Journal articles on the topic 'Smart vehicle'
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Bharathi, V. C. "Smart Parking System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 20, 2021): 1823–26. http://dx.doi.org/10.22214/ijraset.2021.36746.
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In the modern age, many people have vehicles. Vehicle is now a primary need. Every place is under process of urbanization. There are many supermarkets and shopping centers etc. There are many creative places where people used to go for refreshing and relaxation. All these places are full of with people so they need a parking space where people can park their vehicles safely and easily. Every parking area needs a website or system that records the detail of vehicles to give the parking facility. With the help of iot based system we can deliver a good service to users/people who wants to park their vehicles into organization’s premises. Present days in parking areas they just maintain the vehicles just with tokens and they have records of vehicle details in books so that during some critical situations like police enquiry of terrorist car or vehicle missing that case it is difficult to find the details of particular vehicle. But with our parking management system it is easy to find within 1 to 2 seconds. By parking the vehicle in public place the vehicle can be claimed by other person but in this case there is no such problem and no need to give fine for anything we can park our vehicle with securely.
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K R, Suma. "Smart Vehicle Sensor System." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 5, 2021): 4–7. http://dx.doi.org/10.22214/ijraset.2021.37004.
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Over recent years production of the vehicle around the world has increased rapidly, vehicle theft has become a shared concern for all citizens. Security and safety have always become a necessity. However, present anti-theft systems lack the tracking and monitoring function. The Wi-Fi module enabled cost-effective solution has been made to protect the vehicles. This paper attempts to utilize two physically disjoint units in conjunction with each other, to provide a fool-proof mechanism against vehicle theft. A prototype has been made using Arduino and Wi-Fi module. Android smartphones are used to design for the user interface that allows access of the vehicle to an intended person only.
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Leeks, Harry. "Smart Electric Vehicle Charging." ITNOW 61, no. 4 (2019): 12–13. http://dx.doi.org/10.1093/itnow/bwz092.
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Abstract What does IT have to do with the charging of electric vehicles? In this article, Harry Leeks, a graduate IT Analyst at National Grid, explains how IT plays a pivotal role in the electric vehicle charging market.
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YAO, Yiwei, Yongjun WANG, Xiaoyan ZHANG, Xingwang YANG, Shaobo QIU, Weimin HE, Hongjian LI, and Jun LI. "Smart vehicle for smart city." SCIENTIA SINICA Informationis 46, no. 5 (May 1, 2016): 551–59. http://dx.doi.org/10.1360/n112015-00294.
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Narmadha, R., R. Madhav, D. Barath, S. Kiruthika, and J. Keerthana. "Smart Moving Vehicle Detection System." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1758–63. http://dx.doi.org/10.1166/jctn.2020.8438.
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In Vehicle detection is a computer skill that determines the locations, direction and speed of running vehicles in arbitrary (digital) images. Using vehicle features and ignores anything else, such as buildings, trees and bodies. Vehicle detection is currently an active research area in the computer vision community. Automobile localization and detection are frequently the primary step in bids such as face gratitude, video observation, vehicle computer interface and image database administration. Speed and tracking vehicle shapes is a prerequisite for recognition and/or vehicle features analysis, although it is often assumed that a normalized moving vehicle image is available. Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. In this paper, a machine learning algorithm created for analyzing vehicle detection, travel direction and speed measurement.
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Phillips, Anthony M., Ryan A. McGee, Johannes G. Kristinsson, and Hai Yu. "Smart, Connected and Electric." Mechanical Engineering 135, no. 03 (March 1, 2013): S4—S9. http://dx.doi.org/10.1115/1.2013-mar-4.
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This article introduces opportunities that are seen at the intersection of electrification, connectivity, and smart controls in the automobile industry. Computational Intelligence provides the vehicle the ability to reason, adapt, and learn based on historical usage data, the present operating conditions, and the predicted future states. Modern automobiles continue to grow in complexity and sophistication. Electrified powertrains now provide vastly improved fuel efficiency by utilizing high-voltage systems to overcome some of the shortcomings of traditional combustion engines. Smart controls have enabled a wealth of new vehicle features ranging from automatic climate control to vehicle dynamic control. Vehicle connectivity, having already empowered the driver through infotainment and telematics, now promises new computing resources and information that can be leveraged directly for improved vehicle performance. At the intersection of these three vehicle mega trends lies a field that is rich for development. In the future, drivers will benefit in everything from enhanced drivability to more durable vehicles.
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Ravi Kumar Palla, Chonika Molli, Gowthami Narayanasetti, Dwarakesh Malla, Sai kiran Katiki, and Venkata Mani Gopal. "Vehicle parking: A smart solution." Global Journal of Engineering and Technology Advances 13, no. 3 (December 30, 2022): 066–71. http://dx.doi.org/10.30574/gjeta.2022.13.3.0209.
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The system proposed in this paper provides an effective solution in finding the vacant space and manages the vehicles entering in and out of parking area. The system consists of Arduino Uno, Liquid Crystal Display (LCD), Servo motor and Infrared (IR) Sensors. Two IR Sensors placed just before and after the entrance of the gate which detects the motion of the vehicle either entering or leaving the gate or slot area. The Arduino Uno, a microcontroller counts the number of available slots based on the entry or exit of the vehicle and the state of each slot i.e., either the slot is empty or full and provides a feedback which is displayed on the LCD. This whole process makes the system fully automated and thus provides a solution for vehicle parking in a smart way. The proposed automated system causes the driver to navigate in reaching to the vacant slot using display thus reduces search time. The proposed system can be implemented at places where heavy number of vehicles needs to be parked.
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Gupta, Rajesh Kumar, L. N. Padhy, and Sanjay Kumar Padhi. "Smart Driving System for Improving Traffic Flow." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 30, 2017): 236. http://dx.doi.org/10.23956/ijarcsse/v7i7/0174.
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Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay. For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.
