This document summarizes a research paper that proposes a smart vehicle management system using sensors and an IoT-based black box. The system aims to reduce traffic accidents by continuously monitoring the driver and vehicle for unsafe conditions like drowsiness, alcohol consumption, speeding, etc. and alerting authorities if needed. It uses sensors like LiDAR, alcohol sensors, cameras and more to detect surrounding objects, the driver's state, and send real-time data to an IoT server. If an emergency occurs, the system can send a rescue signal to nearby police including the vehicle's location using GPS. The system aims to automatically collect evidence and alert authorities to unsafe driving to help reduce accidents and make roads safer.
VEHICLE COLLISION DETECTION & PREVENTION USING VANET BASED IOT WITH V2Vijwmn
EMERGENCY alert in case of any accident is vitally necessitated to rescue the victims. And so, this paper is made to present the results of a major analysis relating to emergency alert conditions at the time of collision (automobile). In this study, the authors have investigated modern Internet of Things (IoT) and VANET (Vehicular Ad hoc Networks) technologies and developed a collection of modern and specialized techniques as well as their characteristics. It has sensors that detect unbalanced circumstances and provide with a warning to the microcontroller if a collision occurs.
Vehicle Collision Detection & Prevention using VANET Based IoT With V2Vijwmn
EMERGENCY alert in case of any accident is vitally necessitated to rescue the victims. And so, this paper is made to present the results of a major analysis relating to emergency alert conditions at the time of collision (automobile). In this study, the authors have investigated modern Internet of Things (IoT) and VANET (Vehicular Ad hoc Networks) technologies and developed a collection of modern and specialized techniques as well as their characteristics. It has sensors that detect unbalanced circumstances and provide with a warning to the microcontroller if a collision occurs. Additionally, the technique can be implemented in such a way that vehicles are alerted of possible closing barriers. Vehicle-to-Vehicle communication (V2V) has a huge impact since it allows vehicles to communicate with each other while in proximity and the buzzer together with the LEDs serves as a safety feature. The primary goal of the system is to carry out the microcontroller functions in every environment and moreover, the concept refers to detect and prevent the collision specially in a foggy weather as well as at night and in other odd circumstances. The Internet of Things (IoT) and the Vehicular Ad-Hoc Network (VANET) have now been merged as the fundamental and central components of Intelligent Transportation System (ITS). Furthermore, while the procedure of obtaining the insurance may be longer for certain people. On the other hand, others may avoid the law after being involved in severe collisions which makes it difficult for the authorities to discriminate between criminal and non-criminal evidence.
VEHICLE COLLISION DETECTION & PREVENTION USING VANET BASED IOT WITH V2Vijwmn
EMERGENCY alert in case of any accident is vitally necessitated to rescue the victims. And so, this paper is made to present the results of a major analysis relating to emergency alert conditions at the time of collision (automobile). In this study, the authors have investigated modern Internet of Things (IoT) and VANET (Vehicular Ad hoc Networks) technologies and developed a collection of modern and specialized techniques as well as their characteristics. It has sensors that detect unbalanced circumstances and provide with a warning to the microcontroller if a collision occurs.
Vehicle Collision Detection & Prevention using VANET Based IoT With V2Vijwmn
EMERGENCY alert in case of any accident is vitally necessitated to rescue the victims. And so, this paper is made to present the results of a major analysis relating to emergency alert conditions at the time of collision (automobile). In this study, the authors have investigated modern Internet of Things (IoT) and VANET (Vehicular Ad hoc Networks) technologies and developed a collection of modern and specialized techniques as well as their characteristics. It has sensors that detect unbalanced circumstances and provide with a warning to the microcontroller if a collision occurs. Additionally, the technique can be implemented in such a way that vehicles are alerted of possible closing barriers. Vehicle-to-Vehicle communication (V2V) has a huge impact since it allows vehicles to communicate with each other while in proximity and the buzzer together with the LEDs serves as a safety feature. The primary goal of the system is to carry out the microcontroller functions in every environment and moreover, the concept refers to detect and prevent the collision specially in a foggy weather as well as at night and in other odd circumstances. The Internet of Things (IoT) and the Vehicular Ad-Hoc Network (VANET) have now been merged as the fundamental and central components of Intelligent Transportation System (ITS). Furthermore, while the procedure of obtaining the insurance may be longer for certain people. On the other hand, others may avoid the law after being involved in severe collisions which makes it difficult for the authorities to discriminate between criminal and non-criminal evidence.
