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WIRELESS SENSOR NETWORKS FOR IOT ENABLED STRUCTURAL HEALTH MONITORING
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ORDINARY APPLICATION
Published
Filed on 20 November 2024
Abstract
Wireless Smart Sensor Networks (WSSNs) have revolutionized Structural Health Monitoring (SHM) by enabling efficient measurement, assessment, and maintenance of civil infrastructure. Over the past decade, remarkable technological advancements have been achieved in both individual sensor nodes and networked systems. This paper explores key innovations, including event-triggered sensing, multimodal sensing, edge/cloud computing integration, precise time synchronization, real-time data acquisition, decentralized data processing, and enhanced long-term reliability. These advancements have significantly improved the performance, scalability, and cost-effectiveness of SHM systems. Furthermore, the paper highlights full-scale implementations and demonstrations of WSSNs in monitoring complex infrastructures, showcasing their transformative potential in real-world applications. In addition to these breakthroughs, the study identifies persistent challenges and outlines future research directions to address issues such as energy efficiency, robustness, and system scalability. By addressing these challenges, WSSNs will continue to play a pivotal role in advancing SHM, ensuring the safety, sustainability, and longevity of critical infrastructure.
Patent Information
Application ID | 202441090161 |
Invention Field | PHYSICS |
Date of Application | 20/11/2024 |
Publication Number | 48/2024 |
Inventors
Name | Address | Country | Nationality |
---|---|---|---|
Dr. D. Rajesh | Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India | India | India |
Dr. T. Saju Raj | Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India | India | India |
Mr. R. Anto Pravin | Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India | India | India |
Dr. C. Edwin Singh | Assistant Professor (Senior Grade), Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India | India | India |
Dr. M. Sankar | Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India | India | India |
Applicants
Name | Address | Country | Nationality |
---|---|---|---|
VEL TECH RANGARAJAN DR. SAGUNTHALA R&D INSTITUTE OF SCIENCE AND TECHNOLOGY | No. 42, Avadi-Vel Tech Road, Vel Nagar, Avadi, Chennai - 600062, Tamil Nadu, India | India | India |
Specification
Description:FIELD OF INVENTION
This invention relates to Wireless Sensor Networks (WSNs) for IoT-enabled Structural Health Monitoring (SHM), focusing on real-time data acquisition, processing, and transmission to assess the integrity of structures. It integrates low-power sensors, efficient communication protocols, and advanced analytics to detect damages, predict failures, and optimize maintenance, ensuring safety and extending the lifespan of critical infrastructures like bridges, buildings, and pipelines.
BACKGROUND OF INVENTION
Structural Health Monitoring (SHM) is essential for ensuring the safety and longevity of critical infrastructures such as bridges, buildings, dams, and pipelines. Traditional SHM methods rely on manual inspections and wired systems, which are labor-intensive, costly, and often lack the ability to provide real-time data. These limitations pose challenges in detecting early signs of structural degradation or damage, increasing the risk of catastrophic failures.
Wireless Sensor Networks (WSNs) have emerged as a transformative solution for SHM, offering cost-effective, scalable, and real-time monitoring capabilities. The integration of Internet of Things (IoT) technologies further enhances the efficiency of WSN-based SHM systems by enabling seamless communication, remote access, and advanced data analytics. IoT-enabled SHM systems leverage low-power sensors, wireless communication protocols, and cloud-based platforms to collect, transmit, and analyze structural health data. These systems can monitor parameters such as strain, vibration, temperature, and displacement, providing actionable insights for predictive maintenance.
Despite their advantages, WSN-based SHM systems face challenges such as energy efficiency, data reliability, network scalability, and environmental robustness. Advances in sensor miniaturization, energy harvesting, machine learning, and communication technologies are driving the development of more reliable and efficient systems.
