Revolutionising Healthcare Resource Management with IoT-Integrated

Fault-Tolerant Digital Twin Technology

V Shreya Sivani, Vandana J, Sonali Mahesh Rane and Animesh Giri

Dept. of CSE, PES University Bangalore, India

Keywords:

Digital Twin, Healthcare Monitoring, Contiki OS, Cooja Emulator, Kafka, Fault Tolerance.

Abstract:

Healthcare systems require reliable monitoring to prevent critical gaps caused by sensor failures, which can

compromise patient outcomes and delay medical interventions. Continuous monitoring of vital signs is cru-

cial for timely care, and the integration of advanced technologies has signiﬁcantly improved accuracy and

efﬁciency. However, traditional systems face challenges such as interruptions due to sensor failures. Digital

twin technology, with its fault-tolerant architecture, addresses these issues by enabling real-time replication

of sensor systems and ensuring uninterrupted operations. The proposed system achieves a Packet Delivery

Ratio (PDR) of 95% for the primary sensor cluster, 80% for the coordinator, and 90% for the backup cluster,

demonstrating robust performance even under fault conditions. This approach not only enhances reliability

and data integrity but also has the potential to revolutionize resource management and operational efﬁciency in

critical healthcare environments.This paper addresses these challenges by introducing a healthcare monitoring

system that leverages digital twin technology.The system employs a dual-layered sensor mechanism where

primary sensors continuously gather data, and backup sensors automatically activate in case of primary sensor

failure, ensuring high availability and fault tolerance. Additionally, by integrating with cloud-based platforms,

the system enables efﬁcient data transmission and real-time monitoring, empowering healthcare professionals

with timely access to critical patient information. Comprehensive performance evaluations demonstrate that

the system maintains continuous data ﬂow, achieving high reliability and low latency, even during sensor fail-

ures. This innovative approach not only transforms patient monitoring systems but also offers a scalable and

dependable solution for hospital settings, ultimately contributing to enhanced patient care.

1 INTRODUCTION

In today’s healthcare landscape, the integration of

cutting-edge technology is critical to addressing the

complexities of patient care, improving operational

workﬂows, and ensuring patient safety. Delays or fail-

ures in continuous patient monitoring are a leading

contributor to critical healthcare incidents, underscor-

ing the need for robust monitoring systems. Tradi-

tional healthcare practices, while effective, often face

limitations in continuously monitoring patients’ vital

signs, which can result in delays in detecting critical

health issues. The need for a robust, real-time health-

care monitoring system is evident.

This work aims to develop a fault-tolerant digi-

tal twin system for continuous healthcare monitoring.

One of the primary challenges in healthcare is the

continuous, uninterrupted tracking of patient data, es-

pecially in environments where any sensor failure can

lead to signiﬁcant gaps in monitoring. Errors in data

collection and transmission can impact the well-being

of patients, making it essential to design systems that

can reliably gather and process real-time health infor-

mation.

This research introduces a digital twin-based

healthcare monitoring network designed to be fault-

tolerant, ensuring seamless data ﬂow and reliable pa-

tient monitoring even during sensor failures. The pro-

posed solution creates a virtual replica of the physi-

cal network, enabling continuous synchronization and

data accuracy (Jacoby and Usl

ander, 2020).

The digital twin technology in this system allows

for real-time mirroring of the healthcare network, pro-

viding healthcare professionals with a comprehensive

view of the system’s functionality and status. By con-

tinuously replicating the behavior of the physical sen-

sors, the digital twin can simulate potential issues and

analyze data trends, thereby enabling predictive anal-

ysis and ensuring continuous patient care. This ca-

pability allows for real-time data analysis and early

126

Sivani, V. S., J, V., Mahesh Rane, S. and Giri, A.

Revolutionising Healthcare Resource Management with IoT-Integrated Fault-Tolerant Digital Twin Technology.

DOI: 10.5220/0013609800004664

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

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 3, pages 126-134

ISBN: 978-989-758-763-4

intervention, thus minimizing the chances of critical

health events going undetected (Erol et al., 2020).