Design and Implementation of an Intelligent Safety and Security System for Ve...Hamzamohammed70
In recent years, the surge in car theft cases, often linked to illicit activities, has become a growing concern. Simultaneously, countries grappling with oil shortages have shifted towards converting vehicles to run on liquid propane gas, presenting new safety challenges for car owners. This paper introduces a novel integrated intelligent system designed to address the challenges of car theft and safety concerns associated with gas-based vehicles. By seamlessly integrating these concerns into a single system, it aims to achieve significantly improved performance compared to traditional alarm systems. The proposed system consists of three primary parts: the car security subsystem, an Internet of Things (IoT)-based real-time car tracking subsystem, and the car safety subsystem. Utilizing key technologies such as the Arduino Microcontroller, Bluetooth module, vibration sensor, keypad, solenoid lock, GSM module, NodeMCU microcontroller, GPS module, MQ-4 gas sensor, flame sensor, temperature sensor, and Bluetooth module, the system aims to provide a comprehensive solution for the mentioned issues. Furthermore, the vibration sensor plays a crucial role in identifying unauthorized vehicle operations. Its significance lies in detecting the vibrations emanating from the running engine. Concurrently, other modules and sensors are utilized for real-time tracking and enhancing vehicle safety. These measures include safeguarding against incidents like fire outbreaks or gas leaks within the gas container. Finally, the system was compiled and practically tested, with results that worked well. This work provides some basic steps to enhance vehicle safety and security, as well as to prevent theft and overcome safety concerns related to gas leaks
Advanced High Speed Black Box Based Vehicle Crash Investigation Systemijtsrd
Nowadays, vehicles have transformed into a basic bit of our step by step lives. In India, the amount of vehicles has created in a giant rate which makes people lives more straightforward and better. The world has ended up being totally dependent upon vehicles for the transportation reason anyway on account of overpowering arrangements and less than ideal driving consistently accidents happen causing a ton of mishaps. The essential purpose behind our errand is to develop a model of Black Box that can be presented in any vehicle for assessment reason. A Black Box is a device that records all of the activities of the vehicle. This can add to grow progressively secure vehicles, improving the treatment for mishap misused individuals, causing by offering data to the assessment and overhauling road status to reduce the end rate. The subject of the assignment is that certain presentation part of vehicle during or before the mishap is recorded in a memory which will be annihilated after a particular time allotment. By then another course of action of data can be taken care of this. The Event Data Recorder EDR incorporates data on speed at which the vehicle is moving. The checking framework incorporates camcorder front and back in time slip by mode which is utilized to show the situation of mishap in video design on ongoing premise with no misrepresentation and GPS for the area following alongside time. At the point when the vehicle crashes, the framework catches the episode by utilizing camcorder alongside the area and time utilizing GPS. The information has been gathered in a memory. Afterward, the information can be recovered from the memory for examination process. S. Sreejith | A. Abarnaa | S. Dhanush | M. Gowtham | N. Harish "Advanced High Speed Black Box Based Vehicle Crash Investigation System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29393.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/29393/advanced-high-speed-black-box-based-vehicle-crash-investigation-system/s-sreejith
Automated signal pre-emption system for emergency vehicles using internet of ...IAESIJAI
Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.
Traffic accidents are one of the leading causes of fatalities in the world. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%.. By combining smart phones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this application we propose an Android based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. The application reacts to positive detection by sending details about the accident through SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services.
Wireless Reporting System for Accident Detection at Higher SpeedsIJERA Editor
Speed is one of the basic reasons for vehicle accident. Many lives could have been saved if emergency service
could get accident information and reach in time. Nowadays, GPS has become an integral part of a vehicle
system. This paper proposes to utilize the capability of a GPS receiver to monitor speed of a vehicle and detect
accident basing on monitored speed and send accident location to an Alert Service Center. The GPS will
monitor speed of a vehicle and compare with the previous speed in every second through a Microcontroller
Unit. Whenever the speed will be below the specified speed, it will assume that an accident has occurred. The
system will then send the accident location acquired from the GPS along with the time and the speed by utilizing
the GSM network. This will help to reach the rescue service in time and save the valuable human life.
Because of the rapid growth in technology breakthroughs, including
multimedia and cell phones, Telugu character recognition (TCR) has recently
become a popular study area. It is still necessary to construct automated and
intelligent online TCR models, even if many studies have focused on offline
TCR models. The Telugu character dataset construction and validation using
an Inception and ResNet-based model are presented. The collection of 645
letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34×16
Guninthamulu, and 10 Ankelu. The proposed technique aims to efficiently
recognize and identify distinctive Telugu characters online. This model's main
pre-processing steps to achieve its goals include normalization, smoothing,
and interpolation. Improved recognition performance can be attained by using
stochastic gradient descent (SGD) to optimize the model's hyperparameters.
Scientific workload execution on a distributed computing platform such as a
cloud environment is time-consuming and expensive. The scientific workload
has task dependencies with different service level agreement (SLA)
prerequisites at different levels. Existing workload scheduling (WS) designs
are not efficient in assuring SLA at the task level. Alongside, induces higher
costs as the majority of scheduling mechanisms reduce either time or energy.
In reducing, cost both energy and makespan must be optimized together for
allocating resources. No prior work has considered optimizing energy and
processing time together in meeting task level SLA requirements. This paper
presents task level energy and performance assurance-workload scheduling
(TLEPA-WS) algorithm for the distributed computing environment. The
TLEPA-WS guarantees energy minimization with the performance
requirement of the parallel application under a distributed computational
environment. Experiment results show a significant reduction in using energy
and makespan; thereby reducing the cost of workload execution in comparison
with various standard workload execution models.
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Design and Implementation of an Intelligent Safety and Security System for Ve...Hamzamohammed70
In recent years, the surge in car theft cases, often linked to illicit activities, has become a growing concern. Simultaneously, countries grappling with oil shortages have shifted towards converting vehicles to run on liquid propane gas, presenting new safety challenges for car owners. This paper introduces a novel integrated intelligent system designed to address the challenges of car theft and safety concerns associated with gas-based vehicles. By seamlessly integrating these concerns into a single system, it aims to achieve significantly improved performance compared to traditional alarm systems. The proposed system consists of three primary parts: the car security subsystem, an Internet of Things (IoT)-based real-time car tracking subsystem, and the car safety subsystem. Utilizing key technologies such as the Arduino Microcontroller, Bluetooth module, vibration sensor, keypad, solenoid lock, GSM module, NodeMCU microcontroller, GPS module, MQ-4 gas sensor, flame sensor, temperature sensor, and Bluetooth module, the system aims to provide a comprehensive solution for the mentioned issues. Furthermore, the vibration sensor plays a crucial role in identifying unauthorized vehicle operations. Its significance lies in detecting the vibrations emanating from the running engine. Concurrently, other modules and sensors are utilized for real-time tracking and enhancing vehicle safety. These measures include safeguarding against incidents like fire outbreaks or gas leaks within the gas container. Finally, the system was compiled and practically tested, with results that worked well. This work provides some basic steps to enhance vehicle safety and security, as well as to prevent theft and overcome safety concerns related to gas leaks
Advanced High Speed Black Box Based Vehicle Crash Investigation Systemijtsrd
Nowadays, vehicles have transformed into a basic bit of our step by step lives. In India, the amount of vehicles has created in a giant rate which makes people lives more straightforward and better. The world has ended up being totally dependent upon vehicles for the transportation reason anyway on account of overpowering arrangements and less than ideal driving consistently accidents happen causing a ton of mishaps. The essential purpose behind our errand is to develop a model of Black Box that can be presented in any vehicle for assessment reason. A Black Box is a device that records all of the activities of the vehicle. This can add to grow progressively secure vehicles, improving the treatment for mishap misused individuals, causing by offering data to the assessment and overhauling road status to reduce the end rate. The subject of the assignment is that certain presentation part of vehicle during or before the mishap is recorded in a memory which will be annihilated after a particular time allotment. By then another course of action of data can be taken care of this. The Event Data Recorder EDR incorporates data on speed at which the vehicle is moving. The checking framework incorporates camcorder front and back in time slip by mode which is utilized to show the situation of mishap in video design on ongoing premise with no misrepresentation and GPS for the area following alongside time. At the point when the vehicle crashes, the framework catches the episode by utilizing camcorder alongside the area and time utilizing GPS. The information has been gathered in a memory. Afterward, the information can be recovered from the memory for examination process. S. Sreejith | A. Abarnaa | S. Dhanush | M. Gowtham | N. Harish "Advanced High Speed Black Box Based Vehicle Crash Investigation System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29393.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/29393/advanced-high-speed-black-box-based-vehicle-crash-investigation-system/s-sreejith
Automated signal pre-emption system for emergency vehicles using internet of ...IAESIJAI
Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.
