Integration Of Sensor Networks Research Articles

Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis—Part 2: Implementation

The economic cost of repairing corrosion-affected reinforced concrete structures (RCSs) means that reliable and accurate assessment and early detection methods must be sought after. Conventional techniques, such as visual inspections, or measuring either cover layer resistivity or the corrosion potential, are methods that require accessibility and involve personnel having to travel to take in situ measurements. Monitoring by embedded sensors is a much more efficient approach that allows early detection by remote sensing. This work presents the implementation of a new measurement protocol regarding the existing monitoring system called INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring). Along with the corrosion intensity measurement in embedded sensors, it also proposes monitoring the double layer capacity of the sensors’ responses. It aims to determine, along with the rebars’ corrosion rate, the triggering agent of the corrosion process. This study was carried out using three reinforced concrete scaled columns that were exposed to different environments. The results demonstrate with this new protocol that the remote INESSCOM monitoring system can establish the corrosion rate and identify the precursor agent of corrosion (carbonation or chlorides), even when the recorded corrosion rates are similar.

Computer-aided resilience: Advanced techniques for disaster management in smart urban environments

This research paper explores innovative contributions to the field of disaster management in smart urban environments, with a particular focus on integrating advanced computer-aided techniques, specifically GRU-CNN. Three key contributions are highlighted: (1) the development of dynamic risk assessment algorithms utilizing GRU-CNN for real-time analysis and predictive modeling, enabling proactive disaster mitigation; (2) the establishment of an integrated sensor network infrastructure for early warning systems, leveraging various sensors and GRU-CNN-based data analytics to detect and respond to potential disasters at their nascent stages; and (3) the implementation of human-centric resilience planning, utilizing GRU-CNN-based computer-aided tools to simulate disaster scenarios and engage communities in preparedness efforts. The dynamic risk assessment algorithms presented in this paper, powered by GRU-CNN, enable continuous monitoring and analysis of diverse data sources, fostering a proactive approach to disaster preparedness. The integrated sensor network architecture enhances early warning capabilities, allowing for timely responses to emerging threats through the application of GRU-CNN methodologies. Furthermore, the human-centric resilience planning approach introduces virtual simulations using GRU-CNN to model disaster scenarios, facilitating the testing and refinement of strategies in a risk-free environment. This approach not only ensures the adaptability of plans but also engages and educates communities, fostering a culture of preparedness, with GRU-CNN playing a pivotal role in the process. Through these contributions, this research paper seeks to advance the discourse on disaster management in smart urban environments, emphasizing the integration and effectiveness of GRU-CNN in enhancing resilience and response strategies. By leveraging cutting-edge GRU-CNN technologies and prioritizing community involvement, the proposed techniques aim to provide a more robust and adaptive response to the challenges posed by natural and man-made disasters in urban settings.

An intelligent insole based on wide-range flexible pressure sensor

In this paper, an intelligent insole that contains five wide-range flexible capacitive pressure sensors is developed. The output signal of these five sensors is collected and processed by a portable microprocessor system, which is then transmitted to a cloud platform via a Wi-Fi module. The ensuing data visualization is constructed with a WeChat mini-program. The wide-range flexibility is achieved by employing composition materials with a high pressure-sensitive effect. A novel approach is proposed to fabricate flexible pressure-sensitive dielectrics, thereby significantly enhancing the sensitivity and detection range of pressure sensors. The fabrication process involves utilizing polydimethylsiloxane (PDMS) as a flexible substrate, incorporating nano-iron powder and polyvinylidene fluoride as sensitizing materials, and applying an external magnetic field to control the distribution of iron nanoparticles during the curing process of PDMS. Furthermore, a motion detection system tailored for these flexible sensors is developed. By integrating sensor networks with Internet of things technology, the application potential of wide-range flexible pressure sensors in kinematics and medical rehabilitation fields can be effectively realized.

Design of cloud data storage security and financial risk control management early warning system based on sensor networks

The wide application of sensor networks has brought great challenges to cloud data storage, such as transmission security, data privacy protection and financial risk control. In order to deal with these challenges, the system establishes a comprehensive management early warning system to ensure the data security and financial risk control of the sensor network. The system integrates sensor network technology and cloud data storage technology, and stores data in the cloud through the collection and transmission of environmental data by sensor nodes. In order to ensure the security of the stored process, the system adopts a series of security mechanisms, such as data encryption, access control and identity authentication, and also introduces the financial risk control module, which helps users to warn and manage financial risks through real-time monitoring and analysis of data in the sensor network. The system provides a user-friendly management interface, allowing users to easily configure and monitor the operating state of the sensor network, and supports flexible expansion and customization to adapt to the needs of different scenarios. Through the application of this system, users can effectively manage and control the security and financial risks of sensor networks in cloud data storage, and improve the efficiency and reliability of data storage management.

