A Survey on IoT and Blockchain‑Enabled Systems for Automobile

Tracking, Security and Voice Recognition

V. Saranya

and J. Deepa

Department of CSE, Adhiyamaan College of Engineering, Hosur.635109, Tamil Nadu, India

Department of CSE, Easwari Engineering College, Ramapuram, Chennai. 600089, Tamil Nadu, India

Keywords: Internet of Things, Automobile Tracking, Security, Block Chain, Voice Recognition.

Abstract: Many studies have developed new endeavors of automobile tracking, vehicle monitoring, and secured systems

by incorporating IoT and block-chain technology in this paper. The proposed framework is one which at its

core utilizes GPS, GSM, and MMS, working together to create a real-time system addressing issues such as

vehicle theft and accidents. If the vehicle is stolen, the IoT sensors sense unauthorized access to the vehicle,

stops the vehicle engine and captures the image of the driver. This data is transferred securely via blockchain

protocol, which preserves data integrity and prevents manipulation through unauthorized methods. Likewise,

in the event of an accident, the system will also make a note of the car’s location and image of the driver and

send it to the registered emergency contacts or the owner, providing timely assistance. In this context, although

blockchain is very critical for implementing secure, decentralized and immutable data communications inside

the framework. This technology solves the privacy and security problems in IoT network. Additionally, the

system is complemented by the voice recognition capabilities of AI for developing user interaction.

Autonomous systems powered by advanced algorithms allow drivers to operate many functions of their

vehicles while maintaining a hands-free experience – a game changer for convenience and safety. The survey

covers previous solutions, technological developments, and challenges of deploying these systems. It

highlights challenges including scalability, latency and integration costs as prohibitive barriers to adoption,

alongside possible solutions like leveraging edge computing, hybrid blockchain solutions, and better noise-

filtering in voice recognition. This architecture highlights the revolutionary impact achievable through the

merger of IoT and Blockchain technologies. This new method strives to reimagine traditional techniques with

a focus on security, efficiency, and solving current challenges, all to enhance the landscape of vehicle tracking

and monitoring.

1 INTRODUCTION

Natural Language processing the Internet of Things

(IoT) and blockchain technologies have evolved

tremendously and have incorporated countless

industries by providing novel solutions for

automobile security, monitoring, and user interaction.

The combination of all these technologies can

represent a paradigm shift in how vehicles are

tracked, stolen, and accidents are managed,

improving the general user experience thanks to

improvements such as voice recognition systems.

InThis paper investigates how the current state of the

art for Applications of IoT and Blockchain can have

a significant impact on the automobile systems. IoT

is a leading enabler of immediate data collection,

communication, and integration of devices through

GPS, GSM and onboard vehicle sensors. IoT in the

automotive sector: IoT enabled systems facilitate the

accurate location tracking, monitoring status and

events detection, so as vehicle owners can get real-

time notifications in case of car theft/unauthorized

access. GSM technology is used for connecting things

together and can ensure continuous communication

among connected devices, and multimedia messaging

service (MMS) is used for the transmission of images

or recordings required in emergencies to appropriate

stakeholders.

Blockchain technology builds upon IoT

technology by addressing important safety and

privacy issues. The tamper-proof, decentralized and

sys-free nature of blockchain ensures that data

collected from the IoT devices, is securely stored

without risks from unauthorized access or data

Saranya, V. and Deepa, J.

A Survey on IoT and Blockchain-Enabled Systems for Automobile Tracking, Security and Voice Recognition.

DOI: 10.5220/0013885800004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

519-529

ISBN: 978-989-758-777-1

519

manipulation. Seamlessly integrating blockchain

brings added transparency and accountability to

automotive systems, especially in cases of theft or

accidents. For example, the driver’s image and

location data can easily be stored and exchanged

through blockchain in an encrypted manner to

prevent fraudulent claims or unauthorized use. AI-

powered voice recognition systems also add an extra

dimension of innovation and are being integrated

into automobile platforms. Employing complex

neural networks and deep learning algorithms, these

systems allow for natural, hands-free interaction,

enabling voice command control of vehicle functions.

This eliminates distractions and keeps the focus on

the road, which is critical in complex driving

situations. Noise-reduction technologies and

preprocessing units that enhance the reliability of

voice recognition systems, enabling the newly

developed voice recognition systems to801975738 be

effective even for

applications7875406606234824018 under noisy

environments. Though, there are challenges that

prevail while merging IoT with blockchain in

automobiles. Scalability IOT based networks

nowadays need to handle pretty large amounts of

data, and where IoT is in question; scalability means

a lot. Latency, or the delay in the transmission of

real-time data, is another factor to consider,

especially for applications that require immediate

feedback, like accident detection. Moreover, the

expensive costs associated with integrating IoT

devices, blockchain infrastructure, and AI-driven

technologies serve as a stumbling block for mass

adoption. To solve these problems, edge computing

developments are being investigated. This allows for

reduced latency to create a faster response, by

processing data at the edge. Public and private

blockchain hybrids. Optimize security and

performance. AI: Voice Recognition AI research

continues to improve voice recognition AI, with a

focus on developing context-aware systems and noise

filtering techniques to provide more accurate and

reliable AI-driven interfaces. Responsibilities and

Blockchain Integration The merge of IoT and

blockchain is creating transformative opportunities

for automobile security and monitoring. Using a

combination of GPS, GSM, MMS and voice

recognition, these systems claim to be able to provide

greater safety, protection from theft and user

interaction. Continued focus on solving existing

issues and looking to new technology will shape and

improve these solutions for the future to come,

allowing us to reach the next generation of secure,

efficient and user-friendly intelligent transportation

systems.

