Real-time Vehicular Pollution Detection Model using IoT and Distributed Streaming Techniques

• Published in: 2023 3rd International Conference on Advanced Research in Computing (ICARC) pp. 178 - 183
• Main Authors: Rakkesh, S. T., Weerasinghe, A. R., Ranasinghe, R. A. C.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.02.2023
• Subjects: Combustion; Costs; distributed streaming; Internet of Things; IoT; Real-time systems; Tagging; traffic congestion; Urban areas; Wireless sensor networks
• DOI: 10.1109/ICARC57651.2023.10145636

Since the primordial days of human settlements emergence, metropolitan regions and traffic congestion have developed concomitantly. The factors that cause commuters to congregate in modern cities also contribute to often intolerable levels of traffic congestion. Air quality gets severely deteriorated as a result of rising urbanization and traffic congestion. When poor emission standards are instigated by existing older vehicles, this makes air quality even worse. In this paper we propose a real-time pollution detection solution model using cost efficient Internet of Things (IoT) devices and distributed streaming techniques which can detect infringing vehicles instantaneously and calculate the present air quality levels on specific regions to reflect updates regularly. For our study, we have used a four-wheel prototype robot car, radio frequency identification (RFID) tagging unit, NodeMCU V3 Lua board, ATmega328P Arduino board, few electro-chemical toxic gas sensors like MQ-2, MQ-7, MQ-135, etc and a wireless sensor network (WSN) to connect the devices. Also, in this paper we propose a real time complex event processing (CEP) monitoring dashboard which receives feeds from the devices network using Apache Flink distributed streaming technology. Especially in urban areas our proposed solution model can be used cost efficiently to detect and control emission violations.