Enhancing Plant Disease Classification through Manual CNN Hyperparameter Tuning

Diagnosing plant diseases is a challenging task due to the complex nature of plants and the visual similarities among different species. Timely identification and classification of these diseases are crucial to prevent their spread in crops. Convolutional Neural Networks (CNN) have emerged as an advanced technology for image identification in this domain. This study explores deep neural networks and machine learning techniques to diagnose plant diseases using images of affected plants, with a specific emphasis on developing a CNN model and highlighting the importance of hyperparameters for precise results. The research involves processes such as image preprocessing, feature extraction, and classification, along with a manual exploration of diverse hyperparameter settings to evaluate the performance of the proposed CNN model trained on an openly accessible dataset. The study compares customized CNN models for the classification of plant diseases, demonstrating the feasibility of disease classification and automatic identification through machine learning-based approaches. It specifically presents a CNN model and traditional machine learning methodologies for categorizing diseases in apple and maize leaves, utilizing a dataset comprising 7023 images divided into 8 categories. The evaluation criteria indicate that the CNN achieves an impressive accuracy of approximately 98.02%.

A Learning based Secure Routing Approach using Deep Reinforcement Learning in IoT Integrated Wireless Sensor Network

The usage of Wireless Sensor Network (WSN) is ubiquitous in nature. With the emergence of Internet of Things (IoT) technology and its unprecedented use cases, the role of sensor networks as part of IoT application became crucial. WSN became backbone of IoT to realize integration between physical and digital worlds and connectivity to Internet. However, IoT devices are resource constrained with limited computational capabilities. The entire network is distributed in nature and has increased complexity. Routing in such WSN integrated IoT network plays an important role in achieving meaningful communication among objects. In this context, it is indispensable to have more energy efficient routing method. Since the IoT integrated sensor network is highly complicated, it is very dynamic in nature. Thus routing decisions are also dynamic leading to much importance to routing in such use cases. With the emergence of Artificial Intelligence (AI), it became possible to solve complex real world problems through learning based approach which acquires desired intelligence prior to making decisions. In this paper we proposed a deep reinforcement learning based routing mechanism for energy efficient routing in WSN-IoT integrated application. We proposed novel algorithms for network setup, formation of clusters and routing. Our method adapts to network changes due to energy levels, mobility and makes learning based routing decisions. We enhanced the method further with security to ensure its Qualityof Service (QoS) in presence of attacks. Our simulation study using MATLAB has revealed that the proposed secure routing approach outperforms existing protocols.

Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis-Part 1: Laboratory Validation.

Reinforced Concrete Structures (RCS) are a fundamental part of a country's civil infrastructure. However, RCSs are often affected by rebar corrosion, which poses a major problem because it reduces their service life. The traditionally used inspection and management methods applied to RCSs are poorly operative. Structural Health Monitoring and Management (SHMM) by means of embedded sensors to analyse corrosion in RCSs is an emerging alternative, but one that still involves different challenges. Examples of SHMM include INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring), a tool that has already been implemented in different real-life cases. Nevertheless, work continues to upgrade it. To do so, the authors of this work consider implementing a new measurement procedure to identify the triggering agent of the corrosion process by analysing the double-layer capacitance of the sensors' responses. This study was carried out on reinforced concrete specimens exposed for 18 months to different atmospheres. The results demonstrate the proposed measurement protocol and the multivariate analysis can differentiate the factor that triggers corrosion (chlorides or carbonation), even when the corrosion kinetics are similar. Data were validated by principal component analysis (PCA) and by the visual inspection of samples and rebars at the end of the study.

