Journal of IoT-based Distributed Sensor Networks (e-ISSN: 3048-9202)

Gas Guardian: An IoT-Based Real-Time Leakage Detection and Mitigation System

Mon, 01 Jun 2026 00:00:00 +0000

An IoT-based Gas Leakage Detection System is developed to enhance safety by detecting hazardous gas leaks in real time. The system uses an MQ-6 gas sensor to monitor combustible gases such as LPG, propane, and methane with high sensitivity. When the gas concentration exceeds a predefined threshold, the system immediately activates safety mechanisms, including a buzzer alert and an exhaust fan, to reduce gas levels in the environment. The core controller, NodeMCU (ESP8266), enables wireless communication through Wi-Fi, allowing the system to connect with the Blynk mobile application for remote monitoring. It also displays warning messages on an LCD screen, ensuring both local and remote alerts. This dual-alert mechanism helps users take immediate action even when they are not physically present. The system is cost-effective, energy-efficient, and easy to install, making it suitable for residential, commercial, and small-scale industrial applications. By providing continuous monitoring, early detection, and rapid response, it significantly reduces the risk of fire accidents, explosions, and health hazards caused by gas leakage. Overall, the integration of sensor technology with IoT improves safety and helps protect human life and property effectively.

Enhancing Security in Digital Twin-Based Smart IoT Environments

Shikha Tiwari, Nisha Rathore, Vinay Kumar Singh — Tue, 30 Jun 2026 00:00:00 +0000

Digital Twin (DT) technology has emerged as a key enabler of smart Internet of Things (IoT) systems by providing real-time monitoring, predictive analytics, and intelligent decision-making capabilities. However, the integration of DT with IoT introduces significant security challenges due to distributed architectures, continuous data exchange, and heterogeneous devices. This paper presents a comprehensive study of security issues in DT-enabled IoT systems, including threat models across device, network, data, application, and DT layers. To address these challenges, a hybrid security framework integrating Artificial Intelligence (AI), Blockchain, and Edge Computing is proposed. The framework leverages AI-based intrusion detection for real-time anomaly identification, blockchain for secure and tamper-proof data validation, and edge computing for low-latency processing. Mathematical models are developed to evaluate threat detection, intrusion probability, and trust mechanisms. Experimental results demonstrate that the proposed hybrid approach achieves 96% detection accuracy, significantly reduces false alarms, and lowers latency compared to traditional methods. The study highlights the effectiveness of combining AI and blockchain in enhancing security resilience and real-time responsiveness. This work contributes toward developing a unified, scalable, and secure Digital Twin framework suitable for next-generation smart environments such as smart cities, healthcare IoT, and industrial systems.

Hardware-based Efficient Task Offloading in IoT-Fog-Cloud

Nadar Akshayashree Stephan Selvaraj, Maya Nair — Sat, 08 Aug 2026 00:00:00 +0000

The rapid expansion of the Internet of Things (IoT) has led to an exponential increase in data volume, creating challenges for efficient data processing and latency control. Traditional cloud-based systems often experience excessive latency, making them less suitable for real-time applications. This research paper proposes a hardware-based, efficient task offloading framework using an IoT-Fog-Cloud architecture. An ESP8266-based IoT device senses real-time temperature data and transmits it via the MQTT protocol. The fog layer is implemented using Node-RED, which performs real-time data processing and decision-making, and generates alerts based on predefined temperature thresholds. This minimizes the dependency on the cloud for immediate responses. The processed data is then offloaded to the cloud layer using InfluxDB for data storage and Grafana for visualization and analysis. To measure task offloading performance, the proposed system includes a latency comparison between fog-layer processing time and cloud-layer response time. Experimental results demonstrate a significant reduction in response latency at the fog layer (avg. 103 ms) compared to cloud-layer response time (avg. 271 ms), representing approximately 62% lower latency.

Advances and Challenges in Software Architecture for IoT-based Smart Computing in Environmental Applications: A Review

Syed Faizan Haider — Sat, 08 Aug 2026 00:00:00 +0000

The environment around us is changing faster than its traditional monitoring systems were ever designed to handle. Over the last decade, the Internet of Things (IoT) has quietly moved from a research curiosity to the backbone of how to watch over air, water, soil, forests, and cities. Yet the sensors themselves are only half the story. What actually decides whether an environmental IoT system survives in the real world is its software architecture - the way data moves from a muddy field sensor to a decision-maker's screen. This review takes a close look at how software architectures for IoT-based smart computing have evolved in environmental applications. Explaining the dominant architectural styles, from classic layered designs to service-oriented, microservice, and event-driven approaches, and examines how edge, fog, and cloud computing have reshaped where intelligence actually lives in these systems. Then, discussed about recent advances, including lightweight machine learning at the edge, digital twins of natural systems, and low-power wide-area networking, before turning to the stubborn challenges that keep coming up in the literature: interoperability between vendor ecosystems, energy constraints in remote deployments, security of unattended devices, data quality, and the general lack of architectural standardization. Further, a close with a set of research directions that deserve far more attention than they currently receive. This article aims to give researchers and practitioners a realistic picture of where the field stands - what genuinely works, what is still fragile, and where the next round of effort should go.

Smart Healthcare Assistant for Maternal and Child Care Supporting Rural Health Facilitators

V Manjula, Amritha R, Shilpa C — Tue, 11 Aug 2026 00:00:00 +0000

Maternal and child healthcare in rural and semi-urban regions continues to face considerable difficulties due to inadequate access to quality medical facilities and limited technological infrastructure. Community health workers such as ASHA and Anganwadi personnel are essential in delivering primary care services; however, their effectiveness is often restricted by reliance on manual record-keeping and the absence of real-time decision-making support. This study examines the application of Artificial Intelligence (AI) and digital healthcare technologies in maternal care, with particular emphasis on risk assessment, continuous health monitoring, and clinical decision support systems. Existing research indicates that machine learning techniques and mobile health (mHealth) solutions significantly contribute to early risk identification, improved data management, and better communication between healthcare providers and patients. Despite these advancements, several challenges persist, including limited usability for low-literacy users, insufficient multilingual and voice-based interaction capabilities, and a lack of integrated systems that ensure continuity of care. Therefore, there is a strong need for a comprehensive and user-centric platform that combines AI-powered analytics, voice-enabled interfaces, automated notification systems, and efficient immunization tracking. Such a solution can enhance the productivity of healthcare workers and improve overall maternal and child health outcomes.