With the development of IoT and 5G networks, the demand for the next-generation intelligent transportation system has been growing at a rapid pace. Dynamic mapping has been considered one of the key technologies to reduce traffic accidents and congestion in the intelligent transportation system. However, as the number of vehicles keeps growing, a huge volume of mapping traffic may overload the central cloud, leading to serious performance degradation. In this paper, we propose and prototype a CUPS (control and user plane separation)-based edge computing architecture for the dynamic mapping and quantify its benefits by prototyping. There are a couple of merits of our proposal: (i) we can mitigate the overhead of the networks and central cloud because we only need to abstract and send global dynamic mapping information from the edge servers to the central cloud; (ii) we can reduce the response latency since the dynamic mapping traffic can be isolated from other data traffic by being generated and distributed from a local edge server that is deployed closer to the vehicles than the central server in cloud. The capabilities of our system have been quantified. The experimental results have shown our system achieves throughput improvement by more than four times, and response latency reduction by 67.8% compared to the conventional central cloud-based approach. Although these results are still obtained from the preliminary evaluations using our prototype system, we believe that our proposed architecture gives insight into how we utilize CUPS and edge computing to enable efficient dynamic mapping applications.
[1]
Xuemin Shen,et al.
Toward Efficient Content Delivery for Automated Driving Services: An Edge Computing Solution
,
2018,
IEEE Network.
[2]
Toktam Mahmoodi,et al.
Network slicing management & prioritization in 5G mobile systems
,
2016
.
[3]
Tarik Taleb,et al.
Survey on Multi-Access Edge Computing for Internet of Things Realization
,
2018,
IEEE Communications Surveys & Tutorials.
[4]
Yan Zhang,et al.
Cooperative Content Caching in 5G Networks with Mobile Edge Computing
,
2018,
IEEE Wireless Communications.
[5]
Mumbai,et al.
Internet of Things (IoT): A Literature Review
,
2015
.
[6]
Kai Wang,et al.
Enabling Collaborative Edge Computing for Software Defined Vehicular Networks
,
2018,
IEEE Network.
[7]
Doreen Böhnstedt,et al.
Dynamic Map Update Protocol for Highly Automated Driving Vehicles
,
2017,
VEHITS.
[8]
Patrick Weber,et al.
OpenStreetMap: User-Generated Street Maps
,
2008,
IEEE Pervasive Computing.
[9]
Akihiro Nakao,et al.
Application Specific Mobile Edge Computing through Network Softwarization
,
2016,
2016 5th IEEE International Conference on Cloud Networking (Cloudnet).