2 KEY TECHNOLOGIES

These three components turn a smart car into a secure

automobile, with benefits across IoT, blockchain, or

voice recognition technologies. The combination of

these technologies allows for real-time monitoring

and secure data transmission as well as improved user

experience, tackling important matters such as

vehicle theft, motor vehicle accidents and user

accessibility.

2.1 Internet of Things (IoT)

Modern automobile systems rely on the Internet of

Things (IoT) to connect physical devices in real-time

to the internet to collect, share, and communicate

data. IoT allows vehicles to work as interlinked

entities and communicate real-time information

about their condition, location, and surrounding

environment. GPS (Global Positioning System): GPS

is an integral part of IoT in automobiles, enabling

precise location detection. It’s an important

component in tracking vehicles when they are stolen

or during an emergency, providing accurate location

information to vehicle owners or authorities. GSM

(Global System for Mobile Communication): GSM

enables constant communication between vehicles

and centralized systems. This allows for real-time

alerts and notifications to be sent in case of

unauthorized access, movement detection, or

accident alerts.

MMS (Multimedia Messaging Service) — MMS

technology will make IoT-enabled systems richer by

enabling the sharing of multimedia content, e.g.,

images or videos. In such cases, MMS can be

leveraged to capture and dispatch real-time images of

unauthorized access and driver details to aid

situational awareness.

This features a two-way exchange of information

and an interface to monitor, achieve the floating of

information and assist in driving the automobile with

the support of sensors, specifically in8120 native

driving automobiles.

2.2 Blockchain

Automobile systems can leverage the power of

blockchain technology for data security and

transparency. As a distributed ledger, Blockchain

eliminates the single point of failure inherent in

centralized systems, creating a more resilient security

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model for IoT deployments. Its decentralized and

immutable nature further enhances security by

making it nearly impossible to tamper or gain

unauthorized access to sensitive information,

addressing key weaknesses in IoT network security.

Decentralization Since no central control points exist

in blockchain solutions, any information about

vehicle status, location, and driver information is

stored in a decentralized network, lowering

vulnerability to breaches. Immutability: The ledger

system of a blockchain prevents data manipulation,

which improves the integrity and authenticity of

records, making this technology valuable in instances

of theft, as well as accidents. Secure Data Sharing:

Sensitive information like driver images or accident

details can be securely shared with relevant parties

(law enforcement or insurance companies, etc.)

without compromising privacy. Blockchain

technology, builds upon the idea of secure distributed

transactions and thus provides a strong foundation

for data communication in automobile systems and as

such ensures complete verifiability and security of

any interaction among the IoT devices. This not only

increases security but also fosters user trust through

transparency and accountability.

2.3 Voice Recognition

Hands-free operation and intuitive communication

are just some ways voice recognition technology is

changing the way we interact with our cars. More

advanced systems use artificial intelligence, deep

learning, and neural networks to comprehend and

respond to commands given in natural language.

Natural Language Processing: The conversing app or

voice recognition systems will be able to learn other

accents, languages, and contexts by using machine

learning algorithms, enhance their accuracy and

adaptability. Noise Cancellation: One of this system's

features is that it has the preprocessing units which

not only understand your command but also reduce

the effects of background noise so that the command

is interpreted correctly especially in the times when

you are traveling like highways or in urban settings.

Improved Accessibility: Voice recognition enables

drivers to execute critical capabilities, like

navigation, adjusting functions of the vehicle, or

searching for information. Voice recognition in

vehicles allows for great safety and convenience. For

instance, drivers can issue voice commands to enable

security measures, ask for navigation assistance or

file reports on emergencies, reducing the need for

manual input and keeping distractions at bay.

3 LITERATURE REVIEW

3.1 Internet of Things (IoT) Based

Vehicle Tracking Systems

With the advancement of technology, the huge impact

of Internet of things on vehicle tracking system has

changed the real-time monitoring and management

of vehicle. Majority of the IoT-based vehicle tracking

system technology are GPS and GSM that track the

vehicle position accurately and transfer it over

internet. For vehicle owners, fleet managers, and law

enforcement agencies, these systems can be

incredibly useful in providing instant notifications of

unauthorized movement or theft and responding

quickly to an accident. With the help of GPS, accurate

tracking of vehicle geographic locations is easy, and

GSM module is used for clearing data between

vehicle and monitoring system in real-time through

mobile phones, computer systems or cloud systems

(Kumar et al., 2023; Singh & Sharma, 2023).

Advantages of IoT-based tracking systems Theft

prevention, route optimization, and fleet management

are some of the key pros of such IoT-based tracking

systems. Real-time updates make it possible for

owners or fleet managers to monitor vehicles’ precise

locations, optimize routes for fuel efficiency and

switch to timely maintenance.

