Enhanced Task Scheduling for IoT‑Based Healthcare Systems in

Cloud Computing

B. Naga Lakshmi, Kupparamedi Chandrika, Ediga Gowri, Gangula Nagamani and Janupalli Sahithi

Department of CSE, Ravindra College of Engineering for Women, Kurnool, Andra Pradesh, India

Keywords: IoT‑Based Healthcare Systems, Cloud Computing, Scalability, Resource Utilization, Urgency‑Based Task

Prioritization.

Abstract: IoT healthcare platforms, such as wearable devices, sensors, and medical equipment, generate huge volumes

of medical data that have to be processed and analysed efficiently. The current methodology of IoT healthcare

systems in cloud computing faces challenges of using immense volumes of data from IoT systems, which

include non-scalability, ineffectively utilized resources, and were resolved by the proposed system. The

project introduces a refined task scheduling mechanism for the cloud-based IoT-assisted healthcare system.

This project proposes a refined task scheduling method for cloud-based IoT healthcare systems. The suggested

method ranks tasks according to their need and urgency, attempting to ensure that critical tasks remain in

operation to the utmost degree of efficacy as promptly as feasible. Accomplishments of this project are better

task scheduling and greater resource utilization.

1 INTRODUCTION

The merger of IoT and healthcare was a turning point

in healthcare, leading to real-time monitoring,

diagnosis, and treatment. IoT-based healthcare refers

to a network of devices, such as wearable sensors and

medical devices, which gather and send patient data

to cloud servers for notification and analysis.

However, the concomitant complexities and

magnitudes of these systems create some grave

challenges for the optimal processing of task

management, data processing, and resource

allocation.

Dynamic task scheduling in any IoT-based

healthcare system stands out to be one of the major

challenges. Such systems dynamically handle

continuously changing constraints, such as

computational requirements, bandwidth limitation for

data transmission, and real time conditions of the

medical applications.

Unfortunately, the traditional task scheduling is

insufficient to meet the particular exigencies of IoT

healthcare systems with respect to low latency, high

service availability, satisfying energy efficiency, and

more.

It is essential to address the increasing complexity

of task scheduling due to dynamic patient data such

as vital signs. D. The Proposed Scheduling

Framework In this section, we propose an efficient

scheduling framework to leverage the coalition

process of each user in both uplink and downlink

communication systems. With the use of machine

learning for predictive workload management and

dynamic scaling features available in clouds, utilizing

mod- el allows the proactive scheduling which can

improve performance, stability and power savings for

IoT-based health care systems.

2 LITERATURE SURVEY

2.1 A: S - Kumar, R - P - Singh, and M

- K - Gupta, "Enhanced Task

Scheduling for IoT-Based

Healthcare Systems in Cloud

Computing

This article provides a comprehensive review of task

scheduling methods for IoT-based cloud-integrated

healthcare systems. Different methods such as

priority-based scheduling, load balancing, and real-

time processing and machine learning-based methods

for predictive task scheduling are proposed by the

authors. The study highlights the importance of

462

Lakshmi, B. N., Chandrika, K., Gowri, E., Nagamani, G. and Sahithi, J.

Enhanced Task Scheduling for IoT-Based Healthcare Systems in Cloud Computing.

DOI: 10.5220/0013931400004919

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 5, pages

462-466

ISBN: 978-989-758-777-1

efficient utilization of resources in cloud-based

systems for low-latency data processing and efficient

utilization of computational resources, especially in

be employed for forecasting task execution latency

and optimizing the scheduling of tasks based on

present cloud resources for minimizing latency while

maximizing throughput for healthcare applications

real-time patient monitoring and diagnosis.

2.2 B: R - S - Verma, T - P - Joshi, and

K - S - Sharma, "Dynamic Task

Scheduling in Cloud-Based

Healthcare IoT Systems"

This study examined dynamic task scheduling

techniques made for health-care systems with IoT

devices and cloud computing. The authors clarify the

potential use of cloud computing to handle the

computational burden generated by IoT devices

through predictive models for resource allocation on

demand with respect to real-time needs for tasking.

The work also identified scalability, energy

efficiency, and task prioritization as central themes

enhancing the performance of cloud-supported

health-care systems.

2.3 B: T - H - Reddy, V - R - Kumar,

and S - M - Patel, "AI-Driven Task

Scheduling for IoT Healthcare

Applications in Cloud

Environments"

In a similar vein, author B. T. H. Reddy et al propose

improved optimization of task scheduling in the IoT-

based healthcare systems via artificial intelligence

methods like reinforcement learning, neural network

etc. Dominated by how AI can achieve scheduling in

real-time networked health data processing to

optimize cloud resources utilization and decision-

making using real-time health status derived from IoT

devices. The paper focuses on the AI as a means of

enhancing diagnosis and for increasing efficiency in

healthcare cloud. The work is representative of AI’s

ability to forecast workloads, adapt to changing

conditions and reduce delays, while improving

system reliability making health projects smarter,

more energy efficient and with a speedier recovery

from an unpredictable event.