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RAM VARA PRASAD, B., CH PRASANTHI, G. JYOTHIKA SANTHOSHINI, K. J. S. V. KRANTI KUMAR, and K. YERNAIDU. "SMART ELECTRICAL VEHICLE." i-manager's Journal on Digital Signal Processing 8, no. 1 (2020): 7. http://dx.doi.org/10.26634/jdp.8.1.17347.
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Balog, Michal, Žofia Šimeková, and Pavol Semančo. "Smart Vehicle Railroad." Applied Mechanics and Materials 708 (December 2014): 148–52. http://dx.doi.org/10.4028/www.scientific.net/amm.708.148.
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Radio Frequency Identification (RFID) technology introduces the way of automated data collection, and processing to improve accuracy of processed data. In the present time possibilities in development and application of RFID technologies are almost limitless. Increasing expansion of RFID technology in almost any industry, where the RFID tag can be put on any product or material or component is an evidence of the previous assertion. Apart from technical aspects, i.e. security, the financial effect of the RFID technology implementation is also relevant in rail freight transport. Using this technology to keep records of technical condition of the wagons can be avoided a huge disaster caused by lack of knowledge about technical condition of the wagon. It can bring the return of initial investment and cost reduction.
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Tarulescu, Radu, Stelian Tarulescu, Cristian Leahu, and Marius Olaru. "Photovoltaic system for E-Smart electric vehicle." IOP Conference Series: Materials Science and Engineering 1220, no. 1 (January 1, 2022): 012009. http://dx.doi.org/10.1088/1757-899x/1220/1/012009.
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Abstract The vehicles relied on fossil fuels are rapidly being replaced by electric and plug-in hybrid vehicles. But these types of vehicles are still faced with the problem of energy availability. The abundance of solar radiation and its use as the power source in electric vehicles is a necessary condition for environmental pollution limitation. In this study, the authors present photovoltaic systems used as an electricity supply for E-Smart electric vehicles. E-Smart is an electric vehicle obtained through conversion, of a Smart ForTwo City vehicle, from the internal combustion propulsion system to a system that uses a three-phase asynchronous motor supplied from a pack of 32 batteries of LiFePO4 type.
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Jung, In Hwan, Jae Moon Lee, and Kitae Hwang. "Smart Parking Management System Using AI." Webology 19, no. 1 (January 20, 2022): 4629–38. http://dx.doi.org/10.14704/web/v19i1/web19307.
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This paper is aimed to introduce a smart parking lot management system using multiple cameras and artificial intelligence technique. When a vehicle enters a parking lot, it recognizes the vehicle number using embedded camera, tracks which parking space the vehicle is parked in, and updates parking space information. In addition, using a surveillance camera images, it has been also implemented to detect collision accidents that may occur while the vehicle is moving in the parking lot. Vehicle number recognition system uses OCR technique and is implemented on a Raspberry system. By managing the vehicle number recognized at the entrance of the parking lot as an Object ID, it was possible to effectively track the vehicle as a moving object inside the parking lot and finally identify the parking location. In order for accident detection, YOLO with CNN deep learning process is used. More than 500 possible collision images are trained in advance. Experimental results show that the detection accuracy of parking and accident detection increases as the number of training images increases. The accident detection needed more training images because it has more diversity. By using the smart parking system implemented in this paper, it is possible to effectively manage the vehicle's parking location, free space information and possible accidents. Using a cloud system, implemented system can provide drivers an integrated parking lot information over large areas.
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Chopra, Varun. "Cyber Intelligence in Smart Vehicles." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 3 (September 30, 2021): 144–51. http://dx.doi.org/10.35940/ijrte.c6464.0910321.
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In the embryonic stage, the usage of vehicle tracking systems were primarily restricted to getting the geographical location of the vehicular units. This scenario, however, was not perennial and with the escalation from a rudimentary stage to a highly complex archi- tecture for vehicular administration that we witness today, the standards for the vehicles security have also become monumental. With the development of V2X communications, the gamut of facilities provided by smart vehicle services has expanded prodigiously. These technological advancements, however have come at a cost. The gargantuan transition that has taken place over the recent years exacts a lot of security and safety mechanisms to be implemented, adjunct to the products and services it comes equipped with. In this paper, after a comprehensive study in the domain, we imply a security system model comprising of a Microcontroller Unit (MCU), as a part of the Vehicle Tracing Mechanism (VTM), well connected with a Management Hub. The communications be- tween the Vehicular Unit(s), Management Hub and the system Vehicle Tracing Mecha- nism (VTM) are made viable via V2X communications with conducive aid from technolo- gies like Global Positioning System, Radio Frequency Identifications and GPRS network. The paper aims to ameliorate the extant security protocols and improve the security and safety standards of smart vehicles by broaching cyber intelligence in smart vehicles.
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Shokravi, Hoofar, Hooman Shokravi, Norhisham Bakhary, Mahshid Heidarrezaei, Seyed Saeid Rahimian Koloor, and Michal Petrů. "A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques." Sensors 20, no. 11 (June 8, 2020): 3274. http://dx.doi.org/10.3390/s20113274.
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Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.
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S, Thylashri, Manikandaprabu N, Jayakumar T, Vijayachitra S, and Kiruthiga G. "Effective Techniques for Pedestrian Detection in Smart Autonomous Vehicles." Webology 18, no. 05 (October 29, 2021): 1176–83. http://dx.doi.org/10.14704/web/v18si05/web18298.
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Pedestrians are essential objects in computer vision. Pedestrian detection in images or videos plays an important role in many applications such as real-time monitoring, counting pedestrians at various events, detecting falls of the elderly, etc. It is formulated as a problem of the automatic identification and location of pedestrians in pictures or videos. In real images, the art of pedestrian detection is an important task for major applications such as video surveillance, autonomous driving systems, etc. Pedestrian detection is also an important feature of the autonomous vehicle driving system because it identifies pedestrians and minimizes accidents between vehicles and pedestrians. The research trend in the field of vehicle electronics and driving safety, vision-based pedestrian recognition technologies for smart vehicles have established themselves loudly or slowing down the vehicle. In general, the visual pedestrian detection progression capable of be busted down into three consecutive steps: pedestrian detection, pedestrian recognition, and pedestrian tracking. There is also visual pedestrian recognition in the vehicle. Finally, we study the challenges and evolution of research in the future.
