Smart and Secured Healthcare System

Prashant Uppar

, Pratiksha Angadi

, Chandni Kumari

, Swapnil Shahapurkar

and U. V. Somanatti

Department of Computer Science and Engineering, KLE Tech, University’s Dr. MSSCET, Belagavi, India

Keywords:

Network, Security, IOT, Automation, OSPF, Routing, ASA Firewall, VPN, Remote Login.

Abstract:

The growing reliance on digital infrastructure in healthcare institutions necessitates robust solutions to address

data security and operational efﬁciency. This paper presents a smart and secure healthcare network framework

that integrates advanced ﬁrewall conﬁgurations, dynamic routing protocols, and IoT enabled safety automa-

tion. The proposed system ensures data conﬁdentiality, integrity, and availability by implementing optimized

OSPF routing, multi layered trafﬁc ﬁltering through ﬁrewalls, and secure remote access using VPN with SSH

encryption. IoT technologies enhance hospital safety by enabling automated ﬁre detection, temperature con-

trol, and smoke detection systems. Experimental results validate the efﬁciency of this framework in mitigating

unauthorized access, streamlining network management, and automating safety measures. This approach of-

fers a scalable and effective solution to modern healthcare challenges, emphasizing secure communication and

reliable automation.

1 INTRODUCTION

The healthcare sector is increasingly dependent on

digital technologies to manage patient data, stream-

line hospital operations, and automate various safety

measures. However, the digitization of healthcare has

also led to the exponential growth of sensitive in-

formation, making these networks prime targets for

cyber attacks and unauthorized access (Sendelj and

Ognjanovic, 2022). These vulnerabilities not only

compromise patient privacy but can also disrupt hos-

pital operations, potentially leading to severe conse-

quences for both patients and healthcare providers.

Therefore, it is paramount to implement robust secu-

rity measures that protect critical data and ensure the

continuous operation of healthcare systems (Namo

glu

and Ulgen, 2013). Despite the growing importance of

network security, existing systems often fail to sufﬁ-

ciently address the complex needs of modern health-

care environments. Healthcare networks are usually

large and involve various interconnected devices, sys-

tems, and departments. This complexity increases

the difﬁculty of ensuring secure data transmission,

https://orcid.org/0009-0004-2892-9636

https://orcid.org/0009-0003-2499-1646

https://orcid.org/0009-0004-0613-7574

https://orcid.org/0009-0004-4044-4324

https://orcid.org/0000-0002-6930-6628

maintaining privacy, and safeguarding against unau-

thorized intrusions. Moreover, many traditional secu-

rity mechanisms struggle to balance cost efﬁciency,

scalability, and ﬂexibility, often requiring expensive

infrastructure or overly simplistic solutions that fail

to meet all requirements (Wazid et al., 2022).

One of the key challenges in securing healthcare

networks is the diverse range of communication pro-

tocols and devices in use, coupled with misconﬁgura-

tions in routing and inadequate trafﬁc ﬁltering. These

gaps can result in data breaches, downtime, and sys-

tem vulnerabilities, putting sensitive patient informa-

tion at risk. Furthermore, the integration of Inter-

net of Things (IoT) technologies into hospital oper-

ations such as smart ﬁre detection systems, temper-

ature regulation, and smoke detection adds another

layer of complexity. While these devices can au-

tomate safety features and improve operational efﬁ-

ciency, they also introduce new security risks, par-

ticularly in terms of secure communication and re-

mote access (Alsbou et al., 2022). The proposed work

addresses these issues by designing a Smart and Se-

cured Healthcare System that aims to improve both

the security of hospital data and the automation of es-

sential safety functions. The system employs several

advanced techniques, including optimized dynamic

routing with OSPF, advanced ﬁrewall conﬁgurations,

and IoT-based automation for ﬁre detection, tempera-

676

Uppar, P., Angadi, P., Kumari, C., Shahapurkar, S. and Somanatti, U. V.

Smart and Secured Healthcare System.