The fault-tolerant nature of the system ensures

that data collection is not interrupted, even if primary

sensors fail. The proposed network autonomously

switches to backup sensors when faults are detected,

maintaining data integrity and system reliability. This

approach minimizes downtime and ensures that vi-

tal patient information continues to be monitored

and transmitted without interruption, which is essen-

tial for real-time healthcare monitoring applications

(Zaiter and Hacini, 2020).

In healthcare settings, especially in critical envi-

ronments like ICUs, a variety of IoT-enabled sensors

are employed to monitor essential patient parame-

ters. Key sensors include electrocardiogram (ECG)

sensors for heart rate monitoring, pulse oximeters

for tracking blood oxygen levels, blood pressure sen-

sors, temperature sensors, and respiratory rate sen-

sors. Each of these plays a vital role in collecting

continuous patient data and transmitting it to a central

monitoring system for real-time analysis. The inte-

gration of IoT ensures seamless connectivity and ef-

ﬁcient data transmission across the network, allowing

healthcare professionals to monitor patients remotely

and in real time. To address the risk of sensor failure,

the system incorporates redundant backup sensors.

These backup sensors are kept in a standby mode and

automatically activate if a primary sensor experiences

a malfunction, ensuring uninterrupted monitoring and

reliable data collection (Al-kahtani et al., 2022).

At the core of this healthcare network is a robust

communication infrastructure designed to achieve

high reliability and low latency, ensuring that data is

reliably transmitted with minimal delay, even in the

presence of faults. This combination of digital twin

technology, fault-tolerant design, and efﬁcient data

handling forms a comprehensive solution for modern

healthcare environments, supporting continuous and

reliable patient monitoring.

This paper delves into the design and develop-

ment of a fault-tolerant digital twin system tailored

for healthcare environments. It explores the integra-

tion of digital twin technology with IoT-enabled sen-

sors, focusing on ensuring uninterrupted patient mon-

itoring despite sensor failures. The proposed system’s

architecture, implementation, and performance evalu-

ation demonstrate its capability to maintain data in-

tegrity and reliability under fault conditions. Addi-

tionally, the paper highlights the potential of this ap-

proach to enhance resource management and opera-

tional efﬁciency in critical healthcare settings, paving

the way for innovative advancements in real-time

healthcare monitoring.

2 RELATED WORKS

Andreas P. Plageras et al. presented a framework that

integrates Digital Twins and multi-access edge com-

puting (MEC) for IIoT (Plageras and Psannis, 2022),

showing improvements in latency, overhead, and en-

ergy consumption by utilizing edge resources efﬁ-

ciently. Similarly, Chi-Hung Hsiao et al. proposed an

open-source framework to address integration issues

in IIoT, highlighting the importance of choosing ap-

propriate communication protocols for different types

of data. However, these works lack redundancy mech-

anisms to handle critical failures, focusing primarily

on improving communication and resource efﬁciency.

Mesmin Tound

e Dandjinou et al. (Dandjinou

et al., 2020) introduced the F-Hopcount protocol,

which is a fault-tolerant routing mechanism designed

for Wireless Sensor Networks (WSNs) that handles

random node failures while maintaining high network

performance. In (Jain et al., 2008), the Dual-Homed

Routing (DHR) protocol is introduced, which en-

hances packet delivery by using redundant paths but

incurs higher energy and bandwidth costs. The In-

former Homed Routing (IHR) protocol proposed in

(Qiu et al., 2013) improves DHR by reducing en-

ergy consumption through conditional activation of

backup cluster-heads. However, both DHR and IHR

focus on static backup paths, which might not be

adaptable to dynamic network changes.

Younis et al. (Yu and Zhang, 2007) proposed a

routing technique to combat random node failures,

where sentry nodes monitor active nodes. While ef-

fective, this approach introduces extra trafﬁc and re-

source wastage. The Dynamic Energy-aware Fault-

Tolerant Routing (DEFTR) protocol (Haseeb et al.,

2016) optimizes energy consumption by creating uni-

formly sized clusters and balancing routes, address-

ing energy depletion but lacking mechanisms to man-

age temporary node failures. The Distributed Fault-

Tolerant Clustering Routing (DFCR) protocol focuses

on energy efﬁciency and backup cluster heads but

overlooks the impact of transient faults. Similarly,

the Dynamic Fault-Tolerant Routing (DFTR) proto-

col (Chalhoub et al., 2017) utilizes transmission met-

rics and access delay to handle random node fail-

ures with minimal impact on network performance

but does not prioritize redundancy mechanisms for

critical failures.