Traffic accidents are one of the leading causes of fatalities in the world. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%.. By combining smart phones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this application we propose an Android based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. The application reacts to positive detection by sending details about the accident through SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services.
Wireless Reporting System for Accident Detection at Higher SpeedsIJERA Editor
Speed is one of the basic reasons for vehicle accident. Many lives could have been saved if emergency service
could get accident information and reach in time. Nowadays, GPS has become an integral part of a vehicle
system. This paper proposes to utilize the capability of a GPS receiver to monitor speed of a vehicle and detect
accident basing on monitored speed and send accident location to an Alert Service Center. The GPS will
monitor speed of a vehicle and compare with the previous speed in every second through a Microcontroller
Unit. Whenever the speed will be below the specified speed, it will assume that an accident has occurred. The
system will then send the accident location acquired from the GPS along with the time and the speed by utilizing
the GSM network. This will help to reach the rescue service in time and save the valuable human life.
Similar to Smart vehicle management by using sensors and an IoT based black box (20)
Because of the rapid growth in technology breakthroughs, including
multimedia and cell phones, Telugu character recognition (TCR) has recently
become a popular study area. It is still necessary to construct automated and
intelligent online TCR models, even if many studies have focused on offline
TCR models. The Telugu character dataset construction and validation using
an Inception and ResNet-based model are presented. The collection of 645
letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34×16
Guninthamulu, and 10 Ankelu. The proposed technique aims to efficiently
recognize and identify distinctive Telugu characters online. This model's main
pre-processing steps to achieve its goals include normalization, smoothing,
and interpolation. Improved recognition performance can be attained by using
stochastic gradient descent (SGD) to optimize the model's hyperparameters.
Scientific workload execution on a distributed computing platform such as a
cloud environment is time-consuming and expensive. The scientific workload
has task dependencies with different service level agreement (SLA)
prerequisites at different levels. Existing workload scheduling (WS) designs
are not efficient in assuring SLA at the task level. Alongside, induces higher
costs as the majority of scheduling mechanisms reduce either time or energy.
In reducing, cost both energy and makespan must be optimized together for
allocating resources. No prior work has considered optimizing energy and
processing time together in meeting task level SLA requirements. This paper
presents task level energy and performance assurance-workload scheduling
(TLEPA-WS) algorithm for the distributed computing environment. The
TLEPA-WS guarantees energy minimization with the performance
requirement of the parallel application under a distributed computational
environment. Experiment results show a significant reduction in using energy
and makespan; thereby reducing the cost of workload execution in comparison
with various standard workload execution models.
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minimize the energy efficiency and communication overhead; however,
security plays an important role where node security is essential due to the
volatile nature of WSN. Thus, we design and develop proximate node aware
secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional
data is used to secure the original data, and further information is shared with
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state each time. Moreover, the node that does not have updated information is
considered as the compromised node and discarded. PNA-SDA is evaluated
considering the different parameters like average energy consumption, and
average deceased node; also, comparative analysis is carried out with the
existing model in terms of throughput and correct packet identification.
Drones provide an alternative progression in protection submissions since
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rapidly progressed registering frameworks besides 5G officialdoms, the
information from the user is consistently refreshed and pooled. Thus, safety
or confidentiality is vital among clients, and a proficient substantiation
methodology utilizing a vigorous sanctuary key. Conventional procedures
ensure a few restrictions however taking care of the assault arrangements in
information transmission over the internet of drones (IOD) environmental
frameworks. A unique hyperelliptical curve (HEC) cryptographically based
validation system is proposed to provide protected data facilities among
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Monitoring behavior, numerous actions, or any such information is considered
as surveillance and is done for information gathering, influencing, managing,
or directing purposes. Citizens employ surveillance to safeguard their
communities. Governments do this for the purposes of intelligence collection,
including espionage, crime prevention, the defense of a method, a person, a
group, or an item; or the investigation of criminal activity. Using an internet
of things (IoT) rover, the area will be secured with better secrecy and
efficiency instead of humans, will provide an additional safety step. In this
paper, there is a discussion about an IoT rover for remote surveillance based
around a Raspberry Pi microprocessor which will be able to monitor a
closed/open space. This rover will allow safer survey operations and would
help to reduce the risks involved with it.
