Abstract
Using mobile vehicles as “data mules” to collect data generated by a huge number of sensing devices that are widely spread across smart city is considered to be an economical and effective way of obtaining data about smart cities. However, currently most research focuses on the feasibility of the proposed methods instead of their final performance. In this paper, a latency and coverage optimized data collection (LCODC) scheme is proposed to collect data on smart cities through opportunistic routing. Compared with other schemes, the efficiency of data collection is improved since the data flow in LCODC scheme consists of not only vehicle to device transmission (V2D), but also vehicle to vehicle transmission (V2V). Besides, through data mining on patterns hidden in the smart city, waste and redundancy in the utilization of public resources are mitigated, leading to the easy implementation of our scheme. In detail, no extra supporting device is needed in the LCODC scheme to facilitate data transmission. A large-scale and real-world dataset on Beijing is used to evaluate the LCODC scheme. Results indicate that with very limited costs, the LCODC scheme enables the average latency to decrease from several hours to around 12 min with respect to schemes where V2V transmission is disabled while the coverage rate is able to reach over 30%.
Highlights
The Internet of Things (IoT) envisions a promising future for the traditional Internet industry and society [1,2,3]
The proposed latency and coverage optimized data collection (LCODC) scheme focuses on latency-tolerant and loss-tolerant data in smart cities, we demonstrate that Quality of Service (QoS) requirements can be fulfilled with additional mechanisms
We describe three different methods on deciding the locations of data centers for comparison with the LCODC scheme: (1) locations with random distribution, which indicates that we randomly pick out 50 different grids as the locations of data centers; (2) locations with even distribution, which indicates that data centers are evenly distributed, with 10 data centers along the longitude and five data centers along latitude; (3) locations with circular distribution, which indicates that data centers present a circular shape
Summary
Section, preprocessing preprocessing methods methods on on the the T-Drive. T-Drive dataset dataset are are introduced, introduced, along along with with key key parameters used in the experiment. Preprocessing methods include gridding of parameters used in the experiment. Preprocessing methods include gridding of the the city city and and data data filtering
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