While previous works have signiﬁcantly advanced

the ﬁelds of Digital Twin applications and fault-

tolerant protocols, they often fail to address redun-

dancy mechanisms comprehensively or focus solely

on speciﬁc fault types. The proposed system bridges

these gaps by integrating Digital Twin technology

Revolutionising Healthcare Resource Management with IoT-Integrated Fault-Tolerant Digital Twin Technology

127

Figure 1: Sequence of Operations in the Digital Twin-Based Healthcare Monitoring System.

with a fault-tolerant architecture that ensures con-

tinuous healthcare monitoring. Unlike prior ap-

proaches, it emphasizes dynamic redundancy and

seamless switching to backup sensors, thus providing

a more robust solution for critical healthcare environ-

ments.

3 IMPLEMENTATION

The proposed healthcare monitoring system employs

a digital twin architecture to ensure continuous and

reliable data collection from patient wards. This ap-

proach provides a virtual representation of the phys-

ical sensor network, enabling real-time monitoring

and rapid response to sensor failures. The system’s

dual-layered sensor network, comprising both pri-

mary and backup sensors, maintains data integrity

even in the event of sensor downtime, as depicted

in Figure 1,which shows the sequence of operations

within the system.

3.1 Sensor Network Architecture

In the proposed implementation, primary sensors are

deployed in patient rooms to monitor vital signs such

as heart rate, blood pressure, and body temperature.

These primary sensors continuously collect real-time

data critical for effective patient care and timely med-

ical intervention. The physical layer is designed

for low-power, low-data-rate wireless communica-

tion, ensuring reliable data transmission across the

network, even with constrained resources.

The coordinator sensor acts as the central hub of

the network, receiving and aggregating data from all

primary sensors. It compiles and processes incoming

information for further analysis, serving as a critical

point of data collection. Communication between the

coordinator and the sensors is facilitated by Contiki-

MAC, a Medium Access Control (MAC) protocol that

provides collision avoidance and efﬁcient duty cy-

cling, ensuring both reliability and energy efﬁciency.

The network layer employs IPv6, with uIP (a

lightweight TCP/IP stack), which aids in efﬁcient

routing and addressing of data packets. The sta-

tus of primary sensors is continuously monitored by

the coordinator sensor using User Datagram Protocol

(UDP), which is optimal for fast, lightweight, connec-

tionless data transmissions. In case of primary sensor

failures or inactivity, the coordinator sends activation

messages to backup sensors using UDP, ensuring the

network’s integrity and functionality.

Figure 2 illustrates the architecture of the health-

care monitoring system. In each patient room, sen-

sors—including ECG, pulse oximeter, blood pres-

sure, temperature, and respiratory sensors—collect

vital patient data and transmit it to an IoT gateway.

This gateway forwards data to a digital twin platform,

which performs real-time analysis and stores backup

information. The digital twin not only simulates the

patient’s physiological conditions but also predicts

potential health risks, enabling proactive care. Pro-

cessed data is then sent to a central monitoring sys-

tem, equipped with alert capabilities for immediate

response, and stored in the cloud for secure, remote

access by healthcare providers. This architecture en-

sures reliable, continuous monitoring and a rapid re-

sponse to critical changes in patient health, enhanc-

ing patient care. Additionally, it facilitates seam-

less communication between healthcare providers and

patients, promoting better health outcomes through

timely interventions.

3.2 Fault Tolerance Through Backup

Sensors

To mitigate potential failures of primary sensors,

backup sensors are integrated into the system. While

backup sensors are not actively functioning unless

their primary counterparts fail, they constantly moni-

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Figure 2: Digital-Twin based Healthcare Monitoring System.

tor the health and proper functionality of the primary

nodes. In the event that a primary sensor fails to trans-

mit data, the corresponding backup sensor is activated

to ensure that data collection continues without inter-

ruption.