This allows for immediate detection of any

unauthorized movement or tampering and brings

obvious security advantages. Nevertheless, there are

a few challenges that prevent the widespread a

foundation of IoT-based tracking systems. As an

example, IoT device battery life can be a limitation,

especially in older vehicles that lack sophisticated

power management tools (Baba et al., 2023). Lastly,

the quality of the network connection is essential for

good performance, and outages in service will

compromise the system. Data privacy is also a

concern since constant data transmission carries the

potential for unauthorized access or data breaches and

sensitive location data is at particular risk of

exposure. Thus, the competent application of

encryption, data protection, and regulatory

compliance is paramount to the widespread

deployment of these systems (Meena et al., 2024).

3.2 Blockchain in Automotive Security

The explosion of interest in cryptocurrencies like

Bitcoin has brought with it a new paradigm of

blockchain technology, and in the context of Internet

of Things (IoT) networks, blockchain is being

explored for securing automobile systems. By

A Survey on IoT and Blockchain-Enabled Systems for Automobile Tracking, Security and Voice Recognition

521

providing a decentralized, immutable ledger,

blockchain secures data transfer between IoT devices

(i.e. vehicle sensors, GPS modules and

communication modules). This technology can be

used to protect against data tampering and

unauthorized access, as well as cyber-attacks (Sharma

et al., 2024; Gupta et al., 2023), thus making the

automobile industry a lucrative solution.

The inclusion of blockchain in vehicle security

systems has several benefits. A major advantage is

decentralized and tamper-proof storage of all actions

and messages passed through in the network: This

reduces the need for central authorities or

intermediaries where the risk of a single point of

failure is greater, and it builds the overall trust within

the system. It is also able to facilitate secure over-the-

air software updates for vehicles, making sure that

firmware and vehicle software are always up to date

without any risk of malware or data breaches (Patel

et al., 2023). Additionally, the inherent transparency

and auditability of blockchain boost privacy and

security, particularly for sensitive automotive use

cases like vehicle authentication, payment systems

and secure vehicle-to-vehicle communication.

While the potential for blockchain is promising,

there are obstacles to overcome in the automotive

industry. Scalability is still a major issue, as

blockchain networks can essentially slow down and

become inefficient when processing high amounts of

transactions, making it unlikely for use in real-time

applications such as vehicle tracking and

communication. Moreover, the energy consumption

of blockchain networks can be too high for practical

use in resource-constrained environments such as

vehicles (Verma et al., 2023) especially for numerous

of them containing proof-of-work algorithms.

Another challenge is integration with existing vehicle

systems and IoT infrastructure, requiring substantial

changes to existing communication protocols and

hardware. In that sense, before the full application of

blockchain technology to automobile security, there

are still many technical and practical obstacles to be

overcome, and it requires more research and

development effort in the future. Table 1 shows the

methodological comparison of smart vehicle tracking

and security approaches.

Table 1: Methodological Comparison of Smart Vehicle Tracking and Security Approaches.

Paper

No.

Methods Used Dataset Evaluation Metrics

Marite

(

Advanta

)

Demartite

(

Disadvanta

)

[1]

IoT-based vehicle

tracking using

GPS/GSM, energy

efficiency techniques

Simulated vehicle

data, GPS tracking

data

Energy

consumption,

system reliability,

real-time tracking

accurac

Real-time location

updates, energy

efficiency focus

High dependency on

external network

conditions, battery

constraints

[2]

Blockchain for IoT

security, smart

contracts for secure

communication

Not specified

(general

automotive

communication

data)

Security, data

integrity,

unauthorized access

prevention

Blockchain

provides strong data

security, tamper-

proof

communication

Scalability issues,

latency in real-time

vehicle systems

[3]

AI/Deep learning,

NLP techniques for

voice recognition

Not specified

(voice datasets,

car-related

interactions)

Recognition

accuracy, noise

resilience, real-time

rocessing accurac

Hands-free control,

reduced distractions

for drivers

Voice recognition

performance can

degrade in noisy

environments

[4]

IoT vehicle tracking

with enhanced

security protocols

IoT vehicle data,

GSM/GPS-based

location data

Data security,

privacy, system

intrusion detection

Enhanced security

features

High complexity in

integrating security

rotocols

[5]

Blockchain for

securing automobile

IoT systems, vehicle

data management

IoT-based vehicle

data, security logs

Data integrity,

system performance,

prevention of

unauthorized access

Secures vehicle

data, reduces risks

of cyber threats

High energy

consumption,

potential integration

issues

[6]

Blockchain

implementation for

secure vehicle

Automotive

communication

data

Data security,

communication

delay, data

transmission s

Improves privacy

and prevents

tampering of

Scalability and

processing time

concerns in real-

time networks

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communication,

decentralized ledgers

transmitted vehicle

data

[7]

Real-time GPS

tracking, IoT-based

communication

GPS-based vehicle

data

Real-time tracking

accuracy, data loss

rate, communication

Real-time location

tracking

Relies on stable

network

connections, GPS

nal interru

tions

[8]