Remote Monitoring and Controlling Electrical Devices for Industrial Application

Abstract: The advent of advanced technologies has revolutionized the industrial landscape, providing new opportunities for enhancing efficiency and productivity. In this context, remote monitoring and controlling of electrical devices have emerged as a crucial aspect of industrial automation. This paper presents a comprehensive study on the design and implementation of a remote monitoring and controlling system for electrical devices in industry. By integrating sensor networks, wireless communication protocols, and cloud-based platforms, the system establishes a seamless connection between the devices and the centralized monitoring station. This allows operators and engineers to have constant access to vital data and control functionalities, regardless of their physical location. The key components of the system include intelligent sensors deployed in the industrial environment, a reliable wireless communication network for data transmission, and a cloud-based platform for data storage and analysis. The sensors collect various parameters such as voltage, current, temperature, and power consumption, providing a comprehensive overview of the electrical devices' operational status. The wireless network facilitates real-time data transmission to the cloud platform, where the data is securely stored and made available for analysis. The cloud-based platform not only offers data storage but also supports advanced analytics and visualization techniques. This enables operators to gain valuable insights into device performance, identify potential issues, and take proactive measures to optimize operations. Furthermore, the system provides remote control capabilities, allowing operators to adjust device settings, troubleshoot problems, and initiate maintenance actions, all through a user-friendly. The benefits of implementing this remote monitoring and controlling system are numerous. It enables predictive maintenance, reducing downtime and minimizing the risk of equipment failure. Real-time monitoring helps detect anomalies and abnormal behaviours, enabling timely interventions to prevent potential hazards. Additionally, remote access to device control enhances operational flexibility and reduces the need for on-site presence, leading to significant cost savings and improved overall efficiency.

Static Loads Influence on Modal Properties of the Composite Cylindrical Shells with Integrated Sensor Network

This paper presents the results of experimental and numerical studies of the dynamic parameters of composite cylindrical shells loaded under axial tension. Five composite structures were manufactured and loaded up to 4817 N. The static load test was carried out by hanging the load to the lower part of a cylinder. The natural frequencies and mode shapes were measured during testing using a network of 48 piezoelectric sensors that measure the strains of composite shells. The primary modal estimates were calculated with ARTeMIS Modal 7 software using test data. The methods of modal passport, including modal enhancement, were used to improve the accuracy of the primary estimates and reduce the influence of random factors. To estimate the effect of a static load on the modal properties of a composite structure, a numerical calculation and a comparative analysis of experimental and numerical data was carried out. The results of the numerical study confirmed that natural frequency increases with increasing tensile load. The data obtained from experimental results were not fully consistent with the results of numerical analysis, but showed a consistent pattern, repeating for all samples.

An autonomous microchip for real-time, label-free immune cell analysis

Characterization of cell populations and identification of distinct subtypes based on surface markers are needed in a variety of applications from basic research and clinical assays to cell manufacturing. Conventional immunophenotyping techniques such as flow cytometry or fluorescence microscopy require immunolabeling of cells, expensive and complex instrumentation, skilled operators, and are therefore incompatible with field deployment and automated cell manufacturing systems. In this work, we introduce an autonomous microchip that can electronically quantify the immunophenotypical composition of a cell suspension. Our microchip identifies different cell subtypes by capturing each in different microfluidic chambers functionalized against the markers of the target populations. All on-chip activity is electronically monitored by an integrated sensor network, which informs an algorithm determining subpopulation fractions from chip-wide immunocapture statistics in real time. Moreover, optimal operational conditions within the chip are enforced through a closed-loop feedback control on the sensor data and the cell flow speed, and hence, the antibody-antigen interaction time is maintained within its optimal range for selective immunocapture. We apply our microchip to analyze a mixture of unlabeled CD4+ and CD8+ T cell sub-populations and then validated the results against flow cytometry measurements. The demonstrated capability to quantitatively analyze immune cells with no labels has the potential to enable not only autonomous biochip-based immunoassays for remote testing but also cell manufacturing bioreactors with built-in, adaptive quality control.

Information space of sensor networks: Lagrangian, energy-momentum tensor, and applications

It is a challenge to investigate the interrelationship between the geometric structure and performance of sensor networks due to the increasingly complex and diverse architecture of them. This paper presents two new formulations for the information space of sensor networks, including Lagrangian and energy–momentum tensor, which are expected to integrate sensor networks target tracking and performance evaluation from a unified perspective. The proposed method presents two geometric objects to represent the dynamic state and manifold structure of the information space of sensor networks. Based on that, the authors conduct the property analysis and target tracking of sensor networks. To the best of our knowledge, it is the first time to investigate and analyze the information energy–momentum tensor of sensor networks and evaluate the performance of sensor networks in the context of target tracking. Simulations and examples confirm the competitive performance of the proposed method.