2.4 D: M - Singh, S - K - Sharma, and

R - P - Iqbal, "Optimizing Task

Execution in Cloud for IoT

Healthcare Systems Using Machine

Learning"

The authors discuss in this paper a machine learning-

based methodology for task execution and resource

allocation optimization in cloud-supported IoT

healthcare applications. The authors show in this

research how learning algorithms such as support

vector machines (SVMs) and k-means clustering can

employ for forecasting task execution latency and

optimizing the scheduling of tasks based on present

cloud resources for minimizing latency while

maximizing throughput for healthcare applications.

3 PROBLEM STATEMENT

Problem statement is concerned with the issue of

effectively scheduling and prioritizing healthcare

tasks created by IoT devices in a cloud computing

system.

The aim is to maximize efficient task scheduling,

utilization of resources, reliable processing of urgent

healthcare information, considering the changing

nature of patient requirements.

4 RESEARCH METHODOLOGY

4.1 Proposed System

The system proposed attempts to counter the

limitations of existing IoT-based healthcare systems

by using a new dynamic task scheduling method. The

suggested methodology ensures real-time processing

of data, maximizes the usage of resources, and

conserves energy, thus transforming data

management in the healthcare industry. The heart of

the system is a robust scheduling engine that

intelligently prioritizes tasks based on their urgency

and results in fast response times for life-critical

health data like emergency notifications and

monitoring of vital signs.

Priority-based task scheduling in this section is a

natural approach in IoT-based healthcare systems to

best process the medical tasks with minimal latency.

Various healthcare tasks such as constant real-time

patient monitoring, emergency alerts, and medical

diagnostics exist in these systems generated by

medical devices supported by IoT. All these activities

Enhanced Task Scheduling for IoT-Based Healthcare Systems in Cloud Computing

463

have varying levels of priority, and therefore, the

priority-based scheduling classifies and runs them

according to how critical they are. This allows high-

priority operations like issuing notifications for

urgent health conditions to be run without delay, and

lower-priority operations like recording normal

health details to be run without interfering with

critical Operations.

4.2 System Architecture

Figure 1: Proposed Priority-Based Task Scheduling System

for IoT-Based Healthcare.

Figure 1 the system architecture for patient health

monitoring is designed for hospitalized patients

undergoing treatment. It consists of various

networked devices and mobile or wireless sensor

nodes that continuously monitor and measure the

patient's health parameters. The collected data is then

sent to hardware Hospital workstation (HW) units for

processing. The HWs are edge computing servers,

and the proposed PTS- RA system is installed on

them.

This framework is responsible for categorizing the

tasks in terms of urgency, scheduling them, and

prioritizing them based on their requirements. The

tasks are then passed on to either the HW or the cloud

queue for execution.

Each Hospital workstation has two

functionalities:

(i) applying the suggested approach to task

scheduling and prioritization, and (ii) acting as an

edge computing server. Data that reaches an HW

performs computation on the data if it is resource

sufficient. The outputs are passed on to clinicians or

physicians to inform decisions. But if an HW does not

have resources or the problem is not urgent but

complex, it is computed at the centralized cloud. Then

the results are back-end to the HW after computation.

The system tries to reduce the overall processing

time of jobs, optimize the utilization of resources at

HWs, minimize bandwidth expenses, and improve

overall performance. The monitoring devices of the

patient can store, compute, and connect to the internet

using wired or wireless links. In the hospital, several

HWs are used for storing data and computing. All

HWs share the same computational and storage

power, whereas the centralized cloud possesses much

greater resources and can execute multiple tasks at a

time with ease. Moreover, the system is scalable in

nature, and more HWs can be incorporated when

required to support greater workloads.

4.3 Algorithm

Figure 2: Task Scheduling Flowchart Using Priority Queue

and Scheduling Techniques (FCFS, SJF, EDF).

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4.3.1 Result of the Flowchart Execution

• Assign Task Priority – Each task is assigned

a priority level. Initialize Priority Queue

(PQ) – A priority queue is created to manage

tasks based on their priority.

• Select Scheduling Technique – A scheduling

algorithm is chosen from:

• FCFS (First Come, First Serve): Tasks are

processed in the order they arrive.

• SJF (Shortest Job First): Tasks with the

shortest execution time are processed first.

Table 1: Key Features for Task Scheduling Based on

Priority.

Feature Description

Real-Time Priority

Updates

Priorities of tasks are

dynamically updated

according to varying

conditions so that high-

priority tasks are

completed in a timely

manner.

Priority-Based

Resource Allocation

Resources are

distributed according to

task priority so that

high-priority tasks get

adequate resources for

timely completion.

• EDF (Earliest Deadline First): Tasks with

the closest deadline are prioritized.

• Choose Task Based on Scheduling – The

highest-priority task is selected based on the

chosen technique.

