Internet of Things-based Home Automation with Network Mapper and MQTT Protocol

• Main Authors: Alam T., Rokonuzzaman M., Sarker S., Abadin A.F.M.Z., Debnath T., Hossain M.I.
• Other Authors: 58394481900
• Format: Article
• Published: Elsevier Ltd 2025
• Subjects: Anomaly detection; Cloud storage; Distributed database systems; Fire alarm systems; Network security; Sensor data fusion; Smoke detectors; Steganography; Telemetering systems; Edge computing; Fire detection; Home automation; Influxdb; Internet of thing; Network mapper; Smart homes; Support vector machine; Support vectors machine

The increasing ability of internet-connected daily life electronic gadgets has propelled smart homes into a global trend. The Internet of Things (IoT) enables ambient devices to communicate and interact seamlessly through various sensors. Emerging technical concepts like Web3 and Industry 5.0 require decentralised and intelligent systems near the network's edge. Petabytes of IoT sensor-generated data cause a shortage of storage on the Cloud servers, adding a delay factor to the IoT system. Standard cloud-based IoT systems can't fully function in areas with unstable internet. This paper addresses these challenges and proposes a solution to integrate edge computing concepts. The proposed system is developed using a Raspberry Pi 3 Home Server (RHS) driven by the Support Vector Machine (SVM) algorithm. The designed prototype includes a fire and smoke detection system with MQ2 gas, dust, temperature, and flame sensors. The SVM and these sensors form a data fusion module integrating with Network Mapper (NMAP), Message Queuing Telemetry Transport (MQTT) broker, MariaDB SQL server, and InfluxDB time series database. The experiments demonstrate a fundamental edge operation with a latency of 2.45 ms (milliseconds), while NMAP integration ensures data security and device verification for sensor data storage. The synthetic simulations show positive outcomes for the data fusion-based monitoring system, where alerts are promptly triggered as sensor values change, with an overall system latency of approximately 24 ms. The developed system manages home automation, real-time monitoring for fire, smoke, gas leaks, network scans, anomaly detection, appliance usage tracking, and cloud data backup. A multi-level alert system ensures early threat mitigation, with alarms, SMS, notifications, and email alerts to maximize awareness. ? 2024 The Author(s)