This study aims to identify the most suitable IoT communication protocol for a digital twin architecture, focusing on meeting the demands of real-time data transmission with stability and security. The research methodology involves a comparative performance analysis of MQTT over TCP, MQTT over WebSocket, and HTTP protocols, evaluating their latency, connectivity stability, and IoT application feasibility using an ESP32 microcontroller and DHT22 sensor setup. The experimental results demonstrate that MQTT over TCP achieves the lowest average latency at 290.5 ms, while MQTT over WebSocket exhibits more stable latency profiles with a 193.2 ms standard deviation; HTTP, despite its broader compatibility, showed higher average latency of 342.6 ms with a 307.9 ms standard deviation. These findings provide valuable insights for digital twin implementations, enabling developers to make informed protocol selections based on specific requirements, whether prioritizing real-time performance through MQTT protocols or emphasizing system compatibility and simplicity through HTTP.

Enhancing Sustainable Biogas Generation Through a Real-Time Digital Twin of a Modular Bioreactor

Industrial Internet of Things (IIoT) deployments increasingly rely on low-cost microcontrollers and single-board computers to stream operational telemetry for monitoring, control, and predictive maintenance, yet the canonical “TLS-to-broker” model does not protect message content from a compromised or curious MQTT broker. This study therefore designs and implements a practical, application-layer end-to-end (E2E) encryption pipeline spanning an ESP32 data client (C++/mbedTLS), an untrusted MQTT broker, and a Raspberry Pi gateway (Python/PyCryptodome) using AES-256-GCM with Additional Authenticated Data (AAD). Sensor measurements are serialized as compact JSON, encrypted and authenticated on the ESP32, framed into a binary record, Base64-encoded for MQTT payload carriage, and verified/decrypted only at the gateway. Experiments on ESP32-WROOM-32 and Raspberry Pi 4 show an average ESP32 packet-preparation latency of 41.754 ms (JSON 1.0 ms; AES-GCM 29.5 ms; Base64 11.2 ms), robust rejection of ciphertext tampering and unauthorized devices via MAC verification and whitelist checks, and 99.72% decrypt-and-store success over a one-hour run (718/720 messages). These results indicate that commodity IIoT hardware can support practical and replicable E2E confidentiality and integrity without sacrificing operational throughput, while eliminating the MQTT broker as a de facto man-in-the-middle.

This article presents a comparative analysis of data monitoring and visualization tools—Prometheus and InfluxDB—in the context of digital twins (DTs) applied to industrial settings. DTs optimize production processes using industrial internet of things (IIoT) technologies. Mathematical models assessed the tools based on response time, resource consumption, throughput, and reliability. Prometheus is better suited for high-frequency monitoring, achieving a response time of 0.01 seconds and processing up to 10,000 metrics per second—10–15% better than InfluxDB. It consumes 1.5 times less memory (100 MB versus 150 MB), making it faster and more resource-efficient. Conversely, InfluxDB excels in long-term storage and analytics, handling up to 8,000 metrics per second with a response time of 0.09 seconds. However, it requires more resources, including higher CPU usage (20% versus 15%). Both tools integrate seamlessly with Grafana for visualization, offering flexibility for real-time monitoring and decision-making. The study provides actionable insights for selecting monitoring systems based on project-specific requirements, highlighting Prometheus’s efficiency in dynamic scenarios and InfluxDB’s strength in analytics-focused tasks.

Food manufacturing faces challenges in balancing efficiency, energy use, and quality. This paper presents a Hybrid Digital Twin Architecture (HDTA). It combines simulation, constraint programming, and Industrial IoT into a closed-loop system. The architecture has three layers: simulation for planning, optimization for scheduling, and an edge layer for control. We validated this using a bakery model with 10 products. The results show a 24.4% reduction in production time and 23% energy savings. Simulation results show complete elimination of quality time-window violations (0.0% vs. 13.3% baseline, p < 0.001). The system achieved a 2.4-month return on investment. This work demonstrates how combining these technologies can improve process industries.