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

Area of Science:

• Computer Science
• Data Science
• Network Engineering

Background:

• Smart cities generate vast data from numerous sensors.
• Mobile vehicles as data mules offer an economical data collection method.
• Existing research often overlooks performance optimization for data collection.

Purpose of the Study:

• To propose a Latency and Coverage Optimized Data Collection (LCODC) scheme for smart cities.
• To improve the efficiency of data collection by integrating vehicle-to-vehicle (V2V) transmission.
• To mitigate waste and redundancy in public resource utilization through data mining.

Main Methods:

• Developed an opportunistic routing scheme for data collection.
• Incorporated both vehicle-to-device (V2D) and vehicle-to-vehicle (V2V) data transmission.
• Utilized data mining on smart city patterns to optimize resource utilization.
• Evaluated the scheme using a large-scale, real-world dataset from Beijing.

Main Results:

• The LCODC scheme significantly reduced average data latency from hours to approximately 12 minutes.
• Achieved a coverage rate exceeding 30% compared to schemes without V2V transmission.
• Demonstrated efficient data collection with minimal costs and no need for extra supporting devices.

Conclusions:

• The LCODC scheme offers an effective and economical solution for smart city data collection.
• Integrating V2V transmission is crucial for enhancing data collection performance.
• The scheme optimizes resource usage and reduces latency and improves coverage.