Mobile environmental sensing is a promising strategy for scalable and cost-effective urban monitoring. However, this approach introduces challenges due to the irregular spatial and temporal distribution of measurements, leading to sparse and asynchronous datasets. Traditional interpolation struggles with such sparsity and temporal misalignment. To address these issues, we propose an Edge-Fog-Cloud architecture that gathers data at the Edge and performs hierarchical spatio-temporal interpolation. By leveraging real-world mobility patterns, our framework adapts to varying data densities and temporal gaps, enhancing the accuracy of environmental monitoring in urban settings. Our approach combines classical spatial interpolation methods (e.g., RBF, IDW, Kriging) with temporal-aware strategies that consider the dynamic nature of mobile sensing coverage. Experimental results, conducted using instrumented vehicles and acquired environmental data in an urban setting, demonstrate that time-aware interpolation significantly improves estimation in unsampled areas, reducing the median error and variability.
Nearly five years have passed since the commercial launch of $\mathbf{5 G}$ services. Although the Sub-6 GHz band remains the primary frequency range for base station deployment, the adoption of millimeter wave (mmWave) technology, which was introduced for the first time in mobile communication, has faced challenges owing to its limited coverage and susceptibility to blockage. Consequently, the growth in the number of mmWave base stations stagnated. Addressing the coverage limitations of mmWave in 5G is an essential step toward realizing 6G, where even higher-frequency bands are expected to be utilized in the future. Among the various coverage enhancement technologies across layers 1 to 3, analog repeaters and passive reflectors in layer 1 have gained attention owing to their cost-effectiveness and signal amplification capabilities. In particular, NetworkControlled Repeater (NCR), a type of analog repeater, is standardized in 3GPP Release 18. However, existing research remains largely theoretical, and practical evaluations are limited. This paper presents the development of a prototype NCR with analog beamforming functionality and reports the experimental results for a single hop. The results demonstrate NCR's potential for coverage extension, while highlighting future challenges such as multi-hop operation and orchestrator control.
This paper proposes a Federated Edge AI framework with Adaptive Zero-Trust Security designed for resource constrained IoT networks used mainly in smart cities applications. The framework harnesses and deploys lightweight machine learning models, real-time anomaly detection capabilities, and dynamic trust scoring. Each IoT device can also deploy differential privacy and lightweight encryption as optional features. Dynamic trust scoring enables the federated learning process to identify and mitigate malicious clients in adversarial environments, while trust-weighted aggregation prioritizes reliable model updates. The framework is simulated under both IID and non-IID datasets from IoT-23 dataset, with up to $\mathbf{4 0 \%}$ malicious clients to ensure there was a significant amount of anomalous client behavior, control groups and dependent groups to make comparisons and assess precision, false positive-negative, communication and computational load. The lightweight framework is an applicable idea to real-world like smart cities applications, as it is scalable and highly privacy preserving. Use Case Examples include intelligent traffic management, smart energy grids, and security of UAV swarms - where IoT devices must operate in a secure fashion, under strict latencies, power, and resource limitations.
Understanding pedestrian visual experiences is critical for human-centered urban design, yet methods for analyzing first-person perspectives from actual movement data remain limited. This study proposes a novel methodology to reconstruct and analyze these perspectives by integrating LiDAR-collected pedestrian trajectories with a semantically-encoded 3D city model. Our approach simulates what pedestrians see along their movement paths, enabling quantitative, comparative analysis of their visual trajectories. An experiment in Tokyo captured and classified pedestrian flows. A case study analysis of representative trajectories demonstrates the framework's ability to reveal distinct visual signatures for different pedestrian routes, such as a direct departure from a building versus an ambulatory traversal through a green space. Despite using basic semantic categories, the proposed method offers an extensible foundation for evidencebased urban design by transforming subjective visual experiences into measurable data.
Anomaly detection in urban IoT networks is essential for the resilience and security of smart cities. However, the privacy of sensor data remains a critical concern. In this paper, we present a machine learning-based anomaly detection approach that operates entirely on homomorphically encrypted data using Concrete ML. To enable practical deployment under the computational constraints of Fully Homomorphic Encryption (FHE), we investigate the impact of quantization-aware training (QAT) and unstructured pruning, two established techniques for reducing model complexity. We evaluate different combinations of quantization levels and pruning degrees with respect to classification performance and resource efficiency. Our results indicate that competitive accuracy can be retained even under aggressive model compression, enabling efficient and privacypreserving inference.
