Sensors

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Open AccessCorrection

Correction: Lloret et al. A Data-Driven Framework for Digital Transformation in Smart Cities: Integrating AI, Dashboards, and IoT Readiness. Sensors 2025, 25, 5179

by Ángel Lloret, Jesús Peral, Antonio Ferrández, María Auladell and Rafael Muñoz

Sensors 2025, 25(19), 5938; https://doi.org/10.3390/s25195938 (registering DOI) - 23 Sep 2025

Abstract

There was an error in the original publication [...] Full article

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Open AccessArticle

A Two-Step Filtering Approach for Indoor LiDAR Point Clouds: Efficient Removal of Jump Points and Misdetected Points

by Yibo Cao, Yonghao Huang and Junheng Ni

Sensors 2025, 25(19), 5937; https://doi.org/10.3390/s25195937 (registering DOI) - 23 Sep 2025

Abstract

In the simultaneous localization and mapping (SLAM) process of indoor mobile robots, accurate and stable point cloud data are crucial for localization and environment perception. However, in practical applications indoor mobile robots may encounter glass, smooth floors, edge objects, etc. Point cloud data are often misdetected in such environments, especially at the intersection of flat surfaces and edges of obstacles, which are prone to generating jump points. Smooth planes may also lead to the emergence of misdetected points due to reflective properties or sensor errors. To solve these problems, a two-step filtering method is proposed in this paper. In the first step, a clustering filtering algorithm based on radial distance and tangential span is used for effective filtering against jump points. The algorithm ensures accurate data by analyzing the spatial relationship between each point in the point cloud and the neighboring points, which allows it to identify and filter out the jump points. In the second step, a filtering algorithm based on the grid penetration model is used to further filter out misdetected points on the smooth plane. The model eliminates unrealistic point cloud data and improves the overall quality of the point cloud by simulating the characteristics of the beam penetrating the object. Experimental results in indoor environments show that this two-step filtering method significantly reduces jump points and misdetected points in the point cloud, leading to improved navigational accuracy and stability of indoor mobile robots. Full article

(This article belongs to the Section Radar Sensors)

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872 KB

Open AccessArticle

The Untapped Potential of Ascon Hash Functions: Benchmarking, Hardware Profiling, and Application Insights for Secure IoT and Blockchain Systems

by Meera Gladis Kurian and Yuhua Chen

Sensors 2025, 25(19), 5936; https://doi.org/10.3390/s25195936 (registering DOI) - 23 Sep 2025

Abstract

Hash functions are fundamental components in both cryptographic and non-cryptographic systems, supporting secure authentication, data integrity, fingerprinting, and indexing. While the Ascon family, selected by the National Institute of Standards and Technology (NIST) in 2023 for lightweight cryptography, has been extensively evaluated in its authenticated encryption mode, its hashing and extendable-output variants, namely Ascon-Hash256, Ascon-XOF128, and Ascon-CXOF128, have not received the same level of empirical attention. This paper presents a structured benchmarking study of these hash variants using both the SMHasher framework and custom Python-based simulation environments. SMHasher is used to evaluate statistical and structural robustness under constrained, patterned, and low-entropy input conditions, while Python-based experiments assess application-specific performance in Bloom filter-based replay detection at the network edge, Merkle tree aggregation for blockchain transaction integrity, lightweight device fingerprinting for IoT identity management, and tamper-evident logging for distributed ledgers. We compare the performance of Ascon hashes with widely used cryptographic functions such as SHA3 and BLAKE2s, as well as high-speed non-cryptographic hashes including MurmurHash3 and xxHash. We assess avalanche behavior, diffusion consistency, output bias, and keyset sensitivity while also examining Ascon-XOF’s variable-length output capabilities relative to SHAKE for applications such as domain-separated hashing and lightweight key derivation. Experimental results indicate that Ascon hash functions offer strong diffusion, low statistical bias, and competitive performance across both cryptographic and application-specific domains. These properties make them well suited for deployment in resource-constrained systems, including Internet of Things (IoT) devices, blockchain indexing frameworks, and probabilistic authentication architectures. This study provides the first comprehensive empirical evaluation of Ascon hashing modes and offers new insights into their potential as lightweight, structurally resilient alternatives to established hash functions. Full article

(This article belongs to the Special Issue Blockchain-Based Solutions to Secure IoT)

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21 pages, 4089 KB

Open AccessArticle

A Remote Maintenance Support Method for Complex Equipment Based on Layered-MVC-B/S Integrated AR Framework

by Xuhang Wang, Qinhua Lu, Jiayu Chen and Dong Zhou

Sensors 2025, 25(19), 5935; https://doi.org/10.3390/s25195935 - 23 Sep 2025

Abstract

Augmented reality (AR)-based assisted maintenance methods are effective in completing simple equipment maintenance tasks. However, complex equipment typically requires multi-location remote collaboration due to structural complexity, multiple fault states, and high maintenance costs, significantly increasing maintenance difficulty. This paper therefore proposes a remote maintenance support method for complex equipment based on layered-MVC-B/S integrated AR framework (IAR-RMS). First, clearly define the maintenance content and workflow for multi-person remote collaboration and conduct an in-depth analysis of process control within the task workflow to avoid incomplete or unsystematic maintenance guidance information and processes. Second, analyze collaborative management from the perspectives of maintenance role conflicts and maintenance operation conflicts and implement on-demand permission control and operation sequence management to ensure the timeliness and user-friendliness of multi-person collaboration. Then, integrate the layered architecture, MVC, and B/S architecture to construct a remote maintenance support (RMS) model based on an integrated architecture system, ensuring the reliability and timeliness of the model. Finally, demonstrate the main functional modules of the RMS task process, and use power system disassembly and assembly as an experiment to validate the effectiveness and generalizability of the proposed IAR-RMS method. The results indicate that the proposed IAR-RMS method can effectively realize maintenance support tasks in multi-person remote collaboration scenarios. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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24 pages, 4398 KB

Open AccessArticle

EfficientSegNet: Lightweight Semantic Segmentation with Multi-Scale Feature Fusion and Boundary Enhancement

by Le Zhang, Mengwei Li, Peng Zhang and Peng Liu

Sensors 2025, 25(19), 5934; https://doi.org/10.3390/s25195934 - 23 Sep 2025

Abstract

Semantic segmentation is a crucial task in computer vision with broad applications in autonomous driving, intelligent surveillance, drone vision, and other fields. The current high-precision segmentation models generally suffer from large parameter sizes, high computational complexity, and substantial memory consumption, which limits their efficient deployment in embedded systems and resource-constrained environments. In addition, traditional methods exhibit significant limitations in handling multi-scale targets and object boundaries, particularly during deep feature extraction, where the loss of shallow spatial information often results in blurred boundaries and reduced segmentation accuracy. To address these challenges, we propose EfficientSegNet, a lightweight and efficient semantic segmentation network. This network features an innovative architecture that integrates the Cascade-Attention Dense Field (CADF) module and the Dynamic Weighting Feature Fusion (DWF) module, effectively reducing computational resource requirements while balancing global semantic information and local detail recovery. Experimental results demonstrate that EfficientSegNet achieves an excellent balance between segmentation accuracy and computational efficiency on multiple public datasets, providing robust support for real-time segmentation tasks and applications on resource-constrained devices. Full article

(This article belongs to the Section Intelligent Sensors)

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Sensors, Volume 25, Issue 19 (October-1 2025) – 5 articles

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