Deep learning for

voice recognition,

safety alerts, real-time

processing

Voice interaction

data

Driver safety

enhancement,

recognition

accuracy, system

Improves driver

experience, reduces

distractions

Environmental noise

can affect

recognition

accuracy

[9]

IoT integration with

GPS/GSM

technologies, real-

time tracking

Simulated data,

vehicle tracking

data

Accuracy of vehicle

location, energy

consumption,

communication

reliabilit

Efficient real-time

tracking, cost-

effective

Dependent on

external signals,

limited real-time

response

[10]

IoT for fleet

management,

optimized route

planning using GPS,

GSM, and IoT

Fleet tracking data

Route optimization

accuracy, system

efficiency, fleet

management

erformance

Optimized routes,

efficient fleet

management

Limited by network

connectivity and

data accuracy

[11]

Energy-efficient GPS

tracking, battery life

optimization

GPS tracking data

Power consumption,

system longevity,

real-time location

tracking accuracy

Reduces energy

consumption,

extends system

lifetime

Performance may

drop with energy

optimization

techniques

[12]

Cloud-based vehicle

tracking with IoT

integration, scalable

architecture

Cloud-based IoT

vehicle data

Real-time

monitoring, system

scalability, data

transfer rates

Scalable, low-cost

solution

Cloud dependency,

data latency

[13]

GSM-based vehicle

tracking, network

reliability in urban

areas

Urban traffic data

Data accuracy,

network reliability,

latency in urban

settin

Reliable in urban

environments, cost-

effective

Limited scalability

in rural or remote

areas

[14]

LPWAN technologies

for vehicle tracking,

IoT platform

comparison

LPWAN-based

vehicle data

System efficiency,

real-time tracking

accuracy, platform

erformance

Low-power, long-

range

communication

Limited bandwidth

and data transfer

speed

[15]

Blockchain for secure

communication in

vehicle systems,

decentralized data

exchan

IoT vehicle

communication

data

Data integrity,

secure transmission,

communication

latency

Enhances security

and privacy

High resource

consumption in

blockchain

operations

[16]

Blockchain for

vehicle-to-vehicle

communication,

secure data sharing

Vehicle

communication

data

Data security,

communication

speed, system

erformance

Enhances data

sharing security,

real-time

communication

Potential integration

complexity in

diverse vehicle

systems

[17]

Blockchain for secure

over-the-air software

updates in automotive

systems

Automotive

software update

logs

Update security,

data integrity,

update speed

Secure software

updates, reduces

tampering risks

Scalability issues in

large-scale vehicle

fleets

[18]

Blockchain for

decentralized vehicle

data management and

securit

Vehicle data logs,

real-time tracking

data

Data security,

privacy,

decentralized

mana

ement

Secure, tamper-

proof data

management

Real-time

transaction

processing

challen

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[19]

Blockchain in real-

time vehicle

communication, low-

latency transactions

Real-time vehicle

communication

data

Transaction latency,

scalability, data

integrity

Provides security,

reduces

unauthorized access

risks

Blockchain

processing time,

scalability in large

networks

[20]

Blockchain integrated

with edge computing

for secure, scalable

vehicle security

systems

Vehicle security

system logs

System scalability,

data integrity, edge

processing

efficiency

Reduces latency,

enhances scalability

Edge computing

complexity,

integration issues

with blockchain

[21]

AI, deep learning,

NLP for voice

recognition, driver

interaction

Automotive voice

data

Recognition

accuracy, user

interaction speed,

environmental noise

resilience

Improves driver

experience and

safety

Environmental noise

can reduce

recognition

accuracy

[22]

Deep learning for

multilingual voice

recognition, accent

adaptation

Voice interaction

data, automotive

command data

Recognition

accuracy across

accents, system

responsiveness

Multilingual

support, enhances

user experience

Performance

degradation in high

noise environments

[23]

AI-driven voice

recognition for

vehicle system

control

3.3 AI-Driven Voice Recognition

By harnessing machine-learning algorithms,

specifically deep learning models and natural

language processing (NLP), AI-based voice

recognition systems as seen in cars enable a hands-

free experience for the drivers while interacting with

the car. These systems allow drivers to use natural

language commands to control various functions in

the vehicle, including navigation, entertainment,

climate control, and communication. Getting

information through voice also improves driver

safety by avoiding distractions (Yadav et al., 2023).

Methods used in machine learning, like deep

learning models and natural language processing

(NLP), assist in improving the accuracy of voice

recognition systems. These systems can understand

voice commands with increasing accuracy, even in

complex settings like background noise, multiple

speakers, or with different accents. With the

advancement of voice recognition technology, it

provides a more integrated and intuitive user

experience, increasing the convenience and

functionality of in-car systems (Singh et al., 2023).

Additionally, voice-controlled AI can work with

additional vehicle technologies like navigation

systems and smart assistants to build a more custom

and productive driving experience.