DOI: 10.5220/0013600000004664

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

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 2, pages 676-684

ISBN: 978-989-758-763-4

ture control, and smoke monitoring. These features

work together to provide a comprehensive solution

that not only secures hospital data but also ensures op-

erational efﬁciency and safety (Alsbou et al., 2022).

The key objectives of this work are, Network Se-

curity,To design and implement a secure network ar-

chitecture for a hospital environment, using subnet-

ting for network segmentation, static routing for ini-

tial trafﬁc management, and dynamic OSPF routing to

ensure optimized, efﬁcient data transfer across depart-

ments. Trafﬁc Filtering and Intrusion Prevention,

To deploy advanced ﬁrewall mechanisms that ﬁlter

trafﬁc, monitor network activity, and prevent unau-

thorized access, ensuring that only authorized person-

nel and devices can access the hospital’s internal sys-

tems. Remote Access Security, To ensure secure,

encrypted communication for remote access to hos-

pital systems via VPN and SSH encryption. The use

of VPN provides a secure tunnel for all internet traf-

ﬁc, while SSH is used for secure remote command

execution and system management. IoT-Based Au-

tomation, To integrate IoT technologies for automat-

ing safety operations within the hospital, including

systems for ﬁre detection, temperature regulation, and

smoke detection. These systems will operate in real

time, ensuring the safety of patients and hospital in-

frastructure.

The structure of the paper is organized as fol-

lows: Section 2 reviews related work, emphasizing

limitations in current approaches and how the pro-

posed work addresses these gaps. Section 3 details the

methodology, including network conﬁguration and

IoT-based safety integration. Section 4 presents the

results, analyzes performance and accuracy, and com-

pares the proposed system to existing solutions. Sec-

tion 5 concludes the paper with a summary of contri-

butions, key ﬁndings, and recommendations for future

research.

2 RELATED WORKS

The evolution of healthcare systems has driven exten-

sive research in security, IoT integration, and automa-

tion. Existing studies explore various methodolo-

gies, including blockchain, ﬁrewalls, and smart IoT

systems, to enhance data privacy, network security,

and operational efﬁciency. While advancements like

Healthcare 5.0 and medical IoT platforms showcase

potential, gaps such as scalability, real world valida-

tion, and emerging technologies persist. This survey

consolidates insights to guide future implementations

addressing these challenges.

In (Priya et al., 2017) reviewed security attacks

in electronic healthcare systems, discussing security

requirements such as authentication, integrity, and

conﬁdentiality. They categorized attacks based on

the data phases of gathering, transmission, and stor-

age. The study offers a comprehensive categorization

of security attacks, emphasizing the importance of

multi layered security and theoretical insights. How-

ever, it lacks practical implementation, experimental

validation, and quantitative benchmarks. The gaps

identiﬁed include the absence of speciﬁc solutions,

a lack of integration with emerging technologies like

AI or blockchain, and limited adaptability to real time

threats.

In (Jin et al., 2019) conducted a survey of secure

and privacy preserving medical data sharing mecha-

nisms, with a focus on blockchain based approaches.

They categorized the mechanisms into permissionless

and permissioned types and analyzed techniques such

as cryptography, anonymization, and SDN. The re-

view highlights the potential of blockchain in health-

care, especially in cryptographic solutions and SDN

integration. However, it relies on off chain storage

due to blockchain limitations, lacks real world imple-

mentation, and does not use a speciﬁc dataset. The

study identiﬁes challenges in ensuring ﬁne grained

access control, compatibility across domains, and the

need for a uniﬁed query mechanism, as well as holis-

tic solutions integrating blockchain with cryptogra-

phy and SDN.

In (Makhdoomi et al., 2022) reviewed conven-

tional and next generation ﬁrewalls (NGFWs), their

deployment methods, and comparative features. They

explored distributed ﬁrewalls and NAT/PAT ﬁrewalls,

highlighting the advanced features of NGFWs such

as IPS and application layer ﬁltering. While the study

provides a thorough theoretical review, it lacks prac-

tical implementation and experimental results. Gaps

include insufﬁcient research on distributed ﬁrewall

policies, topologies, and effective real world imple-

mentations of access control mechanisms.

