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.
Abstract
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.
Gateway-resident intrusion detection can act before IoT traffic reaches cloud services, but early decisions are based on incomplete flow prefixes. This paper presents a reliability-aware edge–cloud framework that treats early detection as a sequential routing problem. At each checkpoint, a lightweight gated recurrent unit (GRU) maps causal packet-prefix features to a malicious-probability estimate. Temperature scaling, asymmetric benign and malicious thresholds, and an eight-packet minimum-evidence gate determine whether a flow exits locally, remains under observation, or is sent for cloud refinement. Short and unresolved flows are classified by regularized logistic regression using a compact 97-feature causal representation. The edge model contains 19,777 parameters, and each cloud submission carries 388 bytes of float32 features. The principal evaluation uses all 309 CIC-IoT-2023 PCAP files under four outer PCAP-disjoint folds, with separate edge-training, calibration, cloud-development, and final-test roles. Across 2,286,754 pooled out-of-fold flows with 88.54% malicious prevalence, the framework resolves 422,190 flows at the edge and routes 1,864,564 for cloud refinement, reducing cloud submissions by 18.46%. The final policy attains 4.47% FPR, 1.89% FNR, 96.82% balanced accuracy, and 98.76% F1 score. Observation-budget analysis identifies 32 packets as a corpus-specific compromise, whereas controlled delays in post-eight-packet information expose the limits of short-prefix detection. On the balanced CICIDS2017 test set, in-domain development attains 97.03% balanced accuracy; zero-shot transfer falls to 86.30%, and target-calibration-only adaptation improves it to 91.65%. Ablation results identify the minimum-evidence gate and cloud-refinement stage as the main reliability controls. Benign false alarms, delayed post-eight-packet information, cross-dataset shift, and scenario/file-level labels remain the principal limitations.
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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.
S. S. Reddy, R. Sukumar· International Journal of Adv...· 0 citations
The Energy-Aware Hierarchical Green Fog framework is presented, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity, and task deadlines into hierarchical fog-cloud decision-making.
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A digital twin (DT)-enabled predictive scheduling framework in which a DT layer co-located with a multi-access edge computing (MEC) control center continuously mirrors the physical network state and generates H-slot look-ahead scheduling decisions before depletion can occur validating the zero-downtime paradigm for large-scale IIoT networks.
Resource-constrained IoT environments require security detection mechanisms that balance responsiveness, computational cost, and detection capability. This paper presents a utility-based decision framework for adaptive task placement of anomaly detection across local, edge, and cloud layers. Five operational metrics are formalised, covering latency and communication cost, energy cost, detection complexity, attack coverage, and context relevance, and integrated into a composite utility function that selects the most suitable processing layer for each incoming event. The framework incorporates confidence-modulated detection scoring and globally normalised context relevance to enable principled escalation of complex or uncertain events. A discrete-event simulation modelling a three-tier IoT architecture with nine attack categories demonstrates that the proposed balanced configuration achieves 97% of cloud-level detection quality while consuming 53% of its energy cost, outperforming all baseline strategies in composite utility. Per-class analysis confirms that the framework routes high-severity events to more capable layers while retaining simple traffic locally. The configurable weight vector further enables operators to navigate the efficiency–detection trade-off according to deployment requirements.
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