Edge Compression of GPS Data for Mobile IoT

Intelligent Transportation Systems (ITS) is a key IoT use case. To enable ITS applications, the location information (GPS) of a vehicle needs to be continuously transmitted to the cloud. Due to bandwidth and latency considerations, there is a limit to the aggregate volume and velocity of all the data transmitted to the cloud from the vehicle. To address this problem, this paper proposes a novel technique for compressing the GPS data before transmission to the cloud. Our algorithm at the edge correlates the GPS data with the the local GIS information to derive high-precision quantized estimates. At the cloud, our algorithm estimates the vehicular speed from the quantized data, to reconstruct the GPS coordinates with minimum error. Thus our algorithm is different from traditional algorithms for GPS trajectory compression. Our proposed technique also achieves the secondary benefit of automatic encryption and obfuscation of the transmitted GPS data, thus improving the privacy and security of ITS systems. Finally we show that, to implement this algorithm in a real deployment, a fog based architecture is needed for addressing the control and management layer functionalities.