The invention of WSNs for IoT-enabled SHM aims to address these challenges, offering a robust framework for real-time structural monitoring. This innovation not only enhances safety and maintenance efficiency but also reduces costs, paving the way for smarter and more sustainable infrastructure management.
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SUMMARY
The invention focuses on the development of Wireless Sensor Networks (WSNs) integrated with Internet of Things (IoT) technologies for Structural Health Monitoring (SHM). This advanced system aims to address the limitations of traditional SHM methods by providing a scalable, cost-effective, and real-time solution for monitoring the health and integrity of critical infrastructures such as bridges, buildings, dams, and pipelines.
The system utilizes a network of low-power, energy-efficient wireless sensors capable of measuring critical parameters like strain, vibration, temperature, and displacement. These sensors communicate wirelessly, eliminating the need for extensive cabling and reducing installation and maintenance costs. Data collected by the sensors is transmitted to a central hub or cloud platform for processing and analysis. IoT integration enables remote monitoring, automated alerts, and predictive maintenance through advanced data analytics and machine learning algorithms.
Key innovations include energy-efficient protocols to extend sensor lifespan, robust communication techniques to ensure data reliability, and adaptive networks that can scale according to infrastructure size and complexity. The system also incorporates environmental robustness to withstand extreme conditions, enhancing reliability and longevity.
This invention enables early detection of structural damages, preventing catastrophic failures and optimizing maintenance schedules. By providing continuous and real-time insights, the system enhances infrastructure safety, reduces maintenance costs, and extends the lifespan of structures. It represents a significant advancement in SHM, aligning with the growing need for smart and sustainable infrastructure management in the modern era.
DETAILED DESCRIPTION OF INVENTION
Over the past decade, Wireless Sensor Networks (WSNs) have established themselves as a transformative, cost-effective platform for deploying extensive sensor networks. These networks have been widely adopted across diverse domains, including commercial, healthcare, military, and industrial sectors. One prominent application is Structural Health Monitoring (SHM), where distributed sensors are used to evaluate the structural integrity of infrastructures. Traditionally, SHM relied on wired sensor networks, but the substantial installation costs and complexity limited their deployment to critical, long-term monitoring scenarios. WSNs, with their reduced costs and ease of maintenance, present a compelling alternative, enabling broader adoption in both short-term and long-term SHM applications.
WSNs facilitate early damage detection, enhancing the lifespan of structures, reducing routine inspection costs, and critically, improving public safety. These systems deploy sensors across key structural locations to collect high-frequency data on parameters such as acceleration, ambient vibrations, loads, and stress. With sampling rates exceeding 100 Hz, WSNs for SHM handle significantly larger data volumes compared to other WSN applications, introducing unique challenges in network design and efficiency.
Sensor nodes transmit data to a central hub, either directly or through multi-hop pathways. Data aggregation and processing, vital for damage detection and localization, can occur at various points in the network, including nodes, cluster-heads, or centralized servers. Structural damage is typically identified by comparing the current modal features-such as mode shapes or natural vibration patterns-to the baseline undamaged state. This integration of WSNs into SHM represents a paradigm shift in infrastructure management, ensuring smarter, safer, and more sustainable structures.
Innovations in Core Functionalities of Wireless Smart Sensors
Wireless smart sensors are typically composed of three to four key subsystems: a sensing interface, a computational core, a wireless communication module, and, in some cases, an actuation interface. Deploying individual wireless smart sensors introduces unique challenges, primarily due to the resource constraints inherent in these edge devices. Among the most significant limitations is restricted battery life, which poses a critical challenge for sustained operation.
To address this issue, event-triggered sensing has emerged as a promising approach, optimizing energy usage by activating sensors only when specific conditions are met. In addition, significant advancements have been made to enhance the traditional functionalities of wireless smart sensors, including the integration of multimeric sensing capabilities and edge computing. These innovations enable sensors to perform more complex tasks, such as analyzing data locally, reducing the reliance on centralized processing, and improving real-time responsiveness.
