Consolidated Digital Twin (cDT) for IoT Industrial Monitoring

This project implements a Consolidated Digital Twin (cDT) based on the architectural framework defined in the survey paper:

"Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models"
— Minerva et al.

The system bridges the Physical Space (Atoms) and the Virtualization Space (Bits) to create a persistent software counterpart featuring:

• ✅ Strong Entanglement
• ✅ Reflection
• ✅ Memorization
• ✅ Replication & Specialization
• ✅ Persistency

---

🏗️ System Architecture: The Three-Space Model

1️⃣ Physical Space (The Atoms)

Using Wokwi, we simulated an industrial sensing unit that generates a continuous data stream (temperature, humidity, proximity) and reacts to remote actuation commands.

🔧 Components

Component	Role
ESP32	Physical Object (PO) Controller
DHT22	Thermal Attribute Sensor
HC-SR04	Proximity/Distance Sensor
Servo Motor	Actuator for Strong Entanglement feedback

Figure 1: Physical Object (PO) wiring and sensor integration.

---

2️⃣ Virtualization Space (The Middleware)

The middleware layer handles the Entanglement between atoms and bits.

• Broker: HiveMQ Cloud
• Orchestration: Python-based Master Logical Object (LO)

🔁 Reflection Property

The Physical Object mirrors its status to the cloud in real time using JSON format.

Figure 2: Verisimilar representation of PO attributes in the cloud.

---

🧠 Master LO Replication

A Python-based Master Logical Object (LO) orchestrates data distribution.
It prevents overloading the physical device by replicating data into:

• Real-time monitoring stream
• Historical logging stream

Figure 3: Master LO replicating status to specialized virtual instances.

---

3️⃣ Application Space (The Bits)

The Application Space contains specialized services consuming LO data.

---

A. 🧠 Cognitive Engine (Augmentation)

• Monitors the Real-Time LO
• Applies autonomous logic
• If temperature > 25°C → sends actuation command
• Simulates cooling vent opening (Servo activation)

Figure 4: Autonomous feedback loop triggering Strong Entanglement actuation.

---

B. 💾 Memorization Layer (Status Log)

Implemented using Node-RED.

• Captures object history
• Stores data in persistent CSV file
• Enables trend analysis & forensic inspection

Figure 5: Node-RED flow connecting Virtualization Space to the Memorization Status Log.

---

🚀 Technical Features Implemented

• Strong Entanglement
  Bidirectional communication between LO and PO (Servo control)

• Promptness
  Negligible synchronization delay between PO and LO

• Replication & Specialization
  Master Replica serving multiple application instances

• Persistency
  LO maintains last known state even if sensors go offline

---

🛠️ Setup Instructions

1️⃣ Physical Layer

Load the code inside:

into Wokwi ESP32 simulator.

---

2️⃣ Middleware Setup

Install dependencies:

pip install paho-mqtt