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

ABSTRACT

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.

KEYWORDS:

• Environmental monitoring
• mobile sensing
• mobility patterns
• smart city
• spatio-temporal phenomena

Acknowledgments

The authors would like to thank the members of the MIT Senseable City Lab Consortium for supporting this research.

Additional information

Funding

The work of A. Anjomshoaa is supported in part by Science Foundation Ireland [grant 13/RC/2094] and co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero—the Science Foundation Ireland Research Centre for Software (www.lero.ie).

Notes on contributors

Amin Anjomshoaa

Amin Anjomshoaa is a senior research scientist at Senseable City Lab, Massachusetts Institute of Technology, Cambridge, US. He is also associated with Lero—the Irish Software Research Centre, National University of Ireland. Anjomshoaa defined the problem, designed the solution and models, and wrote the paper.

Paolo Santi

Paolo Santi is a principal research scientist at Senseable City Lab, Massachusetts Institute of Technology, Cambridge, US. He is also associated with Istituto di Informatica e Telematica del CNR, Pisa, Italy. Santi supervised the research and contributed to the writing.

Fabio Duarte

Fabio Duarte is a principal research scientist at Senseable City Lab, Massachusetts Institute of Technology, Cambridge, US. He is also associated with Pontifícia Universidade Católica do Paraná, Brazil. Duarte contributed to the writing and the visualization of the results.

Carlo Ratti

Carlo Ratti is the director of Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, US. Ratti secured the financial support and supervised the research.