In (Sendelj and Ognjanovic, 2022) systematically

analyzed cybersecurity challenges in healthcare, iden-

tifying risks, consequences of attacks, and best prac-

tice recommendations. The study offers a compre-

hensive overview, leveraging recent statistical data to

provide actionable insights. However, it is primar-

ily descriptive, lacks empirical testing of solutions,

and does not cover all emerging technologies. The

gaps identiﬁed include the need for case studies on

successful implementations, exploration of emerging

technologies, and research on the effectiveness of pro-

posed frameworks.

In (Wazid et al., 2022) proposed a secure frame-

work for Healthcare 5.0, analyzing its applications,

Smart and Secured Healthcare System

677

security requirements, threat models, and existing

mechanisms. The framework provides a robust ap-

proach to addressing security challenges and com-

pares existing performance metrics. However, it re-

mains theoretical, with no extensive empirical valida-

tion. Gaps include the need for practical case studies,

exploration of emerging technologies, and further re-

search on framework effectiveness.

In (Pandey et al., 2020) conducted a systematic

literature review and scientometric analysis to assess

healthcare data integrity techniques. The study offers

a roadmap for future research by highlighting effec-

tive techniques like blockchain. However, it is lim-

ited by a focus on previously used methods and a lack

of exploration of new approaches. The gaps include

a need for comprehensive studies addressing multi-

faceted data integrity challenges in healthcare.

In (Namo

glu and Ulgen, 2013) conducted a case

study in a 150 bed private hospital in Turkey, exam-

ining vulnerabilities in hospital information systems

and proposing a secure network infrastructure. The

study identiﬁes security vulnerabilities and provides

best practices based on standards like HIPAA and ISO

80001. However, the lack of extensive sample data

and security concerns in revealing the hospital’s iden-

tity are limitations. Gaps include insufﬁcient compli-

ance with healthcare standards, inadequate staff train-

ing, and a lack of privacy agreements with external

users.

In (Alsbou et al., 2022) designed and simulated an

IoT based smart hospital using Cisco Packet Tracer.

The system integrates IoT devices like sensors and ac-

tuators for real time patient data transmission. It en-

hances patient care and response times, but the sim-

ulation is limited by scalability challenges, network

congestion, and insufﬁcient evaluation of data secu-

rity in complex environments. Gaps include the need

for real world testing, scalability assessment, and ad-

vanced security measures for IoT based systems.

In (Walia et al., 2023) developed a safe and secure

smart home using IoT technology in Cisco Packet

Tracer. The system includes RFID based access con-

trol, burglary detection, ﬁre and smoke systems, and

water and temperature monitoring. The study en-

hances security and safety but relies heavily on sta-

ble internet connections and lacks features like voice

recognition or predictive analytics. Gaps include ro-

bust cybersecurity measures, scalability exploration,

and advanced features for smart home ecosystems.

In (Fu et al., 2023) developed a comprehensive

Medical IoT platform to unify healthcare scenarios

and devices, addressing data fragmentation through

technical standards. The platform supports seamless

data collection, real time decision making, and health

data fusion but faces challenges with interoperability

and validation of data reliability. Gaps include insuf-

ﬁcient device integration, improved data sharing stan-

dards, and comprehensive evaluations of platform ef-

fectiveness in real world settings.

In (Roy et al., 2023) implemented a smart home

automation system in Cisco Packet Tracer, integrat-

ing IoT devices for ﬁre safety and enhanced network

security. While the system provides comprehensive

automation, it is limited by simulation constraints and

device interoperability challenges. Gaps include fur-

ther research on real world applicability, advanced se-

curity measures, and diverse IoT device integration

for enhanced functionality.