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greenhouse gases, but the shadow of man-made aerosols should not be
underestimated. These tiny particles play a pivotal role in disrupting Earth's
radiative equilibrium, yet many mysteries surround their influence on various
physical aspects of our planet. The root of these mysteries lies in the limited
data we have on aerosol sources, formation processes, conversion dynamics,
and collection methods. Aerosols, composed of particulate matter (PM),
sulfates, and nitrates, hold significant sway across the hemisphere. Accurate
measurement demands the refinement of in-situ, satellite, and ground-based
techniques. As aerosols interact intricately with the environment, their full
impact remains an enigma. Enter a groundbreaking study in Morocco that
dared to compare an internet of thing (IoT) system with satellite-based
atmospheric models, with a focus on fine particles below 10 and 2.5
micrometers in diameter. The initial results, particularly in regions abundant
with extraction pits, shed light on the IoT system's potential to decode
aerosols' role in the grand narrative of climate change. These findings inspire
hope as we confront the formidable global challenge of climate change.
The use of technology has a significant impact to reduce the consequences of
accidents. Sensors, small components that detect interactions experienced by
various components, play a crucial role in this regard. This study focuses on
how the MPU6050 sensor module can be used to detect the movement of
people who are falling, defined as the inability of the lower body, including
the hips and feet, to support the body effectively. An airbag system is
proposed to reduce the impact of a fall. The data processing method in this
study involves the use of a threshold value to identify falling motion. The
results of the study have identified a threshold value for falling motion,
including an acceleration relative (AR) value of less than or equal to 0.38 g,
an angle slope of more than or equal to 40 degrees, and an angular velocity
of more than or equal to 30 °/s. The airbag system is designed to inflate
faster than the time of impact, with a gas flow rate of 0.04876 m3
/s and an
inflating time of 0.05 s. The overall system has a specificity value of 100%,
a sensitivity of 85%, and an accuracy of 94%.
The fundamental principle of the paper is that the soil moisture sensor obtains
the moisture content level of the soil sample. The water pump is automatically
activated if the moisture content is insufficient, which causes water to flow
into the soil. The water pump is immediately turned off when the moisture
content is high enough. Smart home, smart city, smart transportation, and
smart farming are just a few of the new intelligent ideas that internet of things
(IoT) includes. The goal of this method is to increase productivity and
decrease manual labour among farmers. In this paper, we present a system for
monitoring and regulating water flow that employs a soil moisture sensor to
keep track of soil moisture content as well as the land’s water level to keep
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In order to provide sensing services to low-powered IoT devices, wireless sensor networks (WSNs) organize specialized transducers into networks. Energy usage is one of the most important design concerns in WSN because it is very hard to replace or recharge the batteries in sensor nodes. For an energy-constrained network, the clustering technique is crucial in preserving battery life. By strategically selecting a cluster head (CH), a network's load can be balanced, resulting in decreased energy usage and extended system life. Although clustering has been predominantly used in the literature, the concept of chain-based clustering has not yet been explored. As a result, in this paper, we employ a chain-based clustering architecture for data dissemination in the network. Furthermore, for CH selection, we employ the coati optimisation algorithm, which was recently proposed and has demonstrated significant improvement over other optimization algorithms. In this method, the parameters considered for selecting the CH are energy, node density, distance, and the network’s average energy. The simulation results show tremendous improvement over the competitive cluster-based routing algorithms in the context of network lifetime, stability period (first node dead), transmission rate, and the network's power reserves.
The construction industry is an industry that is always surrounded by
uncertainties and risks. The industry is always associated with a threatindustry which has a complex, tedious layout and techniques characterized by
unpredictable circumstances. It comprises a variety of human talents and the
coordination of different areas and activities associated with it. In this
competitive era of the construction industry, delays and cost overruns of the
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handling of materials at the construction site. In this paper, we propose
developing a system that is capable of tracking construction material on site
that would benefit the contractor and client for better control over inventory
on-site and to minimize loss of material that occurs due to theft and misplacing
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sensors and transmit sensed values to the indoor node. We transferred the data
received by the master node to the cloud using the Adafruit cloud service. The
system can operate with a coverage of 4.5 km, where the optimal distance
between outdoor sensor nodes and the indoor master node is 4 km. To further
predict fall detection, various machine learning classification techniques have
been applied. Upon comparing various classifier techniques, the decision tree
method achieved an accuracy of 0.99864 with a training and testing ratio of
70:30. By developing accurate prediction models, we can identify high-risk
individuals and implement preventative measures to reduce the likelihood of
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people has proven to be the most beneficial application of this technology.
The effectiveness of adaptive filters are mainly dependent on the design
techniques and the algorithm of adaptation. The most common adaptation
technique used is least mean square (LMS) due its computational simplicity.
The application depends on the adaptive filter configuration used and are well
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Smart vehicle management by using sensors and an IoT based black box
1. International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol. 11, No. 3, November 2022, pp. 284~294
ISSN: 2089-4864, DOI: 10.11591/ijres.v11.i3.pp284-294 284
Journal homepage: http://ijres.iaescore.com
Smart vehicle management by using sensors and an IoT based
black box
Mohammad Minhazur Rahman1
, A. Z. M. Tahmidul Kabir1
, Al Mamun Mizan1
,
Kazi Mushfiqur Rahman Alvi1
, Nazmus Sakib Nabil1
, Istiak Ahmad2
1
Department of Electrical and Electronic Engineering, American International University-Bangladesh, Dhaka, Bangladesh
2
Department of Computer Science and Engineering, American International University-Bangladesh, Dhaka, Bangladesh
Article Info ABSTRACT
Article history:
Received Jul 9, 2022
Revised Aug 11, 2022
Accepted Sep 19, 2022
As the number of transports on the road increases every day, so does the
number of accidents. Reckless driving and consuming alcohol are two of the
leading causes of accidents. Apart from these issues, the safety of humans
and vehicles is also critical. A thorough investigation is required to minimize
the accident rate and improve human safety, particularly if an incident
occurs. The purpose of this study is to develop a few sensor-based black box
system that will help us reduce traffic accidents by continuously providing
precise guidance to the driver. At the same time, the evidence will be
uploaded to its server for further evaluation. This system also includes a way
of detecting drowsiness in the driver. Finally, using global positioning
system (GPS) and global system for mobile communications (GSM), the
relevant authorities will get information on the vehicle's condition and
whereabouts. For security purposes, a panic button is introduced here to get
emergency help from the security personnel by detecting the victim’s area.
