Abstract
The number of devices and network use on the Internet of Things has exploded in this modern era. But due to usage of large number of network devices, the more energy consumption is required. Energy consumption is now became the state of art in the view to achieve the green IoT consistency and the implementation of smart world. Intelligent transportation is one of the important aspects of technically smart world. To develop a viable agile world, an energy efficient environment must be created to decrease the carbon dioxide (CO2) emissions by sensors, devices used in the application, and services of IoT. The use of IoT is also increased in smart transportation (car, train, bus, etc.). Nowadays, most of the transportation vehicles are Wi-Fi enabled which also needs energy consumption. In order to prevent more energy consumption, some effective ways should be provided. This article overviews the Green IoT, challenges and issues in Green IoT, various technologies used in Green IoT, and need of Green IoT in smart transportation, and also it focuses on the case study.
Keywords: Green IoT, smart transportation, challenges of IoT and Green IoT, Green IoT case study
10.1 Introduction
In recent years, the Internet of Things (IoT) has exploded in popularity. This had an effect on the performance of network and energy resources used by IoT devices. There has been a massive transition in almost every field of work as a result of rapid advancements in digital technology. The IoT has grown in popularity as a result of a growing trend toward the use of technologically advanced and networked devices. The IoT is a global network infrastructure made up of sensors, actuators, and equipment integrated in physical devices that can monitor, process, and share data over the internet. IoT models waste energy by remaining ON even though they are not in use because they are not built for energy consumption. As a result, a large amount of energy is consumed when it is turned ON and transmitting data 24 hours a day, all days in a week.
10.2 Challenges of IoT
The IoT is a leading-edge and hi-tech technology that has the potential to revolutionize the IT industry, but it comes at a price [1]. Security and privacy, which are described in [2] and [3] as one of the key areas on which experts must concentrate in order to gain users’ trust, are one of the various challenges faced by IoT technologies.
According to the mentioned paper, RFID tags can track an individual without their permission or knowledge, which could cause widespread mistrust among the public. However, energy would be the most significant obstacle in the implementation of IoT. The National Intelligence Council of the United States has estimated that by 2025, everyday things such as food, pens, and other related items incorporated in the internet. This means that the internet will be accessible to billions of users [4]. Depending on its functionality, each active RFID needs a very little quantity of power to operate, and active RFIDs are critical for efficient services. Consider how many billions of such devices consume energy every day; sensors relay millions of gigabytes of data, which must be processed or edited by huge data centers, necessitating massive processing and analytical capabilities. Furthermore, CO2 emissions from ICT products are steadily increasing, causing harm to our atmosphere [5] and are expected to continue if appropriate steps are not taken to resolve this issue. The Green IoT is a crucial subject for resolving these vital issues.
10.2.1 Green IoT
The Green IoT is focused on energy conservation in IoT principles. Green IoT refers to IoT methods that are energy-efficient and can minimize or eliminate the greenhouse gas emissions emitted by current applications. The environmentally friendly and energy-efficient characteristics of the IoT are referred to as “GREEN”. These characteristics are achieved by the use of energy-efficient methods and techniques on both the hardware level and software level, which aids to reduce energy demand, CO2 emissions, and the greenhouse effect associated with current IoT systems, goods, and services. Green IoT guarantees that the system is only switched on when it is needed and that it is inactive or turned off when not needed.
Proper ventilation of heat produced by servers and data centers, as well as energy efficiency using smart IoT technology, are two examples of Green IoT solutions that can save a large amount of energy. In order to achieve green IoT reliability and a smart environment, energy use is being state of the art [6]. The planet is becoming more sophisticated as science and modern technology progress at an exponential rate. In such a smart world, smart and intelligent devices (e.g., computer systems, mobile phones, and watches), smart environment (e.g., workplaces, factories, and homes), smart mobility (e.g., trains, cars, and buses), and so on will serve people automatically and collectively [7].
For example, a Global Positioning System (GPS) can constantly upload a location of an individual person to a device, which will immediately return the quickest path to the travel destination of individual, preventing traffic jams. Furthermore, any abnormality in an individual’s voice will be immediately identified and sent to a server, which will compare the abnormality to a series of collected voiceprints to decide if the person is sick. In a smart society, all facets of people’s cyber, physical, emotional, and mental environments would be inevitably interconnected and intellectual.
Green IoT Techniques are based on the following:
- • Hardware
- • Software
- • Habitual
- • Awareness
- • Recycling
10.2.1.1 Hardware-Based
The majority of IoT energy consumption models [1] depend on algorithm used and hardware improvements, but categorizing objects in an IoT network can be very useful in making it “GREEN”. The MECA algorithm [8], which is used to solve the optimization problem, uses a three-layer architecture to construct the network of GREEN Technology goals. Although Active RFID Optimization was discussed in [9], advances in Passive RFID [10] and the Wireless Identification and Sensing Platform (WISP) [11] will result in to more effective and low power computation in the IoT. A series of energy-efficient commands can also trigger communication delays between sensor nodes and interrogators, resulting in substantial energy waste. In an IoT network, designing of IC’s, i.e., an Integrated Circuits are important in terms of energy conservation. Green Sensors on Chip (SoC) [12] enhance IoT network architecture by integrating sensors and computing power on a single chip, resulting in lower traffic carbon footprint, e-waste, and overall energy consumption. While the Sleep Walker model saves energy through the use of a Green SoC, this model can save even more energy through the use of recyclable materials.
10.2.1.2 Software-Based
Although data centers are critical components of an energy-efficient IoT network, they must be properly maintained before being used for IoT. In [13], researchers introduces a Policy-Based Architecture, i.e., E-CAB, that employs an Orchestration Agent (OA) in a Client-Server Model, which is responsible for context assessment of servers in terms of utilization of resources, as well as management.
The stored data is then sent back to client devices via intelligently chosen servers. This design, however, necessitates the installation of OA on each client-side computer, as well as the use of backup servers, which can lead into a very high energy consumption. To boost energy efficiency, C-MOSDEN [14], a context-aware sensing device, uses selective sensing. The results show that energy consumption has decreased, but there are a few minor overheads that, if eliminated, might make this model extremely efficient.
