Quantifying the Spatio-Temporal Potential of Drive-by Sensing in Smart Cities

Recently, portable sensors, with high accuracy and embedded communication technologies, have become available and affordable. By deploying such sensors on various urban vehicles that routinely navigate through city streets, vehicles can form a dynamic network for comprehensively and efficiently monitoring the urban environment. This drive-by sensing approach benefits also from the lower costs of sensor deployment and maintenance compared to stationary sensor networks. However, the data sampling frequency and spatial granularity of measurements are constrained by factors such as topology of the underlying street network and mobility pattern of sensor-equipped vehicles. In this paper we investigate the effect of street network topology on the quality of data captured through drive-by sensing. To this end, we first study the temporal aspects of drive-by sensing and present a quantitative method for comparing various street networks. Then, we consider the spatial aspects of drive-by sensing by defining a sensing-potential indicator for urban areas based on the geometrical properties of the street networks. This indicator is then combined with vehicle mobility patterns derived to measure the sensing potential of routes and cycles. In this context, we define the novel concept of Sensogram for describing the spatial sensing potential of network cycles using dedicated vehicles.