In (Karunamurthy et al., 2023) implemented the

Routing Information Protocol (RIP) for managing IoT

devices across different LANs. The study highlights

enhanced IoT management and automation but fo-

cuses on simulation in controlled environments. Gaps

include research on scalability and emerging tech-

nologies for better IoT management solutions.

In (Yudidharma et al., 2023) conducted a system-

atic literature review to analyze messaging protocols

and electronic platforms for smart homes. The study

identiﬁes commonly used protocols like MQTT and

CoAP and evaluates performance. However, it fo-

cuses on existing literature, leaving gaps in emerging

technologies and user centric solutions.

In (Abdunabi et al., 2023) developed a secure

system architecture for Body Area Networks (BAN),

incorporating Spatio Temporal Attribute Based Ac-

cess Control (STABAC) and blockchain for policy

integrity. While the study enhances healthcare data

management, it lacks performance evaluation and

consideration of insider threats. Gaps include assess-

ments of mobile user access and the proposed model’s

real world effectiveness.

In (Madhav et al., 2023) designed and simulated a

smart hospital using IoT technologies in Cisco Packet

Tracer. The system integrates automation for safety

and security but lacks real world deployment, predic-

tive analytics, and scalability for larger hospital net-

works. Gaps include real time patient monitoring, ad-

vanced predictive models, and integration of speech

recognition and machine learning for smart hospitals.

In (Alzu’bi et al., 2024), a systematic literature re-

view was conducted to deﬁne research questions, for-

mulate keywords, ﬁlter articles, and classify results.

This study provides a comprehensive overview of pri-

vacy and security concerns in edge computing, iden-

tifying key privacy needs and discussing potential so-

lutions. However, the study is limited in its focus on

practical implementations and does not explore other

computing paradigms in detail. The research identi-

INCOFT 2025 - International Conference on Futuristic Technology

678

ﬁes a signiﬁcant gap in privacy-preserving strategies

and integration studies for intelligent edge systems in

healthcare applications.

In (Samudrala et al., 2024), IoT devices were in-

tegrated using Cisco Packet Tracer for centralized ﬁre

detection employing a star topology. This method-

ology enhances ﬁre detection through the use of

smoke and motion sensors, enabling real-time alerts

and monitoring. Despite its advantages, the study

is simulation-based and does not fully address real-

world variables. Further research is necessary to test

this system in real-world scenarios and integrate it

with other safety measures.

In (Salunkhe et al., 2024), the use of microser-

vices in healthcare was analyzed, particularly for clin-

ical applications. This approach improves scalability,

maintainability, and responsiveness within healthcare

systems. Nevertheless, challenges persist regarding

data consistency, security compliance, and integra-

tion with legacy systems. Future studies should focus

on empirical evaluations and strategies for integrating

user experiences into microservice architectures.

In (Khan et al., 2024), a home server using PCIe

technology for Network Attached Storage (NAS) was

designed, and performance comparisons were con-

ducted. The ﬁndings emphasize the beneﬁts of scal-

ability, centralized management, and data security.

However, the study has limited consideration of en-

ergy efﬁciency and potential security vulnerabilities.

Further research is recommended to develop energy-

efﬁcient solutions and conduct real-world analyses of

such systems.

In (Ghasab et al., 2024), a centralized virtual

network for ﬁre stations in Iraq was proposed us-

ing Cisco Packet Tracer. This approach aims to im-

prove emergency coordination, response times, and

resource allocation. However, the study lacks a de-

tailed analysis of server failures and the resilience of

the proposed network to cyber threats. Further inves-

tigation is required to evaluate the network’s impact

and incorporate enhanced security measures.

In reviewing the existing literature and ap-

proaches, it is evident that current healthcare systems

face signiﬁcant limitations, particularly in areas such

as network segmentation, security, scalability, and

IoT integration. Many systems rely on ﬂat network

topologies, outdated security protocols, and manual

monitoring, which can lead to performance bottle-

necks, data vulnerabilities, and slower response times

during emergencies. Furthermore, existing systems

often lack scalability, requiring expensive overhauls

to accommodate growing needs. The proposed Sys-

tem addresses these gaps by implementing advanced

network management techniques like subnetting and

dynamic routing, enhancing security with modern en-

cryption and automated access controls, integrating

IoT devices for real time monitoring and response,

and ensuring scalability and cost efﬁciency for long

term adaptability.