The ContikiMAC protocol plays a critical role in

this process by enabling low-power operation and ef-

ﬁcient data transmission. Its asynchronous duty cy-

cling ensures that the sensors conserve energy during

idle periods while maintaining readiness to respond to

faults. Additionally, ContikiMAC’s ability to rapidly

wake up backup sensors and manage data retransmis-

sion ensures minimal latency and reliable fault recov-

ery, thereby maintaining high availability and efﬁcient

operation of the system.

3.3 Digital Twin and Simulation Setup

Using Contiki OS and Cooja

The Cooja simulator integrates seamlessly with Con-

tiki OS, an open-source operating system tailored for

resource-constrained Internet of Things (IoT) devices.

This combination provides a robust platform for de-

veloping and testing large-scale IoT networks, sup-

porting various networking protocols optimized for

IoT applications.

In this project, a digital twin of the healthcare sen-

sor network was implemented using Contiki OS, of-

fering a virtual replica for real-time performance anal-

ysis, failure management, and robust data collection

through simulation.

Cooja and Contiki OS are utilized to simulate

client-server communication over the User Datagram

Protocol (UDP). The coordinator.c ﬁle sets up a

UDP server that listens for incoming packets on port

UDP PORT 1234. Upon receiving a message indicat-

ing an active sender, it sends activation commands to

backup sensors. The main.c ﬁle serves as the sender,

generating sensor data and toggling its active state to

simulate faults.

The backup nodes, managed by backup.c, are ac-

tivated upon receiving speciﬁc commands from the

coordinator, ensuring continuous data collection even

in the event of primary sensor failures. The backup

nodes periodically check for active status and send

data only when activated. This design guarantees

the system’s resilience by maintaining data integrity

through backup sensors.

To enhance simulation realism, a backoff timer

(etimer) introduces randomized delays before send-

ing packets, reducing potential network congestion.

The cluster of devices within the Cooja emulator, as

shown in Figure 3, illustrates the arrangement of the

primary, coordinator, and backup nodes in the simu-

lation environment.

In addition, Figure 4 demonstrates the simula-

tion of sensor data transmission and the activation of

backup nodes, providing further insight into the dy-

namic operations of the system. The key simulation

parameters are summarized in Table 1.

The algorithm presented below outlines a health-

care monitoring system that employs a dual-layered

Revolutionising Healthcare Resource Management with IoT-Integrated Fault-Tolerant Digital Twin Technology

129

Figure 3: Cluster of devices with the Cooja emulator.

Table 1: Simulation Parameters.

Components Speciﬁcations

Operating System Ubuntu and Contiki OS

Simulator Version Cooja

Radio Medium Unit Disk Graph Medium (UDGM)

Inference Range 100 m

Transmission Range 150 m

Time of Simulation 300 seconds

Distribution Linear

Number of Nodes (Primary) 5

Number of Nodes (Coordinator) 1

Number of Nodes (Backup) 5

Mote Type Sky

RX Ratio 100%

TX Ratio 100%

sensor mechanism to ensure high availability and

reliability in critical health data monitoring. In this

system, primary sensors are deployed to continuously

transmit critical health data (S2), while backup

sensors initially operate at a lower priority, sending

non-critical data (S1). The system continuously

evaluates incoming values (vg), ensuring that critical

parameters such as heart rate and oxygen levels are

monitored in real time. In the event of a primary

sensor failure, its corresponding backup sensor is

activated, and its data is treated as critical (S2) to

maintain uninterrupted health monitoring.

Once data is classiﬁed, S1 values are routed to

a general monitoring queue for historical analysis,

while S2 values are sent to an urgent monitoring

queue, triggering real-time event detection and medi-

cal responses. By integrating this algorithm with Con-

tiki OS in an IoT-based healthcare system, hospitals

can ensure fault tolerance, efﬁcient resource alloca-

tion, and continuous patient monitoring, preventing

gaps in critical health assessments.