AI voice recognition systems has several benefits

but still faces some challenges. A major problem is

the accuracy of voice recognition in busy

environments, like when there are several people

speaking or traffic noise is heavy. Although the

applications of deep-learning models have

progressed dramatically in voice perception abilities,

accuracy in every driving situation is difficult to

achieve (Jain & Sharma, 2023). There are also

privacy issues related to the collection and processing

of voice data. However, these systems pose a

dilemma in terms of data security and user privacy, as

they are designed to always listen and process voice

inputs. Sensitivity and security matter as it is

important to have voice data safely stored, processed,

and transmitted to gain users confidence (Kumar et

al., 2024)

4 PROPOSED FRAMEWORKS

To enhance the security and safety, and improve the

experience of interaction with vehicles, the proposed

system employs advanced technologies such as IoT,

Blockchain and AI. This framework is built to tackle

three of the most serious challenges that plague

modern transportation: vehicle theft, accident

reaction, and driver safety all accomplished through

natural voice interaction. The proposed system

addresses these significant concerns in automotive

security by leveraging IoT for instantaneous

monitoring, blockchain for secure data storage and

management, and voice recognition powered by AI to

augment user experience.

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4.1 Vehicle Theft Prevention

One of the main aims of this system is to avoid

vehicle stealing and unauthorized usage.

Conventional anti-theft systems based on basic

alarms and simple tracking systems can easily be

circumvented or penetrated. With the integration of

IoT sensors, GPS Technology, and usage of

blockchain for data storage and transmission, the

proposed system provides security to the vehicle.

IoT Sensors and GPS for Real-Time

Monitoring:

The system utilizes a network of IoT

sensors placed in the vehicle. These sensors monitor

for unauthorized access attempts, like a door opening

or tampering with the ignition system. The system

issues an alert when it detects unauthorized users and

can even automatically turn off the engine of the

vehicle, leaving the vehicle unable to move any

further. GPS system keeps updating the vehicle’s

position and location, thus continuously providing the

owner with the vehicle tracking in real-time.

MMS Technology for Driver Identification: In

order to make the security mechanism more secure,

the system uses Multimedia Messaging Service

(MMS) technology to capture the face image of the

driver during the theft attempt. A camera that is

mounted within the vehicle captures an image of the

driver’s face, which is securely transmitted and

backed up to the blockchain network. With this

transmission, only authorized users can have access

to the data, which will protect against tampering and

unauthorized dissemination of sensitive data. Being

immutable, data written into the blockchain (like the

captured image and location of the vehicle) cannot be

changed or deleted.

Blockchain for Data Integrity and Security:

The system can use blockchain for secure storage and

transmission of data. A decentralized ledger stores

all information that relates to vehicle access (location,

time, and image of unauthorized persons)

permanently, ensuring it cannot be tampered with.

Even if the vehicle's internal systems are

compromised, the blockchain data cannot be altered,

ensuring a verifiable source of evidence that can be

used in case of a theft or legal dispute.

4.2 Accident Response System

Vehicle safety is another response to accidents. In

case of an accident, the right information at the right

time could be the difference between life and death.

Traditional systems may alert you to danger, but can't

call emergency services with the relevant

information. Such a system can automate data capture

and transmission and enhance response to the

accident as immediate action can be taken.

• Automatic Mishap Detection: The framework is

outfitted with an effect recognition instrument

outfitted with accelerometers and sensors to

perceive quickly declining or impact, which can

flag a smash. When an accident is detected, the

system begins recording the vehicle’s GPS

location and taking a photo of the driver or the

inside of the vehicle with an in-car camera. This

data is automatically transmitted to the car

owner and emergency services.

• Conveying Details to Emergency Services: The

data (location and driver image) captured is

securely delivered to emergency contacts and

the nearest emergency services. This data is vital

information for first responders, as it allows

them to find the vehicle and get an idea of the

circumstances. Using blockchain, accident data

cannot be altered or tampered, so the

transmitted data is complete and secure.

• Blockchain for Data Security and Privacy: In the

accident response system, blockchain

technology serves a crucial role. Because data

are shared via a decentralized blockchain

network, no third party can access or modify

sensitive information. This can be especially

relevant for legal matters, where the integrity of

the evidence is paramount. In addition, in the

encrypted form of medical records, or

information about the contact number of the

person in case of an emergency, the system can

also store, this will also help in protecting

privacy and security.

4.3 Artificial Intelligence Based Voice

Interaction for Safety and Control

Driver distraction is one of the biggest causes of

collisions on our roads, and one of the most common

distractions comes from using in-car systems.

Whether they are climate control, music, or

navigation commands, these actions require manual

input that takes a driver’s attention off the road. The

current approach will be to create a handsfree control

mechanism for the car to eliminate the need to operate

controls with their hands.

Artificial Intelligence in Voice Recognition:

The AI-powered voice recognition system in the car

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is in charge of understanding the natural human voice

command and interpreting it in a mechanical sense.

With this system, a driver can control most of the in-

vehicle functions like navigation, climate control and

music simply by speaking. This minimizes the need

for manual interaction with the vehicle’s interface,

which could be distracting and dangerous while

driving. In addition to process quite different voice

input properly, the AI system is also trained to know

the context. Preparing Signals for Processing: Voice

recognition systems work most accurately when the

input signal is of high quality. In scenarios with a lot

of surrounding noise such as traffic or road noise, or

when the weather conditions are not favorable, voice

recognition may omit speech. The proposed system

consists of advanced signal preprocessing units that

eliminate background noise and distinguish the

driver’s voice. This process involves various

techniques that are applied to the input audio to

improve its quality, reduce noise, and enhance the

relevant features, which ultimately allows the system

to recognize and process voice commands in real-

time and dynamic environments.