10.2.1.3 Policy-Based
The various policies and approaches focused on real-time data from IoT sensors will aid in large-scale energy savings. Monitoring (different contexts of energy consumption), knowledge processing, customer input, and an automation mechanism are all stages of developing policies for achieving energy efficiency. We may use data obtained from various areas of a building where occupants’ behavior is observed. Automation systems can assist in identifying the position of a building’s occupants as well as environmental improvements, allowing us to make energy-saving choices. City Explorer [15], the home automation solution used in [16], is divided into three levels, each of which is responsible for storage of data, processing of data, and providing services such as energy efficiency. As the previously discussed, policy-based system is used in real-life situations, and energy demand is decreased by 20%.
10.2.1.4 Awareness-Based
While public awareness campaigns are effective at reducing energy consumption, their effectiveness varies by culture and country, making it difficult to guess how many people will respond, listen, and support such campaigns. Using Smart Metering Technology, we can provide homeowners with real-time reports on their energy use from various sources in their homes, workplaces, and buildings, and then advise them on how to track and minimize their energy use based on the real-time data. This results in saving 3%–6% of the energy consumed [17].
10.2.1.5 Habitual-Based
Another strategy for improving energy quality and lowering carbon emissions is to implement a few basic practices that reduce energy consumption in our daily lives. While this is a small-scale measure, when the small savings are added up on a global scale, it adds up to a significant difference. One solution is to monitor energy use patterns in workplaces, homes, and factories using the automation systems suggested in [18–20] and then alleviate energy losses in our everyday activities. Though we should not rely too heavily on this technique, it can be useful.
10.2.1.6 Recycling-Based
Recyclable materials are used in the design of IoT network equipment would aid in its environmental friendliness. Mobile phones, for example, are manufactured by using some of the most costly resources available naturally, such as copper, and contain non-biodegradable materials that, if not properly disposed of when no longer in use, will contribute to the greenhouse effect. According to reports, there are 23 million no longer used mobile phones in drawers and cupboards in Australia [21], and 90% of the material used for making phones is recyclable, and therefore, recycling is becoming increasingly important if we have to address the problem of greenhouse gas emissions and huge energy consumption. Although 90% waste recovery is a lofty goal, it has the potential to save a significant amount of energy. Many strategies for improving the smart phone’s power usage and performance were proposed in [22]. EEE (electric and electronic equipment) has recently used the efficient collection and recovery mechanism of the basic feature for each EEE type as a source of metal [23]. When the charger is connected, solar energy is favored more than 20% of the time, according to the sensitivity study [24].
10.3 Green IoT Communication Components
Green IoT is primarily comprised of networking technologies [25] such as Green RFID [26], Green WSN [27], Green CC [28], Green Machine to Machine (M2M) [29], and Green DC [30]. Cloud computing is abbreviated as CC, Machine to Machine is abbreviated as M2M, and data centers is abbreviated as DC. Figure 10.1 shows the cycle.
10.3.1 Green Internet Technologies
Green Internet Technologies necessitate the use of specially designed hardware and software that consume less energy without losing performance while optimizing power use.
10.3.2 Green RFID Tags
RFID tags can hold data or information at a low level for any items that are connected to them. RFID transmission necessitates RFID systems with a few meter radius. Passive RFID tags lack support of an active battery source, whereas active RFID tags have built-in batteries that allow them to continuously transmit their own signal. The reader’s energy is stored in them. An RFID tag’s size can be decreased, which aids in the reduction of non-biodegradable waste.
Figure 10.1 Green cycle.
10.3.3 Green WSN
The Wireless Sensor Network (WSN) has a large number of sensor nodes, but their power and storage capacity are limited. Green energy management, radio optimization, green routing techniques that minimize mobility energy consumption, and smart data algorithms that reduce storage space and data size requirements can all contribute to the development of a green WSN.
10.3.4 Green Cloud Computing
We are aware that there are cloud computing frameworks such as IaaS, PaaS, and SaaS. In a green system, hardware and applications have to be used in such a manner that energy demand is reduced. Energy-efficient policies must be implemented. Green cloud computing scheme–based technologies such as communication and networking are also to be used.
10.3.5 Green DC
Data centers are in charge of collecting, handling, sorting, and disseminating all forms of data and software. Data centers should be built with clean energy resources in mind. Aside from that, routing protocols should be configured to be energy sensitive, turning off idle network devices and incorporating energy parameters into packet routing.
10.3.6 Green M2M
Since a huge number of computers are involved in M2M communication, there should be energy-saving transmitting capacity and streamlined communication protocols, as well as routing algorithms. There should be search passive nodes in order to save resources.
10.4 Applications of IoT and Green IoT
The numerous applications of IoT and Green IoT are as follows in Figure 10.2.
- • Smart Home
- • Smart Industries
- • Smart City
- • Smart Healthcare
- • Smart Transportation
Out of these, this post mainly focuses on the smart transportation.
Figure 10.2 Green IoT applications.