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Mansour, Ayman M. "Cooperative Multi-Agent Vehicle-to-Vehicle Wireless Network in a Noisy Environment." International Journal of Circuits, Systems and Signal Processing 15 (February 22, 2021): 135–48. http://dx.doi.org/10.46300/9106.2021.15.15.
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With the rapid development of vehicle communication and the goal of self-driving vehicle, research in this area is still ongoing, as car companies aspire for more studies and effective communication methods between vehicles. In this research, we have developed an intelligent, innovative and fully integrated multi agent model, which is used for vehicle-to-vehicle communications. The developed model is supported by an intelligent system based on a Nonlinear External Neural Network (NARX) and signal estimation theory. The system is built using real vehicles sensors, Arduino, GSM and RF technologies. The system is tested by applying different scenarios and observing vehicle behaviors. The results show that the smart system is able to make the appropriate decision based on both the vehicle's current condition and sensor readings. The developed system is able to operate effectively in a noisy environment in an excellent manner.
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Sah, Jitesh, Akshay Rawat, Manish Dhiman, Ankur Bhat, and Anoopshi Johari. "Smart Vehicle Monitoring System." International Journal of Science and Engineering for Smart Vehicles 1, no. 1 (July 30, 2017): 7–14. http://dx.doi.org/10.21742/ijsesv.2017.1.1.02.
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Kamble, K. P. "Smart Vehicle Tracking System." International Journal of Distributed and Parallel systems 3, no. 4 (July 31, 2012): 91–98. http://dx.doi.org/10.5121/ijdps.2012.3410.
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Anusha, K. B. "Smart Vehicle Headlight Dimmer." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2222–25. http://dx.doi.org/10.22214/ijraset.2021.35448.
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Smart Headlight Dimmer is a component in which we can decrease the number of accidents that are causing due to high intensity of light during night times .This helps to reduce the causing of accidents due to Troxler effect .This device helps us to adjust the intensity of the beam of the headlight according to the intensities of the surrounding light .When the intensity of the surrounding light is high ,then the headlight automatically dims .It also helps in other cases like if the Ultrasonic sensor does not works ,then if any accidents occur it will send a message to the authorised contacts along with the location using the GPS and GSM Modules. GSM module send the alert message on your mobile with the location of the accident. The advancing technology has made our day today lives easier. Since every coin has two sides similarly technology has its benefits also as its disadvantages. the rise in technology has increased the speed of road accidents which causes huge loss of life. The poor emergency facilities available in our country just increase this problem. Our project goes to provide a solution to this problem also.
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Wan, Liangtian, Mingyue Zhang, Lu Sun, and Xianpeng Wang. "Machine Learning Empowered IoT for Intelligent Vehicle Location in Smart Cities." ACM Transactions on Internet Technology 21, no. 3 (August 31, 2021): 1–25. http://dx.doi.org/10.1145/3448612.
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Intelligent Transportation System (ITS) can boost the development of smart cities, and artificial intelligence and edge computing are key technologies that support the implementation of ITS. Vehicle localization is critical for ITS since the safety driving and location-aware serves highly depend on the accurate location information. In this article, we construct a vehicle localization system architecture composed of multiple Internet of Things (IoT) with arbitrary array configuration and a large amount of vehicles in smart cities. In order to deal with the coexisting of circular and non-circular signals transmitted by vehicles, we proposed several vehicle number estimation methods for non-circular signals. Based on the machine learning technique, we extend the vehicle number estimation method into mixed signals in more complex scenario of smart cities. Then the DOA estimation method for non-circular signals based on IoT is proposed, and then the performance of this method is analyzed as well. Simulation outcomes verify the excellent performance of the proposed vehicle number estimation methods and the DOA estimation method in smart cities, and the vehicle positions can be achieved with high estimation accuracy.
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Allam, Mohammed Shaibaz. "Smart Vehicle Service Management System Using IoT." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3872–77. http://dx.doi.org/10.22214/ijraset.2022.44767.
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Abstract: To ensure the proper operation and efficient operation of the vehicle, regular vehicle service is necessary to monitor various parts of the vehicle. This paper “Smart Vehicle Service Management System using IoT” is being proposed to detect some problems in vehicle and also it provides a platform to book online service to the vehicles. It sends an alert message in form of SMS to the user if any problem is detected and suggests that servicing to vehicle is now required. The system is integrated with different sensors like FSR, adxl, DHT11, level etc to detect problem in vehicle and being displayed in LCD, so if any problem is detected the buzzer will give alert sound indicating the vehicle has a problem. Along this a Node MCU and GSM module is being placed to get live updates in cloud and also in a form of SMS and the same data is stored data in cloud. The alert message received in form of SMS is redirected to the SVSMS website which will allow the user to book online service. The proposed system helps the user to detect the problems in prior and act precisely so that efficiency of the vehicle is maintained and cost/ inconvenience could be avoided.
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Himanshu, Jayprakashyadav, Jain Akshat, and Singh Alok. "Fully protected and secure smart vehicle." i-manager’s Journal on Embedded Systems 10, no. 2 (2022): 9. http://dx.doi.org/10.26634/jes.10.2.18705.
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Security while traveling is a primary concern for everyone. The purpose of this paper is to find a solution to reduce the annual occurrence of road traffic accidents, which are one of the main causes of death. Designing vehicles with sensors based on microcontrollers could be a solution to achieve this goal. These sensors are made up of tiny devices that work in such a way as to determine the parameters of the vehicle. The ultrasonic sensors will be able to help the driver to drive the vehicle in foggy conditions and prevent frontal collision with the last moment emergency braking system. The vibration detector is used to detect the annoying vibrations of the vehicle. For example, if the vehicle suspension is loose and needs maintenance, it is detected by the vibration sensor. The overheating of electric vehicle motors and batteries causes them to ignite, so flame sensors are also used, and these signals are sent to the microcontroller, which analyses the signals and generates message signals accordingly. If any accident occurs, the vibration detector and the ADXL collision detector will detect the accident and send a signal to the microcontroller. The microcontroller will get the location of the vehicle via NEO 6M Global Positioning System (GPS) and automatically send the message signal via SIM 900A General Packet Radio Services (GPRS) cellular communication system to the nearest police station and hospital for ambulance service. This system not only tries to prevent traffic accidents but also helps to get treatment facilities as quickly as possible. This function can be implemented in any type of vehicle.