Advanced & accurate voice recognition:

The

Voice Recognition technology is calibrated and tuned

to consider the specific vehicle infrastructure. This

integration enables functions like navigation, phone

calls, and entertainment systems to be controlled in

real-time via voice commands. Another aspect of AI

that is implemented in the system is Private Mode —

meaning that the system gets to know a driver, a little

bit at a time, and over the following drives learns the

preferences of the driver, to provide a more intelligent

experience. Through external systems, such as smart

home, the voice interface can create an

interconnected environment for the driver.

The suggested framework provides a

comprehensive strategy for addressing some of the

key challenges in the realm of automotive security,

safety, and user experience. The integration of these

IoT, blockchain, and AI technologies in the proposed

system provides an advanced and improved solution

not only for vehicle theft prevention and accident

response, but also for a safer and smarter driving

experience using voice recognition technology. IoT

sensors and GPS can be incorporated to allow for a

vehicle to be monitored in real-time, which means its

location is always known and if someone tries to

tamper with or access the vehicle those attempts can

be detected and an appropriate response activated.

Using blockchain ensures data integrity and security

in relation to vehicle access and anti-theft, providing a

tamper-proof record of events. 2- Accident response:

This feature captures and transmits all vital data

necessary for effective and timely emergency

response. Last but not least, the AI-powered voice

recognition system minimizes distraction and

enhances driving safety by allowing hands-free

control of in-vehicle systems, making the driving

experience uninterrupted and secure. This advanced

framework is the future of smart vehicle systems,

integrating state-of-the-art technologies for a secure,

efficient, and user-friendly solution for contemporary

vehicles. With the advancement of these technological

adaptations, the proposed system can also be advanced

to further meet the requirements for safety, security,

enhaced performance and comfort cater to the

demands of the growing automotive domains.

5 COMPARATIVE ANALYSIS OF

RELATED SYSTEMS

Framework for IoT, blockchain, and AI integration

offers distinct advantages over traditional IoT-only

systems and blockchain-enabled systems. A

comparison of key features across three system types

is provided in the table 2 below:

• Data Security: Systems that are IoT-only, often

exhibit low data security, due to the absence of

decentralized data storage or tamper-resistant

methods. On the other hand, systems based on

blockchain have good data security because the

features of the blockchain are the encryption,

immutability, and decentralized validation.

Through the involvement of IoT and

Blockchain, it offers greater security and

flexibility that all data transferred from the IoT

devices of the vehicle gets encrypted and is

securely stored into the blockchain. Our voice-

enabled Tier II solution doesn't add security risk,

it adds utility.

Table 2: Feature-wise Comparison of IoT-Only,

Blockchain-Enabled, and Proposed Vehicle Tracking

Systems

Feature

IoT-Only

Systems

Blockchain-

Enabled

Systems

Proposed

System

Data

Security

Low High High

Real-Time

Monitoring

High Moderate High

Voice

Interaction

Limited Limited Advanced

Scalability Moderate High High

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• Real-Time Monitoring: Most IoT-only

systems offer real-time monitoring features,

particularly for tracking the location and

condition of vehicles. Nonetheless, the large-

scale storage of data on-chain systems raises

concerns, as blockchain consensus algorithms

are often slow, resulting in inherent latency

when it comes to real-time transactions and

monitoring. By exchanging real-time data from

IoT sensors for constant monitoring and analysis

on the edge computing layer, the proposed

system, therefore, offers a more immediate

reactive solution during crises and time-

sensitive environments.

•

Voice Interaction: The IoT-only and

blockchain-enabled systems are not capable of

supporting conversational user interface.

However, in the proposed system, an AI-driven

voice recognition module was integrated. The

Automotive segment has been shaping this

module, enabling hands-free driver-vehicle

system interaction, with improved

convenience, safety, and user experience. Its

advanced voice recognition system is trained on

a huge volume of commands, dialects, and noise

ambient, enabling a sophisticated level of

interaction.

•

Scalability: IoT-only systems provide average

scalability due to how they rely on centralized

data processing which can create bottlenecks as

the number of devices grows. However,

blockchain-enabled systems deliver better

scalability due to their decentralized nature.

This system is designed to leverage the benefits

of blockchain's scalability principles in

conjunction with the real-time performance

potential offered by IoT and edge computing

principles, providing high scalability even when

a rising number of vehicles or devices become

connected.

6 CHALLENGES AND FUTURE

DIRECTIONS

But the convergence of IoT, blockchain, and AI

technologies can significantly improve the vehicle

security and the driver experience while also creating

some challenges. These challenges must be mitigated

for the optimal operation and widespread adoption of

the proposed system. Also, given an ever-updating

technology landscape, there is potential for an

upgrade that could improve the efficiency and

scalability of such systems.