The following subsections delve into these advancements, as illustrated in Figure 1, highlighting how cutting-edge developments are shaping the future of wireless smart sensors for structural health monitoring applications.
Fig. 1. Advances of key functionalities in wireless smart sensors.
Event-Triggered Sensing
Event-triggered sensing emerges as a transformative approach for enabling prolonged, large-scale monitoring in civil infrastructure, addressing the significant challenge of power limitations in Wireless Smart Sensor Networks (WSSNs) reliant on battery power. Key strategies involve optimizing sensor dormancy and employing duty cycling mechanisms. This method, alongside schedule-based sensing, constitutes a primary framework for wireless data acquisition to enhance energy efficiency in long-term deployments.
Popovic et al. (2017) introduced an innovative event-driven WSSN tailored for railroad infrastructure monitoring, where sentinel and monitoring nodes were strategically placed on tracks. These nodes activated upon detecting train events to sense strain and then reverted to a low-power sleep mode, extending battery life to several months. Similarly, Sarwar et al. (2020) proposed an event-based sensing system using an ultra-low-power microcontroller with a customizable detection mechanism, facilitating uninterrupted long-term monitoring. However, both systems encounter challenges such as data loss due to latency in response to transient structural phenomena. Addressing this, Fu et al. (2018) developed a demand-based wireless smart sensor leveraging Xnodes with an event-based programmable switch, which autonomously controls the activation of high-fidelity sensors to mitigate data loss. Lin et al. (2021) further advanced this domain by introducing techniques for rapid reconstruction of missing data caused by sensor malfunctions or transmission errors. Moreover, Fu et al. (2022) unveiled an intelligent wireless monitoring system incorporating ultra-low-power, event-triggered sensor prototypes capable of delivering on-demand, high-fidelity sensing for unpredictable impact events, ensuring superior performance and reliability.
Multimeric Sensing
In structural health monitoring (SHM), single-metric sensing often fails to provide sufficient data for addressing complex challenges, such as estimating nonlinear residual deformations solely from acceleration records. Multimeric sensing bridges this gap by collecting diverse data types-such as acceleration, strain, and temperature-thereby enabling a more comprehensive understanding of structural behaviors. This multifaceted approach reduces the need for additional sensors while ensuring accuracy.
Sarwar et al. (2020) introduced a versatile sensor capable of activation via vibration, strain, or timer thresholds, minimizing power consumption. Building on this, they enhanced the design to incorporate multimeric event-driven sensing, triggering the system through both vibration and strain. Advanced platforms, such as those equipped with flexible sensor integration, significantly expand the scope of WSSN. For example, Dong et al. (2014) developed the Martlet system, a modular wireless sensing platform allowing integration of multiple sensor boards for simultaneous data acquisition from heterogeneous sensors. The Xnode platform further advances multimeric capabilities with five external sensing channels, enabling innovations like capacitance-based crack monitoring (Taher et al., 2022) and wind hazard analysis via anemometers (Shaheen et al., 2022). These advancements underscore the pivotal role of multimeric sensing in next-generation SHM systems.
Edge Computing
Edge computing has emerged as a critical paradigm in WSSNs, empowering sensors with local processing capabilities to transform raw data into actionable insights directly at the network edge. By decentralizing computation, edge computing enhances efficiency, scalability, and decision-making speed while alleviating the burden on centralized systems and addressing issues like data inundation (Park et al., 2013).
Advanced studies have focused on optimizing edge computing for WSSNs. For instance, Spencer et al. (2017) significantly improved the computational and concurrent execution capabilities of Xnodes. Fu et al. (2016) implemented preemptive multitasking algorithms to maximize resource efficiency and ensure real-time application performance. On the algorithmic front, Hoang and Spencer (2022) introduced a lightweight, onboard displacement estimation technique capable of transforming raw acceleration data at speeds up to 100,000 times faster than conventional methods. Despite challenges such as limited memory, power constraints, and processor speed, hardware-software co-design approaches continue to push the boundaries of edge computing in SHM applications.
