• Author: Ionel Patrascu
• Email: ionel.patrascu3101@stud.acs.pub.ro
• Master: SRIC

The rise of multi-tasking and the increasing demand for seamless user experiences have led to a need for more intuitive and efficient ways to manage information. While traditional methods like pop-up windows or notifications can be disruptive, a dedicated display offering real-time metrics could provide a more discreet and focused approach to managing relevant data.

Current solutions for displaying metrics during active tasks often interrupt the user's workflow. This can be particularly detrimental in activities requiring focus and precision, such as gaming. There is a lack of a dedicated, non-intrusive display solution that can present relevant metrics without requiring users to switch between applications or lose their current focus. This project aims to address this gap by developing an IoT system that utilizes an LCD screen to provide a continuous stream of metrics without disrupting the user's primary activity.

• ESP32 Development Board with WiFi and Bluetooth 4.2 RIP
• ESP8266 WiFi Development Board
• 1602 LCD with I2C Interface and Yellow-Green Backlight
• Breadboard
• Wires

The circuit diagram showcases a basic setup for the ESP8266 microcontroller, connected to an I2C LCD display and a joystick. The ESP32, a powerful and versatile microcontroller, serves as the central processing unit for the system. It's connected to the LCD display via a set of wires, for 5V, GND, SDA, and SCL. The ESP32 is also connected to the joystick via a set of wires: 3.3V, GND, VRx (analog), SW (digital).

The Pinout scheme of the ESP8266 board that I used is the following:

This Python code sets up a Flask web server to provide system hardware information through a REST API endpoint. It first initializes the .NET runtime and imports the OpenHardwareMonitorLib to access hardware data. A Computer object is created, enabling monitoring of CPU, GPU, and RAM. The code then defines a Flask route '/info' that responds to GET requests by collecting data from the enabled hardware sensors, processing it into a dictionary, and returning it as a JSON response. This allows external applications, such as the ESP8266 microcontroller, to retrieve real-time system metrics from the server for display or other purposes.

# Initialize the .NET runtime which we'll use for
# information about the hardware.
clr.AddReference(r'.\OpenHardwareMonitorLib')
from OpenHardwareMonitor.Hardware import Computer
 
# Create a new Computer instance and enable the CPU, GPU
# and RAM sensors.
c = Computer()
c.CPUEnabled = True # get the Info about CPU
c.GPUEnabled = True # get the Info about GPU
c.RAMEnabled = True # get the Info about RAM
c.Open()
 
app = Flask(__name__)
 
@app.route('/info', methods=['GET'])
def get_info():
    # return all the info available
    response = {}
    for hardware in c.Hardware:
        hardware.Update()
        response[hardware.Identifier.ToString()] = {}
        for sensor in hardware.Sensors:
            response[hardware.Identifier.ToString()][sensor.Identifier.ToString()] = sensor.Value
 
    return jsonify(response)

The API response is a JSON object containing detailed information about the system's hardware components, including CPU, GPU, and RAM. Each component is represented by a unique identifier, and its individual metrics, such as clock speed, load, power consumption, and temperature, are organized under corresponding sub-identifiers.

{
  "/intelcpu/0": {                           // CPU 0
    "/intelcpu/0/clock/0": 99.9998779296875, // Bus Speed
    "/intelcpu/0/clock/1": 3699.99536132813, // Core 0
    "/intelcpu/0/clock/2": 3699.99536132813, // Core 1
    "/intelcpu/0/clock/3": 3699.99536132813, // Core 2
    "/intelcpu/0/clock/4": 3699.99536132813, // Core 3
    "/intelcpu/0/load/0": 4.15613651275635,  // Load total
    "/intelcpu/0/load/1": 3.78488302230835,  // Load core 0
    "/intelcpu/0/load/2": 4.3637752532959,   // Load core 1
    "/intelcpu/0/load/3": 4.15459871292114,  // Load core 2
    "/intelcpu/0/load/4": 4.32131290435791,  // Load core 3
    "/intelcpu/0/power/0": 2.94126510620117, // Power CPU Package
    "/intelcpu/0/power/1": 1.45460450649261, // Power cores
    "/intelcpu/0/power/2": 0,                // Power graphics
    "/intelcpu/0/power/3": 0.542671918869019,// Power DRAM
    "/intelcpu/0/temperature/0": 51,         // Temperature Core 0
    "/intelcpu/0/temperature/1": 54,         // Temperature Core 1
    "/intelcpu/0/temperature/2": 53,         // Temperature Core 2
    "/intelcpu/0/temperature/3": 52,         // Temperature Core 3  
    "/intelcpu/0/temperature/4": 54          // Temperature CPU Package
  },
  "/nvidiagpu/0": {                          // GPU 0
    "/nvidiagpu/0/clock/0": 135,             // GPU Core
    "/nvidiagpu/0/clock/1": 324.000030517578,// Memory
    "/nvidiagpu/0/clock/2": 270,             // Shader           
    "/nvidiagpu/0/control/0": 25,            // Fan
    "/nvidiagpu/0/fan/0": 1066,              // Fan Speed
    "/nvidiagpu/0/load/0": 0,                // Load Core
    "/nvidiagpu/0/load/1": 7,                // Load Frame Buffer
    "/nvidiagpu/0/load/2": 0,                // Load Video Engine
    "/nvidiagpu/0/load/3": 0,                // Load Bus Interface
    "/nvidiagpu/0/load/4": 23.1866836547852, // Load Memory 
    "/nvidiagpu/0/power/0": 27.2940006256104,// Power GPU
    "/nvidiagpu/0/smalldata/1": 3146.2734375,// VRAM Free
    "/nvidiagpu/0/smalldata/2": 949.7265625, // VRAM Used
    "/nvidiagpu/0/smalldata/3": 4096,        // VRAM Total
    "/nvidiagpu/0/temperature/0": 43,        // Temperature GPU
    "/nvidiagpu/0/throughput/0": 0.00390625, // Throughput PCIE Rx
    "/nvidiagpu/0/throughput/1": 0.0009765625 // Throughput PCIE Tx
  },
  "/ram": {
    "/ram/data/0": 13.5922622680664,         // RAM Used
    "/ram/data/1": 18.3470077514648,         // RAM Free
    "/ram/load/0": 42.556583404541           // RAM Load
  }
}