DDSV: Optimizing Delay and Delivery Ratio for Multimedia Big Data Collection in Mobile Sensing Vehicles

Abstract

The large number of mobile-sensing vehicles traveling in cities offer a novel solution to the collection of vast amounts of multimedia data packets. When a vehicle passes through the data center (DC), the collected multimedia data packets will be transmitted to the DC. Due to the mobile characteristic of vehicular sensor networks, the main challenge lies in how to improve the multimedia data delivery ratio and balance the data packet collections. In this paper, in consideration of delay and delivery factors, a novel routing method is proposed to optimize multimedia data collections in mobile sensing vehicles (DDSVs). This method targets at balancing multimedia data collections, improving the delivery ratio of the multimedia data, and reducing the delay ratio in Internet of Things (IoT) networks. In the DDSV scheme, two rules are designed for improving the collection of multimedia data in the IoT. These rules pertain to: 1) data and 2) vehicular priorities. First, different regions hold different priorities of data packet transmission, which can improve the delivery ratio in the suburban areas and reduce the delay ratio. Meanwhile, this scheme is capable of guaranteeing the balance of multimedia data collection. Second, the vehicular priority is proportional to the probability of a vehicle reaching a DC. Therefore, the data should be forwarded to vehicles with higher priorities, that is, the vehicles which are more likely to pass by the DC. By using these two rules, the DDSV scheme can improve the performances of the multimedia data delivery ratio, compared with the conventional optimal vehicular data forwarding scheme. In the simulation experiments, the DDSV scheme utilizes multidatasets of Beijing city, where the average delay for data collection can be decreased by 17.3% in general, and by 41.8% in the suburban areas; the average data delivery ratio can be improved by 16.9% in comparison to the previous studies.