Advancements in Wireless Smart Sensor Networks
Following local sensing and processing, sensor nodes transmit data to gateway nodes and, ultimately, to end-users for further structural health monitoring (SHM) applications. However, challenges such as time synchronization, transmission delays, and data loss pose significant barriers. To address these issues, substantial advancements have been achieved across several functionalities, including time synchronization protocols, real-time data acquisition techniques, decentralized data processing, integration with cloud computing, and enhanced long-term reliability of WSSNs. These innovations are pivotal for the effective and efficient operation of SHM systems, as illustrated in Fig. 2.
Fig. 2. Advances of key functionalities in wireless smart sensor networks.
Time Synchronization
Accurate time synchronization is essential for data collection in wireless sensor networks (WSSNs) with independent clocks. Protocols such as the Time-sync Protocol for Sensor Networks (TPSN), Reference Broadcast Synchronization (RBS), and Flooding Time Synchronization Protocol (FTSP) are widely used to address this challenge. Despite their effectiveness, issues like software processing delays, low-quality clock crystals, prolonged sensing durations, and temperature variations can still lead to discrepancies in clock synchronization. These inconsistencies can cause errors in measurement data, impacting applications like system identification and damage detection. Researchers have proposed various strategies to mitigate these challenges. For instance, a two-stage synchronization method combining linear clock drift compensation with resampling has achieved microsecond-level accuracy. Building on this approach, nonlinear drift compensation methods and GPS-assisted synchronization strategies have been introduced for improved accuracy in diverse environments. Recent advancements involve preemptive multitasking and real-time synchronization via beacon exchange, enhancing synchronized sensing capabilities and enabling event monitoring with high precision.
Real-Time Data Acquisition
Transmitting sensor data wirelessly after collection can introduce delays due to bandwidth limitations and radio interference, posing challenges for applications requiring real-time processing. Real-time data acquisition necessitates that sensors collect and transmit data within short intervals, which may lead to scheduling conflicts in event-driven systems like TinyOS. Simultaneously, efficient, lossless data transmission across multiple nodes is vital to optimize network throughput. Techniques such as comprehensive timing analyses, improved time-division multiplexing, and dual-processor schemes have enhanced real-time acquisition performance, achieving significantly higher throughput. Additionally, adaptive scheduling and advanced multitasking protocols have further addressed these challenges, ensuring efficient data handling for large-scale sensor networks.
Decentralized Data Processing
Decentralized data processing, or distributed computing, is critical for efficient operation in WSSNs. This involves organizing networks into hierarchical levels where neighboring nodes collaboratively process raw data, reducing transmission requirements and improving scalability. However, challenges such as uneven resource distribution, sensor failures, and multi-hop communication in large networks can impact performance. Solutions include power-optimized reprogrammable systems and topology reconfiguration strategies to address sensor failures. Algorithms for synchronized multi-hop communication and time-division techniques have been developed to enhance reliability and power efficiency while ensuring high data accuracy in distributed systems.
Cloud Computing
The increasing scale and complexity of WSSNs necessitate cloud-based solutions for data storage, management, and analytics. Cloud infrastructure provides robust resources for monitoring and visualization in structural health monitoring (SHM) applications. However, challenges remain in developing frameworks and computational techniques for real-time and long-term monitoring. Advanced platforms integrating database technologies and real-time modeling have been proposed to address these needs, enabling efficient big-data analytics. Leveraging parallel computing and machine learning techniques, recent systems can perform damage detection and proactive maintenance with high accuracy and efficiency, demonstrating feasibility through real-world infrastructure applications.