10.4.1 Green IoT in Transportation
In recent years, the number of cars on the road has steadily increased. By 2030, it is estimated that it will rise to two billion people. This is due in part to global urbanization. In coming years, there will be significant change in transportation modes [31], particularly as demand of electric cars grows in the market [32] due to enormous increase in fuel prices. The impending ban of diesel-powered vehicles due to environmental concerns [33] and the introduction of new energy technologies such as hydrogen-powered vehicles [34] would alter the future shape of transportation systems. More environmentally friendly transportation options are needed in general, and these are being built incrementally with market penetration in mind. Vehicles, bicycles, buses, trains, and highways have recently received sensors, actuators tags, and the processing power required to relay vital information to traffic control websites. Modern smart transportation systems make it easier to better route traffic, provide visitors with accurate transportation statistics, and track the status of goods or products being transported. To ensure desirable vehicle autonomy, unique vehicle technologies require the construction of transportation infrastructure. The internet of vehicles definition [35] has recently emerged, demonstrating the IoT’s potential in this important field. In the case of the automated smart car (vehicles) model, IoT is the most important application field [36]. The smart car idea takes into account the use and optimization of various internal functions in the vehicle that are enabled by IoT technologies. The driver’s experience, as well as their comfort and safety, will be enhanced by the use of IoT. The smart car gathers data and correlates it with key operating parameters such as tyre pressure, charging, early detection of potential faults, and routine maintenance indicators, among others. A modern vehicle has evolved into a sensor network that gathers data from the surrounding environment. Data processed by a computer which is on-board and used for navigation, pollution control, and traffic management, among other items. Fast data processing, on the other hand, necessitates the use of a powerful computer which is on-board. This is one of the reasons why high-end vehicles with advanced driver assistance systems are so costly. Heavy computing activities should be able to be uploaded to the cloud through the Internet to prevent the use of costly equipment. As a result, in addition to the data already gathered by cars, IoT will assist traffic control centers in gathering additional data. In general, the targeted use of IoT technologies can result in improved service and added value for customers, which can help car manufacturers compete more effectively in the automotive industry. The most challenging part of IoT deployment when it comes to self-driving vehicles is in [37]. The autonomous vehicle’s location, path, and planned route could be aided by the IoT in general, as well as autonomous vehicle safety system monitoring [38]. The production of fully autonomous vehicles is rapidly increasing, fueled by automotive industry competition and electro mobility. Vehicles must use local and global V2V communication in this context to allow for smoother, more efficient, and comfortable driving, as the vehicle would be able to detect hazardous situations ahead of time, even if they are out of sight due to a curve or other vehicles in front. Crash prevention and avoidance is the most challenging problem with automated vehicles, which could be addressed by strategically deploying IoT devices [39]. In recent years, the growing availability of vehicles has created a problem of finding vacant parking spaces, especially in major cities. This condition leads to emissions, fuel waste, and dissatisfaction among motorists. Smart Parking Systems, which provide real-time information and are a cost-effective and safe solution based on IoT technologies [40], can help solve this issue. Traffic issues, such as traffic congestion, are becoming more common as the world’s population grows. Using the technology of vehicular ad hoc networks (VANETs), it is possible to avoid traffic congestion, which allows vehicles to communicate with one another and share road data in order to gain a better understanding of road conditions [41, 42]. Again, sensor technology advancements, such as smart parking sensors, are crucial for delivering reliable and accurate service [43]. The important issues that must be addressed, such as the rising number of people killed in car accidents and global environmental degradation. As a result, for using ICT to solve transportation problems, designing intelligent transportation system application is important so that it must have the potential to improve safety. IoT could help with vehicle maintenance and failure prevention [44], which could increase vehicle protection and lifespan. Taking all into account, IoT innovations have the potential to fully transform the driving experience and increase the overall efficiency of transportation systems in a variety of ways. Real-time supply chain monitoring is mainly owing to data collected via RFID, NFC, and sensors. These technologies can also capture product-related data in real time, enabling companies to respond as quickly as possible to changing consumer dynamics. In most cases, a typical company requires about 120 days to fulfil a customer’s request. Enterprises that use innovative technology, such as Wal-Mart and Metro, on the other hand, just require a few days to meet consumer demands [45, 46].
Intelligent Transportation, in which vehicles are regarded as intelligent mobile devices capable of connecting to the network and exchanging knowledge about their environment, is a topic that has a big impact on how a smart city allocates resources intelligently. In fact, for governments and modern economic growth in general, improving the transportation management system and promoting sustainability is crucial. Lower environmental impacts, energy savings, and time and money savings will all benefit from transportation system optimization. Despite the many benefits of the 5G era and the IoT, there are still technical challenges to overcome.
10.5 Issues of Concern
10.5.1 End User Viewpoints
Though various IoT devices used for autonomous vehicles, its acceptance and adaptation depends on its users. End user viewpoints, i.e., simplicity and user friendliness are needed to be consider while designing and developing IoT devices and applications.
10.5.2 Energy Conservation
As various sensors used in autonomous vehicles, sensors’ continuous sensing often causes them to rapidly deplete their resources. Various sleeping strategies have been suggested in the past to save space. In certain cases, extrapolating from previous data is a viable option [47], and this is an area that has the potential to save significant amounts of energy for sensor devices and deserves more research in the upcoming years.
10.5.3 Data Security and Privacy
There is a risk of unauthorized access to users’ personal information, just as there is with self-driving cars in cities, where all of our data is shared in the cloud. Security threats exist, but they are tackled from the perspectives of preventing deadly accidents in one case and protecting private information in the other.
10.5.4 Preserving Contextual Data
The IoT aims to link billions or trillions of smart devices to the Internet, ensuring a bright future for smart cities. These objects can generate massive amounts of data or information and send it to the cloud for processing, which is especially useful for finding information and taking subsequent action. Detecting all possible data items collected by a smart object and then sending the entire recorded data to the cloud, on the other hand, is less useful in practice. In addition, such a tactic would be a waste of money (e.g., network bandwidth and storage space). Collecting massive volumes of data without context would be of no benefit in the future and will waste a enormous time. Out of the most complex and difficult issues that IoT faces is maintaining the context or background of the data generated so that further research can provide more meaningful and useful results.
10.5.5 Bandwidth Availability and Connectivity
The growing number of IoT devices on the market would result in increased competition for usable bandwidth, including increased interference [48]. Future IoT devices must be designed to operate in congested environments while minimizing interference from other IoT devices and using the least amount of resources possible.
Previously, system extensions were often hardware-based and not very versatile. Thanks to today’s advancements in network technology, high speed processors, and cheaper memory, expanding the functionality of a computer, such as an IoT system, is now as simple as uploading a smartphone application to the device. As a result, it is easier and quicker to link to new products as they hit the market. This ensures that future IoT devices can take advantage of the variety and user-friendliness of various mobile applications.
