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Syed Muhammad Aarim

FAST · 2021 · 17I-0548

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Phone

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GitHub

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Academic

Program

BS Computer Science

CGPA

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Year

2021

Education

SEECS

Address

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DOB

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Career

Current role

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Target role

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Skills

LPC 1768, SIM 808, CAN Transceiver IC, mbed OS, VueJS, Google Firebase, AWS IoT Core

Verbatim text

The exact text the LLM saw on the page (or the booklet text from the old import). This is what powers semantic search.

Telematics based Fleet Monitoring & Diagnostics 
This project aims to design and implement a fleet management system consisting of two vehicles. 
This system locates vehicles in real-time, estimates the fuel consumption, and diagnoses ignition 
faults in vehicles for the fleet operator (transporter). OBD2 (On-board diagnostics) scanner is a device which communicates with the diagnostic system of the vehicle using a connector. The scanner reads the data from ECU (electronic control unit) to find out what malfunction the vehicle is facing. The data read from the vehicle is sent to AWS IoT Core which communicates with the web application. 
Vehicle diagnostics has become an essential issue in the transport industry as fraud with the fleet operators have increased. Drivers tend to lie to their owners about the fuel and save a lot of fuel and in return charge them with a bigger amount. Additionally, the mechanics are also involved in fraud such as if there is a minor issue with the vehicle, they wrongly charge the owner for other non-existent issues. 
Implementing our system will allow the fleet operators to monitor their vehicles remotely which will help them save their fuel cost and maintenance charges. 
 
 
 
 
Technology Used: 
LPC 1768, SIM 808, CAN Transceiver IC, mbed OS VueJS,  
Google Firebase, AWS IoT Core 
Supervisor Name: 
Engr. Azhar Rauf 
Group Members:   
Adil Saleem (17I-0456) 
Talha Siddique (17I-0483) 
 
 
     Syed Muhammad Aarim (17I-0548)

AI enrichment

Syed Muhammad Aarim holds a BS in Computer Science and worked on a telematics fleet monitoring project using LPC 1768, SIM 808, and AWS IoT Core. The system involved real-time vehicle tracking, fuel estimation, and diagnostics via OBD2 scanners, with data visualization handled by VueJS and Firebase.

Skills (AI)

["C/C++", "Embedded Systems", "AWS IoT Core", "VueJS", "Firebase", "OBD2", "CAN Bus", "mbed OS", "Hardware Integration"]

Status: ai_done

Provenance

Source file: Graduate Directory FAST School of Engineering - 2021 (Final) (1).pdf
From job #21 page 89
Created: 1778141601