Data: Sensor data from primary and backup

sensors.

Result: Real-time monitoring with

prioritized sensor data handling.

S1 = Situation 1 (non-critical data from

inactive backup sensors);

S2 = Situation 2 (critical data from active

primary sensors and activated backup

sensors);

vg = Value generated by a device;

Primary sensors: High priority (critical data,

S2);

Backup sensors: Lower priority (initially

non-critical data, S1);

Set Sensor Data Flow:;

Primary sensors: Send data as critical (S2);

Backup sensors: Initially send data as

non-critical (S1);

while monitoring is active do

Check device priority;

Process data from primary sensors ﬁrst

(critical data, S2);

If a primary sensor fails, activate the

corresponding backup sensor and treat

its data as critical (S2);

for each vg generated by a device do

Check if vg is non-critical (S1) or

critical (S2);

if vg = S1 (non-critical data from

inactive backup sensors) then

Route vg as non-critical data to a

general monitoring queue;

Archive data if it is only for

historical reference;

end

else if vg = S2 (critical data from

active primary sensors or activated

backup sensors) then

Route vg as critical data to the

urgent monitoring queue;

Trigger real-time processing for

critical event response;

end

else

Send vg directly to a central

monitoring system for analysis

and storage;

end

Algorithm 1: Healthcare Monitoring System with Dual-

Layered Sensor Mechanism.

3.4 Data Management and

Visualization

All sensor data, which includes temperature, ECG,

and fault state statistics, is transmitted via a Kafka

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Figure 4: Output of the Simulation Showing Sensor Data Transmission and Backup Node Activation.

stream to the Conﬂuent Cloud by the coordinator sen-

sor. For real-time monitoring, a lightweight transport

layer is essential; thus, data is transmitted via UDP

over IPv6. Each data packet includes timestamp, par-

tition, and offset for accurate tracking of the sensor

behavior.

Data is processed quickly and categorized effec-

tively within Conﬂuent’s Kafka, creating a robust

platform for efﬁciently handling both live and histor-

ical data. The ContikiMAC protocol manages the ra-

dio duty cycle, allowing sensors to transmit data only

when necessary while remaining idle otherwise. This

setup ensures that critical events, such as sensor data

transmission failures or node inactivity, are promptly

captured and made readily available for further anal-

ysis and visualization, as demonstrated in Figure 5.

4 RESULT AND ANALYSIS

Achieving effective data transmission is vital in

healthcare monitoring systems. The simulation re-

sults demonstrated the effectiveness of the energy-

aware fault-tolerant mechanism and the Digital Twin

in maintaining uninterrupted data ﬂow. During the

simulation, primary sensors were intentionally deac-

tivated to test the response of backup sensors, which

successfully activated and took over data transmis-

sion, ensuring no data loss. The Digital Twin pro-

vided real-time insights and optimizations, enhancing

the system’s fault tolerance and resource allocation.

4.1 Packet Delivery Ratio (PDR)

The Packet Delivery Ratio (PDR) measures the per-

centage of packets successfully delivered from the

source to the destination within a network. A higher

PDR indicates better network performance and relia-

bility in terms of packet delivery.

PDR =

Number of packets successfully delivered

Number of packets sent

(1)

Packet Delivery Ratio (PDR) is crucial in eval-

uating how reliably data is transmitted in the sys-

tem. As illustrated in Figure 6, both the primary and

backup clusters maintained a high PDR, conﬁrming

stable data reception from the IoT devices. This ro-

bust performance ensured effective data transmission

even during failures, with the network remaining sta-

ble and allowing for the continued operation of the

system across all clusters.

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131

Figure 5: Sensor data in Conﬂuent Kafka.

Figure 6: Packet Delivery Ratio.

4.2 Average Latency

Average Latency calculates the average time taken for

a packet to travel from the source to the destination

in a network. Lower average latency values indicate

faster data transmission and better network respon-

siveness.

Average Latency =

Latency for all packets

Number of packets

(2)

Latency is important for ensuring the system’s

timely responsiveness, particularly in healthcare sce-

narios where timely data transmission is critical.

