Sep 2026· IEEE Internet of Things Journal· Vol 13, pp. 40918-40930· 0 citations· 36 references
Abstract
Urban Internet of Thing (IoT) networks face severe reliability threats from diverse wireless interference, including jamming and spoofing, which are difficult to detect and localize in multipath-rich environments. Existing schemes often suffer from high false alarms, poor generalization, and low localization accuracy. This article presents an end–edge–cloud interference detection and localization framework integrating zero-shot detection, game-theoretic collaborative sensing, and GNN-based localization. Experiments on a city-scale prototype show that the system achieves 98.5% detection accuracy with recall of 96.7%, while reducing false alarms to 3.2%. The proposed graph neural network (GNN) reduces median localization error to 12.3 m, significantly outperforming baseline methods. Furthermore, the architecture reduces energy consumption by nearly 40% compared with cloud-only designs and maintains end-to-end latency under 120 ms. These results demonstrate that the proposed system enables robust, real-time interference awareness for large-scale IoT deployments, paving the way toward resilient 6G smart cities.
A hierarchical edge-fog anomaly detection framework that integrates lightweight edge-level filtering with a fog-level Temporal Convolutional Network (TCN) detector, which suggests that hierarchical edge intelligence is a practical but calibration-sensitive direction for scalable anomaly monitoring in EV charging infrastructures.
H. Jeong· Computers, Materials & C...· 0 citations
Mega-events present acute challenges in crowd safety, requiring sub-second monitoring, heterogeneous sensing, and strict privacy compliance at scale. We present SmartCrowd-IoT, a multi-modal crowd analytics framework built on a three-tier (sensor, edge, coordination) architecture incorporating (i) temporally aligned, reliability-aware weighted fusion across RGB, thermal, WiFi/BLE, acoustic, and RFID streams; and (ii) lightweight edge inference with federated differential privacy, enabling continuous model improvement without raw data leaving the venue. Evaluated on PETS2009, UCY, Mall, and a custom 61.3-h multi-modal corpus across three controlled mega-event simulations, SmartCrowd-IoT achieves 92.6% crowd-density accuracy, 77 ms end-to-end latency, 92.9% anomaly detection precision, and 83.4% backbone bandwidth reduction. Ablation studies confirm that both temporal alignment and reliability-aware fusion contribute significantly to these gains. The framework provides a deployable, privacy-by-design solution for mega-event crowd safety that scales to 200 edge nodes and 3000 sensors while maintaining sub-100 ms emergency response.
Dense IoT networks require reliable communication despite limited spectrum and substantial multi-user interference while maintaining manageable receiver complexity. This work introduces a deep-learning-based end-to-end multi-user communication design for interference-limited finite-blocklength IoT scenarios, focusing on short and medium blocklengths. We extend a prior 2-user SiameseNet transceiver framework to accommodate 2, 4, and 8 users, leveraging learned redundancy for interference suppression and noise robustness. Compared to conventional non-orthogonal access baselines, our method demonstrates strong Block Error Rate (BLER) performance across various scenarios without resorting to joint detection; the per-user decoder scales roughly linearly with the number of users. Further, we examine the robustness under interference mismatch and unequal interference strengths, critical for practical deployments with heterogeneous devices. The Latent-space analysis reveals that the learned codeword distance increases as the effective per-user rate decreases, corroborating with the observed BLER improvements. In addition, we also present preliminary results for a 2X2 MIMO setup under fixed-channel CSIT and CSIR, indicating potential for extending the framework to IoT gateways with multiple antennas.
Arkadeep Sinha, Shubham Paul, R. Manivasakan· 1 citation
The expansion of the Internet of Things (IoT) has considerably intensified the demand for radio spectrum, which remains a limited and highly valuable resource. Cognitive Radio (CR) technology provides an effective approach by enabling the dynamic identification of unoccupied frequency bands and enabling opportunistic spectrum access, thus improving spectral efficiency. This paper addresses the problem of spectrum sensing in Cognitive Radio-based IoT (CR-IoT) networks using real IoT signals transmitted by LoRa modules and captured with an RTL2832U software-defined radio receiver. Energy detection is employed as the sensing method due to its low computational complexity and suitability for unknown signals. The effectiveness of the proposed method is assessed using Receiver Operating Characteristic (ROC) analysis, highlighting its ability to reliably distinguish between occupied and idle frequency bands. The results confirm the feasibility of deploying energy detection-based CR-IoT systems in real-world scenarios, providing a practical foundation for future opportunistic spectrum access techniques in IoT networks.
H. Semlali, A. Maali, Khadija Lahrouni et al.· 2026 6th International Confe...· 0 citations
This study demonstrates the efficacy of the synergy between federated learning and edge computing in IoT security contexts, providing a scalable and privacy-centric solution for anomaly detection across large-scale distributed devices.
Quan Liu, Yuanyuan Feng· Discover Artificial Intellig...· 0 citations
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.
Siraj Azam, Farheen Naaz, Mikail Mohammed Salim· Electronics· 0 citations
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