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Park, Soyoung, Homin Han, Byeong-Su Kim, Jun-Ho Noh, Jeonghee Chi, and Mi-Jung Choi. "Real-Time Traffic Risk Detection Model Using Smart Mobile Device." Sensors 18, no. 11 (October 30, 2018): 3686. http://dx.doi.org/10.3390/s18113686.
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Automatically recognizing dangerous situations for a vehicle and quickly sharing this information with nearby vehicles is the most essential technology for road safety. In this paper, we propose a real-time deceleration pattern-based traffic risk detection system using smart mobile devices. Our system detects a dangerous situation through machine learning on the deceleration patterns of a driver by considering the vehicle’s headway distance. In order to estimate the vehicle’s headway distance, we introduce a practical vehicle detection method that exploits the shadows on the road and the taillights of the vehicle. For deceleration pattern analysis, the proposed system leverages three machine learning models: neural network, random forest, and clustering. Based on these learning models, we propose two types of decision models to make the final decisions on dangerous situations, and suggest three types of improvements to continuously enhance the traffic risk detection model. Finally, we analyze the accuracy of the proposed model based on actual driving data collected by driving on Seoul city roadways and the Gyeongbu expressway. We also propose an optimal solution for traffic risk detection by analyzing the performance between the proposed decision models and the improvement techniques.
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Niture, Deeplaxmi V., Vivekanand Dhakane, Piyush Jawalkar, and Ankit Bamnote. "Smart Transportation System using IOT." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 434–38. http://dx.doi.org/10.35940/ijeat.e2870.0610521.
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In this paper a Smart Vehicle Assistance and Monitoring system (SVAMS) is presented. SVAMS is an intelligent transportation system (ITS), developed to tackle various traffic related issues. It is a traffic management, monitoring and optimization solution in which all the vehicles are interconnected through Zigbee and are monitored and assisted centrally, by a data center. The system has two parts; one part is mounted in/on the vehicle and the other part is at the data centre. Part one collects data from various sensors and transmits it to central data centre. All the data will be stored on cloud for further analysis, processing and future use. SVAMS is relatively low-cost, compact and has various functionalities such as emergency response, pollution level monitoring, automatic toll collection, traffic rule violation detection, vehicle tracking, etc. The use of SVAMS will help to build up Clean, Corruption free and Crime free (C-3) cities.
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Rachcha, Anish, Arya Mishra, and Kanchan Shinde. "Smart Electric Tractor." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 1145–49. http://dx.doi.org/10.22214/ijraset.2022.46374.
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Abstract: Due to the environmental threats associated with the combustion of fossil fuels, the hike in fuel prices and everyone is looking for an alternate energy sources to propel the vehicle. One such solution is the adoption of electric vehicles, which accounts for high degree of sustainability as compared to the conventional fuel vehicles. This work presents the working prototype of a Smart Electric Tractor. Description of the subsystems which includes the steering system, braking system, electric powertrain, and the chassis frame will be delineated precisely. Major focus will be to infuse IOT in dynamics and its automation. Design calculations will be carried out to obtain an optimized powertrain.
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Babar, Prof Madhuri, Sanket Meshram, Dhanashri Gulhane, Jagdish Kore, Harshad Nikhare, and Aditi Salodkar. "ANTI-THEFT ALERT SYSTEM FOR SMART VEHICLES USING MOBILE APP." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (October 1, 2021): 330–35. http://dx.doi.org/10.33564/ijeast.2021.v06i06.048.
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Theft security of vehicle in common parking places has become a matter to concern. To overcome this problem, we have an idea. There is a system that doesn’t allow tampering with ignition systems of vehicles, it captures the picture of a thief and also shares the location. The system will warn the mobile-phone about the tampering of the vehicles ignition key knob etc. when the parking mode is ON in our app. There is mobile application for warning the owner/mobile-phone. If the vehicle is lifted during parking mode, the app will alert the owner/mobile-phone through the app and also track the vehicle. In 2nd part of system, there is a Feature when an Unknown person allows to tamper with the Vehicle then Pi camera takes a picture and sending to registered application Gmail. There are the functions like this Tampering detection, tampering of ignition wire, attempt to lift, thief attempt to start vehicle without key, App/mobile warning, GPS tracking of vehicle, capturing picture of thief, and extra function apart from mobile application is Image processing system
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Mordillat, Philippe. "RENAULT Smart Cocoon Technology CAE." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 7 (February 1, 2023): 100–111. http://dx.doi.org/10.3397/in_2022_0022.
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RENAULT was the first OEM to introduce mass production Battery Electric Vehicles in Europe with the ZOE in 2012. With the new Megane ETECH, RENAULT set the NVH comfort to higher level to match the customer expectations on BEV vehicles. One of the components that contributes to the silence of Megane is the "Smart Cocoon Technology". This innovative solution has been developed to transform the battery into an acoustically active element. An insulation foam has been embedded between the body floor and the battery casing in order to use the battery mass to improve the floor transparency and damping, and subsequently the Transmission Loss. To support further developments of this technology RENAULT and its CAE partners are developing a simulation methodology to achieve simultaneously three goals: predict the behavior of the smart cocoon technology on the whole vehicle NVH performance, optimize the performance and weight of the insulation package, and secure the assembly process. This paper presents in detail each step of this process. Non-linear simulations of the battery assembly process are used to capture the preloading effects in foam and body panels. The vehicle high frequencies NVH performance is then simulated for vehicle rolling conditions. In a last part of the paper, CAE results are compared with the test measurements on the Megane prototype.
