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% on an average, and reduces the OTA update time
by 5.34%. 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.
