IOT Project

I need to have a complete project about Health IOT system , I already do some part of documentation and design architecture and file of packet tracer. I need some one to complete the whole project with the final report and packet tracer configuration and make sure all IOT devices connected and coding . attached are the full document

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A Systematic Review of IoT Architecture in Healthcare:
HealthIoT Plus
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Table of Contents
1) Abstract ……………………………………………………………………………………………………………….. 3
2) Introduction …………………………………………………………………………………………………………. 3
3) Literature Review …………………………………………………………………………………………………. 4
4) Problem Statement ……………………………………………………………………………………………….. 5
5) Suggested IoT Architecture …………………………………………………………………………………… 6
HealthIoT Plus Architecture: …………………………………………………… Error! Bookmark not defined.
1. Edge Layer:……………………………………………………………………….. Error! Bookmark not defined.
2. Fog Computing Layer:………………………………………………………… Error! Bookmark not defined.
3. Cloud Layer: ……………………………………………………………………… Error! Bookmark not defined.
4. Security Layer: ………………………………………………………………….. Error! Bookmark not defined.
5. Communication Layer: ……………………………………………………….. Error! Bookmark not defined.
6) Expected Challenges and Time Management ……………………………………………………….. 12
Challenges: ……………………………………………………………………………………………………………………… 12
Time Management Table: ………………………………………………………………………………………………….. 13
Conclusion ……………………………………………………………………………………………………………… 15
References ………………………………………………………………………………………………………………. 16
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1) Abstract
This research delves into the HealthIoT Plus system, an IoT framework designed for medical
applications. With its focus on remote monitoring and data analytics, HealthIoT Plus has the
potential to revolutionize patient care. The study analyzes the architecture’s layout, notes the
difficulties it presents, and provides a thorough schedule for bringing the plan to life.
2) Introduction
Incorporating technology into healthcare has a long-standing purpose: to improve treatment
efficiency and quality for patients. The Internet of Things (IoT) has evolved from an intriguing
idea to a practical, game-changing technology in the healthcare sector in recent years. The
Internet of Things (IoT) is the notion of connecting common devices to the internet so that they
can share data, and it has the potential to provide considerable benefits to many different
industries. Global challenges, such as the increasing frequency of chronic illnesses and the
aging population, place enormous strain on the healthcare system, necessitating change and
adaptation. However, what makes such a system important in the present-day context? For
one, global phenomena like the COVID-19 pandemic have underlined the importance of
telehealth solutions and the potential constraints of traditional healthcare systems. Being able
to monitor and treat patients remotely can mitigate the pressure on healthcare facilities and
reduce the risk of contagious disease spread. Furthermore, with a growing elderly population,
the need for solutions that can provide efficient elderly care without always requiring physical
presence has become paramount (Garcia, 2021).
However, developing and implementing such a system is not without its challenges. Data
privacy, interoperability between various devices, power management, and the sheer volume
of real-time data are just some of the hurdles to overcome. This report delves into the HealthIoT
Plus system, dissecting its architecture, the rationale behind its design, and the expected
challenges in its deployment, all while drawing upon existing literature in the domain. As we
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step into a future where technology and healthcare become even more intertwined,
understanding, and perfecting systems like HealthIoT Plus will be instrumental in defining the
next phase of medical care.
3) Literature Review
Scholarly interest in the expanding influence of the Internet of Things (IoT) on healthcare is
widespread. Healthcare delivery might be completely rethought with the help of device and
system integration, which could make it more efficient, patient-focused, and cost-effective.
Purposefully emphasizing research that have focused on IoT architecture in healthcare and the
underlying problems and possibilities it brings, this literature review attempts to throw light on
the most important contributions in this area.
In their 2018 study, “IoT in Contemporary Healthcare: Architectural Trends and Future
Directions,” Smith et al. This study gave a nice summary of how the Internet of Things is
changing medicine. Smith and his co-authors stressed the need of scalable, flexible
infrastructures capable of processing the vast amounts of data produced by modern medical
equipment. The report suggested a multi-tier design, with distinct levels for edge, fog, and
cloud computing. While the edge layer was focused on real-time patient monitoring, the cloud
layer emphasized long-term storage and complex analytics. Their work shed light on the
importance of ensuring scalability in healthcare IoT systems, given the rapid growth and
evolution of medical devices and sensors.
Jones and Patel (2019), in their seminal work, “Wearable Sensors in Medical IoT:
Architectural Insights and Data Challenges,” presented a deep dive into wearable sensors’ role
within the healthcare landscape. The authors articulated that the proliferation of wearable
devices necessitates architectures that can handle real-time data processing. Their insights into
data requirements, particularly the need for immediate feedback from wearable devices to
provide real-time alerts, were noteworthy. The authors also explored the challenges of
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integrating heterogeneous data sources, emphasizing the role of standardization in ensuring
effective data integration.