Long-Term Reliability
The long-term reliability of WSSNs is crucial for consistent performance in extended deployments and harsh environments. Common issues include power and hardware failures, software malfunctions, and environmental stresses. Reliability assessments often focus on communication performance and fault detection within the network. Enhancements in hardware design, power management, and anomaly detection algorithms contribute to improving network stability. Advanced methods like hybrid machine learning models and non-parametric prediction techniques have been developed for effective detection and diagnosis of anomalies, ensuring sustained reliability and functionality over time.
Full-Scale Applications in SHM
WSSNs play a vital role in monitoring critical infrastructure such as bridges, skyscrapers, and stadiums, enhancing public safety and extending structural lifespans. However, deploying these systems in real-world scenarios introduces challenges such as signal interference, network topology complexities, and long-distance data transmission, which differ significantly from controlled lab environments. Overcoming these challenges through innovative solutions has enabled the effective application of WSSNs in full-scale SHM projects, ensuring reliable performance and valuable insights into infrastructure health.
The applications of wireless sensor networks (WSNs) for structural health monitoring have been demonstrated in various studies involving diverse structures and purposes. For instance:
• Phanish et al. deployed 40-50 sensor nodes and 5-10 cluster heads using LIS344ALH sensors by STMicroelectronics at the Bobby Dodd Stadium to evaluate the accuracy of a new synchronization algorithm, measuring acceleration.
• Potenza et al. utilized 16 accelerometers (unspecified models) to monitor the Basilica S. Maria di Collemaggio building, assessing its response to seismic events.
• Häckell et al. tested and validated six Martlet wireless sensor nodes integrated with three-axis accelerometers on a wind turbine for acceleration measurements.
• Liu et al. used 29 Martlet units to verify the reliability of the Martlet wireless sensing system on a concrete highway bridge, measuring acceleration, strain, and displacement.
• Badon et al. validated the performance of Kionix KXR94-2050 accelerometers on the Pietratagliata bridge, employing 10 sensors for acceleration monitoring.
• Fu et al. employed eight Xnode wireless smart sensors to ensure data acquisition fidelity on a suspension bridge, focusing on acceleration.
• Hoang et al. assessed the displacement-based condition of nine timber trestle and two steel truss railroad bridges using Xnode sensors. They deployed 2-3 sensors on each trestle bridge and 6-8 sensors on each steel truss bridge to measure acceleration.
• Luo et al. installed 323 sensors, including 49 accelerometers, 100 wind sensors, and 174 vibrating wire and temperature sensors, at Hangzhou East Metro Station to monitor the station's state and internal force redistribution. Measurements included stress, acceleration, wind load, and temperature.
A WSSN-based SHM platform was developed and deployed at Bobby Dodd Stadium at Georgia Tech. This platform utilized a power-efficient, scalable, and clustered WSN testbed to collect real-time data during football games and other major events. The system monitored the stadium's structural behavior and its correlation with spectator activities. The sensing devices achieved synchronization without GPS or beacons, ensuring sufficient accuracy for modal analysis. A cognitive radio backhaul link was established for communication between the WSSN in the stadium and the lab's servers.
Phanish et al. conducted permanent seismic monitoring of the Basilica S. Maria di Collemaggio, a historic building that was partially damaged during the L'Aquila earthquake. They deployed 16 accelerometer sensors, 8 extensometers, 3 wall inclinations, and 1 node gateway on the structure to monitor its condition over a three-year seismic monitoring period. The study analyzed the acceleration data collected in both the frequency and time domains, revealing the complex interaction between the masonry structure and temporary protective devices.
Häckell et al. proposed a three-layer algorithmic framework for SHM systems and applied it to a 3 kW wind turbine. Six three-axis accelerometers and six Martlet wireless sensor nodes were installed to collect data on acceleration, environmental conditions, and operational states. The data, including lateral acceleration at various heights and wind speed and direction, were analyzed to explore the framework's modularity and its ability to monitor the wind turbine's health.
