https://matjournals.net/engineering/index.php/JIBDSN/issue/feedJournal of IoT-based Distributed Sensor Networks (e-ISSN: 3048-9202)2025-05-28T14:43:01+00:00Open Journal Systems<p><strong>JIBDSN</strong> is a peer reviewed journal in the discipline of Computer Science published by the MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of fundamental research papers on all areas of IoT-based Distributed Sensor Networks. The Journal aims to promote high quality empirical Research, Review articles, case studies and short communications mainly focused on sensor networks, Sensor Network Tasking and Self-Organization, Distributed Sensor Networks , Networking / Caching Issues, Sensor Networks for Internet of Things (IoT), Architecture, Algorithms, and Complexity Issues,Information Fusion Methodologies Based on Statistical Decision Theory, Distributed Detection / Classification Methods,Learning Patterns from Distributed Sensor Sources, Coordination, Integration, and Synchronization in Distributed Sensor Networks</p>https://matjournals.net/engineering/index.php/JIBDSN/article/view/1946Breathe Safe: An IoT-Based Air, Quality Monitoring System2025-05-28T14:32:32+00:00Pranjali Patilptlpranjali1802@gmail.comMaithili Pawarptlpranjali1802@gmail.comPratap Sanapptlpranjali1802@gmail.comShruti Rohomptlpranjali1802@gmail.comSaee Salunkeptlpranjali1802@gmail.comT. Bhaskarptlpranjali1802@gmail.com<p><em>Paper presents an innovative IoT-Based air quality monitoring system that utilizes the IoT to provide real-time data on various pollutants, including CO2, CO, NH3, and airborne particles. By integrating low-cost MQ135 sensors with NodeMCU ESP8266 modules, the system offers an affordable and efficient solution for monitoring air quality in both urban and rural settings.</em></p> <p><em>We’ve collected data by these sensors which is transmitted wirelessly to the ThingSpeak cloud platform, where it is analyzed and visualized for easy interpretation. This cloud- based approach not only allows for real-time monitoring but also enables users to access historical data, helping them understand trends in air quality over time. The system has been tested in a variety of environments, including industrial areas, Farm areas, and traffic zones, achieving an impressive accuracy rate of 93% in pollutant detection when compared to EPA standards.</em></p> <p><em>To enhance public awareness and engagement, the system features an alert mechanism that sends notifications via Twitter whenever pollutant levels exceed safe limits. This functionality empowers communities to respond quickly to air quality issues, fostering a proactive approach to environmental health. Overall, this scalable and cost-effective solution addresses the limitations of old air quality monitoring methods, making it a practical choice for widespread implementation and contributing to healthier living conditions for all.</em></p>2025-05-28T00:00:00+00:00Copyright (c) 2025 Journal of IoT-based Distributed Sensor Networks (e-ISSN: 3048-9202)