A paper by Mendez et al. (2020) titled “Security Implications and Solutions in Healthcare IoT:
Architectural Perspectives,” brought to the fore the crucial aspect of security. As healthcare
data is particularly sensitive, ensuring its integrity and privacy is of paramount importance.
Mendez and the team dissected potential security threats, including data breaches and
unauthorized access, and then went on to propose architectural remedies. Their advocacy for
end-to-end encryption and a decentralized approach, leveraging blockchain technology for data
integrity, showcased forward-thinking solutions to address pressing security concerns.
Garcia et al. (2021)’s work on “Environmental Sensors in Healthcare IoT: Opportunities and
Architectural Challenges,” is also of particular relevance. The study dove into how
environmental sensors, monitoring aspects like air quality, can complement wearable devices
to provide a holistic view of a patient’s health and surroundings. Garcia and his team argued
for the integration of both wearable and environmental sensors within a unified architecture,
emphasizing the need for a seamless flow of data between these sensors. Their insights into
ensuring energy efficiency, particularly for battery-operated environmental sensors, were
crucial.
Another pivotal contribution was by Roberts and Khan (2022) in their paper, “IoT Healthcare
Systems: The Role of Artificial Intelligence in Data Processing and Analytics.” In this work,
the authors investigated the complementary nature of IoT and AI in the medical field. Roberts
and Khan argued that the flood of data produced by IoT devices renders conventional data
processing processes insufficient. The capacity of artificial intelligence to analyze large
datasets, spot trends, and conduct predictive studies suggests that it may form the backbone of
future IoT healthcare designs. They found that machine learning algorithms are useful for
tracking patients, anticipating medical emergencies, and facilitating prompt treatment.
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In conclusion, the literature emphasizes the revolutionary potential of IoT in healthcare, with
a common agreement on the need for scalable, secure, and efficient systems. The foundation
of future healthcare systems is the incorporation of wearables, environmental sensors, and
cutting-edge technology like artificial intelligence. Data security, interoperability, and realtime processing are just a few of the obstacles along the way. The research described above
provide light on the obstacles that must be overcome before the full promise of IoT in
healthcare can be realized.
4) Problem Statement
While the Internet of Things (IoT) has tremendous promise for improving healthcare, special
consideration must be given to address issues such as patient privacy, energy efficiency, and
device compatibility. HealthIoT Plus aims to solve these problems by providing the best
possible alternative.
5) Suggested IoT Architecture
Protocol and structural diagram:
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Components of IoT:
1. Edge Layer (Devices Layer):
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Devices: Wearables and sensors to measure heart rate, blood sugar levels, ambient
temperature, and air quality.
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Edge Processing: Lightweight data processing capabilities for real-time alerts.
2. Network Layer:
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Gateways: Devices that aggregate data from multiple sensors and send it to the
cloud. They can also receive commands from the cloud and send them to devices.
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Connectivity: Protocols like MQTT, CoAP for efficient and reliable data
transmission.
3. Cloud Layer:
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Data Storage: Store historical and real-time data.
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Data Processing: Analyze data for trends, predictions, and more in-depth
insights.
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Application Layer: User interfaces, APIs, and other applications to interact with
the data.
4. End-User Layer:
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Healthcare Providers: Receive alerts and monitor patient data.
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Patients: Monitor their own health metrics and receive notifications.
Rationale:
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Edge Processing: Given the critical nature of health data, real-time processing at the
edge (device level) ensures immediate action can be taken without waiting for data to
travel to the cloud and back.
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Network Layer: Using efficient protocols like MQTT ensures data is transmitted
reliably and efficiently, which is crucial for health data.
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Cloud Layer: Centralized storage and processing allow for more in-depth analysis,
trend spotting, and integration with other systems.
Simulation:
For simulating this system in Packet Tracer:
1. Devices: Represented by generic IoT devices.
2. Gateways: Represented by routers or switches.
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3. Cloud: Represented by servers.
4. End-Users: PCs or end-user devices.
Wearables and Sensors:
a. Heart Rate Monitor:
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Description: A device worn by the patient, often as a wristband or chest strap, to
continuously monitor heart rate.
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Features:
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Real-time heart rate monitoring
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Wireless data transmission
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Battery-powered with efficient power consumption
b. Blood Sugar Level Monitor (Glucometer):
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Description: A device that measures blood sugar levels. Some advanced versions can
provide continuous monitoring.
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Features:
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Capable of quick and accurate readings
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Minimal or non-invasive blood sampling
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Wireless data transmission
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c. Ambient Temperature Sensor:
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Description: A sensor that measures the surrounding temperature, which can be
crucial in understanding certain health conditions.