As illustrated in Figure 7, the primary cluster ex-

hibits low latency, enabling swift decision-making

and prompt updates on vital signs. This low latency

guarantees reliable data monitoring even during pri-

mary sensor failures.

Figure 7: Average Latency.

4.3 Comparative Analysis and

Signiﬁcance

The improvements in both PDR and latency under-

score the advantages of integrating the proposed fault-

tolerant mechanism and Digital Twin in healthcare

monitoring. By addressing critical gaps in existing

systems, the proposed solution ensures uninterrupted

and efﬁcient monitoring, which is crucial in life-

saving scenarios. The graphs and metrics are inter-

preted in the context of healthcare applications, where

consistent, reliable data transmission directly impacts

patient safety and care quality.

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5 CONCLUSIONS

This research demonstrates the potential of combin-

ing backup sensor systems with Digital Twin technol-

ogy to enhance the reliability and fault tolerance of

patient monitoring systems in healthcare. The pro-

posed system ensures continuous data transmission,

even in the event of sensor failures, addressing one of

the most critical requirements in healthcare monitor-

ing.

By leveraging Digital Twin technology, real-time

shadowing and analysis of sensor networks become

feasible, enabling healthcare providers to maintain

high-quality care with minimal delays. The predictive

functionality incorporated into backup sensors further

strengthens the system, reducing downtime and pre-

serving service continuity.

The broader implications of this work suggest that

such resilient and fault-tolerant systems can signiﬁ-

cantly transform critical care environments like ICUs.

Continuous monitoring with minimal latency ensures

that vital patient data—such as heart rate, oxygen lev-

els, and blood pressure—is always available, enabling

prompt decision-making and reducing the likelihood

of adverse events. The system’s ability to seamlessly

activate backup sensors during primary sensor fail-

ures is particularly vital in ICUs, where every sec-

ond can impact patient outcomes. By reducing in-

tervention delays, this technology can support health-

care providers in responding to emergencies faster

and more effectively, ultimately improving the quality

of care and saving lives.

Furthermore, the scalability of this approach po-

sitions it as a viable solution for larger and more

complex healthcare networks. As healthcare facili-

ties grow in size and complexity, the ability to ex-

tend this system to accommodate an increasing num-

ber of patients, sensors, and data streams is essen-

tial. The modular design of the system ensures it can

be adapted to different hospital settings, from single

ICU units to entire hospital networks, while maintain-

ing its core fault-tolerant capabilities. This scalability

not only makes it suitable for large-scale deployments

but also opens opportunities for integration into na-

tional healthcare infrastructures, where uninterrupted

patient monitoring is critical.

This work provides evidence that incorporating

predictive functionality into backup sensors, com-

bined with real-time insights from Digital Twins, can

contribute signiﬁcantly to resilient healthcare sys-

tems. These systems are well-equipped to handle di-

verse operational challenges, ensuring continuity of

care and advancing the ﬁeld of digital healthcare.

6 FUTURE WORKS

Incorporating machine learning algorithms into the

analysis of sensor data presents a signiﬁcant opportu-

nity for enhancing the healthcare monitoring system.

Integrating models such as Long Short-Term Mem-

ory (LSTM) networks or Random Forests for predic-

tive maintenance can enable the system to anticipate

sensor failures with high accuracy. By analyzing his-

torical and real-time data patterns, these models fa-

cilitate the prediction of potential issues before they

occur, allowing for timely interventions and ensuring

uninterrupted patient monitoring.

Moreover, integrating these machine learning

models with hospital management systems can en-

able seamless data handling and improved decision-

making across the healthcare infrastructure. This in-

tegration would ensure that predictive insights are

efﬁciently relayed to healthcare personnel, enabling

proactive maintenance and reducing operational dis-

ruptions. Collaborative efforts between data scientists

and healthcare professionals are crucial for tailoring

these solutions to the speciﬁc challenges of monitor-

ing systems. Additionally, establishing robust valida-

tion processes through real-world testing in hospital

environments will be critical to ensuring the reliabil-

ity and efﬁcacy of these algorithms in practical health-

care scenarios.

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