Faster Fog Computing Based Over-the-Air Vehicular Updates: A Transfer Learning Approach

Abstract

Fog computing is a promising option for time sensitive vehicular over-the-air (OTA) updates, as it can offer enhanced network durability and lower communication delays, as compared to the cloud. Fog node utilization for updates is non-deterministic, largely owing to the patterns in vehicular traffic. The resultant over provisioning of resources manifests itself in increased communication and handover delays. Based on an analysis of the regional traffic pattern for a particular time period, our proposed algorithm determines the optimal number of fog nodes required for OTA updates. In order to pinpoint the traffic load and perform fog node distribution, we employ k-means clustering. The efficacy of our proposed approach is demonstrated using a case study that considers handover delay, propagation delay, transmission rate and vehicular mobility to predict the OTA update time. We employ a machine learning model for predicting the communication delay between fog devices and vehicles. Using the European WiFi hotspot signal strength NYC dataset and the 5G dataset, we observe that the proposed approach increases the net reserve fog resources by 26.57 percent on an average, and reduces the OTA update time by 5.34 percent. We test the scalability of the proposed approach by analyzing the performance in terms of average throughput while varying the number of vehicles and OTA update size. We observe that a system with less traffic and small update size overall delivers a higher average throughput of 46 Mbps versus one with more traffic and large update size overall, which provides an average throughput of 30 Mbps. The performance of the proposed OTA update scheme on simulations has been corroborated by implementation on a real-world testbed.