EasiSee: Real-Time Vehicle Classification and Counting via Low-Cost Collaborative Sensing

In the field of traffic-information acquisition, one pervasive solution is to use wireless sensor networks (WSNs) to realize vehicle classification and counting. By adopting heterogeneous sensors in a WSN, we can explore the potential of using complementary physical information to perform more complicated sensing computation. However, the collaboration among heterogeneous sensors, such as the collaborative sensing mechanism (CSM), is not well studied in current state-of-the-art research. In this paper, we design and implement EasiSee, a real-time vehicle classification and counting system based on WSNs. Our contributions are as follows. First, we propose a CSM, which coordinates the power-hungry camera sensor and the power-efficient magnetic sensors, reducing the overall system energy consumption and maximizing system lifetime. Second, we propose a robust vehicle image-processing algorithm, i.e., a low-cost image processing algorithm (LIPA). LIPA reduces environment noise and interference with low computation complexity. In the verification section, the vehicle detection accuracy turned out to be 95.31%, which pave the way for CSM. The time of image processing is around 200 ms, which indicates that our LIPA is computationally economical. With the overall energy consumption reduced, EasiSee achieves classification accuracy of 93%. Based on these experiments and analysis, we conclude that EasiSee is a practical and low-cost affordable solution for traffic-information acquisition.