10.6 Challenges for Green IoT
Around the world, transportation and traffic have become a major concern. Congestion is bad for public health, efficiency, and the environment, and reducing dependence on private cars will help solve many interconnected problems in these areas. Green transportation alternatives would also help to reduce the need for expensive infrastructure investments and contentious traffic regulations. Implementing new types of programs that provide community members with situational conscious information in multiple ways is one way to do this. Green technology will be critical in enabling an energy-efficient IoT. There are several difficult topics that must be tackled.
10.6.1 Standard Green IoT Architecture
The present and potential visions for smart cities are of modern urban development that integrates knowledge and the IoT to manage and track the majority of the city’s activities and resources. Smart city infrastructure is required as IoT devices are dispersed throughout the city and information is shared through various sensors attached to it. Energy consumption though these devices should be less to prevent environment from CO2 emission.
It is essential to develop such an architecture which is energy efficient and based on existing standard TCP/IP model. So developing standard Green IoT architecture is a research area to be considered on priority. WSN plays vital role in IoT.
10.6.2 Security and Quality of Service
Huge data flows in network hold a lot of personal privacy data, including identity, location, and private content. Privacy breaches may have serious consequences in some situations. When it comes to IoT implementation, security or safety and privacy are major issues to be considered. The implementation of protection algorithms necessitates a significant amount of processing on the part of computers. The promise of energy-efficient and safe mechanisms, which are still in their infancy, could entice further research and development [49]. As the huge amount of data is store and shared on cloud, security of data is of primary concern to maintain confidentiality and privacy of users’ data from hackers. Data integrity must be preserved by delivering high-quality service.
10.6.3 Data Mining and Optimization
Millions of IoT devices are increasing in coming years in the internet leading to increase in data stored on clouds. Storing data is not a big deal but to extract useful content from data is important. So developing data mining algorithm that provides optimal solution is an area of research to be considered by academicians and scientists.
10.6.4 Various Traffic Management and Scheduling-Based Smart Public Transport Solutions
The traffic monitoring system [50] contains many IoT-enabled devices. These complex interconnected devices needs interoperability that depends on alteration and self-administering actions is one of the challenges of overall transportation. Interoperability between different standards, data collection, heterogeneous gear, customs, resource create, programming, and database frameworks is the key problem in IoT. Another problem is the need for a user experience as well as access to different organisations and applications. This gives an idea that adaptable administrators are a valuable device for dealing with these problems to handle IoT interoperability and communication among such devices. In cases of low bandwidth, insignificant messages through systems to undefined objectives, and handling IoT interoperability, a versatile administrator is an excellent option. The TCP/IP Protocol is used to set up all notification exchanges among administrators. An expert of product is an independent executable program that monitors and responds to circumstances while acting to achieve predetermined goals. The operators should be able to move between organized devices, sharing their knowledge, implementation states, and should be possible to converse with other expert or humans. New approach involves implementing operator creativity during the period allotted for traffic monitoring and control. Such inventions are elegantly suits for traffic monitoring and regulating system as it follows self-ruling, adaptability, structurally, and flexibility. Experts are also helpful to send messages through proper networks where the actual position of the targeted traffic device is not known. A product expert (an operator) is used to answer to every traffic query. In this system, an astonishingly large number of devices would be linked and communicated to by their own smart operator who collects data and responds to other devices. Operators may demonstrate their worth as administrators. Every device contains an operator, and each device must support all professional capabilities, such as relocation and execution. The system as a whole can be regulated by a specific application. The system’s portable specialists inside the device move from one center to the next, allowing the devices to send and receive data, retrieve data, and locate available assets.
10.6.5 Scheduling and Admission Control for Independent Vehicle Public Transportation System
The Autonomous Vehicle Transportation System (AVTS) focuses on the system’s two main issues [51]:
- 1. Scheduling—to create the most cost-effective plan to meet transportation demands
- 2. To maximize benefit, admission control determines the collection of admissible requests among all requests.
A control center coordinates all vehicles, handles all service activities, and assigns vehicles to respond to requests. To evaluate arrival time and configure travel schedules, Dijkstra’s algorithm is used to analyze the scheduling problem. The admissions control problem is addressed by considering that all timetables are capable of transporting such solicitations with the shortest possible ride time. The above problem is addressed using a Genetic Algorithm–based methodology.
The study discovered that profit is highly dependent on the various parameters focusing on the individual demands, and its benefits from various cases cannot be exactly comparable, despite the fact that more benefit can be gained if the number of vehicles increases.
10.6.6 Impacts on Public Transportation Management by Applying AVLs
The paper [52] describes the various characteristics of monitoring framework and analyzing public transportation not in big but particularly for area of a medium sized. In this application, it is planned to implement and test a model for predicting bus positions and arrival times based on real-time data as well as past data from previous transportation operations for the same route as the forecast.
The investigation was carried out in the Nis’s Siberian city. A server was installed in the transportation system of city to collect positional updates from vehicles. A monitoring system was mounted on 150 buses, and an information monitor was installed at a specific bus stop. The prediction algorithm was found to be more specific than previous Kalman filters for transport data.
10.6.7 Lisbon and Portugal’s Bus Ride Study and Prediction of Transport Usage
The focus of the research paper [53] is mainly on accuracy with which people use public transportation. It finds out the data of accessibility of large trip records from large number of transport clients in Lisbon, Portugal through an electronic ticketing system. An electronic access history associated with the cardholder is registered once an explorer loads a transport. This data of bus transportation are being mined to analyze the scope and prediction of bus rider’s transport behavior. The prediction algorithm is used for the purpose as follows:
- 1. To analyze the extent of user rides prediction.
- 2. Depending on the prediction accuracy, categorization of bus according and bus usage behavior characteristics [53].
As per the data collected from Automatic Fate Collection Systems as well as Automatic Vehicle Location system, it is observed that for buses which run in regular fashion can be predicted with a high degree of accuracy for a maximum number of trips.
