International Journal of Wireless and Ad Hoc Communication

Heterogeneous Internet-of-Things deployments expose wireless sensor networks to a diverse and continuously evolving threat landscape encompassing distributed denial-of-service flooding, network reconnaissance scanning, and brute-force credential attacks. Existing intrusion detection approaches predominantly adopt single-classifier architectures and binary labelling, which are ill-suited to the multi-class, class-imbalanced traffic characteristic of real-world IoT sensor deployments. This paper proposes WS-STACK, a Weighted Stacking ensemble that combines five heterogeneous base learners—Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbours, and Gradient Boosting—under an ℓ2-regularised Logistic Regression meta-learner trained on cross validationgenerated probability features. A three-stage feature engineering pipeline comprising mutual information filtering, variance inflation factor pruning, and correlation-based elimination reduces the 83 dimensional RT-IoT2022 feature space to 20 informative features, and the Synthetic Minority Over-Sampling Technique corrects the six-fold class imbalance prior to training. Evaluated on 83,000 labelled network flow records from the publicly available RTIoT2022 benchmark spanning four benign traffic patterns and seven attack categories, WS-STACK achieves 99.61% classification accuracy, a weighted F1-score of 0.9960, and an AUC-ROC of 0.9978, outperforming every individual base classifier and five recently published state-of-the-art baselines. The false positive rate is reduced to 0.0006, and ten-fold cross-validation confirms μacc = 0.9959 (σ = 0.0004). Ablation experiments identify SMOTE as the single most critical preprocessing component, and noise robustness tests confirm 98.81% accuracy under 20% Gaussian feature perturbation. The framework is grounded through a formal variance-reduction proof and a channel-energy anomaly model that establishes the physical motivation for packet-rate features as the dominant intrusion detection signal in constrained wireless sensor networks.

Dense Wi-Fi deployments are often tuned by changing channel width or adding access points, while the joint effect of antenna gain, operating frequency, wall loss, and network interference receives less systematic attention. This paper presents a frequency-aware antenna configuration model for Wi-Fi communication networks operating in the 2.4, 5, and 6 GHz bands. The model combines an indoor link budget, antenna-pattern classes, bandwidth dependent noise, an airtime-overlap penalty, and a coverage-assurance score that balances signal quality, throughput, latency, and packet error. A reproducible design-space table is generated from a validated Wi-Fi engineering model and analyzed across five deployment scenarios, three antenna families, four channel widths, and multiple client distances. The results show that higher frequency bands improve short-range capacity but deteriorate faster under distance and wall loss, while directional antenna gain can recover a substantial part of the lost link margin. The paper provides planning rules for selecting antenna type, frequency band, and channel width according to coverage, capacity, and interference risk. The work is intended for Wi-Fi network designers who need interpretable engineering evidence rather than a black-box prediction model.

Battery-powered sensor nodes expire when their energy reserves are depleted, terminating data collection regardless of the physical integrity of the hardware. Solar harvesting offers a viable path to perpetual operation, but only when the routing layer can continuously track the time-varying energy state of every node and steer traffic away from nodes likely to be power-starved in the near future. Classical clustering and chain-based protocols select forwarding paths without regard to harvested energy, leading to premature node death even when sufficient solar income would have been available to sustain operation. This paper presents a deep reinforcement learning framework in which each sensor node operates an independent Deep Q-Network agent that adapts its next-hop forwarding decision based on local battery state, short-horizon solar energy forecasts, link quality estimates, and the residual energy levels of candidate neighbours. A lightweight LSTM sub-model provides the solar prediction horizon that the agent uses as part of its state representation, enabling it to distinguish nodes that are temporarily depleted but will recover from those whose batteries are trending toward permanent failure. Extensive simulation across a 100-node deployment over 3,000 operational rounds confirms that the proposed approach substantially extends network lifetime, improves packet delivery, and reduces wasted harvested energy compared with five competitive baselines. Reward function ablation, scalability experiments, and an energy neutrality verification further validate the design choices and confirm stability across a wide range of deployment conditions.

Wireless sensor IoT networks are moving from simple measurement pipelines toward distributed systems where sensing, interpretation, filtering, and coordination are divided across devices, fog nodes, and cloud services. This review examines that transition through the lens of tiny intelligence, with special attention to how small models, local event filters, federated updates, service placement, and privacy controls reshape fog-assisted wireless sensor deployments. The paper does not treat fog computing as a generic latency layer. Instead, it studies fog as a governance and orchestration layer that decides which data should stay at the device, which events should be aggregated nearby, and which models require cloud-level supervision. A structured comparison of prior studies is provided across architecture, TinyML, federated learning, placement, security, benchmarking, and lifecycle coverage. The synthesis shows that the literature has matured in modelling fog resources and building lightweight inference functions, but remains fragmented in lifecycle management, cross-layer wireless awareness, privacy accounting, and reproducible evaluation. The review concludes with a research agenda for sensor-to-fog intelligence pipelines that are adaptive, auditable, energy-aware, and suitable for long-lived cyber-physical deployments.

Wireless sensor Internet of Things (IoT) devices increasingly generate time-sensitive traffic that cannot be efficiently inspected only in a remote cloud. Fog computing reduces the distance between sensing devices and decision logic, but fog nodes must jointly manage latency, queue pressure, wireless channel variability, energy use and security risk. This paper presents FogSense-TSA, a trust-aware and anomaly-aware scheduling model for wireless sensor IoT traffic in fog computing environments. The model integrates traffic intensity, wireless link behaviour, fog-resource state and temporal trust into a local decision process that determines whether a device-window should be accepted, quarantined at the fog layer or escalated to cloud inspection. The empirical analysis is conducted using a reduced analysis-ready file aligned with a recent public IoT device-identification and anomaly-detection setting. The proposed formulation introduces three algorithmic components: online trust-risk scheduling, load-aware fog placement and adaptive threshold calibration. Mathematical analysis is provided for evidence aggregation, trust stability, latency decomposition, energy cost, constrained placement and computational complexity. The results show that fog placement substantially reduces service latency relative to cloud-only routing while preserving high anomaly-discrimination capability. The strongest predictors are trust score, flow intensity, jitter, fog CPU load, payload entropy and queue pressure, indicating that fog-layer security should be coupled with wireless access and resource conditions rather than treated as a separate classifier. The study provides a reproducible and interpretable basis for designing lightweight security and scheduling modules for wireless sensor IoT deployments.

Wireless sensor IoT devices increasingly operate under strict energy, latency, and security constraints while generating high-frequency telemetry that cannot be forwarded continuously to remote clouds. This paper presents an event-selective fog microbatching model for wireless sensor streams in which local novelty scoring, fog-side buffering, risk-preserving retention, and energy-aware scheduling are jointly optimized. Unlike conventional anomaly-detection pipelines, the proposed method treats communication reduction as a primary design objective and binds it mathematically to attack-evidence preservation. A reduced feature-level experimental file following the public Edge-IIoTset label structure and selected network/sensor attributes is used to evaluate traffic selectivity, uplink reduction, fog latency, energy saving, and detection performance. The model assigns each observation window a novelty score, suppresses redundant low-information traffic, and groups retained events into load-aware microbatches at the nearest fog node. The proposed model is extended with stochastic retention bounds, microbatch-delay stability, radio-energy equations, and risk-constrained threshold calibration. Experimental results show that the design reduces uplink load and radio-energy consumption while preserving strong attack discrimination across distributed wireless sensor traffic. The findings support a broader use of fog computing as a selective communication-control layer for dense, security-sensitive wireless sensor IoT deployments.