Emulating Smart City Sensors Using Soft Sensing and Machine Intelligence: A Case Study in Public Transportation

This paper proposes a new framework for emulating the functionality of a sensor by using multiple available soft sensors and machine intelligence algorithms. As a case study, the localization of city buses in a smart city setting is investigated by using the accelerometer and microphones of the passengers and a Support Vector Machine (SVM) running in the cloud; in this application, the GPS functionality is emulated by using these two soft sensors. What makes such an emulation feasible is the statistical dependence of the location data (which would normally be obtained from a GPS) on the accelerometer and microphone data; while accelerometers capture data that relate to the typical stop/start patterns of the buses, microphone capture enter/exit patterns of the passengers through the sound levels inside the bus. We evaluate our proposed scheme through simulations and show that the proposed framework can operate with more than 90% accuracy in estimating the location of public buses while preserving the actual location privacy of the smartphone users. This approach results in smartphone battery energy savings of 38-46% (as compared to GPS-based approaches) due to the elimination of the power-hungry GPS devices.