucial role in an IoT-based ecosystem. Similarly, in vehicular IoT, sensors monitor different environmental conditions and help to make the system more economical, efficient, and robust. Traditionally, two types of sensors, internal and external, are used in vehicular IoT systems.
• Internal: These types of sensors are placed within the vehicle. The sensors are typically used to sense parameters that are directly associated with the vehicle. Along with the sensors, the vehicles are equipped with different electronic components such as processing boards and actuators.
• The internal sensors in a vehicle are connected with the processor board, to which they transmit the sensed data. Further, the sensed data are processed by the board to take certain predefined actions.
• A few examples of internal sensors are GPS, fuel gauge, ultrasonic sensors, proximity sensors, accelerometer, pressure sensors, and temperature sensors.

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• External: External sensors quantify information of the environment outside the vehicle. For example, there are sensors used in the smart traffic system that are capable of sensing vacant parking lots in a designated parking area.
• The still images and videos from cameras are important inputs to generate decisions in a vehicular IoT system. Therefore, on-road cameras are widely used as external sensors to capture still images and videos.
• The captured images and videos are processed further, either in the fog or in the cloud layer, to take certain pre-programmed actions. As an example, camera sensor can capture the image of the license plate of an over speeding vehicle at a traffic signal; the image can be processed to identify the owner of the vehicle to charge a certain amount of fine.
• Similarly, temperature, rainfall, and light sensors are also used in the vehicular IoT infrastructure.

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• External: External sensors quantify information of the environment outside the vehicle. For example, there are sensors used in the smart traffic system that are capable of sensing vacant parking lots in a designated parking area.
• The still images and videos from cameras are important inputs to generate decisions in a vehicular IoT system. Therefore, on-road cameras are widely used as external sensors to capture still images and videos.
• The captured images and videos are processed further, either in the fog or in the cloud layer, to take certain pre-programmed actions. As an example, camera sensor can capture the image of the license plate of an over speeding vehicle at a traffic signal;
• the image can be processed to identify the owner of the vehicle to charge a certain amount of fine. Similarly, temperature, rainfall, and light sensors are also used in the vehicular IoT infrastructure.

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• Satellites: In vehicular IoT systems, automatic vehicle tracking and crash detection are among the important available features. Satellites help the system to track vehicles and detect on-road crashes. The satellite image is also useful for detecting on-road congestions and road blocks.
• Wireless connectivity: As vehicular IoT deals with connected vehicles, communication is an important enabling component. For taking any action or making decisions, the collective data from internal and external sensors need processing.
• For transmitting the sensed data from multiple sensors to RSU (roadside unit) and from RSUs to the cloud, connectivity plays an indispensable role.
• Moreover, in the vehicular IoT scenario, the high mobility of the vehicles necessitates the connectivity type to be wireless for practical and real-time data transmission.
• Different communication technologies, such as Wi-Fi, Bluetooth, and GSM, are common in the vehicular IoT systems.

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• Road Side Unit (RSU): The RSU is a static entity that works collaboratively with internal and external sensors. Typically, the RSUs are equipped with sensors, communication units, and fog devices.
• Vehicular IoT systems deal with time critical applications, which need to take decisions in real time. In such a situation, the fog devices attached to the RSUs process the sensed data and take necessary action promptly.
• If a vehicular system involves heavy computation, the RSU transmits the sensed data to the cloud end. Sometimes, these RSUs also work as an intermediate communication agent between two vehicles.

Cloud and fog computing

• We have already discussed the importance of fog computing and cloud in the context of IoT applications.
• In vehicular IoT systems, fog computing handles the light-weight processes geographically closer to the vehicles than the cloud.
• Consequently, for faster decision making, fog computing is used in vehicular IoT systems. However, for a heavy-weight process, fog computing may not be a suitable option.
• In such a situation, cloud computing is more adept for vehicular IoT systems.
• Cloud computing provides more scalability of resources as compared to fog computing..

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• Therefore, the choice of the application of fog and cloud computing depends on the situation.
• For example, the location and extent of short on-road congestion from a certain location can be determined by fog computing with the help of sensed data.
• Further, the congestion information can be shared by the RSU among other on road vehicles, thereby suggesting that they avoid the congested road.
• On the other hand, for determining regular on-road congestion, predictions are typically handled with the help of cloud computing.
• For the regular congestion prediction, the cloud end needs to process a huge amount of instantaneous data, as well as, historical data for that stretch of road spanning back a few months to years.

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• Analytics: Similar to different IoT application domains, in vehicular IoT, analytics is a crucial component. Vehicular IoT systems can be made to predict different dynamic and static conditions using analytics. For example, strong data analytics is required to predict on-road traffic conditions that may occur at a location after an hour.

Advantages of vehicular IoT

• The evolution of IoT resulted in the development of a connected vehicular environment. Moreover, the typical advantages of IoT architectures directly impact the domain of connected vehicular systems. Therefore, the advantages of IoT are inherently included in vehicular IoT environments. A few selected advantages of vehicular IoT are depicted in Figure 13.3.

