2026· International Journal of Advanced Computer Science and Applications· Vol 17· 0 citations· 39 references
TL;DR
This study proposes a modified Lyapunov-based severity-aware MEC offloading framework for heterogeneous 5G/B5G IoT systems that significantly reduces average task delay, energy consumption, and QoS violation rate, while improving long-term system stability.
Abstract
The rapid growth of latency-sensitive and computation-intensive IoT applications in 5G and Beyond-5G (B5G) networks has increased the demand for efficient Multi-access Edge Computing (MEC) offloading strategies. Current MEC frameworks have several limitations: 1) binary QoS modeling without considering deadline violation severity, 2) a lack of severity-aware optimization in IoT applications, 3) insufficient consideration of different task criticality, and 4) poor handling of dynamic latency and energy trade-off in large-scale IoT environments. This study proposes a modified Lyapunov-based severity-aware MEC offloading framework for heterogeneous 5G/B5G IoT systems. The proposed framework utilizes task deadlines, task criticality, queue states, wireless channel conditions, and MEC resource availability as input for adaptive offloading optimization. A QoS Violation Severity Index is introduced to jointly capture deadline violation magnitude and task criticality. Furthermore, severity-aware virtual queues are integrated with a modified Lyapunov Drift-Plus-Penalty optimization framework to dynamically minimize QoS violation severity while balancing latency and energy consumption. Experimental evaluation demonstrates that the proposed framework significantly reduces average task delay to 82 ms, energy consumption to 6.0 mJ, and QoS violation rate to 4.8%, while improving long-term system stability compared with existing MEC offloading approaches in dynamic 5G/B5G IoT environments.
The rapid growth of lightweight Internet of Things (IoT) applications has intensified the need for effi- cient task scheduling mechanisms in fog computing environments, where delay sensitivity and resource constraints are critical concerns. To address these challenges, this paper proposes MPTS, a Multi-Queue Priority-Based Task Scheduling algorithm designed to minimize service delay while ensuring fair resource allocation for heterogeneous and delay-sensitive IoT workloads. The proposed algorithm classifies incom- ing tasks into short and long jobs based on burst time and schedules them using multiple priority queues with a dynamic time-frame mechanism, effectively mitigating starvation and improving response time. The performance of MPTS is evaluated using a Cooja-based simulation environment implemented on Con- tiki OS, considering realistic fog–IoT network settings. The proposed approach is compared against two benchmark scheduling schemes: Greedy Knapsack-based Scheduling (GKS) and Delay and Performance Optimization in Fog Computing (DPOFC). Simulation results demonstrate that MPTS achieves approx- imately 18–24% reduction in average end-to-end service delay and 47–50% lower network usage, while maintaining comparable energy consumption across varying numbers of IoT devices. These results confirm that MPTS significantly enhances Quality of Service (QoS) by jointly optimizing delay, network utilization, and energy consumption, making it well suited for delay-sensitive and resource-constrained fog-enabled IoT applications.
Annu Malik, Rashmi Kushwah· Informatica· 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
This paper investigates reliability-aware admission-threshold selection for finite-buffer systems with service interruptions, motivated by IoT gateway–cloud architectures and develops a trade-off-driven weighted optimization approach and a constraint-based feasibility-enforcement approach.
Internet of Things (IoT) applications increasingly offload sensing and analytic tasks to edge and cloud resources. Cloud processing offers high computational capacity but can increase round-trip delay and wireless energy consumption, while edge processing reduces access delay but can suffer from queue buildup when many devices select the same nearby node. This study proposes LEAOT: Load-, Deadline-, Latency-, and Energy-Aware Task Offloading for Scalable IoT Edge–Cloud Systems, a lightweight load- and deadline-aware task offloading method for IoT edge–cloud systems. Unlike black-box learning methods, LEAOT uses an explainable online score that combines the estimated communication delay, the First-Come First-Served (FCFS) aggregate queue delay, the execution delay, the device-side energy, the soft deadline pressure, and a dimensionless infrastructure-pressure term. The method also uses an exponentially weighted link estimate and a virtual workload correction step so that stale bandwidth and queue estimates are not treated as fixed constants. A controlled discrete-event evaluation is conducted across 10 independent trials, heterogeneous task sizes, and scalable edge topologies ranging from 2 to 16 edge nodes. Results show that LEAOT reduces deadline violation to 0.69% and maintains low energy of 0.117 J/task under the reference setting, while remaining competitive with delay-resource and drift-plus-penalty baselines. The results also quantify the effect of node scalability and the resource-pressure weight.
Ayman Noor· International Journal of Adv...· 0 citations
The rapid expansion of Internet of Things (IoT) networks necessitates efficient task scheduling and offloading mechanisms to improve energy efficiency, reduce latency, and optimize resource utilization. However, conventional scheduling approaches often suffer from imbalanced workload distribution, high energy consumption, and increased processing delays, resulting in suboptimal system performance. To overcome these issues, this study proposes a Double Fuzzy Clustering-Driven Context Neural Network (DFC-CNN) integrated with the Secretary Bird Optimization Algorithm (SBOA) for energy-aware task scheduling and offloading in Software-Defined Networking (SDN)-enabled IoT environments. The DFC-CNN model dynamically clusters IoT tasks based on contextual attributes, enabling adaptive scheduling, efficient load balancing, and real-time task prioritization. Simultaneously, SBOA optimizes task scheduling and offloading decisions, improving fog and cloud resource utilization while reducing energy consumption and execution delay. Extensive experimental evaluations conducted on benchmark datasets demonstrate that the proposed framework achieves up to 35% lower energy consumption, 28% shorter task completion time, and 40% higher system throughput compared with state-of-the-art methods, including PSO, FA, SSA, HHO, MOMFO, and ABC. By integrating context-aware task clustering with nature-inspired optimization, the proposed framework enhances scalability, improves resource utilization, and supports sustainable and energy-efficient computing in large-scale IoT environments.
Unknown authors· International Journal of Com...· 0 citations
Vehicular fog computing (VFC) enhances compute-intensive task processing by exploiting idle vehicle resources. However, existing offloading mechanisms may fail due to dynamic factors, such as vehicle mobility, unstable links, and service overload. This paper proposes an offloading-failure-aware (OFA) task offloading scheme (OFA-offloading). Although the exact offloading failure probability is difficult to obtain, it is determined by the service capability of the selected service vehicle (SV). Thus a new tractable metric, i.e., vehicle service capability (VSC), is defined to reflect the offloading failure probability, which is a function of vehicle mobility, resource availability, and link status. Based on VSC of each SV and considering that delay is important for VFC networks, an OFA delay utility is designed. Aiming to maximize this utility, a joint offloading SVs selection and computing resource allocation optimization problem is formulated. Since it is NP-hard and the VFC network is highly dynamic, a novel Graph Neural Network based federated Advantage Actor-Critic (GNN-FAC) algorithm is proposed to solve the problem. GNN-FAC can proactively predict environmental dynamics and incorporate VSC as a critical criterion for offloading decisions. Simulation results demonstrate that compared with existing offloading algorithms, OFA-offloading can improve the OFA delay utility by up to 40%.
Yihao Wu, Yanli Qi, Yiqing Zhou et al.· IEEE Transactions on Network...· 0 citations
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