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Features:
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Accurate temperature readings
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Wireless data transmission
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Compact and unobtrusive design
d. Air Quality Sensor:
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Description: A device that measures the quality of the air in the patient’s
environment, detecting pollutants and other harmful substances.
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Features:
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Can detect common pollutants like CO2, PM2.5, VOCs, etc.
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Real-time monitoring and alerts
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Wireless data transmission
e. MCU PT:
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Description: This MCU can be used as a bridge between sensors and the network, to
control device operations, or to interpret data from a variety of sensors.
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Features:
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Allows it simple to alter the settings on the device and add new features.
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Allows for integration with a wide variety of sensors and devices for
command and control or processing of data.
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Able to transmit data from sensors to a network or to receive instructions
from another device or the cloud.
f. Alarm:
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Description: A system that can notify the user or healthcare professional when a
certain condition or threshold has been reached. For instance, the alarm can be set
off whenever the patient’s heart rate exceeds a certain threshold, alerting either the
patient or their caretaker.
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Features:
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Allows the user or a healthcare professional to configure alert thresholds or
conditions.
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Lighting that alerts the user to a particular condition.
g. Motion sensor:
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Description: Its main use in healthcare is the tracking of patients’ motion and the
detection of falls, particularly among the elderly.
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Features:
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The use of heat signatures to detect motion is a common technique.
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Allows the user to adjust the sensitivity to reduce the number of false
positives.
2. Gateways:
a. IoT Gateway Device (Home Gateway):
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Description: A device that acts as a bridge between the sensors and the cloud. It
aggregates data from multiple sensors and sends it to the cloud.
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Features:
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Supports multiple connectivity options (Wi-Fi, Bluetooth, Zigbee, etc.)
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Secure data transmission protocols
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Scalable to support multiple connected devices.
3. Cloud:
a. Cloud Server:
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Description: A server that stores, processes, and manages the data received from the
IoT devices.
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Features:
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High storage capacity
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Advanced data processing capabilities
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Secure data storage and access controls
4. End-User Devices:
a. Healthcare Provider Interface:
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Description: A computer or tablet interface for healthcare providers to monitor
patient data and receive alerts.
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Features:
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User-friendly dashboard
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Real-time data visualization
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Secure access controls
b. Patient Interface:
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Description: A mobile or computer application for patients to monitor their own
health metrics and receive notifications.
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Features:
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Intuitive user interface
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Personalized health insights
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Secure data access
These devices, when integrated, will provide a comprehensive health monitoring system,
ensuring timely alerts and insights for both patients and healthcare providers.
6) Expected Challenges and Time Management
Challenges:
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Data Security: Ensuring the privacy and integrity of patient data.
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Interoperability: Ensuring various devices communicate effectively.
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Scalability: Handling the increasing number of devices.
Time Management Table:
Major Task
Subtasks
Responsible
Time Frame
Device Team
Week 1-2
Network Team
Week 3-4
Cloud Team
Week 5-7
UI/UX Team
Week 8-10
Procure devices
Device Setup
Configure devices
Set up gateways
Network Setup
Configure connectivity
Set up cloud storage
Implement data processing
Cloud Integration
algorithms
Design UI
User Interface
Implement alert system
Test real-time alerts
Testing
Test data storage and retrieval
QA Team
Week 11-12
Major Task
Subtasks
Responsible
Time Frame
Device Team
Week 1-2
Network Team
Week 3-4
Procure devices
Device Setup
Configure devices
Set up gateways
Network Setup
Configure connectivity
Simulation Using Packet Tracer: To simulate HealthIoT Plus, use Packet Tracer to model
data flow between sensors, aggregators, and the cloud. Expected traffic modeling would
require artificial intelligence to predict patient data flow patterns and ensure efficient
bandwidth usage.
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Integration with Real Cloud Services: Utilize Packet Tracer’s cloud services integration
feature to link the architecture to real-world cloud platforms, enhancing simulation realism.
Network Design Topology:
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Communication:
Defense and Justification:
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Edge Processing: According to Shi et al., 2016, edge computing provides cloud
resources closer to data sources, enabling low-latency and efficient processing.
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MQTT Protocol: As per Hunkeler et al., 2008, MQTT is a lightweight and efficient
protocol designed for low-bandwidth, high-latency networks, making it ideal for IoT.
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Centralized Cloud Storage: Yi et al., 2015 highlight the benefits of cloud storage for
scalability, reliability, and integration capabilities.
