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 km2) 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 m2, 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.
Highlights
Smart urbanization will soon significantly improve our lifestyle by enabling communication technologies and plenty of Internet-of-Things (IoT) applications like smart homes, smart vehicles, smart grids, eHealth, and much more
We have proposed Algorithm 1 for grid clustering to reduce the complexity of big data analysis, which is inspired by the Statistical Information Grid Approach to Spatial
We propose an alternate network in a smart city that does not require the deployment of expensive infrastructure to collect delay-tolerant data
Summary
Smart urbanization will soon significantly improve our lifestyle by enabling communication technologies and plenty of Internet-of-Things (IoT) applications like smart homes, smart vehicles, smart grids, eHealth, and much more. Therein, smart sensors can play a vital role to observe different environmental variables, disseminate their data, and provide in-time actions based on advanced big data analytics. We explore the collection of big data produced by a massive number of smart devices using vehicular sensor networks as an alternate data dissemination channel. We may piggyback accumulated data on moving taxi-cabs for data delivery at data centers of the corresponding service by using the opportunistic contacts between the sensors and the microscopic movement of vehicles with their Global Positioning System (GPS) location information This will reduce the load on expensive cellular links, and eliminates the need for new infrastructure deployment. We analyze our proposed network of vehicles for data transmission service scenario from the service provider to smart sensors using software update code.
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