6.1 Challenges

• The Scalability of IoT Networks: Scalability

is one of the big challenges related to IoT-based

vehicle systems. They consist of billions of

separate inter-connected devices that produce

massive volumes of data; Another major

challenge is the efficient management of the

data, ensuring continuous communication

between algorithms and consumers and

keeping the network intact as the number of

devices keeps increasing.

• Data Management and Storage: With every

new component added to the IoT network, the

amount of data created from sensors, GPS

systems and so on doubles exponentially. This

also makes it more difficult to store and process

this data in real time. The difficulty is in

handling that massive data as efficiently as

possible while retaining the system's

speed/accuracy. More advanced storage

solutions, for example, edge computing (which

we will discuss more below) can ease some of

these problems by processing data nearer the

edge in real-time, taking the load off of central

servers, and minimizing latency.

• Communication Overhead: The IoT system is

a network of devices that communicate with

each other to send data to a central server or

receive commands from other users. However,

as the number of devices grows, so does the

complexity of these communications.

Performance can be degraded due to network

congestion, interference, and signal loss.

Improving communication protocols and

utilizing better wireless technologies like Low

Power Wide Area Network (LPWAN) address

these issues.

• One of the major concerns in real-time data

transmission: Latency- in the systems where

real-time Kernel responses are most important

(vehicle tracking, accident detection, and

emergency response). So, the time required for

the data to be sent from IoT devices to the

centralized server or to the emergency services

as a whole, all the processing time, can

significantly delay the response time. Network

Congestion: On networks where too many

devices are connected to a limited number of

available resources, data transmission delays

can occur due to network congestion especially

in a densely populated area. However, high

network load and limited bandwidth generally

lead to lower data delivery times, which delay

A Survey on IoT and Blockchain-Enabled Systems for Automobile Tracking, Security and Voice Recognition

527

important information on a vehicle’s location,

an emergency or an intrusion.

• Processing Time: Although blockchain

transaction systems are secure and transparent,

they introduce additional processing time due to

transaction validation and time writing to the

blockchain. In an IoT-based system that

demands real-time observation and decision-

making, the processing times required for

blockchain consensus algorithms can introduce

latency, especially in systems with high

transaction frequencies. Considerations Of

TimingPoA(Proof of Authority) or other more

lightweight consensus algorithms could be

considered to remove latency from hash

functions while maintaining a basic layer of

security.

6.2 Future Directions

Regardless, the future holds many applications for

more scalable, efficient, and secure Lot solution for

vehicle types, along more efficient solutions for

integrated blockchain and AI technology. IoT-based

vehicle system are revolutionized with edge

computing and advanced the technology that improve

performance, security, and user experience. With data

processing closer to the source on the vehicle or local

network latency, as well as the dependence on

centralized cloud servers, is reduced. By processing

data locally, applications such as accident detection

can capture data and act in real time, while reducing

the load on cloud servers and bandwidth

requirements. It also helps make privacy and security

better by keeping sensitive data such as driver

identity and the circumstances of an accident local

and encrypted. Hybrid blockchain also combines

security and performance optimally. A private

blockchain can hold sensitive data securely by

making the information accessible only to authorized

parties, while public blockchains maintain auditable

records of vehicle transactions, which contributes to

transparency. This creates a separation between

privacy and trust. Advancements are happening with

AI-driven voice recognition systems as well. In the

future, we could expect improvements to integrate

noise-canceling technologies to improve performance

in noisy environments and add contextual

understandings to prioritize commands about safety.

However, multilingual and multimodal capabilities

that integrate voice, gestures, and visuals hold the

potential for a more accessible and intuitive user

experience. Combined, these innovations enable

IoT-equipped automobiles systems to function at

scale, securely and responsively while overcoming

the challenges of privacy, latency, and transparency.

7 CONCLUSIONS

The recent paper is titled: Integration of IoT and

Blockchain for Secure and Efficient Automotive

Tracking and Monitoring. The framework is

successfully implemented to critical problems such as

vehicle theft and accidents by using GPS for location

tracking, GSM for real-time communication, and

MMS for multimedia sharing. The incorporation of

IoT sensors along with blockchain’s decentralized

and secure architecture guarantees input integrity and

safeguards against tampering communication

channels, effectively overcoming security

vulnerabilities commonplace in traditional IoT

environments. The integration of AI-powered voice

recognition also enhances the experience by

providing convenience and safety, allowing drivers to

interact with their vehicles hands-free. It highlights

challenges such as scalability, latency, and

integration cost and provides potential solutions such

as edge computing, hybrid blockchain models, and

better voice recognition algorithms. Such technology

advancements are essential for the adoption of these

integrated systems in the automotive sector. With the

advancement of technology and high-level

integration, the proposed framework aims to address

these issues by maximizing accuracy and reliability

on the task of the proposed model; dynamic barricade

management can significantly secure the vehicle, thus

requiring immediate attention in terms of monitoring

to ensure human safety.

REFERENCES

Baba, A., Meena, G., & Kumar, A. (2023). Challenges and

advancements in IoT-based vehicle tracking systems.

International Journal of Vehicle Engineering, 17(3), 34-

42.