• Execute Task – The selected task is executed.

• Mark Task as Finished & Remove from

Queue – The task is marked as completed and

removed from the priority queue. Check if

Queue is Empty:

If not, return to the scheduling step and continue

selecting and executing tasks. If yes, all tasks are

completed, and execution ends.

Table 1 show sthe

Key Features for Task Scheduling Based on Priority.

5 RESULTS AND DISCUSSIONS

The following table 2 gives a collection of tasks, with

each task having a specified arrival time, execution

time, priority value, and deadline. Task T1 arrives at

time 0 and has an execution time of 4 units. It has the

highest priority (1 - High) and needs to be done prior

to time 9. Task T2 shows up at time 1 with an

execution time of 2 units and a medium priority (2)

and needs to be done prior to time 7. Task T3 shows

up at time 3 with an execution time of 5 units. It is

also a high-priority task (1).

with deadline 12. Task T4 is late, at time 5, low

priority (3), takes execution time of 3 units and needs

to be finished by time 10. Lastly, T5 shows up at time

7, has an execution time of 6 units, medium priority

(2), and a deadline of 14. All these tasks can be

efficiently scheduled by priority scheduling

algorithms or deadline scheduling so that the system

ensures timely completion on the basis of urgency

and resource constraints.

Table 2: Task Dataset for Priority-Based Scheduling in IoT

Healthcare Systems.

Tas

k ID

Arri

val

Tim

Execu

tion

Time

Priority

(1=High,2=Medi

um,3

=Low)

Deadl

ine

T1 0 4 1(High) 9

T2 1 2 2(Medium) 7

T3 3 5 1(High) 12

T4 5 3 3(Low) 10

T5 7 6 2(Medium) 14

Figure 2. illustrates priority-based task scheduling

by showing the execution time for tasks T1 to T5

according to their respective priority levels. The tasks

are coded with colors: red representing high-priority

tasks (1), blue representing medium-priority tasks (2),

and green representing low-priority tasks (3). This

aids in visualizing how execution time changes across

various priority levels.

Figure 3: Execution Time Analysis of Priority-Based Task

Scheduling in IoT Healthcare.

Enhanced Task Scheduling for IoT-Based Healthcare Systems in Cloud Computing

465

T1 and T3 from the graph are of high priority, with

execution times of 4 and 5 units, respectively. T2 and

T5, which are of medium priority, have execution

times of 2 and 6 units, respectively. T4 is of low

priority, with an execution time of 3 units.

Observably, T5 has the longest execution time of 6

units and T2 has the shortest of 2 units. Figure 3

shows the Execution Time Analysis of Priority-

Based Task Scheduling in IoT Healthcare.

6 CONCLUSIONS

Finally, the system enhances IoT- based healthcare by

introducing an intelligent task scheduling algorithm

that tackles priority medical tasks. The system

prioritizes critical medical tasks to ensure that urgent

health data, such as emergency alerts and vital sign

monitoring, is processed efficiently and in a timely

manner. Through optimized utilization of resources

and real-time data processing, the system increases

scalability, minimizes costs, and enhances overall

efficiency. Finally, this solution enables faster and

more efficient delivery of healthcare services, thus

providing improved patient care.

REFERENCES

Kaur, A., & Kumar, R. (2020). "Dynamic resource

allocation and task scheduling for cloud- based

healthcare IoT systems." Procedia Computer Science,

167,106-116. https://doi.org/ 10.1016/ j.procs.

2020.03.058

Khan, M, A., & Khan, F. A. (2020) "Energy-efficient task

scheduling for IoT healthcare systems in cloud

computing." Journal of Ambient Intelligence and

Humanized Computing, 11(4), 1591-1602.

https://doi.org/10.1007/s12652-019-01438-w

Li, X., Liu, L., & Wang, H. (2020). "Smart task scheduling

for energy-efficient healthcare systems based on IoT."

Computers, Materials & Continua, 63(3), 1003- 1019.

https://doi.org/10.32604/cmc.2020.011463

Saxena, N., & Mishra, A. (2021). "A survey of task

scheduling and resource management in healthcare IoT

systems." International Journal of Computer

Applications, 176(12), 1- 10. https://doi.org/10.5120/ij

ca2021916899

Sharma, R., & Soni, M. (2021). "Task scheduling and

resource management for healthcare applications in

IoT- cloud integrated systems." IEEE Access, 9,

16916- 16929. https://doi.org/10.1109/ACCESS.2021.

3050219

Zhang, L., & Deng, L. (2019). "A review on scheduling

algorithms in cloud computing for healthcare

applications." Journal of Healthcare Engineering,

2019, 1-15. https://doi.org/10.1155/2019/4826067

Zhang, X., & Wang, L. (2020). "Efficient task scheduling

for healthcare systems in cloud computing

environments." Journal of Cloud Computing:

Advances, Systems and Applications, 9(1), 20-30.

https://doi.org/10.1186/s13677-020-00208-7

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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