Efficient Sensor Processing Technique Using Kalman Filter-Based Velocity Prediction in Large-Scale Vehicle IoT Application

Abstract

Sensor nodes that operate as edge devices in Internet-of-Things (IoT) networks have various limitations, such as insufficient power supply and small memory size. Therefore, the sensor node must be able to use resources efficiently to achieve the specified software behavior of the target application. The application in this study involves an IoT network in which the sensor node requests the position from a moving vehicle and estimates the velocity by using a Kalman filter. Using the same sensing cycle for all vehicles improves accuracy regardless of the predicted velocity of the sensor node but increases unnecessary computations. The proposed technique can be used to weigh the communication distance from the sensor node as the speed of the vehicle decreases, to enable the sensor node to adaptively determine the data request period based on the state of the vehicle. When a slow-moving vehicle intermittently communicates with a sensor node, the time required for the computation performed by the sensor node can be significantly reduced. To evaluate the proposed technique, we experimented with a traffic simulator that was implemented in MATLAB. Compared with the increment in the root mean square error of the reference velocity that sensed the position at every time step, the decrement in the processing time of the sensor node was considerable. Experiments with four manually determined distance weights and a number of spawned vehicles showed that the sensor node processing time was reduced by up to 72.91%.