Intelligent Edge-Cloud Data Management with a Predictive Smart Offloading Proxy for 5G Internet of Vehicles
TL;DR
In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated.
Abstract
Connected vehicles generate video, sensor, and bulk data that must be uploaded, cached, and forwarded across edge and cloud resources under short contact durations and congested backhaul. This paper studies selected data-management functions of a Smart Offloading Proxy (SOP) for 5G Internet of Vehicles (IoV): deadline-constrained scheduling of uploads already accepted at the edge proxy, and a radio-quality allocation signal. Control-plane functions are described but not evaluated; no end-to-end architecture validation is claimed. For proxy-side forwarding, six bandwidth-scheduling policies are formalized and evaluated in NS-3 against a first-come-first-served serve-one baseline. With bursty arrivals, a 60 Mbps bottleneck, and a 30 s dwell deadline, the shortest-remaining-k equal-allocation policy (SRK-EQ) is the strongest of the six scheduling policies, completing 92.5 ± 4.6% over 20 seeds versus 75.2 ± 12.0% for the all-jobs baseline; the serve-one baseline attains higher completion (98.0 ± 2.4%) for homogeneous 10 MB jobs. Under a heterogeneous 1/10/50 MB workload, SRK-EQ delivers lower latency (median 3.15 s versus 19.84 s) with overlapping completion estimates and a large-job fairness trade-off. In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated.