Intelligent Sensor Network Research Articles

Retracted: Application Research of Advanced Intelligent Big Data Analysis Based on Intelligent Sensor Network in the Design of Personalized Education Management System and the Construction of Innovation System

Retracted: Application Research of Advanced Intelligent Big Data Analysis Based on Intelligent Sensor Network in the Design of Personalized Education Management System and the Construction of Innovation System

Retracted: A Study on the Application of a Multisubject Collaborative Model Based on Intelligent Sensor Networks in Sports Training

Retracted: A Study on the Application of a Multisubject Collaborative Model Based on Intelligent Sensor Networks in Sports Training

Roadside sensor network deployment based on vehicle-infrastructure cooperative intelligent driving

The sensor network for intelligent roadways, comprised of devices like cameras, laser radars, millimeter-wave radars, and weather stations, is an integral part of the roadside digital infrastructure. One of the main challenges in building intelligent highway sensor networks is to create a controllable, manageable, and useable sensor network with multi-modal sensors deployed on highways. This network should not only facilitate global and scene sensing but also enable collaborative sensing and control functions. Therefore, this study aims to define the concept, main features, and technical connotation of Vehicle-Infrastructure Cooperative Intelligent Driving (VICID). It also outlines the development of a cloud-native cloud control platform for intelligent roadways and refines the technology requirements and indices. This platform is designed to support open services for innovative applications, such as addressing bottlenecks, managing roadworks zones, and implementing dynamic lane assignments for automated driving. Lastly, we introduce Beijing's highway pilot projects, which can serve as a guide and reference for designing and constructing sensor network equipment for intelligent roadways in China, as well as for key technology research and development.

Intelligent Wireless Sensor Networks for Healthcare: Bridging Biomedical Clothing to the IoT Future

The science of achieving a healthy mind, body, and spirit through objectives and activities is known as personal health (PH). We must be aware of our mental, bodily, and social well-being. The term hygiene refers to a wide variety of healthy behaviours. Individuals' healthcare costs and quality of life increased by avoiding or reducing the long-term effects of the disease through knowledge and skills. Biomedical apparel includes sutures, vascular grafts, and biodegradable clothes (BC). Biomedical clothing is anything implanted or incorporated into the human body and used near tissue, blood, or cells. Quick, dependable, and energy-efficient connectivity between wireless sensor networks is necessary (WSNs). Physical layers, media access control, networking layers, and control requirements must be co-designed. For those with lesser means, health insurance will increase in cost. There are difficulties with privacy and cyber security, a higher chance of malpractice claims, and increased time and financial expenditures for doctors and patients. In this study, wireless sensor networks-based personal health biomedical clothing (PH-BC-WSN) was utilized to increase access to high-quality healthcare, increase food production through precision agriculture, and raise the standard of human resources. More effective healthcare and medical asset monitoring systems can be developed thanks to the Internet of Things. Eavesdropping on medical data, modification, fabrication of warnings, denial of service, user tracking and location, physical interference with equipment, and electromagnetic threats were extensively discussed. The article gives several instances of current technology, discusses design challenges including energy efficiency, security, and scalability, provides various demonstrations of current technology, and provides a complete analysis of all the advantages and disadvantages. Despite their many benefits, body sensor networks have several significant obstacles and unresolved research problems, which are described along with some potential answers. As a result, the experimental findings demonstrate that PH-BC-WSN enhances accuracy and reduces response time in inpatient health monitoring.

Retracted: Advanced Intelligent Big Data Analytic‐Based Automated Layout for Environmental Design Intelligent Sensor Networks

Retracted: Advanced Intelligent Big Data Analytic‐Based Automated Layout for Environmental Design Intelligent Sensor Networks

Retracted: Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Retracted: Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Fairness-Aware Harvested Energy Efficiency Algorithm for IRS-Aided Intelligent Sensor Networks with SWIPT

Fairness-Aware Harvested Energy Efficiency Algorithm for IRS-Aided Intelligent Sensor Networks with SWIPT

Open Issues for Intelligent Connectivity Wireless Sensor Networks (WSNs) and IoT: State of the Art

Open Issues for Intelligent Connectivity Wireless Sensor Networks (WSNs) and IoT: State of the Art

An Energy-efficient and Privacy-aware Decomposition Framework for Edge-assisted Federated Learning

Deep Learning (DL) is an essential technology for modern intelligent sensor network and interactive multimedia applications, having problems with user data privacy when training on a central cloud. While Federated Learning (FL) motivates to preserve user privacy, it also causes new problems of lower user terminal usability and training efficiency, which caused substantial energy consumption. This article proposes a novel energy-efficient and privacy-aware decomposition framework to improve user-side FL efficiency under pre-defined privacy requirements with the assistance of Mobile Edge Computing (MEC) and Software Decomposition. It takes the propagation of each neural layer as the migrating unit and considers the tradeoff relationship between privacy and efficiency. We also propose an online scheduling algorithm to optimize the framework’s training performance. Furthermore, we summarize eight privacy-sensitive information classes on which existing privacy attacks base and design configurable privacy preservation mechanisms for each class. Simulations and experiments prove the effectiveness of our framework and algorithm in FL efficiency improvement and the effects of different privacy constraints on the overall training efficiency.

Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing

Due to the global uncertainty caused by social problems such as COVID-19 and the war in Ukraine, companies have opted for the use of emerging technologies, to produce more with fewer resources and thus maintain their productivity; that is why the market for wearable artificial intelligence (AI) and wireless sensor networks (WSNs) has grown exponentially. In the last decade, maintenance 4.0 has achieved best practices due to the appearance of emerging technologies that improve productivity. However, some social trends seek to explore the interaction of AI with human beings to solve these problems, such as Society 5.0 and Industry 5.0. The research question is: could a human-in-the-loop-based maintenance framework improve the resilience of physical assets? This work helps to answer this question through the following contributions: first, a search for research gaps in maintenance; second, a scoping literature review of the research question; third, the definition, characteristics, and the control cycle of Maintenance 5.0 framework; fourth, the maintenance worker 5.0 definition and characteristics; fifth, two proposals for the calculation of resilient maintenance; and finally, Maintenance 5.0 is validated through a simulation in which the use of the worker in the loop improves the resilience of an Industrial Wireless Sensor Network (IWSN).

A market study of early adopters of fault detection and diagnosis tools for rooftop HVAC systems

Retrofit fault detection and diagnosis (FDD) products are undergoing major advances in their ability to optimize the operation and maintenance of building heating, ventilation, and air conditioning (HVAC) systems as a result of advances in artificial intelligence, cloud computing, and low-cost sensor networks. However, many market barriers still exist to their widespread adoption. This paper is the first to study the market and barriers for FDD products from the perspective of early adopters of HVAC rooftop retrofit FDD products as part of a field study of early adopters. Researchers engaged multiple sites and installed different FDD products at these sites for the purposes of (1) evaluating market readiness, market barriers, and user experience, (2) evaluating energy and demand savings, (3) evaluating the purchase, installation, training, and use process, and (4) determining overall FDD product effectiveness. This paper presents and focuses on the market study goals, methods, results, and findings determined based on survey data collected from key stakeholders participating in the study. These stakeholders included FDD providers, energy efficiency program managers and engineers, HVAC contractors, building owners, facility managers, energy managers, and consultants. The study provides results and analyses concerning (1) current knowledge and awareness levels for FDD technologies, (2) attitudes towards FDD products, (3) market barriers and resources available for FDD, and (4) insights on how stakeholders perceive and determine the value and cost for purchasing, using, and adopting FDD technologies and products. The study produced twelve (12) key findings, which provide valuable input for energy efficiency programs in their development of more effective FDD marketing programs that can increase adoption of FDD technologies and reduce barriers to widespread adoption.

Application Research of Advanced Intelligent Big Data Analysis Based on Intelligent Sensor Network in the Design of Personalized Education Management System and the Construction of Innovation System

With the continuous innovation and development of information technology and communication technology, human beings have entered a huge data center. In terms of big data, in-depth discussion on the in-depth application of big data is the current situation and development direction. Based on the intelligent sensor network, this paper conducts theoretical and empirical analysis combined with the application of individualized teaching in teaching and provides some reference opinions for the future development of individual pedagogy. On the basis of the research, the meaning of big data is explained from multiple perspectives. In the empirical research part, based on big data, based on real big data, and using relevant technologies in data mining, the relevance of each training program is analyzed. Mining was carried out using the FP-Growth method to analyze the correlation of each training scheme and optimize the FP-Growth method accordingly. The results of this experiment show that the use of big data can not only help teachers understand their students’ daily life, study habits, and consumption records but also make teachers’ work more dynamic, early warning, and subsidized. The research results are of great significance for guiding colleges and universities to formulate educational programs that are beneficial to students’ development, keep pace with the times, and cultivate modern educational technicians with modern significance.