Keywords:
Accident prevention
Black box
Light detection and ranging
Raspberry Pi
Real-time notification
Smart vehicle
This is an open access article under the CC BY-SA license.
Corresponding Author:
A. Z. M. Tahmidul Kabir
Department of Electrical and Electronic Engineering, American International University-Bangladesh
Dhaka-1229, Bangladesh
Email: tahmidulkabir@gmail.com
1. INTRODUCTION
Autonomous vehicles are the most emphasized invention in the automobile industry. Due to
simplicity, such as sustainable mobility and less complexity, autonomous vehicles play a major role in the
world. The worldwide transport network is continuing to evolve, and new automobiles are being introduced
to the road on a daily basis to make life easier. As a result, traffic accidents are increasing. According to the
National Highway Traffic Safety Administration, 94% of accidents are caused by drivers who make poor
choices on the road [1]. Drowsy driving, poor vision, racing, getting drunk, and other factors all have a part
in severe traffic accidents. According to one study [2], drowsy driving is responsible for about 6000 traffic
incidents each year. Another statistic [3], says that sleepy driving causes 100,000 fatalities each year. 36300
crashes occur in the USA each year, with lower vision issues accounting for roughly 17% of all crashes [4].
In Bangladesh, a media article [5], states that 60 persons die in every 10,000 road accidents because of poor
vision. Alcohol-related incidents accounted for more than 25% of all road accidents in 2019, while exceeding
the speed limit accounted for roughly 26% [6], [7]. Another research [8], found that in 2019, 47% of
passengers in automobiles were not wearing seat belts, resulting in 22,215 fatalities. After the collision, an
analysis is needed to define the exact damage and a feasible solution to prevent a repeat of the accident.
Moreover, additional research is necessary to solve the police case. Due to a lack of adequate real-time
evidence, the judgment process might become distorted. Along with the vehicle, human safety is also critical.
2. Int J Reconfigurable & Embedded Syst ISSN: 2089-4864
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285
Many drivers and passengers have reported experiencing robbery when traveling. A few days ago, a doctor
from Bangladesh, was robbed and tortured for 8 hours on a bus [9]. A person carrying his company's money
in a car was stopped at a checkpoint by fake cops, who robbed the money and the vehicle [10].
Based on the above-mentioned circumstances, we propose an internet of things (IoT)-based black
box for automobiles in this research. The IoT is a popular study in modern science, especially in the field of
monitoring systems [11]-[15]. This black box contains a variety of sensors and cameras. The major purpose
is to gather data while driving and preserve it for later examination. We developed a system to detect all-
sided data in order to identify any vehicle or objects and give the driver a safe driving path. Our prototype
will begin buzzing after it has identified the current state of both eyes and the driver's degree of alcohol
consumption while driving. In dangerous circumstances, this black box is capable of transmitting a rescue
signal to the nearby police stations, allowing for immediate assistance. The following are the primary
features of the paper:
− A smart vehicle algorithmic approach has been presented by this system.
− This system detects surrounding objects, amount of alcohol, and driver drowsiness by using sensors.
− This prototype can identify location zones, sub-zones and send a rescue signal to police or volunteers.
− This technology recommended using a camera to collect real-time video footage.
− This system produced a prototype that transmits an SMS to officials through GSM in case of an accident.
2. LITERATURE REVIEW
The light detection and ranging (LiDAR) sensing technology uses a data point clustering algorithm
to identify objects in a driver assistance system [16]. Another study [17], suggests a technique in which
dedicated short range communications (DSRC) roadside units can detect disconnected vehicles and transmit
the signal to neighboring automobiles. Another study [18], unveiled a collection of technologies, even in the
presence of numerous barriers, that allow their algorithm to precisely extract the road borders. Following a
high-resolution database, LiDAR point cloud and red, green, blue (RGB) imaging technologies are combined
to recognize every object and generate a 3D rectangle box [19]. One research [20], has explored an
algorithmic approach for identifying the driver's facial expression. The authors further stated that their
method will carry out an operation for finding a safe parking space, allowing their autonomous vehicle to
travel there on its own. In one paper [21], the combination of drowsiness, alcohol, and overload detection
sensors is recommended to diminish the accident rate. This technology uses the eye blinking ratio to
determine whether or not to slow down the vehicle. In a separate investigation [22], facial landmark points
were used to identify the person's eye flickering rate. An application to develop human-computer interaction
for determining the degree of tiredness was suggested by researchers in [23]. In that situation, they estimate
the movements of the head, both eyes, and yawning without the aid of any worn accessories. The authors of
[24], used a microcontroller and an alcohol-detection sensor to determine whether alcohol was present in the
vehicle. Additionally, a global system for mobile communications (GSM) module is included to alert the
vehicle's owner of the driver's intoxication. In a study [25], radio frequency identification (RFID) and alcohol
detection sensors are described. The publication claims that the speed of the vehicle will be restricted if the
amount of alcohol consumption is above the limit and that the automatic toll will be collected after the
vehicle passes through a toll plaza. Liu et al. [26], suggests a technique through which blind spots are
detected both during the day and at night using various sensors, radars, and frequencies. In the event that it
notices any unusual movement within the targeted zone, this method can simultaneously transmit a warning
signal to the driver. Kim et al. [27], creates a projection map to alert the driver if the motion vectors identify
any blind spots. Marigowda et al. [28], employed RFID and user architecture to create a secure transportation
system. Their system allows the host server to instantaneously evaluate all of the driver's and vehicle's
paperwork. Li and Zhuang [29], claimed to use an RFID-based smart toll collecting system that does not
require the driver to brake in order to pay the toll.
