Simulation results show that MoReSP achieves the lowest admission-adjusted system cost across all evaluated scenarios, and demonstrate that MoReSP provides a reliable and balanced scheduling solution for dynamic V-IoT environments.
Abstract
Vehicular Internet of Things (V-IoT) networks require reliable scheduling for safety-critical communication, cooperative awareness, and cooperative perception under dynamic mobility and limited roadside infrastructure. This paper proposes MoReSP, a Mobility- and Reliability-aware Scheduling Policy for roadside unit (RSU)-assisted V-IoT networks. MoReSP uses mobility-regime inference, structured action scoring, safety projection, and episodic parameter adaptation to select among deny, grant, preempt, coexist, and handoff actions. Its multiobjective formulation jointly minimizes average delay, communication energy consumption, admission-adjusted reliability loss, a penalty for cooperative perception message (CPM) delivery/freshness, and RSU-load imbalance. The framework is evaluated under vehicle-load variation, Nagel–Schreckenberg (NaSch) density variation, and RSU-capacity scaling using admission-adjusted metrics that penalize excessive blocking and interruption. MoReSP is compared with five literature-grounded benchmark families: Age of Correlated Information (AoCI)-Heuristic, RSU-Coop, Handoff-Aware, vehicle-to-everything (V2X)-Priority, and Adaptive Learning-based Task Offloading multi-armed bandit (ALTO-MAB). Simulation results show that MoReSP achieves the lowest admission-adjusted system cost across all evaluated scenarios. At nominal RSU capacity, MoReSP reduces the system cost by 43.6% compared with the best baseline. Under high vehicle load, it reduces the cost by 54.4% at arrival scale 2.0 and maintains effective packet and CPM delivery ratios of 0.849 and 0.828, respectively. These results demonstrate that MoReSP provides a reliable and balanced scheduling solution for dynamic V-IoT environments.
The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed et al.· IEEE Transactions on Network...· 0 citations
An interpretable intelligent scheduling framework, where intelligence refers to state-aware, service-aware, energy-aware, and resilience-aware adaptation rather than purely black-box learning, is presented, indicating that intelligent 5G-Advanced slice scheduling is a promising standards-aligned approach for service-differentiated and dependable smart-grid communications.
Xianyang Zhang· Journal of ICT Standardizati...· 0 citations
IoT gateway–cloud systems support large-scale sensing, monitoring, and control applications, but must operate under finite buffering, dynamic traffic demands, and service interruptions caused by gateway and cloud failures. These challenges can lead to backlog accumulation, congestion, and degraded service performance, making admission-threshold selection an important reliability-management problem. This paper investigates reliability-aware admission-threshold selection for finite-buffer systems with service interruptions, motivated by IoT gateway–cloud architectures. A finite level-dependent quasi-birth-and-death (LD-QBD) model is developed to jointly capture probabilistic admission control, finite buffering, and gateway–cloud failures. Exact stationary analysis yields a multidimensional performance-characterization framework based on effective service deliverability, congestion-regime probability, saturation probability, and soft normalized headroom. The admission threshold is shown to govern the trade-off between service deliverability and congestion protection under failure-induced backlog dynamics. To address this trade-off, two complementary threshold-selection paradigms are developed: a trade-off-driven weighted optimization approach and a constraint-based feasibility-enforcement approach. Numerical results show that the weighted formulation exhibits a well-defined knee point, whereas the constraint-based method produces reliability-aware threshold adjustments when operational constraints become active. The results further indicate that increasing traffic load or failure intensity generally requires more conservative admission policies. Although motivated by IoT gateway–cloud systems, the proposed framework is applicable to a broader class of finite-buffer service systems with unreliable resources.
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations
Vehicle-to-Everything (V2X) communication requires low-latency, reliable, and context-aware connectivity across vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian, and vehicle-to-network services. Heterogeneous cellular networks can support these services by combining 5G New Radio, cellular sidelink, roadside units, Wi-Fi or DSRC links, and multi-access edge computing; however, high mobility, variable traffic density, frequent handovers, congestion, and static resource-allocation policies can degrade end-to-end delay and packet delivery. This paper presents a software-defined networking assisted framework for latency-aware resource management in heterogeneous V2X environments. The proposed framework separates network control from forwarding, maintains a global view of vehicular network state, classifies V2X flows by service criticality, and dynamically selects routes and bandwidth allocations using delay, congestion, handover, and priority constraints. A mathematical formulation is developed for minimizing weighted end-to-end latency under link-capacity, minimum-bandwidth, packet-delivery, congestion, and controller-processing constraints. The paper also defines the Latency-Aware SDN Resource Allocation Algorithm for V2X Networks and specifies a reproducible simulation methodology using NS-3/SUMO or OMNeT++/Veins with multiple baselines, confidence intervals, and ablation analysis. Because the source report did not include raw simulation data, the revised results section includes clearly labelled hypothetical numerical plots and reporting values only to demonstrate result formatting and interpretation; these values are not validated empirical findings. The study provides a rigorous foundation for validating SDN-MEC orchestration in next-generation vehicular communication systems
Swadhin Singh, Swatantra Kumar, Mr. Rahul Kumar· International Journal of Adv...· 0 citations
The rise of mobile Internet of Things (IoT) applications has made reliable, low-latency task offloading to nearby fog nodes essential. However, user mobility, time-varying wireless links, short-packet transmission errors, and fog-queue congestion make the execution of delay-sensitive tasks challenging. Existing schemes typically address reliability, replication, or offloading separately, without jointly considering mobility-aware link variation. To address this gap, this paper proposes a mobility-aware reliability-driven offloading framework for fog-enabled IoT networks. The proposed scheme combines a lightweight multilayer perceptron (MLP)-based SINR predictor with a Lyapunov driftplus-penalty (LDPP) controller to select among local execution, single-fog offloading, and replicated-fog execution. Simulation results show that the proposed method reduces task delay, reliability violations, and energy consumption by $17 \%, 13 \%$, and 41%, respectively as compared to existing scheme.
Rajasekhar Dasari, Satyajit Mohapatra, S. Nayak· International Conference on...· 0 citations