Baba, V., Kumar, P., & Gupta, S. (2023). Energy Efficiency

in IoT-Based GPS Vehicle Tracking Systems. Journal

of Wireless Communications and Networking, 31(4),

459-472.

Gupta, P., Verma, S., & Jain, V. (2023). Blockchain-based

automotive security: Opportunities and challenges.

Blockchain Applications, 15(1), 55-67.

Gupta, S., Singh, M., & Kaur, A. (2023). Blockchain for

Securing Communication in IoT-Based Vehicle

Systems. IEEE Access, 11, 10201-10214.

Jain, K., & Sharma, M. (2023). Advancements in AI-Based

Voice Recognition Systems for In-Vehicle Interaction.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

528

Journal of Artificial Intelligence in Automotive

Systems, 12(3), 134-146.

Jain, P., & Sharma, R. (2023). Deep learning in voice

recognition for automotive systems: A review. Journal

of AI in Automotive Systems, 10(2), 101-112.

Kumar, N., & Kumar, P. (2024). Reliability of GSM in IoT-

Based Vehicle Tracking in Urban Environments.

Wireless Communications and Mobile Computing,

24(6), 678-692.

Kumar, R., & Patel, A. (2024). AI-Based Voice

Recognition in Vehicles for Seamless Integration with

In-Car Systems. Journal of Automotive Engineering

and AI, 9(2), 112-125.

Kumar, S., Gupta, R., & Sharma, P. (2023). Integration of

IoT with Vehicle Tracking Using GPS and GSM

Technologies. Journal of Intelligent Transportation

Systems, 15(2), 122-136.

Kumar, S., Meena, G., & Patel, D. (2024). Data security

concerns in IoT-based vehicle tracking systems. Journal

of Transportation Security, 12(4), 150-159.

Meena, R., Rajendran, S., & Patel, A. (2024). Cloud-Based

IoT Vehicle Tracking System for Scalable Real-Time

Monitoring. International Journal of Cloud Computing

and Vehicle Systems, 5(1), 40-52.

Meena, S., & Ramesh, V. (2024). Comparative Study of AI-

Driven Voice Recognition Technologies for In-Car

Systems. Journal of Machine Learning in Automotive

Technology, 11(2), 145-157.

Patel, M., & Kumar, J. (2023). Deep Learning and NLP in

Voice Recognition Systems for Automotive

Applications. Journal of Natural Language Processing

in Vehicles, 2(4), 78-92.

Patel, R., Singh, D., & Yadav, S. (2023). Blockchain

integration in automobile IoT systems. Journal of

Automotive Technologies, 21(2), 101-115.

Patel, S., & Joshi, P. (2023). Blockchain for Over-the-Air

Software Updates in Vehicles. Journal of Computer

Networks and Communications, 2023, 1-12.

Patel, S., Joshi, H., & Shah, R. (2023). Comparative

Analysis of IoT Platforms for Vehicle Tracking Using

LPWAN Technologies. Journal of Embedded Systems

and IoT, 9(4), 123-137.

Sharma, A., Yadav, M., & Verma, S. (2024). Blockchain

for secure automobile communication networks.

International Journal of Blockchain and IoT, 5(1), 22-

29.

Sharma, R., & Pandey, S. (2024). Decentralized Vehicle

Data Management Using Blockchain for Privacy and

Security. International Journal of Blockchain and

Vehicle Technology, 8(2), 44-58.

Singh, A., & Sharma, P. (2023). Real-time GPS-based

vehicle tracking using IoT technologies. International

Journal of Wireless Communications, 11(4), 89-97.

Singh, A., & Sharma, R. (2023). IoT-Based Fleet

Management and Vehicle Tracking for Optimized

Route Planning and Security. International Journal of

Vehicle Technology and Traffic Engineering, 19(3),

88-101.

Singh, A., & Sharma, V. (2023). Improving Voice

Recognition Performance in Automobiles Using AI for

Driver Safety and Control. International Journal of

Intelligent Transportation Systems, 17(5), 221-233.

Singh, N., & Yadav, S. (2024). Challenges in Real-Time

Blockchain Applications for Vehicle Communication

Networks. Blockchain Technology and Applications,

3(1), 22-34.

Verma, D., & Kumar, H. (2023). Vehicle-to-Vehicle

Communication Using Blockchain for Secure

Information Exchange. IEEE Transactions on

Vehicular Technology, 72(9), 10550-10563.

Yadav, P., & Gupta, S. (2023). AI-Driven Deep Learning

for Automotive Voice Recognition with Accent and

Language Variations. IEEE Transactions on Neural

Networks and Learning Systems, 34(11), 1425-1437.

Yadav, R., Kumar, D., & Singh, R. (2023). AI-driven voice

recognition for improving driver safety and experience.

AI in Automotive Technology Journal, 16(3), 67-74.

Yadav, R., & Patel, M. (2024). Blockchain and Edge

Computing Integration for Scalable Vehicle Security

Systems. International Journal of Automotive

Computing, 6(3), 56-67.

A Survey on IoT and Blockchain-Enabled Systems for Automobile Tracking, Security and Voice Recognition

529