Liu et al. deployed 29 Martlet units on a prestressed concrete highway bridge in Georgia. These units, equipped with accelerometers, strain gauges, and magnetostrictive displacement sensors, measured the bridge's response to traffic and environmental stimuli. Hammer tests were also conducted, and modal analysis of the collected acceleration data demonstrated the reliability of the Martlet wireless sensing system.
Bedon et al. conducted an experimental verification of wireless MEMS accelerometers on a cable-stayed bridge in Pietratagliata, Italy. Ten sensors were deployed to monitor the deformation of the bridge slab, and the dynamic parameters of the bridge were estimated using Structural Modal Identification Toolsuite software. The results showed that MEMS accelerometers, even in the prototype stage, offer a reliable and cost-effective solution for bridge monitoring.
Fu et al. performed field tests on a pedestrian bridge in Lake Woods, Illinois, using Xnode wireless smart sensors. Eight Xnodes were deployed, with one serving as a gateway and the others measuring dynamic bridge responses. A modal analysis was conducted comparing wireless and wired sensor data, and the results demonstrated a high level of fidelity in the wireless data acquisition system.
Hoang et al. implemented a wireless monitoring system on nine timber trestle and two steel truss railroad bridges in Marion, Illinois. The system recorded 944 datasets, including 419 train crossing events. The data showed efficient energy usage, with battery power readings consistently above 3.5 V, and dynamic displacement measurements were analyzed using Statistical Process Control (SPC) for structural condition assessment.
Luo et al. applied a WSSN at the Hangzhou East Railroad Station. The system included 323 sensors of various types, such as vibrating wire sensor nodes for monitoring strain, acceleration, temperature, and wind load. The sensors were integrated into a flexible tree-type network, collecting data throughout the structure's life cycle to better understand its states and internal force redistribution during both construction and service phases.
Challenges and Future Research Directions
Based on the reviewed literature, several key challenges remain in the implementation of Wireless Sensor Networks (WSSN) for Structural Health Monitoring (SHM). Notably, these challenges include data gaps due to event-triggered sensing, increasing complexity in SHM algorithms, limitations in data processing speed, and the need for high sampling frequencies and resolution. The following subsections highlight critical issues, including limited power resources, insufficient computing capabilities, and environmental vulnerabilities, and propose directions for future research.
Limited Power Resources
Wireless sensors are often powered by batteries, which have finite capacities. Once the battery depletes, the sensor node either becomes inactive or requires manual replacement. Remote replacement is costly, labor-intensive, and in some cases, impractical. Although extensive research has focused on optimizing power consumption, many wireless sensor lifetimes still fall short for intensive sensing applications. The limited power supply remains one of the main barriers to replacing wired systems with WSSN for SHM. To address this, future research should focus on optimizing cluster sizes, utilizing low-power sensor modes, and exploring energy harvesting techniques. Solar and wind power have been widely explored, though they face challenges due to their intermittent nature, especially when sensors are deployed in sheltered locations. Vibration energy harvesting offers a promising alternative but is constrained by limitations such as the low energy generated by traffic-induced vibrations. Future studies should explore more efficient and reliable energy harvesting strategies. Additionally, adaptive techniques for putting WSSNs into low-power or deep sleep modes, either through rule-based methods or data-driven algorithms, could help optimize power consumption.
Insufficient Computing Capabilities
As WSSNs generate vast amounts of data, sufficient computational resources are required to handle big data challenges. Edge and cloud computing have emerged as solutions for transforming raw data into actionable information. However, limited resources in WSSNs, such as memory and processing power, hinder the application of complex algorithms, such as machine learning. These limitations also affect real-time execution performance, such as visualizing data in real time. To address these issues, future studies should focus on developing high-efficiency algorithms and models optimized for the limited computational resources of WSSNs. In addition, leveraging powerful edge, fog, or cloud computing architectures, combined with advanced hardware and flexible software, could significantly improve the capabilities of WSSNs for data-driven, high-rate processing and real-time execution.