PSIV-A-3 Body Temperature Monitoring Using Subcutaneously and Vaginal Sensors in Grazing Cows

Abstract Body temperature is an indicator of animal health and can be used to detect harmful physiological events in advance of clinical symptoms. Traditionally, body temperature in cattle is determined with a rectal thermometer and requires the animal to be restrained. Although systems to automatically monitor cattle health in confinement operations exist, assessing the real-time temperature of grazing cows is not common. Our object was to demonstrate an integrated sensor network for monitoring pastured cattle body temperature in comparison with temperature measured by a vaginal logger. The integrated sensor network (Mahindra & Mahindra; Mumbai, India) communicates with the data obtained from a subcutaneous temperature sensor (EmbediVet, Livestock Labs Inc.; Pittsburg, PA) to a cloud-based data storage platform. The vaginal temperature was measured by an implantable temperature logger (Micro-T 16-bit; Star Oddi, Iceland). Vaginal devices were inserted into the vagina using a controlled internal drug release (CIDR) cleaned of progesterone. The subcutaneous temperature sensors were surgically implanted through a 2cm vertical incision in the neck. Data from the implantable sensor were transmitted via Bluetooth communication to a solar-powered base station. The subcutaneous sensors and vaginal loggers were deployed on 10 grazing cattle over a period of 6 months. Data from only 4 subcutaneous sensors consistently reported data. Temperature values measured by the subcutaneous sensors had no statistical relationship (P=0.595) with body temperature measured by the vaginal temperature loggers. However, the subcutaneous sensors were able to detect changes in body temperature over time within-animal, indicating that errors in predicting temperature were more likely due to frameshift issues than an inability to track true temperature. Further improvements in data-processing and higher sample size are needed to maximize the usability of this sensor network for monitoring cattle body temperature.

Secure node ID assignment for internet integrated sensor network

Secure node ID assignment for internet integrated sensor network

Secure node ID assignment for internet integrated sensor network

Secure node ID assignment for internet integrated sensor network

IPAWL: An integrated power aware Wireless sensor network and Location-Based social network for incidence reporting

One of the constraints in Wireless Sensor Network (WSN) is the limited power of distributed interconnected sensor nodes over the space. There are several proposed architectures for handling power consumption in a WSN. Traditional clustering and routing methods improve the Cluster Head (CH) selection of WSN internodes for balancing energy in the network. In such clustering methods, every message is transmitted from a source node to its CH, from the CH to other CHs, and finally to the BS (Base Station). Therefore, a high amount of energy should be consumed for routing when the source nodes are far from CHs or the BS. This paper proposes an integrated LBSN (Location-Based Social Network) and WSN routing and CH selection method for reducing routes from source nodes to BSs to reduce the energy consumption of WSN. In the integrated network, LBSN nodes can be added to the WSN as BS or WSN sensor nodes. In the first situation, the integrated network is called IPAWL I and the latter builds the IPAWL II network.To evaluate the method, we considered a scenario of smoke detection and reporting on the campus of the University of Calgary and generated a simulation dataset for the LBSN and WSN. Then, results of implementing IPAWL I, IPAWL II, and a stand-alone WSN on the dataset were compared in terms of network energy consumption and message transmission latency. Results demonstrated a 42% reduction in energy consumption in IPAWL I and 33% in IPAWL II. Moreover, a significant reduction of time latency was observed in bigger size networks.

Closed-loop feedback control of microfluidic cell manipulation via deep-learning integrated sensor networks.

Microfluidic technologies have long enabled the manipulation of flow-driven cells en masse under a variety of force fields with the goal of characterizing them or discriminating the pathogenic ones. On the other hand, a microfluidic platform is typically designed to function under optimized conditions, which rarely account for specimen heterogeneity and internal/external perturbations. In this work, we demonstrate a proof-of-principle adaptive microfluidic system that consists of an integrated network of distributed electrical sensors for on-chip tracking of cells and closed-loop feedback control that modulates chip parameters based on the sensor data. In our system, cell flow speed is measured at multiple locations throughout the device, the data is interpreted in real-time via deep learning-based algorithms, and a proportional-integral feedback controller updates a programmable pressure pump to maintain a desired cell flow speed. We validate the adaptive microfluidic system with both static and dynamic targets and also observe a fast convergence of the system under continuous external perturbations. With an ability to sustain optimal processing conditions in unsupervised settings, adaptive microfluidic systems would be less prone to artifacts and could eventually serve as reliable standardized biomedical tests at the point of care.

