Crack Detection in Concrete Structures Using Deep Learning

Sustainability, Journal Year: 2022, Volume and Issue: 14(13), P. 8117 - 8117

Published: July 2, 2022

Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically maintain its reliability and structural health. Visual signs of cracks depressions indicate stress wear tear over time, leading failure/collapse if these are located at critical locations, in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long times rely on their empirical subjective knowledge. This lengthy process results delays that further compromise the infrastructure’s integrity. To address this limitation, study proposes a deep learning (DL)-based autonomous crack detection method using convolutional neural network (CNN) technique. improve CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training pretrained VGG16 architecture create different models. The chosen methods (grayscale, thresholding, edge detection) have been used image processing (IP) detection, but not DL. found grayscale models (F1 score 10 epochs: 99.331%, 20 99.549%) had similar 99.432%, 99.533%), with increasing greater rate more (grayscale: +2 TP, +11 TN images; RGB: +4 images). thresholding edge-detection reduced compared (20-epoch F1 −0.723%, −0.402%). suggests DL does colour. Hence, model has implications automated concrete infrastructures gathered information.

Language: Английский

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Wireless Body Area Network (WBAN): A Survey on Architecture, Technologies, Energy Consumption, and Security Challenges

Journal of Sensor and Actuator Networks, Journal Year: 2022, Volume and Issue: 11(4), P. 67 - 67

Published: Oct. 18, 2022

Wireless body area networks (WBANs) are a new advance utilized in recent years to increase the quality of human life by monitoring conditions patients inside and outside hospitals, activities athletes, military applications, multimedia. WBANs consist intelligent micro- or nano-sensors capable processing sending information base station (BS). Sensors embedded bodies individuals can enable vital exchange over wireless communication. Network forming these sensors envisages long-term medical care without restricting patients’ normal daily as part diagnosing caring for patient with chronic illness after surgery manage emergencies. This paper reviews WBAN, its security challenges, sensor network architecture functions, communication technologies. The work reported this investigates significant security-level challenge existing WBAN. Lastly, it highlights various mechanisms increasing decreasing energy consumption.

Language: Английский

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Chained-Drones: Blockchain-based privacy-preserving framework for secure and intelligent service provisioning in Internet of Drone Things

Computers & Electrical Engineering, Journal Year: 2023, Volume and Issue: 110, P. 108772 - 108772

Published: June 15, 2023

Language: Английский

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Digital Twin-Driven Trust Management in Open RAN-Based Spatial Crowdsourcing Drone Services

IEEE Transactions on Green Communications and Networking, Journal Year: 2024, Volume and Issue: 8(3), P. 1061 - 1075

Published: May 21, 2024

We introduce "TMIoDT," a pioneering framework aimed at bolstering communication security in the Internet of Drone Things (IoDT) integrated with Open Radio Access Networks (Open RAN), specific focus on bushfire monitoring applications. Our novel contributions include seamless integration digital twin technology blockchain to establish robust trust management system IoDT context. This approach addresses critical vulnerabilities associated unsecured wireless networks IoDT, such as data integrity issues and susceptibility cyber threats. The TMIoDT encompasses mutual authentication mechanism secure interactions key exchanges among entities, including drones Unmanned Ground Vehicles (UGVs). Furthermore, it leverages for credible employs twins model UGV servers accurately, enhancing relationship modeling. An advanced Intrusion Detection System (IDS), utilizing Stacked Variational Autoencoder (SVA) Attention-based Bidirectional LSTM (ABL), is implemented anomaly detection, complemented by blockchain-based transaction writing scheme verification. comprehensive evaluation, ToN-IoT ICIDS-2017 network intrusion datasets, confirms TMIoDT's effectiveness significantly improving reliability IoDT.