Environmental Vulnerabilities
Ensuring the reliability of WSSNs is a challenge, especially in harsh environmental conditions. Sensors deployed outdoors are vulnerable to natural phenomena like rain, snow, wind, and extreme temperature variations, which can lead to frequent errors and accelerated sensor degradation. For instance, temperature fluctuations can cause strain gauges to drift, requiring compensation methods. Additionally, WSSNs deployed in large-scale civil infrastructures often face issues with signal transmission, particularly over long distances, which can drain sensor batteries and reduce system reliability. Traditional signal processing techniques can detect sensor faults but often require significant human intervention to diagnose and resolve issues. Furthermore, distinguishing between actual events and measurement errors when anomalous data is detected remains a challenge. Currently, there is a lack of an automatic fault detection and localization system suitable for large-scale, long-term deployment. Future research should focus on advanced signal processing techniques, efficient algorithms for sensor fault detection and diagnosis, and adaptive network topology management to enhance the reliability and performance of WSSNs.
WSSNs offer a promising solution for long-term SHM due to their ease of installation and lower costs compared to traditional wired systems. Recent advancements in event-triggered sensing, multimeric sensing, and edge computing have enhanced the capabilities of individual sensor nodes for complex applications. Technological improvements in time synchronization, real-time data acquisition, decentralized data processing, and cloud computing have also contributed to the effective operation of WSSNs. However, challenges related to limited power resources, insufficient computing capabilities, and environmental vulnerabilities still exist. Addressing these challenges will require interdisciplinary efforts across civil, mechanical, electrical, and computer science engineering fields to further improve and enable the widespread deployment of WSSNs for complex structural monitoring tasks.
DETAILED DESCRIPTION OF DIAGRAM
Fig. 1. Advances of key functionalities in wireless smart sensors.
Fig. 2. Advances of key functionalities in wireless smart sensor networks. , Claims:1. Wireless Sensor Networks for IoT Enabled Structural Health Monitoring claims that WSSNs offer a cost-effective alternative to traditional wired systems for SHM, reducing installation and maintenance costs.
2. The wireless nature of WSSNs enables easier and faster installation, especially in large or remote structures, without the need for extensive wiring.
3. WSSNs are highly scalable, making them suitable for monitoring both small and large-scale structures by easily adding more sensor nodes.
4. WSSNs enable real-time data acquisition from various sensors, facilitating timely decision-making and proactive maintenance of structures.
5. The integration of IoT allows WSSNs to collect vast amounts of data, which can be processed using advanced algorithms and machine learning models for more accurate decision-making.
6. IoT-enabled WSSNs allow for remote monitoring and management, enabling engineers and operators to monitor structures from a distance, reducing the need for physical inspections.
7. The limited power resources of sensor nodes, typically reliant on batteries, pose a significant challenge for long-term deployment, necessitating energy-efficient solutions such as energy harvesting or low-power modes.
8. WSSNs often face constraints in memory and processing capabilities, which hinder the ability to deploy complex algorithms like machine learning directly on sensor nodes.
9. Sensors deployed in harsh outdoor environments face risks from weather conditions (e.g., rain, snow, temperature variations), which can impact the reliability and durability of the network.
10. Ensuring reliable data collection and sensor fault detection remains a challenge, as traditional methods require human intervention, and automatic detection systems are still under development.
Documents
Name | Date |
---|---|
202441090161-COMPLETE SPECIFICATION [20-11-2024(online)].pdf | 20/11/2024 |
202441090161-DRAWINGS [20-11-2024(online)].pdf | 20/11/2024 |
202441090161-FORM 1 [20-11-2024(online)].pdf | 20/11/2024 |
202441090161-FORM-9 [20-11-2024(online)].pdf | 20/11/2024 |
202441090161-POWER OF AUTHORITY [20-11-2024(online)].pdf | 20/11/2024 |
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