3 PROPOSED METHODOLOGY

Fig.1, illustrates the logical architecture of the pro-

posed system, focusing on how different layers and

components interconnect to achieve efﬁcient health-

care network management. This design emphasizes

the conceptual structure and modularity of the system,

ensuring scalability, reliability, and security across all

layers

Figure 1: Logical Architecture of the Proposed System

Centralized Management layer ensures secure

storage of patient data, centralized conﬁguration,

and access control. It acts as the primary control

hub for the entire system. Network Communication

Comprising departmental subnet routers and trafﬁc

routing mechanisms, this layer manages data ﬂow and

network connectivity across departments. IoT and

Device Integration section is dedicated to integrating

IoT safety devices and ensuring seamless device

conﬁguration and coverage for real time operations.

Hospital Operations is Focused on hospital staff and

monitoring systems, this layer supports applications

that facilitate operational workﬂows and device

interactions. Security and Privacy ,Spanning all

layers, this ensures robust encryption, ﬁrewalls, and

authentication mechanisms, safeguarding the system

from vulnerabilities. This logical design serves as

a blueprint, providing a high level view of how the

system functions conceptually.

Smart and Secured Healthcare System

679

The logical design has been seamlessly trans-

lated into a virtual implementation using Cisco Packet

Tracer. This practical setup incorporates the conﬁg-

uration of essential networking components, includ-

ing routers, switches, IoT devices, and safety sys-

tems, while closely aligning with the blueprint pro-

vided by the logical architecture. The representation

of the Cisco Packet Tracer implementation is shown

below in Fig 2.

Figure 2: Cisco Network Implementation and Architecture

Centralized Core Network represents the back-

bone of the network, hosting the central data server

and managing communication between subsystems

and Conﬁgured with routing protocols (e.g., OSPF)

to optimize data ﬂow and ensure redundancy. Depart-

mental Subnetworks are segregated based on hospital

departments, each with its own routers, switches,

and IoT devices. and Dynamic routing protocols

and ﬁrewalls are implemented to ensure secure

communication and adaptability to varying network

demands. In Safety and Automation Systems, IoT

enabled safety mechanisms include ﬁre detection,

temperature control, and smoke detection systems.

and Devices are connected through IoT protocols

and integrated with monitoring dashboards for real

time alerts. In Interconnectivity, all systems are

interconnected via secure VPN tunnels and encrypted

communication channels to safeguard sensitive data.

Load balancing ensures high availability and fault tol-

erance across departments. This design emphasizes

modularity, scalability, and security, aligning with the

logical architecture’s blueprint while addressing real

world constraints.

In the proposed healthcare system, OSPF (Open

Shortest Path First) routing ensures dynamic and efﬁ-

cient communication across hospital subnetworks. It

connects departmental routers to the centralized data

server, enabling optimal data ﬂow for IoT devices and

monitoring systems. OSPF dynamically calculates

the shortest, most reliable paths for data, adapting to

changes like link failures or congestion to maintain

uninterrupted healthcare operations. Its hierarchical

structure segments the network into zones, reducing

routing overhead while ensuring high speed commu-

nication. This protocol facilitates seamless integra-

tion of IoT sensors for ﬁre detection, temperature con-

trol, and patient safety, ensuring robust connectivity,

operational efﬁciency, and scalability.

VPN (Virtual Private Network) establishes a se-

cure and encrypted connection between devices over

an untrusted network, such as the internet. It ensures

that data exchanged between endpoints remains con-

ﬁdential and protected from unauthorized access. By

creating a private network tunnel, VPNs prevent po-

tential threats like eavesdropping or data interception.