10.6.8 Smart Assistance for Public Transport System
This section explains the concept of transparent smart assistance in the public transportation system. The project has been carried out in favor of public transportation (for example, Pune Municipal Transportation in Pune). It includes the entire smart assistance system that is needed for public safety and well-being. The elegant structure also provides security for women. The accident site and the observing office are two additional modules in this mission. It was also simple to use the app for customers to monitor their transportation on their mobile phones. Both GSM and GPS modules can be used to plan the structure. In this case, to get the accurate coordinates for the disconnected (GSM) structure framework, the GPS structure is mostly used. People can get the information about the availability of seat in the transport, similarly stop information and the time when the transport arrives at the next stop. It also provides the facility for handicapped and older people to allow them a straight entry to take advantage of the vehicle. It also has an RFID-based driver verification system [54]. The framework additionally has several extra highlights to make people most familiar and to provide easy going transport facility.
10.7 Green IoT in Smart Transportation: Case Studies
10.7.1 Smart Traffic Signal
As it we see, most of the people do not follows rule especially at traffic signal. As a result, accident happened on traffic signal. Due to this, many people loss their life. According to NDTV repot 400 people loss their life every day in road accident. The main reason behind this is to violate rules. One smart system should be there which will automatically note the vehicle number which has violated rules. To avoid this, a smart chip has been designed. Once this chip inserted in the vehicle as shown in Figure 10.3, if any vehicle driver did not follows traffic signal, then the vehicle number is automatically display on screen. Once the unique number of vehicle gets catches now the system can send E-chalan to that driver. As the E-chalan application is already is there we directly transfer the unique to code to control room. Control room identifies the driver and E-chalan can issue to him. In this system, we use Arduino Uno as a microcontroller, passive RFID chip, and RFID sensor. RFID chip contains the unique code (engine number) of vehicle and to sense the unique code RFID sensor is used. To uniquely identify an object, animal, and person, the RFID (Radio Frequency Identification) uses electromagnetic or electrostatic coupling in radio frequency portion of electromagnetic spectrum, and it comes under the wireless communication.
Figure 10.3 Smart traffic signal system.
The working of this model is based on microcontroller. The RFID reader collects the unique code from RFID chip and displays it on screen. The RFID sensor is ON when the traffic signal is RED and get OFF when it get GREEN. When any vehicle driver break signal when traffic light is RED, RFID sensor read the unique code from RFID chip and display it on screen.
10.7.1.1 Description
A RFID reader is a device that collects data from a tag of RFID that is used to monitor a vehicle’s unique id number. Radio waves play an important role for transmission of data from the tag to the reader. Because of high cost and the need to mark each object individually, RFID tags have not yet replaced bar codes. An RFID tag is used to store digital data in RFID technology. RFID consists of integrated circuits with a small antenna for transmitting data to an RFID transceiver. Since water absorbs wavelengths in the 2.4 GHz range, they are restricted. The Light Dependent Resistor (LDR) is made up of a piece of uncovered semiconductor material, such as cadmium sulfide, that changes its electrical resistance from thousands of Ohms in the dark to only a few hundred Ohms when light shines on it, causing hole-electron pairs to form in the material. It is made up of semiconductor substrates such as lead sulfide (PbS), lead selenide (PbSe), and indium antimonide (InSb) that detect light in the infrared range, with cadmium sulfide (CdS) being the most widely used of all photoresistive light sensors (CdS).
10.7.1.2 Application
- 1. It reduces the human effort in traffic control.
- 2. It is used to take immediate action against the violation of rules.
- 3. It is economic to use because the cost of chip is less.
- 4. Lost vehicle can be identified.
10.7.1.3 Advantages
- 1. The cost of chips as well as reader is less.
- 2. RFID chip life is 10 years.
- 3. Work of traffic police get reduces.
- 4. No power supply is required for chip.
- 5. Size of chip is compact.
- 6. In case of accident when it unable to find of the owner of vehicle, at that time by scanning the unique code the details of owner can be get.
10.7.1.4 Disadvantages
- 1. The chips should be inserted in vehicle and should have its engine number as its unique code.
- 2. The reading range is only 3 ft.
10.7.2 Cloud-Based Smart Parking System
The application described in [55] has different components. The simple slot detection is achieved with infrared sensors that are used to send data to a database. The user must register and book a slot, and payment must be made online. RFID tags are used to detect entries and exits at the entrance. The Rpi3 microprocessor is used in the device. In a large parking lot, the proposed system reduces waiting time and helps to ensure more effective usage of the entire parking lot. This method is also efficient in terms of reducing paper use and lowering costs. A prototype including an Android application on the MIT app inventor was linked to cloud storage via fire-base as a proof of concept (POC). Once the reservation is done, the hardware system which is implemented at the car parking will wait for the user’s arrival and, after identification at the doors, will let them in. To retrieve the data, the RPi3 connects directly to the firebase. Every parking bay’s IR sensors, LEDs, and LCD are all connected to the NodeMCU. On the LCD at the entrance, the NodeMCU shows the total count inside the parking lot as a way to be reminiscent customers looking for parking. The overall block connectivity diagram for two parking lots are operated by the same system as shown in Figure 10.4.
Two Arduino Uno are used at Parking 1 [Car Parking (CP1)] and [Car Parking 2 (CP2)], one at each entry and exit of CP1 and CP2. In addition, infrared sensors have been introduced in place of ultrasonic sensors. If the scanned RFID tag is approved, at the entrance, Arduino Uno controls the RFID tags and the servo motor that lifts the gate. It also checks the length of the vehicle and shows messages on the various LCDs. Similar features are available on the Arduino Uno at the exit, but it lacks the length sensor. To share data about number plate authentication and RFID anti-pass-backvalues, the Arduino Uno communicate serially with the RPi3.
The proposed framework is more integrated and linked with each other using serial and cloud networking, as seen in the overall block diagram as shown in Figure 10.5. The system’s inner working is described in detail below.
Figure 10.4 Overall architecture of cloud-based parking system.
