TL;DR
An Energy-Optimized Federated Aggregation architecture of Predictive Networking in Vehicular Cloud Architectures (EOFA-PNVC) integrating client selection, gradient compression, and an energy-aware weighting scheme with a forecasting head that handles short-horizon state prediction of networks is suggested.
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
SOVANET+ is presented, an extended scheduling technique that jointly accounts for service criticality, network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services, supporting its viability for next-generation intelligent transportation systems.
Vehicular edge computing (VEC) has emerged as a key paradigm to support computation-intensive and delay-sensitive vehicular applications by offloading tasks from vehicles to nearby multi-access edge computing (MEC) servers. However, in realistic urban environments, task processing performance is heavily affected by heterogeneous vehicle-MEC interactions, spatiotemporal traffic dynamics, and continuously varying vehicle populations. To address these challenges, this paper considers a traffic-aware embodied edge intelligence-enabled vehicular network (EEIVN), where edge intelligence is grounded in the physical traffic environment by integrating VLM-based semantic perception with edge decision making. Based on this architecture, we formulate a reliability-constrained delay minimization problem (RDMP) by jointly optimizing task offloading ratio, computing resource allocation, and vehicle association, while constraining the queue reliability to mitigate queue-induced tail delay. To solve the NP-hard RDMP, we propose a VLM-multi-agent proximal policy optimization (VLM-MAPPO) approach that integrates a VLM-based traffic awareness method, a vehicle-adaptive MAPPO algorithm, and a vehicle association scoring and selection mechanism. Extensive simulations based on SUMO and CARLA demonstrate that the proposed VLM-MAPPO approach outperforms benchmarks in terms of task completion delay and tail delay, while maintaining comparable vehicle energy consumption and exhibiting robust scalability under dynamic traffic conditions and varying vehicle densities.
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.