3. METHOD
In Figure 1, with the assistance of Raspberry Pi, the system's principal microcontroller, the
architecture is working properly. A bunch of sensors such as LiDAR, alcohol, and speed detectors are also
utilized to improve the accuracy of the functions. The RFID module and camera are also part of the system,
with the camera observing seat belt status and eye blinking ratio.
The autonomous vehicle section includes all of the sensors. The driver assistance system includes a
display monitor in front of the driver that displays the results obtained from the sensors. The essential
measurements and warning messages will be shown on the screen. A buzzing alarm is also installed in the
3. ISSN: 2089-4864
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286
vehicle's dashboard to alert the driver if drowsiness is detected. Concerning authorities can get the real-time
data and scenarios from the firebase.
Figure 1. The architecture of the proposed system
3.1. LiDAR
A LiDAR sensor is used here to compute the distance from any sided vehicle by collecting all side
information. The laser pulses are thrown to all sides by LiDAR, which then receives the reflected pulses. The
calculation procedure then begins by calculating the total pulses and time for the reflected laser beams. The
results from the LiDAR are then collected by Raspberry Pi. After receiving the data, LiDAR creates a 3D
point cloud to get a more specific visualization of any targeted obstacle. The point cloud's Bird's-eye view
provides a sketch that includes the object's height and width. The density of the object, as well as the updated
map, will be displayed on the driver's screen [19]. Following the map, the driver will be aware of other
nearest vehicles. Figure 2 depicts the entire situation. In this system, LiDAR identifies the vehicle in front of
it and, according to its principles, makes a sketch of it to alert the driver. This sketch is generated using the
object's measured height and width. This is how the LiDAR operates in this proposed system.
Figure 2. Working procedure of LiDAR in this system
3.2. Drowsiness detection
Sixty-eight facial landmark points (FLP) is the most accurate method for detecting the driver's
drowsiness. Pre-trained 68 FLP capture data from the eyes, mouth, and nose. By utilizing those parameters, it
can recognize any facial expressions and the rate of eye blinking in both normal and sleepy situations. The
eye aspect ratio (EAR) is calculated by using a total of 12 points, with 6 points for each eye as. A camera
mounted on the dashboard takes footage of the driver's eye condition, and the 68 FLP evaluates the ratio
according to its functioning mechanism.
EAR =
||𝑝2 − 𝑝6|| + ||𝑝3 − 𝑝5||
2 ||𝑝1 − 𝑝4||
Figure 3 shows six points on each eye, numbered p1 to p6. The prototype starts calculation to
identify the EAR after scanning these spots for both eyes. In Figure 3(a) the system recognizes the eyes as
4. Int J Reconfigurable & Embedded Syst ISSN: 2089-4864
Smart vehicle management by using sensors and an IoT based black box (Mohammad Minhazur Rahman)
287
open if the measured ratio exceeds the threshold (0.25). In Figure 3(b) the system considers the eye status
closed if the ratio is less than 0.25. The prototype uses the average value of a few ratios over a period of time
to obtain more accurate results [20]. If the average value remains below the threshold, the system sends a
signal to the alarm to begin buzzing and alerting the driver. At the same time, a text SMS will be sent to the
associated phone number. This is a continual procedure that looks for signs of tiredness in the driver's eyes
and any unusual facial expressions.
(a) (b)
Figure 3. EAR points (a) open condition and (b) close condition
3.3. Alcohol and smoking sensor
For detecting alcohol consumption, a sensor named MQ3 is used here. This sensor can detect the
presence of alcohol in a person's breath as well as in the nearby surroundings. This sensor is capable of
detecting concentrations ranging from 25 to 500 parts per million (ppm). If the amount of alcohol
consumption is higher, the sensor initiates a procedure in which the LED blinks rapidly. The more the
consumption, the more blinking there will be. The MQ2 sensor is used in this system to detect the level of
smoke or any combustible gas created inside the vehicle. MQ2 can detect any volume between 200 and
10,000 ppm. This sensor's sensitivity increases if it detects any gas or smoke in close surroundings. For both
circumstances, a text SMS will be sent to the appropriate authority.
3.4. Radio frequency identification
In this project, a RFID-based locker system is used to improve the vehicle's security system. Figure
4 shows the implementation and working process of RFID. The important pieces of information about the
vehicle (registration, insurance) as well as the information about the owner and driver (name, address, driving
license) must be included in this RFID card. A driver should have to punch his card into the RFID reader
before starting the engine. The relay will deliver a positive signal to the electric control unit (ECU) if all of
the data matches the pre-defined information. The ECU will only allow the driver to start the car after
receiving a positive signal from the relay. Otherwise, the driver will not be able to start the engine. Before
starting the engine, the entire operation will take no more than 4-5 seconds. Figure 4(a) displays the working
procedure, whereas Figures 4(b) and 4(c) depict the RFID hardware implementation.
(a)
(b) (c)
Figure 4. Implementation of RFID (a) flow diagram of RFID, (b) RFID module, and (c) hardware setup
5. ISSN: 2089-4864
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Furthermore, while parking in a restricted area or a shopping center, the driver's and owner's names
and phone numbers, as well as the vehicle's number, must be written down. Scanning the RFID’s QR code
and collecting that information digitally may save a lot of time. When an unfamiliar vehicle enters any major
government buildings or in hilly areas, this procedure might be performed.
3.5. Tracking system
The "Panic button" is integrated into this system to ensure human safety. The primary goal of this
button is to catch the attention of any law enforcement agency during a life-threatening situation. The
prototype collects GPS data when the button is pushed. The system has several pre-defined "zones" and "sub-
zones". A zone is defined as a specific region, and the roads or blocks inside that zone are called sub-zones.