Figure 10.5 Overall block diagram.
10.7.2.1 Hardware—Car Parks CP1 and CP2
Once the car enters the parking and after detecting as a light vehicle (based on length), the access card get checked by the Arduino Uno with its RFID tag and if the card is flashed, the Arduino Uno checks with the RPi3 to see if the card has been used to enter the parking before closing the gate. With this process a pass back get avoided. If the card is not flashed, the Arduino Uno sends the checking command to RPi3 to activate image processing portion.
10.7.2.2 Smart Airport Management System
Airport management is a dynamic structure that is separated into phases. In this part, we suggest an IoT-based airport management application [56, 57]. Each component of the device is regarded as a thing. In this smart airport application system, the terms are described the following: the object of the operations office, the object of the desk for check-in, the object of the departure lounge, the passengers, baggage, the aircraft, and the crew member.
Each device object is self-contained and communicates with other components. Figure 10.6 depicts the system’s elements and their relationships.
Figure 10.6 Architecture of airport management system.
The different steps for airport management described as follows:
- • The airport’s important section is the operations room. It is in charge of the check-in counters, departure lounges, and flights. As a result, the assignment of check-in desk to a flight is carried out by the operations room, and the list of already booked passengers is also provide to the officers. The flight’s departure lounge is also assigned by the operations room.
- • When passengers with their baggage arrive at the check-in counter, each passenger is given an electronic access key, and on each suitcase, an electronic sticker is placed. Passengers with their baggage are now treated as objects by the machine.
- • Passengers and their baggage can take two separate routes to the aircraft. In this article, the main focus is on passenger routes.
- • The registered passenger’s list will be sent to the departure lounge by the check-in-desk assigned to the flight at the conclusion of the check-in process.
To find the appropriate departure lounge, the registered passenger’s list is very useful. If the passenger ID does not appear on the list of departure lounges, then he is led to the correct one.
- • A warning is sent prior to boarding (usually 20 minutes) to notify screened passengers who are not available in the departure lounge.
- • Prior to boarding, the passenger’s list who are available in the departure lounge is provided to the plane.
- • Each member of the crew wears an electronic badge that allows him to be seen in the system.
- • Passengers on board the plane are added to the list of enplaned passengers.
- • Prior to take-off, the number of passengers on board get compared to the passenger’s list preparing to board. If a traveller does not return, then his baggage is withdrawn and the luggage inventory is revised.
- • When the plane lands at the destination, it sends passenger’s list present in plane to the destination airport’s operations room.
10.7.2.3 Intelligent Vehicle Parking System
Sensors are really important in this application [57]. It helps in gathering information about the vehicle’s geographic location, parking lot capacity (as shown in Figure 10.7), prior reservation data, parking status, vehicle details, and information of current traffic. As a result, big data plays a significant role in this case, as it requires real-time implementation with the capability of providing a smart transportation infrastructure. The outcome factors such as occupied or free component influence the vehicle parking judgment. If the location is free and open for parking, then it is labeled as such. If there are cars present, then the position is identified as inhabited. The parking decision is dependent on the implementation of the conclusion, which will be modified over time through sensors. Then, the server is updated with the decision. For the final decision on the parking slot, these features are compared to the specified threshold value.
Figure 10.7 Architecture of intelligent vehicle monitoring system.
10.7.2.4 IoT-Based Smart Vehicle Monitoring System
The SVSM [58] was not a straightforward method, but rather a tool for quickly detecting serious injuries. SVMS also allows the driver to remotely disable the car after a burglary. It also helps the driver to find the location of the car from anywhere in the world. To support all of this, the SVMS contains a Raspberry Pi, as well as various sensors, a GSM/GPRS module, and a GPS module.
Figure 10.8 depicts the overall architecture of the SVMS system. Figure 10.9 depicts the internal architecture of the IoT system. The Raspberry Pi IoT device includes several sensors, a camera, a GSM/GPRS module, and a GPS module.
Any change in acceleration in any direction, as well as any tilt or rotation, can be observed. The impact sensor has been used in cars to detect crashes and deploy airbags in recent years. SVMS uses these two sensors to detect any injuries. The accelerometer readings are continuously monitored when the acceleration or deceleration reaches a threshold value or the sensor is tilted. It will detect an accident if acceleration exceeds the threshold value.
Figure 10.8 Overall architecture of the SVMS system.
Figure 10.9 Internal architecture of IoT system.
10.8 Conclusion
Intelligent transportation is one of the important aspects of technically smart world. The use of IoT is also increased in smart transportation (car, train, bus, etc.). Nowadays, most of the transportation vehicles are Wi-Fi enabled which also needs energy consumption. Similarly, Green IoT is used nowadays in smart transportation. But it has issues such as energy conservation, data security and privacy, preserving contextual data, bandwidth availability and connectivity, and challenges such as security and quality of service are discussed here. The various technologies used in Green IoT are discussed here. The case studies related to various transportation issues are also described in this chapter. The case studies which are discussed here are implemented by focusing on cloud-based parking, smart traffic signal, smart airport management system, etc.
References
1. Arshad, R., Zahoor, S., Shah, M.A., Wahid, A., Yu, H., Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond, Special Section On Future Networks: Architectures. Protocols, and Applications, IEEE ACCESS, Received February 1, 2017, accepted February 26, 2017, date of publication July 31, 2017, date of current version August 22, 2017. Digital Object Identifier 10.1109/ACCESS.2017.2686092. pp. 15667-15681.
2. Weber, R.H., Internet of Things–New security and privacy challenges. Comput. Law Secur. Rev., 26, 1, 23–30, 2010.
3. Singh, D., Tripathi, G., Jara, A.J., A survey of Internet-of-Things: Future vision, architecture, challenges and services, in: Proc. IEEE World Forum Internet Things, Mar. 2014, pp. 287–292.
4. Lee, C.-S., Kim, D.-H., Kim, J.-D., An energy efficient active RFID protocol to avoid overhearing problem. IEEE Sens. J., 14, 1, 15–24, Jan. 2014.
