A survey on local transport mode detection on the edge of the network

Abstract

We present a survey of smartphone-based Transport Mode Detection (TMD). We categorize TMD solutions into local and remote; the first ones are addressed in this article. A local approach performs the following steps in the smartphone (and not in some faraway cloud servers): 1) data collection or sensing, 2) preprocessing, 3) feature extraction, and 4) classification (with a previous training phase). A local TMD approach outperforms a remote approach due to less delay, improved privacy, no need for Internet connection, better or equal accuracy and smaller data size. Therefore, we present local TMD solutions taking into account the above mentioned four steps and analyze them according to the most relevant requirements: accuracy, delay, resources consumption and generalization. To achieve the highest accuracy (100%), studies used a different combination of sensors, features and Machine Learning (ML) algorithms. The results suggest that accelerometer and GPS (Global Position System) are the most useful sensors for data collection. Discriminative ML algorithms, such as random forest, outperform the other algorithms for classification. Some solutions improved the delay of the proposed system by using a small window size and a local approach. A few studies could improve battery usage of their system by utilizing low battery-consuming sensors (e.g., accelerometer) and low sampling rate (e.g., 10Hz). CPU usage is primarily dependent on data collection, while memory usage is related to the features and complexity of the ML algorithm. Finally, the generalization requirement is met in studies that consider user, location and position independency into account.