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Barth, Matthew, Michael Todd, and Susan Shaheen. "Intelligent Transportation Technology Elements and Operational Methodologies for Shared-Use Vehicle Systems." Transportation Research Record: Journal of the Transportation Research Board 1841, no. 1 (January 2003): 99–108. http://dx.doi.org/10.3141/1841-11.
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There has been significant interest and activity in shared-use vehicle systems as an innovative mobility solution. Shared-use vehicle systems, that is, carsharing and station cars, consist of a fleet of vehicles used by several different individuals throughout the day. Shared-use vehicles offer the convenience of a private automobile and more flexibility than public transportation alone. From the 1990s to today, varying degrees of intelligent transportation system technologies have been applied to shared-used systems, providing better manageability and customer service. Many shared-use vehicle service providers today include some degree of advanced technologies (online reservations, vehicle tracking, smart card access) in their operations. Currently, there is a developing need for interoperability among shared-use vehicle service providers (e.g., smart card access among carsharing organizations) and transit operators (e.g., transit fare collection via smart cards). Interoperability will likely result in higher customer satisfaction and use, leading to greater market penetration. Similarly, some standardization will likely unfold for overall operational techniques (online reservations and insurance policies), customer interactions, and to some degree vehicle interfaces. Because shared-use vehicles systems are still a relatively new mobility concept, an industrywide standardization approach is still premature. Nevertheless, there are attempts to identify many of the important issues that will play a significant role in interoperability discussions among shared-use vehicle providers and the development of industry standards in the future. There are key elements in intelligent shared-use vehicle system operations and trade-offs encountered during the pioneering stage of shared-use vehicle system developments. Topics to discuss include vehicles, user–system interactions, user–vehicle interactions, and system operations.
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Dharsini, Visnu, V. Gireesh, S. Vimal, and Yash Nair. "Smart Vehicle Automation Using Internet of Things." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1610–15. http://dx.doi.org/10.1166/jctn.2020.8410.
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The field of automobile automation, though relatively new, has taken over the world by storm with the advent of driverless cars and smart vehicles. While these vehicles provide various automation features like autopilot, auto cruise control and automatic braking which enhance the driving experience and ensure safety, there are not many provisions for further safety, driver protection and problem prevention. Keeping this in mind, we have proposed a very inexpensive model to monitor the driver’s state and the in-vehicle safety, with an automatic braking system, using various sensors—gas (detecting highly poisonous toxic gases), IR (seatbelts), alcohol (if the driver is drunk), temperature/flame (for fire inside and temperature recording), humidity, LDR (automatic headlights) and ZIGBEE (automatic braking). A 12 V DC motor (wheel) controlled by L298N. The vehicle status and the motor stop reason is displayed in a basic LCD display. The data collected in all these sensors is displayed in the cloud for analyses. Arduino was used for interconnection, automation and system control. NodeMCU for cloud connection. The whole system is powered by a 5 V power source.
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Vaidya, Binod, and Hussein T. Mouftah. "Smart electric vehicle charging management for smart cities." IET Smart Cities 2, no. 1 (March 2020): 4–13. http://dx.doi.org/10.1049/iet-smc.2019.0076.
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Naren Thiruvalar, V., and E. Vimal. "Smart Traffic Management for Collision Avoidance Using V2V." Bonfring International Journal of Industrial Engineering and Management Science 11, no. 1 (February 16, 2021): 01–07. http://dx.doi.org/10.9756/bijiems/v11i1/21001.
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The main objective of this project is to connect the vehicles together and avoid accidents by using V2V Communication. The vehicles are to be connected together by means of DSRC algorithm which is used for transceiving alert messages among the connected vehicles, in case of any emergency situation such as accidents. The Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) technologies are specific cases of IoT and key enablers for Intelligent Transportation Systems (ITS). V2V and V2I have been widely used to solve different problems associated with transportation in cities, in which the most important is traffic congestion. A high percentage of congestion is usually presented by the inappropriate use of resources in vehicular infrastructure. In addition, the integration of traffic congestion in decision making for vehicular traffic is a challenge due to its high dynamic behaviour. An increase in the infrastructure growth is a possible solution but turns out to be costly in terms of both time and effort. Various applications that target transport efficiency could make use of the vast information collected by vehicles: safety, traffic management, pollution monitoring, tourist information, etc.
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Chen, Kai, Cheng Xu, Hongzhe Liu, Pengfei Wang, and Ziyi Chen. "Blockchain-Based Dangerous Driving Map Data Cognitive Model in 5G-V2X for Smart City Security." Security and Communication Networks 2022 (April 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/8922289.
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The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety.
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Wang, Junmin. "Energy Consumption and Tailpipe Emission Reductions by Personalized Control of Connected Vehicles." Mechanical Engineering 139, no. 09 (September 1, 2017): S5—S11. http://dx.doi.org/10.1115/1.2017-sep-4.
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This article demonstrates several approaches to the vehicle energy consumption and tailpipe emission reduction opportunities. The article leverages the vehicle storage dynamics through smart and personalized optimization and control approaches in the context of connected vehicles. Recent advances in vehicle connectivity and automation have brought unprecedented information richness and new degrees of freedom that can be synergized with insightful understanding of vehicle powertrain and aftertreatment physical systems. Vehicle automation also provides new degrees of freedom that can be further leveraged by the vehicle control systems to improve vehicle energy efficiency and reduce tailpipe emissions. While vehicle automation levels probably will keep increasing, humans will still be involved in vehicle operations at various levels for the foreseeable future. The prediction of future vehicle’s power demand based on vehicle connectivity can significantly benefit tailpipe emission reductions and fuel economy.
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Mo, Tiande, Yu Li, Kin-tak Lau, Chi Kin Poon, Yinghong Wu, and Yang Luo. "Trends and Emerging Technologies for the Development of Electric Vehicles." Energies 15, no. 17 (August 28, 2022): 6271. http://dx.doi.org/10.3390/en15176271.