5. Gelenbe, E. and Caseau, Y., The impact of information technology on energy consumption and carbon emissions, in: Proc. Ubiquity, Jun. 2015, pp. 1–15.
6. Zhu, C., Leung, V.C.M., Shu, L., Ngai, E.C.-H., Green Internet of Things for Smart World. Special Section On Challenges For Smart Worlds Received IEEE ACCESS, October 9, 2015, accepted October 17, 2015, date of publication November 3, 2015, date of current version November 16, 2015. Digital Object Identifier 10.1109/ACCESS.2015.2497312. pp. 2151–2162.
7. Han, Q., Liang, S., Zhang, H., Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world. IEEE Netw., 29, 2, 40–45, Mar./ Apr. 2015.
8. Caragliu, A., Del Bo, C., Nijkamp, P., Smart cities in Europe. J. Urban Technol., 18, 2, 65–82, 2011.
9. Weber, R.H., Internet of Things New security and privacy challenges. Comput. Law Secur. Rev., 26, 1, 23–30, 2010.
10. Occhiuzzi, C., Caizzone, S., Marrocco, G., Passive UHF RFID antennas for sensing applications: Principles, methods, and classifcations. IEEE Antennas Propag. Mag., 55, 6, 14–34, Dec. 2013.
11. Sample, A.P., Yeager, D.J., Powledge, P.S., Smith, J.R., Design of a passively-powered, programmable sensing platform for UHF RFID systems, in: Proc. IEEE Int. Conf. RFID, Mar. 2007, pp. 149–156.
12. Boll, D. et al., Green SoCs for a sustainable Internet-of-Things, in: Proc. Faible Tension Faible Consommation (FTFC), pp. 1–4, 2013.
13. Peoples, C., Parr, G., McClean, S., Scotney, B., Morrow, P., Performance evaluation of green data centre management supporting Sustainable growth of the Internet of Things. Simul. Model. Pract. Theory, 34, 221–242, May 2013.
14. Perera, C., Talagala, D.S., Liu, C.H., Estrella, J.C., Energy-efficient location and activity-aware on-demand mobile distributed sensing platform for sensing as a service in IoT clouds. IEEE Trans. Comput. Soc. Syst., 2, 4, 171–181, Dec. 2015.
15. Zamora-Izquierdo, M.A., Santa, J., Gómez-Skarmeta, A.F., An integral and networked home automation solution for indoor ambient intelligence. IEEE Pervasive Comput., 9, 4, 66–77, Oct./Dec. 2010.
16. Moreno, M.V., Úbeda, B., Skarmeta, A.F., Zamora, M.A., How can we tackle energy efficiency in IoT based smart buildings? Sensors, 14, 6, 9582–9614, 2014.
17. McKerracher, C. and Torriti, J., Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays. Energy Effic., 6, 2, 387–405, 2013.
18. Occhiuzzi, C., Caizzone, S., Marrocco, G., Passive UHF RFID antennas for sensing applications: Principles, methods, and classifcations. IEEE Antennas Propag. Mag., 55, 6, 14–34, Dec. 2013.
19. Fensel, A., Kumar, V., Tomic, S.D.K., End-user interfaces for energy efficient semantically enabled smart homes. Energy Effic., 7, 4, 655–675, 2014.
20. Moreno-Cano, M.V., Zamora-Izquierdo, M.A., Santa, J., Skarmeta, A.F., An indoor localization system based on artificial Neural networks and particle filters applied to intelligent buildings. Neurocomputing, 122, 116–125, Dec. 2013.
21. Foteinos, V., Kelaidonis, D., Poulios, G., Vlacheas, P., Stavroulaki, V., Demestichas, P., Cognitive management for the Internet of Things: A framework for enabling autonomous applications. IEEE Veh. Technol. Mag., 8, 4, 90–99, Dec. 2013.
22. Falaki, H., Lymberopoulos, D., Mahajan, R., Kandula, S., Estrin, D., A rst look at traffic on smartphones, in: Proc. 10th Annu. Conf. Internet Meas. (IMC), p. 281, 2010.
23. Oguchi, M., Murakami, S., Sakanakura, H., Kida, A., Kameya, T., A preliminary categorization of end-of-life electrical and electronic equipment as secondary metal resources. Waste Manage., 31, 9-10, 2150–2160, 2011.
24. Zink, T., Maker, F., Geyer, R., Amirtharajah, R., Akella, V., Comparative life cycle assessment of smart phone reuse: Repurposing vs. refurbishment. Int. J. Life Cycle Assess., 19, 5, 1099–1109, 2014.
25. Ahmed, R., Asim, M., Khan, S.Z., Singh, B., Green IoT—Issues and Challenges. 2nd International Conference on Advanced Computing and Software Engineering (ICACSE-2019).
26. Namboodiri, V. and Gao, L., Energy-aware tag anticollision protocols for RFID systems. IEEE Trans. Mob. Comput., 9, 1, 44–59, 2010.
27. Lin, Y.-H. et al., Optimal and maximized configurable power saving protocols for corona-based wireless sensor networks. IEEE Trans. Mob. Comput., 14, 12, 2544–2559, 2015.
28. Farahnakian, F. et al., Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans. Serv. Comput., 8, 2, 187–198, 2015.
29. Chang, C.–H., Chang, R.Y., Hsieh, H.–Y., Highfidelity energy–efficient machine–to–machine communication. Personal, Indoor, and Mobile Radio Communication (PIMRC), 2014 IEEE 25th Annual International Symposium on. IEEE, 2014.
30. Shuja, J. et al., Survey of techniques and architectures for designing energy-efficient data centers. IEEE Syst. J., 10, 2, 507–519, 2016.
31. Jonkeren, O., Francke, J., Visser, J., A shift-share based tool for assessing the contribution of a modal shift to the decarbonisation of inland freight transport. Eur. Transport Res. Rev., 11, 1, 1–15, 2019.