The main advantage of selecting a zone is to get immediate assistance from anybody. Because robbery is a
rapid activity that takes place in a short period of time. As a result, the victim needs immediate assistance. If
a person is attacked in one location, but assistance arrives from 20 kilometers away, it will not be a feasible
option. To avoid this situation, the concept of selecting a zone and subzone appeared to send an emergency
rescue attempt to the victim. For the highway cases, per kilometer is considered a sub-zone. The GPS data for
a running vehicle will be updated every 30 seconds to provide the specific location.
In the algorithm, there are some pre-defined emergency numbers for police stations, checkpoints,
and voluntary organizations. The prototype sends an emergency signal to the nearby police checkpoints as
well as the volunteers after pressing the button. In case of the unavailability of the nearest check post, the
system immediately transmits all relevant data to the national emergency number for further action.
Figure 5 shows how the vehicle tracking system will work. In Figure 5(a), a "zone" is considered as
a particular region while Figure 5(b) indicates a block that is regarded as a "sub-zone". One car (victim)
became trapped in a risky scenario and used the panic button to get assistance. After receiving a signal from
the vehicle, one police car approaches the victim to provide security. In Addition, according to the victim's
GPS, one volunteer from the voluntary organization is heading to the location to offer support to the victim.
This is how it works with the panic button. This prototype utilizes google map to collect the coordinates to
generate a bounding box. Each of the four corners of this box has four sets of latitude and longitude, each
with a maximum and minimum point. The victim’s vehicle also bears one location coordinate. All locations
within this range can easily be recognized by following the inequalities listed (1) and (2) [30].
𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑙𝑎𝑡𝑖𝑡𝑢𝑑𝑒 ≤ 𝑣𝑒ℎ𝑖𝑐𝑙𝑒′𝑠 𝑙𝑎𝑡𝑖𝑡𝑢𝑑𝑒 ≤ 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑙𝑎𝑡𝑖𝑡𝑢𝑑𝑒 (1)
𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑙𝑜𝑛𝑔𝑖𝑡𝑢𝑑𝑒 ≤ 𝑣𝑒ℎ𝑖𝑐𝑙𝑒′𝑠 𝑙𝑜𝑛𝑔𝑖𝑡𝑢𝑑𝑒 ≤ 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑙𝑜𝑛𝑔𝑖𝑡𝑢𝑑𝑒 (2)
If the vehicle's latitude and longitude endpoints are equal or inside the bounding box, the vehicle is
generally considered to be within that sub-zone by this prototype. Figure 5(c) presents the coordinate points
for the bounding box, as well as the victim's location. The victim's latitude and longitude must be between
the highest and lowest latitude and longitude, according to inequalities (1) and (2). The victim's latitude and
longitude are 23°48'51.4"N and 90°25'39.3"E, respectively. As a result, this latitude and longitude are inside
the maximum and minimum points. By using this approach, the sub-zone computation is done.
If the prototype fails to identify all four sets of latitude and longitude, another formula known as the
"Haversine formula" will be used. Two sets of latitude and longitude are required to use this formula. Among
them, the victim's vehicle will continually transmit one set. So, this formula will work if the prototype can
locate at least one set from the map. The formula is employed in this system to obtain the accurate coordinate
value. This formula can be used to calculate the great circle distance between two spots [30], [31]. For given
two points on a sphere:
haversin (
d
r
) = haversin (α2 − α1) + cos (α1). cos (α2). haversin (β2 − β1) (3)
here,
𝑑/𝑟 = 𝑐𝑒𝑛𝑡𝑟𝑎𝑙 𝑎𝑛𝑔𝑙𝑒 (𝑟𝑎𝑑𝑖𝑢𝑠) (4)
in this formula,
ℎ𝑎𝑣𝑒𝑟𝑠𝑖𝑛 = 𝐻𝑎𝑣𝑒𝑟𝑠𝑖𝑛𝑒 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 [haversin(θ) = sin2
(
θ
2
) =
1−cos(θ)
2
] (5)
in (4), by applying inverse haversine,
6. Int J Reconfigurable & Embedded Syst ISSN: 2089-4864
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d = r. haversin−1
(h) = 2r. arcsin(√h)
d = 2r. arcsin(√haversin (α2 − α1) + cos (α1). cos (α2). haversin (β2 − β1) )
d == 2r. arcsin(√sin2 (
∆α
2
) + cos (α1). cos (α2). sin2 (
∆β
2
) )
(6)
(a)
(b)
(c)
Figure 5. Tracking system in an area (a) zone, (b) sub-zone, and (c) coordinate points of the bounding box
The prototype can calculate the distance using this formula. If the measured distance is within 2
kilometers, the prototype will send out a rapid response signal within 2 kilometers on each side of the car. If
the distance is greater, the prototype will choose the next subzone to send the rescue signal to the pre-set
phone numbers. Figure 6 depicts the flow diagram of this process. The Raspberry Pi stores all of the
information. Following that, various pieces of data are uploaded to the Firebase server for further processing.
For many IoT-based applications, Firebase is an efficient server for preserving real-time data [32], [33]. This
server saves all of the data associated with the vehicle's registration numbers. All of the variables and
scenarios are kept in JavaScript object notation (JSON) format in firebase. These data can be obtained in
CSV format if required. Any data may be checked at any moment to assess the state of the vehicle. This file
also contains real-time data, allowing for a proper inquiry following an accident.
7. ISSN: 2089-4864
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Figure 6. Flow diagram of tracking system
3.6. System prototype
The simulated design before building the system and the corresponding prototype are shown in
Figure 7. Figure 7(a) depicts a three-dimensional image of our suggested model, comprising LiDAR, display,
buzzer, and camera. The other one, 7(b), is a front view of our car. Figures 7(c) and 7(d) depict the hardware
model and the vehicle in motion, with the driver seated in his seat. This prototype vehicle is considered for a
single person and a few loads.
(a) (b) (c) (d)
Figure 7. Prototype of (a) full proposed model, (b) front view, (c) hardware model, and (d) running condition
3.7. System evaluation
Figure 8 shows the outputs of the various warning features of the system through the display screen.
