Smart City Taxi Trajectory Coverage and Capacity Evaluation Model for Vehicular Sensor Networks

Salman Naseer, William Liu, Nurul I. Sarkar, Muhammad Shafiq and Jin-Ghoo Choi
Additional contact information
Salman Naseer: Department of Information Technology, University of the Punjab Gujranwala Campus, Gujranwala 52250, Pakistan
William Liu: Department of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand
Nurul I. Sarkar: Department of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand
Muhammad Shafiq: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Jin-Ghoo Choi: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea

Sustainability, 2021, vol. 13, issue 19, 1-24

Abstract: In a smart city, a large number of smart sensors are operating and creating a large amount of data for a large number of applications. Collecting data from these sensors poses some challenges, such as the connectivity of the sensors to the data center through the communication network, which in turn requires expensive infrastructure. The delay-tolerant networks are of interest to connect smart sensors at a large scale with their data centers through the smart vehicles (e.g., transport fleets or taxi cabs) due to a number of virtues such as data offloading, operations, and communication on asymmetric links. In this article, we analyze the coverage and capacity of vehicular sensor networks for data dissemination between smart sensors and their data centers using delay-tolerant networks. Therein, we observed the temporal and spatial movement of vehicles in a very large coverage area (25 × 25 km 2 ) in Beijing. Our algorithm sorts the entire city into different rectangular grids of various sizes and calculates the possible chances of contact between smart sensors and taxis. We further calculate the vehicle density, coverage, and capacity of each grid through a real-time taxi trajectory. In our proposed study, numerical and spatial mining show that even with a relatively small subset of vehicles (100 to 400) in a smart city, the potential for data dissemination is as high as several petabytes. Our proposed network can use different cell sizes and various wireless technologies to achieve significant network area coverage. When the cell size is greater than 500 m 2 , we observe a coverage rate of 90% every day. Our findings prove that the proposed network model is suitable for those systems that can tolerate delays and have large data dissemination networks since the performance is insensitive to the delay with high data offloading capacity.

Keywords: smart cities; spatial data mining; grid clustering; big data; delay tolerant network; sensor networks; GPS traces; Internet of Things; intelligent transportation system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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