VPN is utilized to safeguard communication between

hospital networks and remote users. This implemen-

tation provided encrypted channels for accessing sen-

sitive information like patient records, ensuring se-

cure data exchange across different departments and

external entities. The use of SSH (Secure Shell) en-

cryption further enhanced the security of the VPN by

adding an additional layer of protection to data trans-

fers, ensuring only authorized users could access the

network resources.

Figure 3: Firewall Trafﬁc Filtering.

As shown in Fig. 3, the ﬁrewall acts as a security

barrier between Subnet 1 and Subnet 2, monitoring

and controlling data ﬂow based on predeﬁned rules.

Requests and responses passing through the ﬁrewall

are evaluated against these rules to determine their

validity. Valid trafﬁc is allowed to proceed, ensuring

seamless communication between clients and servers,

INCOFT 2025 - International Conference on Futuristic Technology

680

while invalid trafﬁc is blocked to protect the network

from unauthorized access or malicious activity. This

conﬁguration safeguards sensitive data, prevents po-

tential breaches, and ensures compliance with secu-

rity policies. Additionally, the ﬁrewall enforces trafﬁc

ﬁltering and access control.

Figure 4: Fire Detection.

As shown in Fig. 4, the diagram illustrates the

process of ﬁre detection and response. The system

begins with a ﬁre sensor monitoring the environment

for potential ﬁre events. If the sensor detects a ﬁre, it

triggers the activation of the ﬁre sprinkler system to

mitigate the threat. If no ﬁre is detected, the sprin-

klers remain inactive, ensuring resource efﬁciency.

This automated system collects real time environmen-

tal data to promptly identify ﬁre hazards. Upon detec-

tion, sprinklers are activated to minimize damage and

enhance safety. The integration ensures seamless co-

ordination and effective hazard management.

Figure 5: Temperature Control.

As shown in Fig. 5, the diagram depicts the mon-

itoring and control of temperature through a ther-

mostat. The system evaluates the temperature and

initiates corresponding actions. If the temperature

falls below 18°C, the furnace is activated to main-

tain warmth. Conversely, if the temperature reaches

or exceeds 35°C, the air conditioning (AC) is turned

on to cool the environment. This setup automates

temperature-based actions to maintain optimal envi-

ronmental conditions. By dynamically controlling the

heating and cooling systems, it ensures effective re-

source utilization while addressing extreme tempera-

ture scenarios efﬁciently.

Figure 6: Smoke Detection.

As shown in Fig. 6, the diagram illustrates a

smoke detection system. The process begins with

a smoke detector monitoring the environment. If

smoke is detected, the system activates a blower to

mitigate the smoke and improve air quality. If no

smoke is detected, the blower remains off to conserve

energy and resources. This conﬁguration ensures

timely detection and response to smoke, preventing

potential hazards while maintaining efﬁcient oper-

ation in normal conditions. The automated control

enhances environmental safety by addressing smoke

related risks effectively.

4 RESULTS AND DISCUSSION

As shown in Fig. 7, The graph illustrates the accu-

racy and efﬁciency of OSPF routing over a 24 hour

period, with packets sent, delivered, and overall ef-

ﬁciency analyzed. High alignment between pack-

Smart and Secured Healthcare System

681

Figure 7: OSPF Routing Accuracy Analysis

ets sent and delivered demonstrates OSPF’s ability

to adapt to dynamic network conditions by maintain-

ing updated routing tables. Efﬁciency remains consis-

tent at most intervals, reﬂecting stable network perfor-

mance, while occasional drops, such as at hours 5 and

20, suggest transient issues like increased network

trafﬁc or link updates. These variations highlight the

protocol’s robustness in maintaining data ﬂow despite

disruptions. The consistent performance underscores

the reliability of OSPF in ensuring accurate packet de-

livery and optimal resource utilization across diverse

conditions.

Figure 8: Temperature, VPN Success, Network Trafﬁc and

Latency Analysis Over Time

As shown in Fig. 8, The graph showcases the in-

terplay between trafﬁc analysis (in Mbps), tempera-

ture (°C), VPN usage (%), and latency (ms) within

the conﬁgured smart healthcare system. An upward

trend in trafﬁc analysis and VPN usage is observed,

indicating increased network activity and secure com-

munication demands during peak operational periods.