Figure 8(a) depicts how LiDAR identifies an item near the smart car and presents warning information on the
monitor. The car exceeded the limit, as shown in Figure 8(b). Figure 8(c) identifies the eye’s condition as
sleepy when the EAR is less than 0.25. As a result, the motorist receives an SMS warning. If alcohol is
discovered in the vehicle, the system will provide the information indicated in Figure 8(d). The suggested
model's performance and implementation consequences are shown below.
(a) (b) (c) (d)
Figure 8. Display screen (a) object detected, (b) over speeding, (c) drowsiness detected,
and (d) alcohol detected
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Figure 9 shows the output from the RFID and camera modules. Two sets of notification text are
shown in Figures 9(a) and 9(b), which illustrate the RFID state. Figures 9(c) and 9(d) displays the state of a
driver's eyes for detecting tiredness. In Figure 9(c), the EAR is 0.18, which is below the 0.25 criterion. As a
result, the suggested prototype considered the situation as the eyes were closed. Figure 9(d) shows that the
EAR is 0.36 and classed as an open eye condition. Figure 10 shows some warning information from the GSM
module, where 10 (a) shows drowsy driving and 10 (b) shows over speed driving notification.
(a) (b)
(c) (d)
Figure 9. RFID (a) not matched (b) matched, Eyes (c) closed (EAR=0.18), and (d) opened (EAR = 0.36)
(a) (b)
Figure 10. Warning information (a) drowsiness detected and (b) over-speed condition
4. COMPARISON BETWEEN EXISTING AND PROPOSED SYSTEM
Each project has some unique qualities. Those qualities increase the project's profitability and
productivity. Among the many contrasts between the existing and suggested models, these are a few of the
main fundamentals. To highlight the differences, a few sets of comparisons between today's technology and
our suggested approach is included in Table 1.
Table 1. Comparison between existing and proposed system
Features Proposed systems Existing systems
Storage of Information Firebase SD Card [34], [38], [45]
Working microcontroller Raspberry Pi PIC16F877A microcontroller [35], Arduino
UNO [41], Arduino Mega 2560 [42]
Drowsiness detector EAR through 68 landmark points Eye blink sensor [37], [43]
Object detection LIDAR Ultrasonic ranging module HC-SR04 [38],
proximity sensor [44]
Security of the Vehicle RFID key Blynk mobile application [39]
Seat belt detection Camera Seat belt sensor [40]
Speed check In-vehicle display and text via GSM Flashing light [36]
Information about Danger Panic button signal by creating zone and sub-zone Piezoelectric sensor [43]
Sending information Using GSM through Firebase server Bluetooth module [43]
Alcohol/smoking check MQ3 sensor, MQ2 sensor Gas sensor [44]
5. NOVELTY
One of the key considerations of this prototype is to capture all information and then store it for later
study to reduce the rate of accidents. The firebase server is a better way of storing data rather than the secure
digital (SD) card even without the internet. Another important aspect of this system is that it sends the rescue
signal to the nearest police station. When the panic button is pressed or an accident occurs, the system will
detect the location. Another addition is identifying the driver's drowsiness using 68 FLP, which is a standard
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way of assessing the mouth and eye aspect ratios. In the case of an occurrence, the authorities can get the
information from the server.
6. CONCLUSION
Image processing can be used in the system to obtain precise readings. Many artificial intelligence
(AI) based technologies, such as machine learning (ML) and computer vision (CV), may be employed in this
prototype to determine the precise facial expression of the driver. Safe parking assistance and an automated
speed reduction method can be introduced in this black box device. Although the rate of accidents will not be
lowered immediately, this technique may assist to minimize it. To keep track of these records, a committee of
experts could be formed, with the goal of determining the true cause of any accident and proposing a better
way to prevent it. As a result, we may predict a community with fewer accidents in the future.
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BIOGRAPHIES OF AUTHORS
Mohammad Minhazur Rahman received his B.Sc. Eng. Degree in Electrical and
Electronic Engineering from American International University-Bangladesh. He is currently
pursuing his Master of Engineering (M. Eng) in Electrical and Computer Engineering (ECE) at
the University of New Brunswick. His research interests include automation, smart vehicles,
robotics, and internet of things. He can be contacted at email: minhaz.nayem790@gmail.com.
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A. Z. M. Tahmidul Kabir received his B.Sc. Eng. Degree in Electrical and
Electronic Engineering from American International University-Bangladesh. He has authored
or coauthored more than 15 publications: 13 conference proceedings and 3 journals, with 4 H-
index and more than 50 citations. His research interests include embedded systems, IoT,
robotics, and autonomous vehicles. He can be contacted at email: tahmidulkabir@gmail.com.
Al Mamun Mizan is currently pursuing his MEEE Degree at American
International University-Bangladesh. Also received his B.Sc. Eng. Degree in Electrical and
Electronic Engineering from the same institute. His research interests include IoT, robotics,
and electronics circuit. He can be contacted at email: almamuneee15@gmail.com.
Kazi Mushfiqur Rahman Alvi received his B.Sc. Eng. Degree in Electrical and
Electronic Engineering from American International University-Bangladesh. He is currently
pursuing his MEEE in Electrical and Electronic Engineering (EEE) at American International
University-Bangladesh. His areas of interest for research include smart vehicles, robotics, and
the internet of things. He can be contacted at email: kmralvi@gmail.com.
Nazmus Sakib Nabil received his B.Sc. Eng. Degree in Electrical and Electronic
Engineering from International Islamic University Chittagong. Then he completed his Masters
of Engineering in Telecommunication Engineering from American International University
Bangladesh. His research interests include wireless communication, IoT, and digital signal
processing. He can be contacted at email: nabil.eee.15@gmail.com.
Istiak Ahmad is currently pursuing his B.Sc. in Computer Science and
Engineering (CSE) at American International University-Bangladesh. His research interests
include computer vision, IoT, blockchain, and software engineering. He can be contacted at
email: istiakahmad003@gmail.com.