Language: Английский

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Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement

Sensors, Journal Year: 2022, Volume and Issue: 22(6), P. 2129 - 2129

Published: March 9, 2022

Nowadays, the rapid deployment of Wireless Sensor Networks (WSNs) and integration Internet Things (IoT) technology has enabled their application to grow in various industrial fields our country. Various factors influence success WSN development, particularly improvements Medium Access Control (MAC) protocols, for which WSNs-IoT are deemed vital. Several aspects should be considered, such as energy consumption reduction, performance, scalability a large nodes, clustering intelligence. However, many protocols address this aspect constrained view handling medium access. This work presents state-of-the-art review recently proposed MAC protocols. Different methods approaches enhance main performance factors. issue considered attribute that protocol support. A comparison table is given provide further details about using these algorithms improve issues, network throughput, end-to-end delay, packet drop, translated into consumption.

Language: Английский

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An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm

Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 231 - 231

Published: June 2, 2023

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and protection can also avoid invasive damage caused by traditional methods. An adaptive cooperative optimization seagull algorithm optimal coverage heterogeneous sensor networks is proposed order to address the issue blind zone redundancy initial random deployment network nodes sensing layer Things. Calculate individual fitness value according total number nodes, radius, area edge length, select population, aim at maximum rate determine position current solution. After continuous updating, when iterations maximum, global output output. solution node's mobile position. A scaling factor introduced dynamically adjust relative displacement between individual, which improves exploration development ability algorithm. Finally, fine-tuned opposite learning, leading whole move correct given search space, improving jump out local optimum, further increasing accuracy. experimental simulation results demonstrate that, compared with energy consumption PSO algorithm, GWO basic SOA PSO-SOA this paper 6.1%, 4.8%, 1.2% higher than them, respectively, reduced 86.8%, 68.4%, 52.6%, respectively. method based on improve reduce cost, effectively network.

Language: Английский

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DroneSSL: Self-Supervised Multimodal Anomaly Detection in Internet of Drone Things

IEEE Transactions on Consumer Electronics, Journal Year: 2024, Volume and Issue: 70(1), P. 4287 - 4298

Published: Feb. 1, 2024

In this study, we introduce a pioneering framework, DroneSSL, that integrates the concept of spatial crowdsourcing with TinyML to enhance anomaly detection in Internet Drone Things (IoDT). This innovative approach leverages drones and unmanned ground vehicles (UGVs) for expansive data collection environments are typically inaccessible or hazardous, such as during Australian bushfire incidents. By employing lightweight machine learning models alongside advanced communication technologies, DroneSSL transcends traditional spatial-temporal analysis methods. It efficiently processes multimodal from diverse Points-of-Interest (PoIs), significantly improving quality speed analysis. The framework's integration temporal feature extraction module Graph Neural Network (GNN) its adaptable, scalable GNN architecture tailor real-time operations resource-constrained IoDT environments. Achieving an 89.6% F1 score, marks substantial 4.9% improvement over existing approaches, highlighting effectiveness critical applications environmental surveillance emergency response. advancement not only showcases potential combining but also sets new standard efficient, detection, paving way future innovations IoT edge devices monitoring systems.

Language: Английский

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Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks

Soft Computing, Journal Year: 2023, Volume and Issue: 27(16), P. 11853 - 11867

Published: June 14, 2023

Language: Английский

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Dynamic GNN-based multimodal anomaly detection for spatial crowdsourcing drone services

Digital Communications and Networks, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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BC-IoDT

Published: Oct. 17, 2022

We leverage blockchain technology for drone node authentication in internet of things (IoDT). During the procedure, credentials nodes are examined to remove malicious from system. In IoDT, drones responsible gathering data and transmitting it cluster heads (CHs) further processing. The CH collects organizes data. Due computational load, their energy levels rapidly deplete. To overcome this problem, we present a low-energy adaptive clustering hierarchy (R2D) protocol based on distance, degree, residual energy. R2D is used replace CHs with normal biggest energy, shortest distance BS. cost keeping big volume high. employ Interplanetary File System (IPFS), address issue. Moreover, IPFS protects user using industry-standard encryption technique AES-128. This standard compares well other current methods. Using consensus mechanism proof work requires high amount computing resources transaction verification. suggested approach leverages known as authority (PoA) problem. results simulations indicate that system model functions effectively efficiently. A formal security analysis conducted assess smart contract's resistance attacks.

Language: Английский

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