Despite the rise in trafﬁc, latency remains consistently

low (around 20–25 ms), signifying efﬁcient trafﬁc

management and robust VPN implementation. Tem-

perature, a critical IoT metric, remains relatively sta-

ble, reﬂecting the efﬁcacy of temperature control sys-

tems. The synchronization between high VPN usage

and minimal latency highlights the VPN’s effective-

ness in securely transmitting data without signiﬁcant

delays. These results validate the system’s capabil-

ity to handle dynamic workloads while ensuring data

security and system performance.

Figure 9: Firewall Trafﬁc Filtering Analysis

As shown in Fig. 9, graph represents ﬁrewall ac-

tivity within the Smart and Secured Healthcare Sys-

tem over a 24 hour period, highlighting the number

of blocked and allowed requests. Red bars corre-

spond to blocked requests, representing unauthorized

access attempts or malicious trafﬁc, while green bars

depict legitimate requests from authorized users and

devices. The consistent blocking pattern, with occa-

sional peaks, underscores the ﬁrewall’s role in safe-

guarding network trafﬁc and maintaining the health-

care system’s secure operation.

Figure 10: Temperature Control and Smoke Detection

Rules

As shown in Fig. 10, IoT conﬁguration in

Cisco Packet Tracer automates temperature control

and smoke detection across different areas in a smart

and secure healthcare network. The system uses ther-

mostats and windows to regulate temperature within

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682

speciﬁed ranges for critical zones such as operat-

ing rooms (OS), ICUs, PACUs, and general wards

(GW). For instance, when the temperature exceeds

32°C, cooling is activated, and windows close; when

it drops below 19°C, heating is enabled, and windows

open. Additionally, a CO2 detector monitors air qual-

ity, triggering an alarm if levels exceed 500 ppm, en-

suring prompt action for safety. This setup enhances

environmental control and safety in healthcare facili-

ties.

The comparison of the proposed System to

existing healthcare solutions , Network Conﬁgu-

ration and Routing in the proposed system utilizes

subnetting and OSPF for efﬁcient data ﬂow and

reduced congestion. In contrast, existing systems

often use ﬂat topologies, leading to bottlenecks and

poor scalability due to lack of dynamic routing.

Security and Trafﬁc Management are strengthened

in the proposed system through VPN with SSH

encryption and a robust ﬁrewall. Existing systems

typically rely on basic security protocols and lack

modern encryption, making them more vulnerable

to breaches. Integration of IoT and Smart Features

enhances safety in the proposed system with auto-

mated IoT devices like ﬁre detection and temperature

control. Many existing systems still use manual

monitoring methods, which are prone to delays and

human error. Scalability and Adaptability are built

into the proposed system, allowing easy expansion

with new devices and protocols as the hospital grows.

Existing systems often struggle to scale, requiring

costly upgrades due to rigid architectures. Cost

Efﬁciency of the proposed system leverages existing

infrastructure and optimizes resources, reducing both

upfront and long term costs. Existing systems tend

to have high initial costs and ongoing maintenance

expenses, with limited scalability.

5 CONCLUSION

In conclusion, this paper presented the design and im-

plementation of a Smart and Secured Healthcare Sys-

tem, integrating advanced networking technologies

such as OSPF routing, VPN with SSH encryption, and

IoT based safety features. The system demonstrated

efﬁcient routing, secure data transmission, and reli-

able environmental monitoring, as evidenced by the

performance analysis of network trafﬁc, latency, and

IoT metrics. Key ﬁndings include the robustness of

OSPF routing in dynamic environments, the effective-

ness of VPN in ensuring data conﬁdentiality, and the

stability of IoT driven temperature and smoke con-

trol systems. Future research could explore the im-

plementation of the system with upcoming protocols

to enhance scalability and adaptability further.

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