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. 2017 Apr 18;17(4):888.
doi: 10.3390/s17040888.

A Latency and Coverage Optimized Data Collection Scheme for Smart Cities Based on Vehicular Ad-hoc Networks

Yixuan Xu  1 , Xi Chen  2 , Anfeng Liu  3 , Chunhua Hu  4   5
Affiliations

A Latency and Coverage Optimized Data Collection Scheme for Smart Cities Based on Vehicular Ad-hoc Networks

Yixuan Xu et al. Sensors (Basel). .

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%.

Keywords: VANET; data collection; data mining; opportunistic routing; smart city.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
One application of the LCODC scheme.
Figure 2
Figure 2
State transition diagram of the running states of mobile vehicles.
Figure 3
Figure 3
An illustration of vehicle to vehicle (V2V) transmission.
Figure 4
Figure 4
Schematic diagram used to summarize the LCODC scheme.
Figure 5
Figure 5
Summary statistics of the T-Drive dataset. (a) Percent of grids with different traffic flow information; (b) Number of grids with different traffic flow information.
Figure 6
Figure 6
Visualization of the filtered T-Drive dataset (© OpenStreetMap Contributors).
Figure 7
Figure 7
Final locations of data centers with different clustering algorithms.
Figure 8
Figure 8
Distribution based on grids covered by uploaded data packets.
Figure 9
Figure 9
Distribution based on the latency of uploaded data packets.
Figure 10
Figure 10
Detailed distribution of the average latency.
Figure 11
Figure 11
The number of remote and urban areas covered by uploaded data packets.
Figure 12
Figure 12
Distributions of remote and urban areas covered by uploaded data packets.
Figure 13
Figure 13
Total number of uploaded data packets with different number of data centers.
Figure 14
Figure 14
Average latency and coverage with different number of data centers.
Figure 15
Figure 15
Distribution of grids covered by uploaded data packets with 100 data centers.
Figure 16
Figure 16
Locations of data centers with three different distributions.
Figure 17
Figure 17
Average latency and coverage of LCODC scheme with different internal memory sizes (measured by the number of data packets).
Figure 18
Figure 18
Distributions of covered grids at different times in a day.
See this image and copyright information in PMC

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