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In response to severe environmental and energy crises, the world is increasingly focusing on electric vehicles (EVs) and related emerging technologies. Emerging technologies for EVs have great potential to accelerate the development of smart and sustainable transportation and help build future smart cities. This paper reviews new trends and emerging EV technologies, including wireless charging, smart power distribution, vehicle-to-home (V2H) and vehicle-to-grid (V2G) systems, connected vehicles, and autonomous driving. The opportunities, challenges, and prospects for emerging EV technologies are systematically discussed. The successful commercialization development cases of emerging EV technologies worldwide are provided. This review serves as a reference and guide for future technological development and commercialization of EVs and offers perspectives and recommendations on future smart transportation.
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Singh, Pritpal, Tanjot Sethi, Bibhuti Bhusan Biswal, and Sujit Kumar Pattanayak. "A Smart Anti-theft System for Vehicle Security." International Journal of Materials, Mechanics and Manufacturing 3, no. 4 (2015): 249–54. http://dx.doi.org/10.7763/ijmmm.2015.v3.205.
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Mazzei, M. "A SMART DATA APPROACH TO ANALYZE VEHICLE FLOWS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W5-2022 (October 14, 2022): 105–11. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w5-2022-105-2022.
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Abstract. In the logic of Smart Cities it is of fundamental importance to analyze the traffic situation through dedicated sensors and networks. According to this approach and through the potential of smart data is based this study. Improve prediction of traffic patterns by analyzing and counting vehicles in a virtualized scene in real time. In the past, the technique of hardware inductive coils was used that were dropped in the asphalt to exploit the principle of magnetic induction in order to verify the transit of vehicles. This technique is not able to classify vehicles or estimate their speed, unless using multiple inductive coils. The proposed system provides for the virtualization of an area of interest which requires a selection and mapping of the areas where the control areas are to be included. The “image detection” techniques allow us to classify the vehicles in transit. With the techniques of “machine learning” can to able to verify the flow, count the vehicles present in the scene and classify them by vehicle type in real time. The vehicle counting and classification data available in the cloud platform allow to model and update the main nodes of the network in order to improve the prediction and estimates of the best routes of the road network according to the degree of saturation of the flows and the length of the line of the graph. The model can also indicate additional information of an environmental nature in an ITS system present in the cloud.
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Taufiq, Reny Medikawati, Sunanto Sunanto, and Yoze Rizki. "INTEGRATED SMART TRAFFIC CONTROL SYSTEM MENUJU PEKANBARU SEBAGAI SMART CITY." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 7, no. 1 (December 14, 2020): 67–74. http://dx.doi.org/10.33330/jurteksi.v7i1.942.
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Abstract: Pekanbaru still using conventional traffic light control system. Pekanbaru as the capital of Riau Province is predicted udergo the increased of urban population by 54.5% in 2025. It is important for Pekanbaru to immediately implement smart and efficient traffic management system, so that traffic congestion can be resolved quickly. This research paper provides a design solution for smart traffic light management (Smart Traffic Control System), based on object detection technology that uses deep learning to detect the number and type of vehicles. The number of vehicle is the basis for determining the green light timer automatically. The Smart Traffic Control System (STCS) is integrated with a web based geographic information system (smart map) that can display the current condition (picture, the number of vehicle, congestion level) of each STCS location. This integrated system has been tested on a traffic light prototype, using a mini computer and a miniature vehicle. This integrated system is able to detect 9 out of 12 vehicles, and able to send data regularly to the smart map. Keywords: deep learning; smart mobility; smart traffic control system Abstrak: Pengaturan lampu lalu lintas di Kota Pekanbaru masih dilakukan secara konvensional. Pekanbaru sebagai ibukota Provinsi Riau diprediksikan akan mengalami peningkatan jumlah penduduk perkotaan sebesar 54,5% pada tahun 2025. Dengan melihat predikisi ini, penting bagi kota Pekanbaru untuk segera memiliki tata kelola lalu lintas yang cerdas dan efisien agar kemacetan dapat ditanggulangi dengan cepat. Penelitian ini memberikan rancangan solusi untuk tata kelola lampu lalu lintas cerdas (Smart Traffic Control System), berbasis teknologi object detection yang menggunakan deep learning untuk mendeteksi jumlah dan jenis kendaraan. Jumlah kendaraan menjadi dasar penentuan timer lampu hijau secara otomatis. Smart Traffic Control System (STCS) terintegrasi dengan sistem informasi geografis berbasis web (smart map) yang secara kontinu menerima informasi kepadatan (gambar terkini, jumlah kendaraan, level kepadatan), kemudian menampilkannya diatas peta Kota Pekanbaru. Solusi sistem terintegrasi ini telah diujikan pada sebuah prototipe lampu lalu lintas, menggunakan komputer mini dan miniatur kendaraan. Sistem terintegrasi ini mampu mendeteksi 9 dari 12 kendaraan, dan mampu mengirimkan data secara berkala kepada smart map. Kata kunci: deep learning; smart mobility; smart traffic control system
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R, Karthik Rao, Sai kiran P, and Phaneendra Babu Bobba. "Communication between PHEV’s and Smart Grid using Zigbee Protocol." E3S Web of Conferences 87 (2019): 01018. http://dx.doi.org/10.1051/e3sconf/20198701018.
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Plug-in-hybrid electric vehicles commonly known as PHEV’s are hybrid electric vehicles that use rechargeable batteries for operation. Since PHEV’s run on electric batteries, they require charging after the charge reaches a certain minimum level. The batteries can be charged using external sources usually a smart grid. This requires a wireless technology that can be used to send the information of the battery charge to the smart grid so that it can be charged. This paper is a detailed description of how this communication can be achieved using the ZigBee wireless technology. The battery level information can be sent to the smart grid using this technology and the smart grid operator can then decide whether the PHEV needs charging or not. If not, the battery can be used to provide Vehicle-to-grid (V2G) services i.e. the charge from the vehicle can be sent back to the grid depending on the will of the vehicle owner. Thus, in this way a system can be developed where in both the PHEV driver and the grid operator can benefit.
