Project

# Title Team Members TA Documents Sponsor
38 VEHICULAR EDGE COMPUTING SYSTEM
 Mingjun Wei
Shaohua Sun
Ye Yang
Yinjie Ruan
 design_document1.pdf
final_paper2.pdf
proposal2.pdf
 Meng Zhang
# TEAM MEMBERS
- Shaohua Sun (shaohua6)
- Ye Yang (yeyang3)
- Mingjun Wei (mingjun9)
- Yinjie Ruan (yinjier2)

# VEHICULAR EDGE COMPUTING SYSTEM

# PROBLEM:

As more and more research has been conducted on mobile edge computing, we propose that a mobile edge computing server in application can be deployed on-board a vehicle. But when performing tasks, the server will heat up very quickly and traditionally, the air-conditioner is needed. We try to avoid the use of air-conditioner, but put the server exposed to the air.

# SOLUTION OVERVIEW:

The vehicular mobile edge computing server is designed with a general server installed on-board vehicle. To make full use of the server, it will be accessed to the Internet and realize functionalities according to the existing theory of edge computing. To solve the problem of heating when performing intensive computational tasks, we utilize the wind to cool it down while designing waterproof to protect the server from rain.

# SOLUTION COMPONENTS:

## Modules on Waterproof and Shelter:

- The waterproof: To protect the server from rain or snow.

- The shelter: To carry the server with high stability.

- The airpath on the shelter: To utilize the wind to cool down the server effectively, even in relatively low car speed.

## Server Modules:

- The wireless communication access to the Internet.

- The server can perform relatively complex tasks like deep learning effectively.


# CRITERION FOR SUCCESS:

- Functionality: The mobile edge computing server can do computation tasks in the complexity level of deep learning, and access to the Internet to send or receive data. The waterproof and shelter should be stable and firm to fasten the server and protect it from rain. Also it can dissipate heat effectively.

- User experience: The user can get real-time access via the Internet and enjoy plentiful services like online video, etc.

- Durability and stability: The server needs to maintain a stable access to the Internet, and it can be used in rainy environment.

# DISTRIBUTION OF WORK:

- ME STUDENT SHAOHUA SUN:

Design how to set a waterproof.

- ME STUDENT YE YANG:

Design how the shelter can be breathable to cool down the server.

- EE STUDENT MINGJUN WEI:

Model a mobile edge computing server being able to take complex computing tasks.

- EE STUDENT YINJIE RUAN:

Make the edge computing server connected to the Internet.

ML-based Weather Forecast on Raspberry Pi

Xuanyu Chen, Zheyu Fu, Zhenting Qi, Chenzhi Yuan

Featured Project

#Team Members

Zheyu Fu (zheyufu2@illinois.edu 3190110355)

Xuanyu Chen (xuanyuc2@illinois.edu 3190112156)

Chenzhi Yuan (chenzhi2@illinois.edu 3190110852)

Zhenting Qi (qi11@illinois.edu 3190112155)

#Problem

Weather forecasting is crucial in our daily lives. It allows us to make proper plans and get prepared for extreme conditions in advance. However, meteorologists always get it wrong half of the time and still keep their job :) To overcome the limitations of traditional weather forecasting, machine learning models have become increasingly important in weather forecasting. Building our own weather forecast ML system is a perfect idea for us to analyze vast amounts of area data and generate more accurate and timely weather predictions on the go in our surrounding areas.

#Solution Overview

A weather forecast system can be created by using a few different hardware components and software tools. Our solution mainly consists of two parts. For weather measurement and data collection, temperature, humidity, and barometric pressure sensors are considered the main components. A machine learning-based algorithm is to be applied for data analysis and weather predictions.

#Solution Components

##Hardware Subsystem

Due to the complexity of weather conditions, our system incorporates the following weather indicators and their corresponding collectors:

-a barometric pressure sensor, a temperature sensor, and a humidity sensor

-a digital thermal probe for heat distribution

-an anemometer for wind speed, wind vane for wind direction, and rain gauge for precipitation

The aforementioned equipment would be integrated into a single device, and weatherproof enclosures are needed to protect it. Plus, a Raspberry Pi, either with built-in wireless connectivity or a WiFi dongle, is required for conducting computations.

##Software Subsystem

A practically usable weather forecast system is supposed to make reliable predictions for real-world multi-variable weather conditions. We apply Machine Learning techniques to suffice such generalization to unseen data. To this end, a high-quality dataset for training and evaluating the Machine Learning model is required, and a specially designed Machine Learning model would be developed on such a dataset. Once a well-trained system is obtained, we deploy the such model on portable devices with easy-to-use APIs.

#Criterion for Success

1. The weather measurement prototype with sensors should be able to accurately collect the temperature, humidity, and barometric pressure. etc.

2. A machine learning algorithm should be successfully trained to make predictions on the weather conditions: rainy, sunny, thunderstorm, etc.

3. Our system can forecast the weather in Haining, in real-time, and/or longer-period forecast.

4. The forecasted weather information could be demonstrated elegantly through some UI interface. A display screen would be a baseline, and an application on phones would be extra credit if time permitted.

5. Extra: Make our own weather dataset for Haining. If good, make it open-source.

#Work Distribution

**EE Student Zheyu Fu**:

-Design the sensor module circuit

-Development of visualization interface

**ECE Students Xuanyu Chen & Zhenting Qi**:

-Weather data collection and analysis

-Build and test Machine Learning model on Raspberry Pi

**ME Student Chenzhi Yuan**:

-Physical structure hardware design

-Proper distribution of the sensors to collect accurate data on temperature, humidity, barometric pressure, etc.