32. Capuder, T., Sprčić, D.M., Zoričić, D., Pandžić, H., Review of challenges and assessment of electric vehicles integration policy goals: integrated risk analysis approach. International journal of electrical power & energy systems (01420615) 119 (2020); 105894, 12.
33. Li, J., Han, Y., Mao, G., Wang, P., Optimization of exhaust emissions from marine engine fueled with LNG/diesel using response surface methodology. Energ. Source. Part A Recovery, Util. Environ. Eff., 42, 12, 1436e1448, 2020c.
34. Ajanovic, A. and Haas, R., Economic and environmental prospects for battery electric- and fuel cell vehicles: a review. Fuel Cells, 19, 5, 515e529, 2019.
35. Shen, X., Fantacci, R., Chen, S., Internet of vehicles. Proc. IEEE, 108, 2, 242e245, Article number 8967259, 2020.
36. Chugh, A., Jain, C., Mishra, V.P., IoT-based multifunctional smart toy car. Lect. Notes Netw. Syst., 103, 455e461, 2020.
37. Padmaja, B., Narasimha Rao, P.V., Madhu Bala, M., Rao Patro, E.K., Proceedings of the International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2018, 26 February 2019, p. 18e21, Article number 8653736, 2019.
38. Bylykbashi, K., Qafzezi, E., Ikeda, M., Matsuo, K., Barolli, L., Fuzzy-based Driver Monitoring System (FDMS): implementation of two intelligent FDMSs and a testbed for safe driving in VANETs. Future Gener. Comput. Syst., 105, 665e674, 2020.
39. Abdou, M., Mohammed, R., Hosny, Z., Essam, M., Zaki, M., Hassan, M., Eid, M., Mostafa, H., Proceedings of the International Conference on Microelectronics, ICM, December 2019, vol. 2019-December, p. 103e107, Article number 9021613, 2019.
40. Luque-Vega, L.F., Michel-Torres, D.A., Lopez-Neri, E., Carlos-Mancilla, M.A., Gonzalez-Jimenez, L.E., Iot smart parking system based on the visualaided smart vehicle presence sensor: SPIN-V. Sensors, 20, 5, 2019. DOI: 10.3390/s20051476
41. Xu, Y. et al., Data collection for the detection of urban traffic congestion by vanets, in: Proc. APSCC, pp. 405–410, 2010. DOI: 10.3390/s20051476
42. Xu, Y., Wu, Y., Xu, J., Sun, L., Efficient detection scheme for urban traffic congestion using buses, in: Proc. 26th Int. Conf. WAINA, pp. 287–293, 2012.
43. Perković, T., Solic, P., Zargariasl, H., Coko, D., Rodrigues, J.J.P.C., Smart parking sensors: state of the art and performance evaluation. J. Cleaner Prod., 262, 121181, 2020a.
44. Saki, M., Abolhasan, M., Lipman, J.E., A novel approach for big data classification and transportation in rail networks. IEEE Trans. Intell. Transp. Syst., 21, 3, 1239e1249. Article number 8701707, 2020.
45. Yuan, R.. Shumin L., Baogang, Y., Value chain oriented RFID system framework and enterprise application, Science Press, Beijing, 2007.
46. Hugl, Ulrike, Smart Technologies and Privacy: Running a close race?, 2006.
47. Patterson, D., Liao, L., Fox, D., Kautz, H., Inferring high-level behavior from low-level sensors, in: Proc. UbiComp, pp. 73–89, 2003.
48. Tragos, E., Zeadally, S., Fragkiadakis, A.G., Siris, V.A., Spectrum assignment in cognitive radio networks: A survey. IEEE Tutor. Surv. Commun., 15, 3, 1108–1135, Third Quarter 2013.
49. Caviglione, L., Merlo, A., Migliardi, M., What is green security?, in: Conf. Rec. IEEE 7th IAS Annu. Meeting, pp. 366–371, 2011.
50. Jalaney, J. and Ganesh, R.S., Review on IoT Based Architecture for Smart Public Transport System. Int. J. Appl. Eng. Res. 14, 2, 466–471, 2019.
51. Lam, A.Y.S., Leung, Y.-W., Chu, X., Autonomous-vehicle public transportation system: scheduling and admission control. IEEE Trans. Intell. Transp. Syst., 17, 5, 1210–1226, 2016.
52. Shalaby, A. and Farhan, A., Prediction model of bus arrival and departure times using AVL and APC data. J. Public Transp., 7, 1, 3, 2004.
53. Foell, S., Phithakkitnukoon, S., Kortuem, G., Veloso, M., Bento, C., Predictability of public transport usage: A study of bus rides in Lisbon, Portugal. IEEE Trans. Intell. Transp. Syst., 16, 5, 2955–29, 2015.
54. Rathod, R. and Khot, S.T., Smart assistance for the public transport system, in: Inventive Computation Technologies (ICICT), International Conference on, vol. 3, IEEE, pp. 1–5, 2016.
55. Mukadam, Z. and Logeswaran, R., A Cloud-Based Smart Parking System Based On Iot Technologies. Journal of Critical Reviews, 7, 3, 105-109, 2020.
56. Bouyakoub, S., Belkhir, A., Guebli, W., Smart airport: an IoT-based Airport Management System. ICFNDS ‘17, Cambridge, United Kingdom, July 19-20, 2017.
57. Muthuramalingam, S., Bharathi, A., Rakesh kumar, S., Gayathri, N., Sathiyaraj, R., Balamurugan, B., IoT Based Intelligent Transportation System (IoT-ITS) for Global Perspective: A Case Study, in: Internet of Things and Big Data Analytics for Smart Generation, Intelligent Systems Reference Library, vol. 154, V.E. Balas and et al. (Eds.), Springer Nature Switzerland AG, 2019, https://doi.org/10.1007/978-3-030-04203-5-13.
58. Mallidi, S Kumar Reddy., IoT Based Smart Vehicle Monitoring System. International Journal of Advanced Research in Computer Science, 9, 738–741, 2018.