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T, Sivakumar, Kalicharan L, and Selvanithila R.S. "Smart Vehicle Insurance Verification System." IJARCCE 8, no. 2 (February 28, 2019): 271–78. http://dx.doi.org/10.17148/ijarcce.2019.8253.
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Prasad, K. Venkatesh, Steve Hodges, and Brian Noble. "Smart Vehicle Spaces [Guest Editorial]." IEEE Pervasive Computing 15, no. 1 (January 2016): 19–21. http://dx.doi.org/10.1109/mprv.2016.4.
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Bučko, Boris, Martin Michálek, Katarína Papierniková, and Katarína Zábovská. "Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint." Applied Sciences 11, no. 22 (November 9, 2021): 10514. http://dx.doi.org/10.3390/app112210514.
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The aim of this article is to describe estimates of data difficulty and aspects of the data viewpoint within Vehicle-to-Infrastructure (V2I) communication in the Smart Mobility concept. The historical development of the database system’s architecture, that stores and processes a larger amount of data, is currently sufficient and effective for the needs of today’s society. The goal of vehicle manufacturers is the continual increase in driving comfort and the use of multiple sensors to sense the vehicle’s surroundings, as well as to help the driver in critical situations avoid danger. The increasing number of sensors is directly related to the amount of data generated by the vehicle. In the automotive industry, it is crucial that autonomous vehicles can process data in real time or can locate itself in precise accuracy, for the decision-making process. To meet these requirements, we will describe HD maps as a key segment of autonomous control. It alerts the reader to the need to address the issue of real-time Big Data processing, which represents an important role in the concept of Smart Mobility.
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Zhang, Ru Tong, and Teng Fei Yao. "Energy Model of Electric Vehicle Filling Station." Advanced Materials Research 608-609 (December 2012): 1618–22. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.1618.
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With the increasingly grim world environmental degradation and energy crisis, the industrial development of electric vehicles and charging for electric facilities has risen to national strategy. Based on the introduction of electric vehicle energy supply mode, proposed for power mode is the main push of the State Grid Corporation of business model. The Large-scale construction of electric vehicle filling station presents a challenge to Power grid, but through the application of V2G technology, electric vehicle power battery become smart grid energy storage structure, strengthen the new energy, electric vehicle charging the relationship between the three power stations and smart grid, and accelerate the integration process among the three.
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Kalpana, V., S. Shanthi, A. Sagai Francis Britto, and N. B. Prakash. "Internet of Thing Based Smart Traffic Control Signal Using Solar Energy." Journal of Computational and Theoretical Nanoscience 17, no. 12 (December 1, 2020): 5334–38. http://dx.doi.org/10.1166/jctn.2020.9425.
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Nowadays traffic congestion is major problem in all over the cities. The cities are renovated to “smart cities” by using Information and Communication Technologies (ICT). The IoT are playing a vital role in smart cities. This work proposes Internet of Thing (IoT) based smart traffic control signal using solar energy for smart cities. This signal is always coordinated with the emergency vehicle like ambulance to discover the signal and select the route where road traffic is dynamically controlled and the traffic violation vehicles are identified by traffic monitoring officers through internet. The traffic control signal lights are automatically controlled by Raspberry Pi controller with the help of IR sensor and RF signal. If any emergency vehicle will come nearby traffic control signal then the green signal shows for emergency vehicle and the remaining paths are shows as red signal.
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Ekbote, Amey, Mathew Karvinkoppa, Virendra Bhojwani, and Naveen Patil. "Comprehensive study on smart cooling techniques used for batteries." E3S Web of Conferences 170 (2020): 01028. http://dx.doi.org/10.1051/e3sconf/202017001028.
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Electric vehicles are important in today’s world to reduce pollution. The demand for electric vehicles is increasing day by day. The major component is the battery for an electric vehicle which gives the power to drive motor and drives the vehicle. Continuous operation of the vehicle causes the battery to heat and while heating there are some flammable gases released which may cause a fire. The heating of batteries reduces the performance of the vehicle and reduces the efficiency, therefore there is a need for cooling techniques to keep the temperature of batteries below the critical temperature for safe operating conditions. The present study emphasizes various cooling techniques used for the battery thermal management system. Cooling improves the performance of the battery and reduces the temperature of the battery. It helps in maintaining the temperature of the battery at the desired level. Before cooling the battery, it is necessary to study the thermal behavior of the battery. Various aspects of the thermal behavior are also reported in the paper and the problems associated with the time required for charging the batteries are also discussed.
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Wang, Jie, Xi Li, Zhen Wang, Jiangtao Feng, Weixun Lin, and Jingxuan Peng. "Research on Application Characteristics of Zirconia-Based High-Temperature NOx Sensors." Energies 15, no. 8 (April 15, 2022): 2919. http://dx.doi.org/10.3390/en15082919.
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The zirconia solid electrolyte SOFC (solid oxide fuel cell) has the characteristics of oxygen ion conduction function, high-temperature resistance, thermoelectric coupling effect, etc. A NOx sensor based on zirconia solid electrolyte has common characteristics and problems with the SOFC in principle and application. The research objective of this paper is to solve the application problems of smart NOx sensors in diesel vehicles or gasoline vehicles. Improvements in the application performance of the NOx sensor can help the NOx emissions of gasoline vehicles or diesel vehicles better meet the requirements of emission regulations. The smart NOx sensor is a regulatory sensor required by vehicles for China’s Phase VI Vehicle Exhaust Emission Regulations or Euro Phase VI Vehicle Exhaust Emission Regulations. The smart NOx sensor is a key sensor device for improving fuel efficiency and reducing pollution. Moreover, its measurement performance includes dynamic immunity to interference, response speed, and measurement accuracy, which are key factors affecting vehicle emissions. This paper focuses on the impact of the physical structure, electrode characteristics, and control strategies of the sensor on its performance during the application. An excellent sensor structure, electrode structure, and control strategy are given based on application analysis and experimental testing. The results show that the application performance of this smart NOx sensor meets the requirements of exhaust aftertreatment systems.