The Impact of Smart Sensor Networks on Improving the International Competitiveness of China’s Digital Economy Industry in the Context of the Smart Era

At present, intelligent sensor network technology has become a popular field with a wide range of application prospects. With the continuous development of communication protocols and component technologies, the role of intelligent sensor networks in the digital economy is growing. Intelligent sensor network technology industry is a talent-intensive, knowledge-intensive, technology-intensive, technology-intensive, and technology-intensive industry; is a strategic pillar of national development; and is an important industry to promote industrial upgrading, pulling economic development. The development of intelligent sensor network technology is an inevitable choice for China to take a new road of industrialization. In today’s world, the coordinated development of the environment and the economy is being increasingly valued by countries around the world. Therefore, from a macroscopic point of view, the development of intelligent sensor network technology industry has a very important role to play in improving its international competitiveness.

Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Sensor technology has been deeply into the sports industry, with the help of sensors to monitoring and collection data for the physical training in real-time. In table tennis, wearable sensors can record the amount of training, movement essentials, and the number of strokes of both hands and assist in the testing and evaluation of table tennis. This paper analyzes the application of wearable sensors in table tennis training activities and the details of signal collection and feature extraction. At the same time, machine learning technology often used to recognize and test table tennis training data, and a support vector machine (SVM) is one of the representative classifiers. Applying the processed signal data to the classification and testing of SVM can effectively identify the movement and evaluate the training effect and athletes’ physical fitness. The integration of intelligent sensors and table tennis can effectively improve the evaluation efficiency and quality in the process of teaching and training.

Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes

The accelerated evolution of communication platforms including Internet of Things (IoT) and the 5th generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks to monitor the chemical safety of our surroundings and personal health in real time. However, this application scenario requires a very challenging combination of characteristics of gas sensors including small formfactor, low-cost, ultra-low power consumption, superior sensitivity, and high intelligence. Herein, we demonstrate self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network for the first time. The SINGOR features ultra-high sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10 ppb, 2 ppb, 1 ppb and 1 ppb, respectively, obtained under room temperature sensor operation. Therefore, ultra-low power consumption of 68.6 μW at sensor level and 1.71 mW at the system level has been achieved. As the result, an individual SINGOR system can be self-driven by indoor light with a Si solar cell. Furthermore, each SINGOR consists of an array of nanostructured sensors powered by machine learning algorithm and thus a SINGOR has the capability of gas pattern recognition and classification. And multiple SINGOR systems are wirelessly connected together to form a sensor network for smart home application demonstrations. Intriguingly, the sensor network has successfully demonstrated flammable gas leakage detection and alarm function when they are deployed in multiple rooms. And when the SINGOR network is deployed in one single room, it can achieve gas leakage localization with satisfactory precision. These successes open up the possibility of using nanostructured-gas-sensor network for wide range applications including smart home, smart building and future smart city.

Application of Convolution Neural Network Algorithm Based on Intelligent Sensor Network in Target Recognition of Corn Weeder at Seedling Stage

Grass damage in the seedling corn field has always been an important factor affecting the growth and development of crops. The existence of grass not only compresses the living space of corn seedlings but also easily causes insect damage. Therefore, it is essential for weeding in the seedling corn field. The existing weeding methods usually use manual or chemical herbicide spraying, which is not only time-consuming and laborious but also inefficient. With the development of artificial intelligence and modern agricultural technology, the use of robots for field weeding has become an effective means, which has attracted more and more attention of researchers at home and abroad. Therefore, based on the full investigation of the development of relevant technologies at home and abroad, this paper carried out the research on the real-time target recognition and ranging method of field weeding robot and proposed a target recognition method of weeding corn at the seedling stage by using an intelligent sensor network and deep learning convolution neural network. Among them, the intelligent sensor is mainly used for target ranging and obstacle avoidance, and CNN is mainly used for target recognition. Taking the images of corn seedlings and weeds in the seedling stage under natural environmental conditions as samples, the migration training is carried out through the depth network model of the COCO data set, and the convolution features are shared by the CNN depth network model and Fast-CNN depth network model. VGG and ResNet feature extraction networks are compared. The experimental results show that the CNN depth network model based on this paper has obvious advantages in rape and weed target recognition. The target recognition accuracy of rape and weeds can reach 87.64%, and the recall rate can reach 80.23%. Compared with other models, it has obvious advantages, which proves the effectiveness of this model.

Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch.

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64 × 64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch) by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ∼385 kPa-1 for multi-walled carbon nanotubes concentration of 6%, while the 14% one exhibited fast response time of ∼3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could empower the building of large-scale intelligent sensor networks for next-generation smart robotics.

Smart Sensor Network Optimization and Line Defect Identification Based on UAV Transmission Inspection under 5G Technology

Transmission line is a key link in the transmission line, as the main equipment in the grid are located in the field; in the long-term harsh environmental conditions, the line is very easy to appear broken strands, broken, and other defects, which need to be regularly monitored and maintained. The traditional manual line inspection operation is not only harsh, labor-intensive, inefficient, and low precision but also has a great impact on the personal safety of the operators due to the complicated operating conditions. This project mainly focuses on 5G, intelligent sensor network technology, and the application of UAVs in high-voltage line inspection, detection, and maintenance. The developed inspection UAVs can effectively improve the efficiency of line inspection and quickly discover fault points, which brings convenience to the construction of power systems. In the 5G environment, the use of UAV transmission inspection technology for line fault identification can overcome the shortcomings of manual faults, but also timely and comprehensive fault detection, and improve the effectiveness and standardization of monitoring work.

5G Smart Sensor Network-Based e-Commerce Investment Risk Management System for SMEs

The rapid development of Internet technology in the new era has led to the prosperous development of the e-commerce industry, and more and more traditional retail enterprises are transforming into e-commerce enterprises in line with the development of the times to survive. Based on the relevant financial risk management theories, this paper designs an e-commerce investment risk management system for small and medium-sized enterprises based on the 5G intelligent sensor network, identify the financial risks arising from the e-commerce model, and takes appropriate countermeasures to manage the identified risks. For the optimal coverage problem of homogeneous terminals in the subnetwork of a smart sensing network, this paper introduces the concept of area coverage contribution, transforms the problem of selecting the optimal coverage subset in a large area into the problem of selecting the substrate particles, and organically combines the particle swarm algorithm in the bionic algorithm to consider the network survival period, area sensing coverage, and iteration cost for the hierarchical distributed large-scale IoT architecture. A minimum coverage subset dormancy scheduling algorithm based on homogeneous terminals is proposed. The simulation results are compared with the greedy algorithm and the multioptimized target task assignment algorithm, and it is demonstrated that this algorithm can consider multiple optimization objectives such as energy balance, scheduling cycle, timeout rate, network survival cycle, and system reliability and perform well in the case of large-scale deployment. Finally, an appropriate financial risk evaluation model is constructed to objectively evaluate the identified financial risks, and targeted financial risk control measures and suggestions are proposed based on four aspects: financing, investment, operation, and others.

Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

The application of traditional feature extraction methods in subway mechanical fault diagnosis has attracted the attention of researchers. Based on the 5G intelligent sensor network signal processing theory, this paper constructs a fault diagnosis model for subway machinery and equipment and analyzes the effectiveness of the information redundancy calculation method proposed in this paper by using the on-site vibration data of subway units. The model is obtained by analyzing the entropy characteristics of the vector vibration signal in each bearing section of the subway machinery and equipment. The entropy value quantitatively reflects the vibration complexity of the rotor in the section from different angles and solves the problem of data quantitative analysis. In the simulation process, based on the 5G intelligent sensor network signal and the fuzzy mean clustering information fusion method, in the process of identifying the fault state of the subway unit, a relatively optimistic identification result was obtained. The experimental results show that, no matter whether the Gaussian kernel or the polynomial kernel is selected, the number of kernel principal components whose cumulative contribution rate is greater than 0.85 decreases with the increase of the kernel parameter, and the fault identification support rate is 85%, that is, the diagnosis results of 3 sets of sample data are nonrolling element faults, which significantly improves the performance of the bearing.