Smart City Application Platform

2022 COE Engineering Design Project (MJ06)

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Faculty Lab Coordinator

Muhammad Jaseemuddin

Topic Category

Distributed Systems and Networking

Preamble

With growing urbanization modern cities have been facing enormous challenges to provide services to the residents. Ubiquitous connectivity to Internet and automation facilitate city management.

Objective

In this project, you will design and implement a Smart City application. With growing urbanization modern cities have been facing enormous challenges to provide services to the residents. Ubiquitous connectivity to Internet and automation facilitate city management. In this project, you will design a Smart City application. You will design a software platform that allows launching and interaction of different applications. You will demonstrate viability of your platform by implementing one application, such as smart scheduling of snow removal, etc. You will look at data driven machine learning approach to optimize resource scheduling. The application needs deployment of sensor and localization network. You need to set up this network.

Partial Specifications

• Raspberry PI
• Android phones
• Sensing and localization
• Public cloud (Azure etc.)
• Machine Learning

Suggested Approach

• Use Python, MySQL and JSON to implement the platform and interaction with web client.
• Implement web client in Android
• Use WiFi SSID for location tracking
• Consider only single-hop communication between Raspberry PI (fog node) and the cloud server

Group Responsibilities

• All students will work on the initial design phase to define the ambient information, and decision function.
• All students will be involved in integration and testing.
• All students will be involved in demo set up and performance evaluation.

Student A Responsibilities

• Lead the design and implementation of Smart City application platform.
• Implement the protocol specification in either hardware/software.
• Lead the demo design.

Student B Responsibilities

• Lead the design and implementation of the client and the decision engine.
• Lead the demo set up.

Student C Responsibilities

• Lead the sensor and localization network design and cloud set up.
• Lead the design and implementation of the GUI.

Student D Responsibilities

• Lead the design and implementation of data analytic.
• Assist in the implementation of the GUI.

Course Co-requisites

COE768 and COE718 (recommended)

To ALL EDP Students

Due to COVID-19 pandemic, in the event University is not open for in-class/in-lab activities during the Winter term, your EDP topic specifications, requirements, implementations, and assessment methods will be adjusted by your FLCs at their discretion.