# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 5 Future Aspects of IoSB
### Data Mining  
A suitable information mining strategy and their application scenarios are critical and should be considered to figure out the last challenge in Section 4. In [106], the authors rationally analyzed the relationship between some environmental factors and the usage of a Bicycle-sharing System (BSS) with a mathematical model, so as to select the appropriate point for a new docking station to increase the usage of BSS. Similarly in some big data analysis of cycling released by OFO and Mobike [107; 108; 109], the links between some cycling data including cycling duration, cycling moment, geographical location, cycling path, user gender and age, have been analyzed. The results can directly reflect some characteristics about the city's travel, such as travel peak, pace of city life, user age composition, etc. Considering the above cases, correlation analysis of data in IoSB should be done first. Referring to the relevant work in [86; 92; 110; 111; 112], we have shown some feasible application scenarios of IoSB and the data associated with them in Figure 12.  
Figure 11: Green IoSB vision in future. Deep learning has been widely used in image recognition, natural language processing and speech recognition and can be a good choice. In [86], a fusion method based on human body sensor networks and vehicular Ad hoc network was designed to identify four dangerous situations in the driving process (including drowsiness, drunkenness, abnormal driving and distracted driving) and send out alerts in time. On the basis of the theory, the corresponding experimental driving was conducted to verify, and the real-time monitoring program based on the Android system was developed to monitor the concentration of attention by Electroencephalogram (EEG). However, due to insufficient study on relevant aspects in shared bicycle, improvements to the classical neural network model should be implemented to adjust IoSB. In [112], a high-precision real-time license plate detection method based on Convolutional Neural Network (CNN) is proposed to achieve higher accuracy and lower costs. As shown in Figure 12, the above CNN-based detection method can also be applied to traffic violation investigation based on the image or video collected in IoSB. In [111], a Multi-layer Perception (MLP) deep learning model for congestion prediction is proposed, and Linear Regression (LR) model is added to predict its duration which data sources are from the Singapore Land Transport Authority, Google and Open Maps weather APIs. Experimental results show that the MLP-LR model performs very well in predicting congestion, with an accuracy of 63%. Analogously, it is feasible to replace the data source of the above method with the data collected by IoSB, and the improved method based on the MLP-LR model mentioned above can be used in IoSB to predict congestion and arrange travel plan. Further data mining and analysis we are able to do about urban planning, traffic management, and urban security are summarized as follows:  
1. Combining location data and air monitoring data, we can geographically divide the city into several regions based on the level of pollution, which will help the environmental protection department to find the source of urban pollution and formulate targeted solutions. 2. From the location data of shared bicycles, we can study the daily routine of citizens, so as to better plan the public transportation schedule, such as the start and end time of the bus and subway, as well as their daily frequency. Moreover, the research on the cycling path of shared bicycle can make the urban infrastructure planning more reasonable and efficient. 3. By collecting electrical signals and age data (obtained from a real-name registered app account) from cyclists, an assessment of their health can be made, so as to analyze the physical condition of users at different ages. Figure 12: Application scenarios and data correlation in IoSB.