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• Easy tracking: The tracking of vehicles is an essential part of vehicular IoT. Moreover, the system must know from which location and which vehicle the system is receiving the information. In a vehicular IoT system, the tracking of vehicles is straightforward; the system can collect information at a remote location.
• Fast decision making: Most of the decisions in the connected vehicle environment are time critical. Therefore, for such an application, fast and active decision making are pertinent for avoiding accidents. In the vehicular IoT environment, cloud and fog computing help to make fast decisions with the data received from the sensor-based devices.
• Connected vehicles: A vehicular IoT system provides an opportunity to remain connected and share information among different vehicles.

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• Easy management: Since vehicular IoT systems consist of different types of sensors, a communication unit, processing devices, and GPS, the management of the vehicle becomes easy. The connectivity among different components in a vehicular IoT enables systems to track every activity in and around the vehicle. Further, the IoT infrastructure helps in managing the huge number of users located at different geographical coordinates.
• Safety: Safety is one of the most important advantages of a vehicular IoT system. With easy management of the system, both the internal and external sensors placed at different locations play an important role in providing safety to the vehicle, its occupants, as well as the people around it.
• Record: Storing different data related to the transportation system is an essential component of a vehicular IoT. The record may be of any form, such as video footage, still images, and documentation. By taking advantage of cloud and fog computing architecture, the vehicular IoT systems keep all the required records in its database.

Crime assistance in a smart IoT transportation system

• The system highlights a fog framework for intelligent public safety in vehicular environments (fog-FISVER) [1]. The primary aim of this system is to ensure smart transportation safety (STS) in public bus services. The system works through the following three steps:

(i) The vehicle is equipped with a smart surveillance system, which is capable of executing video processing and detecting criminal activity in real time.

(ii) A fog computing architecture works as the mediator between a vehicle and a police vehicle.

(iii) A mobile application is used to report the crime to a nearby police agent.

Architecture

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• Tier1—In-vehicle FISVER STS Fog: In this system component, a fog node is placed for detecting criminal activities. This tier accumulates the real sensed data from within the vehicle and processes it to detect possible criminal activities inside the vehicle. Further, this tier is responsible for creating crime-level metadata and transferring the required information to the next tier.
• For performing all the activities, Tier 1 consists of two subsystems: Image processor and event dispatcher

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• Image Processor: The image processor inside Tier 1 is a potent component, which has a capability similar to the human eye for detecting criminal activities.
• Developers of the system used a deep-learning-based approach for enabling image processing techniques in the processor. To implement the fog computing architecture in the vehicle, a Raspberry-Pi-3 processor board is used, which is equipped with a high-quality camera.
• Further, this architecture uses template matching and correlation to detect the presence of dangerous articles (such as a pistol or a knife) in the sub-image of a video frame.
• Typically, the image processor stores a set of crime object templates in the fog-FISVER STS fog infrastructure, which is present in Tier 2 of the system.

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• The image processor is divided into the following three parts:

(a) Crime definition downloader: This component periodically checks for the presence of new crime object template definitions in fog-FISVER STS fog infrastructure. If a new crime object template is available, it is stored locally.

(b) Crime definition storage: In order to use template matching, the crime object template definition is required to be stored in the system. The crime definition storage is used to store all the possible crime object template definitions.

(c) Algorithm launcher: This component initiates the instances of the registered algorithm in order to match the template with the video captured by the camera attached in the vehicles. If a crime object is matched with the video, criminal activity is confirmed.

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• Event dispatcher: This is another key component of Tier 1. The event dispatcher is responsible for accumulating the data sensed from vehicles and the image processor. After the successful detection of criminal activity, the information is sent to the fog-FISVER STS fog infrastructure.
• The components of the event dispatcher are as follows:

(a) Event notifier: It transfers the data to the fog-FISVER STS fog infrastructure, after receiving it from the attached sensor nodes in the vehicle.

(b) Data gatherer: This is an intermediate component between the event notifier and the physical sensor; it helps to gather sensed data.

• (c) Virtual sensor interface: Multiple sensors that sense data from different locations of the vehicle are present in the system. The virtual sensor interface helps to maintain a particular procedure to gather data.

• Tier 2—FISVER STS Fog Infrastructure: Tier 2 works on top of the fog architecture. Primarily, this tier has three responsibilities—keep updating the new object template definitions, classifying events, and finding the most suitable police vehicle to notify the event.
• FISVER STS fog infrastructure is divided into two sub-components:
• Target Object Training: Practically, there are different types of crime objects. The system needs to be up-to-dated regarding all crime objects. This subcomponent of Tier 2 is responsible for creating, updating, and storing the crime object definition.
• The algorithm launcher uses these definitions in Tier 1 for the template matching process. The template definition includes different features of the crime object such as color gradient and shape format. A new object definition is stored in the definition database. The database requires to be updated based on the availability of new template definitions.

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• Notification Factory: This sub-component receives notification about the events in a different vehicle with the installed system. Further, this component receives and validates the events. In order to handle multiple events, it maintains a queue.

(iii) Tier 3 consists of mobile applications that are executed on the users’ devices. The application helps a user, who witnesses a crime, to notify the police.