AI4IoT

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Documentation

Runtime Environment

WE RECOMMEND USERS TO INSTALL RUNTIME ENVIRONMENT IN LINUX OPERATION SYSTEM. THE GUIDE FOR SETTING RUNTIME ENVIRONMENT ARE APPLIED FOR LINUX OPERATION SYSTEM FOR THE MOMENT!!

♦ Hardware

–      Server

Mobius server operated by KETI.

–      Client

Raspberry Pi 4 (ARM Cortex-A72 1.5GHz)

Raspberry Pi Camera (RPI CAMERA BOARD)

♦ Software

–      Server

oneM2M Golden Sample server (Mobius)

URL: http://203.253.128.161:7579

–      Client

Linux (version: 2.4.0)

♦  Other environments

–      Network

Connect to the Internet through WI-FI

–      Postman

Version: v8.11.1

–      oneM2M Resource Browser (http://203.253.128.161:7575/#!/monitor)

♦  Installation and configuration

1. Pip

⇒  python get-pip.py

–     Module installation

1. Tensorflow-io

⇒  pip install tensorflow-io

2. Kafka-python

⇒   pip install kafka-python

3. Kafka

⇒  wget https://www.apache.org/dist/kafka/2.7.1/kafka_2.13-2.7.1/kafka_2.13-2.7.1.tgz

⇒  sudo tar xzf kafka_2.13-2.7.1.tgz

⇒  sudo mv kafka_2.13-2.7.1 /opt/kafka

4. Zookeeper

⇒  https://zookeeper.apache.org/releases.html

5. JDK

⇒  sudo apt install openjdk-11-jdk

⇒  apt install default-jre

Get Started

1. Access to root

⇒  su – root

2. Add a user and change permission

⇒  adduser kafka

⇒  usermod -aG sudo kafka

⇒  su – kafka

3. Check Java version (openjdk 11)

⇒  java-version

4. Reflect the edited settings file

⇒  sudo systemctl daemon-reload

5. Confirm status of the Zookeeper and Kafka server

⇒  sudo systemctl start zookeeper

⇒  sudo systemctl start kafka

⇒  sudo systemctl status zookeeper

⇒  sudo systemctl status kafka

6. Run file

⇒  python producer.py (example)

⇒  python consumer.py (example)

⇒  python deeplearning.py (example)

AI4IoT

Description

Ai4iot is an AI enabler for standard IoT platforms. Our goal is to develop open sources to support AI functions on standard IoT platforms such as Mobius, an open source platform of oneM2M, and EdgeX. We also develop various Use Cases using the AI4IoT framework.

<AI4IoT Framework>

Use case: Metaverse-based virtual store

A metaverse-based online store where stores in the real world are created as digital twins in the metaverse virtual space, and users visit a virtual store in the metaverse space to purchase preferred products. Various smart sensors are used to sense real-world products intelligently for real-time synchronization between the real world and the virtual stores in the metaverse. The edge node at the real-world store loads a trained AI/ML model and infers products’ information. The retrieved product data is then transferred to the IoT platform for real-time synchronization.

A user can now purchase products from a virtual store in the metaverse. The purchase info in the metaverse is notified to the administrator and the purchased product is delivered to the user.

<Metaverse-based virtual store>