Skip to content

Latest commit

 

History

History
87 lines (67 loc) · 6.26 KB

README.md

File metadata and controls

87 lines (67 loc) · 6.26 KB
Raw

Internet of Things Environmental station

Project Directories structure:

├──User_Activity_Recognition     # browser user activity recognition code
│   ├── classifier               # folder with dumb python classifier
|   ├── forwarder                # NodeJs API to Bridge the local broker and AWS IoT Core
|   ├── index.html               # HTML5 page accessing the sensors
|   └── main.js                  # JS script embedded into index.html implementing sensor access   
|──Copy_of_UserActivityRecognition_IoTAss4.ipynb  # attempt to classify data with a CNN
|
|
|
├── db                              #SQLite database with real data (.tables to see all available tables)
│   
├── python_simulated_station        # code from the first assignment simulating env. station with python
│   
│── RIOT_LORA_STATION               # code from the third assignement (LoRa comunication)
│   │
│   ├── simulated_station
│   ├── simulated_station2
│   ├── ttn_aws_gw.py
│   └── aws_2_db_consumer.py
│
│   
├── RIOT_simulated_station          # code to simulate env. station in RIOT OS
│   │   
│   ├── lib                         # all the scripts needed to run the gateway (from mosquitto /rsmb/src/clients)                        
│   │   
│   ├── simulated_station           # simulated station #1
│   │   └── bin
│   │   
│   └── simulated_station_2         # simulated station #2 
│       └── bin
│      
└── RIOT_testlab_sensors            # Code to read M3 temperature and pressure sensors

User Activity Monitoring In The Browser Using Generic Sensor API - Assignment 4

A Crowd Sensing system using HTML5, Javascript, Generic Sensor API and AWS IoT Core.

I used Generic Sensor API to access data from the mobile device then we sent them to a simple REST API written in NodeJS in charge of forwarding data to the broker. We are going to detect if the user is either moving or laying, the HTML page will allow the user to select if to use Edge classifier (running locally and sending to the back-end only classification information) or a Cloud classifier (send raw data to the cloud and retrieve classification information).

Links

Attempt to Classify Data with a CNN: Link

System Demo (Youtube): https://youtu.be/gJ93i-Wi2Cw

Tutorial and Description (Linkedin Blog): https://www.linkedin.com/pulse/user-activity-monitoring-browser-using-generic-sensor-nicola-di-santo/



Saving Energy Using LoRaWAN - Assignment 3

A IoT system using RIOT OS, A Python Transparent Gateway, The Things Network and LoRaWAN, AWS IoT Core, Apache Superset and one between {AWS S3 and AWS Athena} or SQLite database.

Cloud and analytics reuse the same code and configuration of Assignemt 1 and 2 (see below). Tutorial on how to replace MQTT-SN with LoRaWAN saving energy during comunication is provieded below. Here we use The Things Network open infrastructure supporting LoRaWAN.

Links

System Demo (Youtube): https://youtu.be/lpCwLwO6V5Y

Tutorial and Description (Linkedin Blog): https://www.linkedin.com/pulse/energy-saving-comunication-iot-lorawan-nicola-di-santo



RIOT OS Station - Assignment 2

A IoT system using RIOT OS, A Python Transparent Gateway, AWS IoT Core, Apache Superset and one between {AWS S3 and AWS Athena} or SQLite database.

Cloud and analytics reuse the same code and configuration of Assignemt 1 (see below). Tutorial on how to build a RIOT OS application and how to build a Gateway to allow comunication between board using MQTT-SN and AWS IoT Core broker is provided below.

Links

System Demo (Youtube): https://youtu.be/MY6dfM6pYLs

Tutorial and Description (Linkedin Blog): https://www.linkedin.com/pulse/designing-environmental-station-based-riot-os-aws-iot-nicola-di-santo



Python Simulated Station - Assignment 1

A Simulated IoT system using Python, AWS IoT Core, Apache Superset and one between {AWS S3 and AWS Athena} or SQLite database. (directory: python_simulated_station)

Links

System Demo (Youtube): https://youtu.be/tRe6Aw1Ow0k

Cutted video (YouTube): https://youtu.be/aOt-4mvsAAo

Tutorial and Description (Linkedin Blog): https://www.linkedin.com/pulse/setting-up-complete-iot-system-aws-core-apache-python-nicola-di-santo

About this repository

I have first realized a Simulated IoT system using Python, AWS IoT Core and Apache Superset. Then also a simulated version of environmental station running on RIOT OS has been implemented toghether with a transparent gateway to forward messages to the cloud.
In detail, following the attached tutorials, We will emulate sensors from an environmental station. The generated data are sent to AWS IoT Core using MQTT protocol. We will explore two possible storage options: one is cloud based with AWS S3, Athena and IoT Core rules, the other is local storage with a Python MQTT consumer and SQLite database but can easily generalize with whatever DB. We will then quickly setup a web-based dashboard using Apache Superset.
The same functionalities running within a real board with real environmental sensors is going to work quickly (it is possible to read sensor but still not possible to send those values to the cloud).
Then I have also replaced the MQTT-SN with LoRaWAN, to save energy during comunication and explore capabilities of this new protocol.

SQLite has been chosen so that we can also upload the populated DB into the GitHub repository (in the db folder) and, if your goal is only to test Apache Superset, you do not need to interact with AWS nor run the script to generate data.

NB: keys used to work with AWS IoT core are actually removed from the directory for security reasons and only the Authority root certificate is left, since it is accessible by everyone.

Acknowledgments

This reposiotry is part of an assignment from IoT course held at Sapienza University - Rome (IT) during the academic year 2019-2020. For details on the assignment visit the course page.