Conclusion
The HealthIoT Plus system, as outlined, stands as a testament to the transformative power of
IoT in revolutionizing healthcare. By seamlessly integrating wearable and environmental
sensors, it seeks to create an ecosystem that is not only responsive but also predictive, ensuring
timely medical interventions. As we navigate the intricacies of its architecture, from edge
processing to cloud analytics, the overarching goal remains unwavering: to provide optimal
patient care. Challenges like power management and data privacy undoubtedly lie ahead. Yet,
with continued research and innovation, we move closer to a future where healthcare is
proactive, personalized, and propelled by technology.
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References
Smith, J., Thompson, A., & Johnson, L. (2018). IoT in Modern Healthcare: Architectural
Trends and Future Directions. Journal of Medical Systems and Technologies, 12(2),
45-58.
Jones, R., & Patel, S. (2019). Wearable Sensors in Medical IoT: Architectural Insights and
Data Challenges. International Journal of Health Informatics, 17(4), 320-337.
Mendez, M., Lee, H., & Kim, Y. (2020). Security Implications and Solutions in Healthcare
IoT: Architectural Perspectives. Journal of Cybersecurity and Systems, 8(1), 15-29.
Garcia, L., Rodriguez, P., & Fernandez, D. (2021). Environmental Sensors in Healthcare IoT:
Opportunities and Architectural Challenges. Health Technology Review, 5(6), 89-105.
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges.
IEEE Internet of Things Journal, 3(5), 637-646.
Hunkeler, U., Truong, H. L., & Stanford-Clark, A. (2008). MQTT-S—A publish/subscribe
protocol for Wireless Sensor Networks. 2008 3rd International Conference on
Communication Systems Software and Middleware and Workshops (COMSWARE ’08).
Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing: Concepts, applications, and issues.
2015 Workshop on Mobile Big Data.
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COE 550: Intro. To Internet of Things
King Fahd University of Petroleum and Minerals
Computer Engineering Department
COE 550- Course Project
Project Requirements:
In this project, students are expected to implement an IoT application using either:
Option 1: Cisco Packet Tracer, or
Option 2: Real hardware platform,
In addition to Cumulocity IoT Cloud Platform. The implementation is based on the design you
have developed in the IBL activity you have been given. The implementation must include all the
following components:
1.
2.
3.
4.
Sensing and actuation.
Edge and Could Computing, (optionally Fog layer).
MQTT and HTTP protocols.
Descriptive Data Analysis and Visualization (Dashboard).
If going with Option 1, students must use good number of sensor nodes. On the other hand,
Options 2 must include at least two sensor nodes. In either option, the application must also
include three functionalities.
All codes must be properly organized and documented. All assumptions must also be clearly
stated. Students are free to make design choices regarding system architecture and the individual
components. However, all choices must be properly justified in the report.
Cumulocity IoT Platform:
Cumulocity IoT platform is provided to us by SoftwareAG, an IoT cloud provider, for academic use.
You will receive an email with to complete your registeration.
Please use the following link: https://kfupm.cumulocity.com
If you have any issues, please contact the course coordinator (Dr. Louai Al-Awami
louai@kfupm.edu.sa) to activate your account.
Performance Evaluation:
You project must include a performance evaluation section where you will evaluate empirically
the performance of the system such as latency, protocol overhead, etc.
Deliverables:
1. A written report (A template will be provided).
2. All source codes used including PT simulation and other scripts.
3. A short instruction manual for running the simulation (setup instructions, limitation,
problems).
Term 221
Louai Al-Awami
02-11-2022
1
COE 550: Intro. To Internet of Things
4. List of any open-source codes used and their sources (if applicable).
Notes:
1. If using PT, you are required to use MQTT in your project to facilitate communication
between different objects.
2. PT API documentations can be found under PT installation directory in
/help/default/iot_python_api.htm.
3. The REST API’s that can be used with Cumulocity can be found in the resource folder on
Blackboard.
4. Students are encouraged to view the examples and documentations provided by the
platforms on how to use different capabilities of PT and Cumulocity.
5. You are free to include any open-source code in your project. All used codes must be
properly cited.
Timeline:
Date
Monday Dec. 25, 2022
Tuesday Dec. 26, 2022
Milestone
Project Submission (Report + Simulation)
Project Demo (online)
Grade Distribution:
1- Project proposal (15%), due Monday Nov. 6, 2023.
2- Progress report (25%), due Saturday 18 November, 2023.
3- Final report (60%), due Dec. 25-26, 2023.
Term 221
Louai Al-Awami
02-11-2022
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Final Report Rubrics
Abstract Introduction Literature Survey
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7
5
Problem
statement
10
Simulation
Setup
Contributions
10
Abstract
Introduction
Literature Survey
Problem statement
Simulation Setup
Contributions
Results and discussion
Conclusion
Baker Hughes Confidential
10
Results and
discussion
Conclusion
15
5