IoT-cloud based traffic honk monitoring system: empowering participatory sensing

The honking events' density reflects the level of traffic noise pollution, road congestion, etc in the urban areas. In this paper, we propose a participatory sensing based traffic honk monitoring system called HonkSense that uses smartphone equipped sensors (e.g. microphone, GPS, etc.). Citizens can take part in monitoring traffic noise pollution due to honking by recording ambient noise on the road. Application running on users' smartphones is used to extract features in real time from recorded audio and then send to the cloud for honk detection and decision making tasks. Here, Mel-Frequency Cepstral Coefficients (MFCCs) are utilized as feature for presenting audio signals in honk detection. This paper uses a deep Convolutional Neural Network (CNN) model that is deployed to cloud for detecting traffic honking events. The end-to-end system provides a privacy-preserving (anonymous data collection), low-power and low-cost solution for participatory sensing based traffic honk monitoring. We evaluate our proposed system on real world participatory sensing based road sound dataset collected by participants. It achieves a classification accuracy of 96.3%. The deep CNN is also evaluated on different benchmark datasets (namely ESC-50 and UrbanSound8K). The results are also compared with the baseline support vector machine (SVM) and k-nearest neighbors (KNN) classification models. Besides, state-of-the-art visualization techniques are used to explore spatial and temporal variability of honking events in urban areas using two case studies.