# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 1 Introduction
proposed an indoor scene constraint-based localization method for accurate scene classification by fusing multiple camera, Wi-Fi, and inertial sensors [17]. IODetector utilizes lightweight sensors, including ambient light sensors, and cellular networks to distinguish between indoor and outdoor scenes using a joint strategy with fixed thresholds and Adaboost classifiers [18]. [PERSON] et al. used the C4.5 algorithm for I0 detection, and features inputted to the model included feature information such as barometric pressure, light intensity, time, base station signals, magnetometer variance, etc. [19]. [PERSON] et al. used an algorithm based on Wi-Fi signal and light intensity fusion features to detect I0 scenes using AdaBoost and semi-conditional random fields [20]. The use of multi-sensor information can reduce the impact of sensor scene arrangement limitations and enriches the feature information so that a more accurate scene classification and recognition model can be obtained, while most multi-sensor feature scene classification models use deep learning or machine learning for the classification. At present, most of the research focuses on scene recognition accuracy and ignores the advantages that the data, temporal information, spatial information, and mathematical statistical information can bring, and it also ignores the role of indoor and outdoor scene discrimination for indoor and outdoor switching as well as the past based on fixed thresholds and special features. It is difficult to achieve good results in indoor and outdoor switching due to the complexity of signal changes when the scene is switched; it is difficult to detect scene switching changes correctly within a short period of time, and in addition to this, it is difficult to detect the scene switching changes correctly. Due to the complexity of signal changes during scene switching, it is difficult to correctly detect scene switching changes in a short time. In addition, the heterogeneity of equipment leads to signal fluctuations and thus causes feature drift, which makes the existing algorithms under-adapted in complex indoor and outdoor scenes. Therefore, this paper introduces a complex indoor and outdoor scene sensing algorithm based on spatio-temporal features. The main contributions of this paper are as follows:  
1. This paper proposes a complex indoor-outdoor scene sensing algorithm based on multiple signal sources, utilizing five types of sensors: global satellite navigation signals, cellular network signals, magnetometers, optical sensors, and Wi-Fi. The algorithm leverages both temporal and spatial features to handle complex scene detection. 2. The five sensors--global satellite navigation signals, cellular network signals, magnetometers, optical sensors, and Wi-Fi--are thoroughly characterized, and their data features are deeply mined. The data are analyzed from temporal, spatial, and mathematical/statistical perspectives to enrich and highlight the scene-specific features, enhancing the classification performance. 3. This paper introduces a scene classification network that uses multi-sensor data, comprising multi-scale CNN and BiLSTM architectures. This network effectively classifies data features and addresses the issue of long-term dependencies in neural networks. 4. The whale optimization algorithm is employed to automatically optimize key parameters of the neural network, including the initial learning rate, regularization parameters, and BiLSTM hidden layer settings. This method reduces the time required for manual parameter tuning and achieves improved performance. The rest of the paper is organized as follows. Section 2 describes the data feature mining approach, data feature pre-processing, the proposed scene-aware classification model and evaluation metrics in this paper. Section 3 describes the dataset, which is followed by ablation experiments and a performance comparison with other methods. Section 4 discusses outlooks, and Section 5 concludes.