Integrated sensor networks with error correction for multiplexed particle tracking in microfluidic chips

Spatial manipulation of suspended cells based on their properties is an essential part of numerous microfluidic assays. To further read and analyze the manipulation result, a microscopy system is typically required, which, however, increases the cost and reduces the portability of the entire system. As an alternative, a network of integrated Coulter sensors, distributed over a microfluidic chip, provide rapid and reliable detection of spatially-manipulated cells. Code-multiplexing of distributed Coulter sensors enables simplification of such integration by offloading the hardware complexity into advanced signal processing techniques that are needed to interpret the coded sensor outputs. In this work, we combine code-multiplexed Coulter sensor networks with an error-correction technique, a strategy typically used in telecommunication systems for controlling errors in data over unreliable communication channels. Specifically, we include redundancy in the physical sensor design to alleviate the ambiguity in the signal-decoding process, so that interfering sensor signals due to coincidently-detected cells can be resolved reliably. The presented sensor technology not only tracks the spatiotemporal state of cells under test but also measures their sizes and flow speeds. To demonstrate the sensor concept experimentally, we fabricated a microfluidic device with 10 distributed Coulter sensors designed to produce distinct signal waveforms and performed experiments with suspended human cancer cells to characterize the performance of the sensor platform.

An improved fusion crossover genetic algorithm for a time-weighted maximal covering location problem for sensor siting under satellite-borne monitoring

Traditional location problems usually focus on spatial and temporal impacts of facilities, but few studies have focused on sensor siting under satellite-borne monitoring in a space-ground integrated sensor network. Given the partial coverage and the requirement for continuous coverage in space and time, a time-weighted maximal covering location problem with partial coverage (TMCLP-PC) model is introduced. This model is solved using a modified genetic algorithm (GA)-based approach that utilizes the spatio-temporal characteristics of potential facility sites for faster convergence. The performance of the new GA is tested on a precipitation station siting problem in the Jinsha River Basin on the upper reaches of the Yangtze River in China. The results demonstrate that the proposed GA can significantly reduce the computation time compared with CPLEX, a commercial integer programming solver, and can outperform the greedy algorithm and the GAs with one-point, two-point, fusion, and uniform crossover operators. The applicability of the proposed method and exploration of the design in the new GA are also discussed.

Modular and Integrated Sensor Network of Intelligent Radiation Monitor Systems for Radiological and Nuclear Threat Response

Increased sensitivity to nuclear safety and security issues has prompted public entities and private institutions to maximize their capability to rapidly assess risks and intervene in the case of accident or threat. Quick intervention and response are achieved through nuclear measurements via airborne, land, and underwater systems. A network of cohesive, well-integrated and easy deployable radiation monitoring systems combined with real-time analysis of data is essential to facilitate and enhance the decision-making process during these most critical moments enhancing the quality of the management plan. The presented radiation monitoring systems can be integrated in several form factors which depend mainly on operational needs and internal battery for autonomous operation. Compact ARM based computers is embedded, which can store large amount of data in their non-volatile memory, run automatic data analysis and trigger alarms in case of exceeding radiation levels. All the systems can communicate with redundant interfaces in failover configuration and upload the acquired environmental information in a central database. The same monitoring systems can alert the emergency response personnel on the field as well, through wireless connection to common tablets or cellphone or SMS, guaranteeing a prompt response in case an illicit transportation of radiological or nuclear material is detected.

IEEE Access Special Section Editorial: Convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things

With growing attention from both academia and industry, Industrial Internet of Things (IIoT), comprised of a multitude of connected devices, is supposed to monitor, collect, exchange, analyze, and instantly act on information to intelligently change the industrial device’s behavior or the industrial environment (e.g., autonomous reaction to unexpected changes in production by effectively detecting failures and triggering maintenance processes). Toward this goal, the convergence of sensor networks, cloud computing, and big data in IIoT is recently identified as an essential component. Specifically, to capture various data in IIoT, sensor networks are deployed. Meanwhile, to store and process data powerfully in IIoT, cloud computing platforms are developed. Moreover, to glean valuable findings from the massive data in IIoT, big data analytics tools are utilized. However, to integrate sensor networks, cloud computing, and big data in IIoT in a robust way, there are a lot of tough issues to be solved with respect to various aspects (e.g., framework, greenness, security, quality of service, etc.).