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Keote, R. S., Prasanna Kale, Chaitanya Raut, Bhaskar Samavedula, Rutuja Khawade, and Abhijeet Dumanwar. "Solar Based Smart Lawn Mower." International Journal of Engineering and Advanced Technology 9, no. 4 (April 30, 2020): 2190–94. http://dx.doi.org/10.35940/ijeat.d9065.04942.
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Our lawn mower is a self grass cutting vehicle running on solar energy which avoiding obstacles, increases the capability of complete mechanized grass cutting devoid of any necessity of any human intervention. Our structure requires a couple of 12V batteries to influence up the motors of the blades and for the movement. Lawn mowing is regarded as one of the most boring or tiring everyday jobs. It is also very reliable private robotic applications. The principle of this paper is to study and re-define the art in robotic lawn mowers to highlight the needs and capabilities of today's devices. A brief survey of available products, some patents and typical test bed prototypes are also provided. Some enabling techniques that make the vehicles more efficient are also suggested. We have utilized a solar board i.e. solar panel to charge the batteries the lawn mower and robot motors are interfaced to an Arduino Uno which governs the implementation of each motor. It is additionally used to interface an ultrasonic sensor for obstacle recognition and IR sensors for stopping the vehicle. The importance of this research lies in the presentation of an overview of a potential big market for personal vehicles.
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Chen, Joy Iong-Zong. "VANET-based Secure Information Exchange for Smart Charging." Journal of Electrical Engineering and Automation 2, no. 3 (January 29, 2021): 141–45. http://dx.doi.org/10.36548/jeea.2020.3.006.
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Smart inventions have led to smart grids, which have paved the way to smart charging means. This smart charging information about a vehicle needs to be properly maintained in order to use it to exchange information between charging stations and electric vehicles. In this work, we have introduced an efficient methodology for managing and disseminating energy from the charging station to the smart vehicle in an urban area where the number of electric vehicles is high. We have designed and implemented a novel concept involving mobile edge computing in VANET. Moreover, we have also used an effective mechanism for communicating the information on charging with the moving electric vehicles and servers. A local relay scheme is used for reducing redundant overheads, increase delivery efficiency of charging information. This scheme is implemented with delay wait model as the base. The output is recorded by means of simulation environment and based on the observations the proposed work is found to be optimal in maintaining, accessing and disseminating the charging information.
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Sengan, Sudhakar, Osamah Ibrahim Khalaf, Priyadarsini S., Dilip Kumar Sharma, Amarendra K., and Abdulsattar Abdullah Hamad. "Smart Healthcare Security Device on Medical IoT Using Raspberry Pi." International Journal of Reliable and Quality E-Healthcare 11, no. 3 (July 2022): 1–11. http://dx.doi.org/10.4018/ijrqeh.289177.
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This paper aims to improve the protection of two-wheelers. This study is divided into two parts: a helmet unit and a vehicle unit. The primary unit is the helmet unit, which contains a sensor, and the second part is known as the alcohol sensor, which is used to determine whether or not the driver is wearing the user helmet correctly. This data is then transmitted to the vehicle unit via the RF transmitter. The data is encoded with the aid of an encoder. Suppose the alcohol sensor senses that the driver is intoxicated. In that case, the IoT-based Raspberry Pi micro-controller passes the data to the vehicle unit via the RF transmitter, which immediately stops the vehicle from using the Driver circuit to control the relay. To stop the consumption of alcohol, the vehicles would be tracked daily. If the individual driving the vehicle is under the influence of alcohol while driving, the buzzer will automatically trigger. The vehicle key will be switched off.
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Galanis, Ioannis, Iraklis Anagnostopoulos, Priyaa Gurunathan, and Dona Burkard. "Environmental-Based Speed Recommendation for Future Smart Cars." Future Internet 11, no. 3 (March 24, 2019): 78. http://dx.doi.org/10.3390/fi11030078.
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Modern vehicles are enhanced with increased computation, communication and sensing capabilities, providing a variety of new features that pave the way for the deployment of more sophisticated services. Specifically, smart cars employ hundreds of sensors and electronic systems in order to obtain situational and environmental information. This rapid growth of on-vehicle multi-sensor inputs along with off-vehicle data streams introduce the smart car era. Thus, systematic techniques for combining information provided by on- and off-vehicle car connectivity are of remarkable importance for the availability and robustness of the overall system. This paper presents a new method to employ service oriented agents that cohesively align on- and off-vehicle information in order to estimate the current status of the car. In particular, this work combines, integrates, and evaluates multiple information sources targeting future smart cars. Specifically, the proposed methodology leverages weather-based, on-route, and on-vehicle information. As a use case, the presented work informs the driver about the recommended speed that the car should adapt to, based on the current status of the car. It also validates the proposed speed with real-time vehicular measurements.
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Urooj, Shabana, Fadwa Alrowais, Yuvaraja Teekaraman, Hariprasath Manoharan, and Ramya Kuppusamy. "IoT Based Electric Vehicle Application Using Boosting Algorithm for Smart Cities." Energies 14, no. 4 (February 18, 2021): 1072. http://dx.doi.org/10.3390/en14041072.
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The application of Internet of Things (IoT) has been emerging as a new platform in wireless technologies primarily in the field of designing electric vehicles. To overcome all issues in existing vehicles and for protecting the environment, electric vehicles should be introduced by integrating an intellectual device called sensor all over the body of electric vehicle with less cost. Therefore, this article confers the need and importance of introducing electric vehicles with IoT based technology which monitors the battery life of electric vehicles. Since the electric vehicles are implemented with internet, an online monitoring system which is called Things Speak has been used for monitoring all the vehicles in a continuous manner (day-by-day). These online results will then be visualized in MATLAB after an effective boosting algorithm is integrated with objective function. The efficiency of proposed method is tested by visual analysis and performance results prove that the projected method on electric vehicle is improved when using IoT based technology. It is also observed that cost of implementation is lesser and capacity of electric vehicle is increased to about 74.3% after continuous monitoring with sensors.