代写comp3211程序代做 IoT Framework
1. Assignment guidance This exercise aims to give you some practical
experience of using serverless architectures. I t will also give you the
opportunity to research on the cloud computing domain of Function as
a Service (FaaS), technologies and performance.
Serverless computing is revolutionising cloud application development
as it offers the ability to create modular, highly-scalable, fault-tolerant
applications, with minimal operational management. In order to contribute to its widespread adoption of serverless platforms, the performance of FaaS serverless platforms is key. This coursework aims to
develop a simple Internet of Things (IoT) simulation Framework for
data acquisition and statistics as well as investigate its performance,
see Figure 1. The IoT’s real power today lies in harnessing data from
diverse sources and converting it into actionable insights.
You should review the lecture notes on the serverless architectures topic
carefully before you start work.
2. Assessment tasks
Figure 1: IoT Framework Components
Task 1: Simulated Da ta The framework collects environmental data
from 20 sensors in the city of Leeds. This data includes the following weather information: sensor ID, temperature (in Celsius), wind
(miles/hour), relative humidity (%), and CO2 level (ppm). All the sensors are simulated using code running in an Azure Function to achieve
high scalability whilst simplifying deployment. This code mimics the
data that would be collected at regular intervals from sensors placed
throughout the city, measuring all the types of data mentioned above
at the same time before it is stored in a database. The framework
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also incorporates random variations to ensure that the data mirrors
real-world sensor data as closely as possible. A snapshot of the data
generated by the sensors at a given time is:
Sensor I D Temperature Wind R.H umidi ty CO 2
1 12 20 45 750
2 13 19 50 600
...
20 11 21 60 1000
Da ta Range
2
Temperature 5 .. 18
Wind speed 12 .. 24
Relative Humidity 30 .. 60
CO2 400 .. 1600
Implement the simulated data serverless function using a language runtime of your choice, e.g. Python, Java. Node.js. Demonstrate its
performance in terms of scalability of the data. Generate a graph.
Task 2: Statistics This simulated data is subsequently processed
through the analytical platform thanks to a second serverless function,
where we can track patterns and carry out all kinds of evaluation to
identify potential issues such as heatwaves or high pollution levels. The
role of this function is to output the minimum, maximum and average
of the data per sensor.
Task 3: Realistic Scenario Propose a technical solution for implementing the following realistic scenario:
(a) the data should be collected from sensors at a regular interval T,
for example every 10 seconds.
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(b) Once the new collected data is stored in the database by the first
function (simulated data), this will automatically trigger the second function (statistics) thanks to a dabase change tracking mechanism.
(c) the second function should output the same results as in Task 2.
Notes:
You should consider creating an Azure SQ L database as part of
the implementation;
You should consider using the Azure SQ L trigger for Functions
for Task 3, see Azure SQ L trigger for Functions documentation
and example on the MS Azure Web site.
3. General guidance and study support Learning resources and useful links are available in COMP3211 area on Minerva. Help is also
available on the module Teams channel.
4. Assessment criteria and marking process
The coursework will be marked out of 50. Coursework feedback and
return of marks will be available on Gradescope.
5. Presentation and referencing
The quality of written English will be assessed in this work. As a
minimum, you must ensure:
Paragraphs are used
There are links between and within paragraphs although these
may be ineffective at times
There are (at least) attempts at referencing
Word choice and grammar do not seriously undermine the meaning and comprehensibility of the argument
Word choice and grammar are generally appropriate to an academic text
6. Submission requirements
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Questions: address the questions in relation the implementation,
discussion of results, and evidence of execution on Gradescope.
Code: submit your code for the exercise. You can either: 1)
provide a link to Git, or 2) create a Zip or tar archive of the files
which make up your system and upload it on Gradescope. If you
have organised your files into a directory hierarchy, then please
package this as a single Zip or tar archive.
Demonstration: produce a short video (maximum 2 minutes
long) to demonstrate your results. You can either: 1) upload it
on Gradescope, or 2) upload on any cloud platform of your choice,
e.g. Youtube and provide the link.
Students working in pair: this is not a must but is allowed for
idea sharing and peer-to-peer learning. The share of tasks is: 1)
student 1, task 1, 100%; 2) student 2, task 2, 100%, and 3) task 3
is equally shared among both students. One single submission on
Gradescope is expected for the pair. There is a form to complete
by 12/11/2025, 10:00am:
https://forms.office.com/e/7ANL84q4s6
One single submission of the form is expected for the pair.
Serverless Platform: you are allowed to work on a different
serverless platform if you wish to, e.g. AWS Lambda, Google
Functions. There is a form to complete by 12/11/2025, 10:00am:
https://forms.office.com/e/9yeDFfzEWv
7. Academic misconduct and plagiarism
Academic integrity means engaging in good academic practice. This
involves essential academic skills, such as keeping track of where you
find ideas and information and referencing these accurately in your
work. By submitting this assignment you are confirming that the work
is a true expression of your own work and ideas and that you have given
credit to others where their work has contributed to yours.
8. Assessment/marking criteria grid
5
Task 1 15
Task 2 10
Task 3 15
Code/scripts 5
Video 5
50
A lateness submision penalty will apply (5% of the maximum available
mark per day).