Things Equipment Research Articles

The detailed investigation paper on network invasion awareness

Intrusion detection is critical for network security, with deep learning-based algorithms gaining traction. This project introduces NIDS - CNNLSTM, a model designed for the Industrial Internet of Things wireless sensing environment. It effectively identifies network traffic data, ensuring Industrial Internet of Things equipment and operations remain secure. Trained on the NSL_KDD data set, it exhibits strong convergence and performance across three data set, accurately classifying traffic types. Comparative analysis underscores NIDS - CNNLSTM efficacy enhancements. Experimental results validate increased detection rates, classification accuracy, and reduced false alarms, making it suitable for Industrial Internet of Things varied network data scenarios.

Handoff Control and Resource Allocation for RAN Slicing in IoT Based on DTN: An Improved Algorithm Based on Actor–Critic Framework

As a three-layer association of Internet of things equipment (IoTE) -network slicing (NS) -base station (BS) in radio access network (RAN) slicing, handoff control and resource allocation has become an important but complicated issue. In addition, the centralized controller has a difficult grasping the network situation in real time. In view of this, the problem of handoff control in the RAN slicing are investigated in the digital twin network (DTN), with the goal of maximizing the long-term utility about user satisfaction and handoff cost. Then an improved algorithm based on actor-critic framework is suggested, which is called HCRA. Specifically, the actor component contains neural networks for Handoff Control and an optimizer for Resource Allocation, and then the critic component evaluates the handoff and resource allocation actions of the actor component to guide the optimization of actions in the actor component. The simulation results show that HCRA can obtain better performance than benchmark algorithms.

CYBER ATTACKS FOR DATA BREACH AND POSSIBLE DEFENSE STRATEGIES IN INTERNET OF HEALTHCARE THINGS ECOSYSTEM

The widespread use of the Internet and the increase in Internet of Things (IoT) equipment paved the way for the development of user-friendly systems. The inclusion of many electronic systems in the IoT ecosystem, especially in homes, has improved remote control and monitoring features. In general, within the concept of smart home, voice, control and joint movements are used as command center and WIFI, Bluetooth, Zigbee and GSM etc. technologies are used for communication. Regular monitoring of some health problems of individuals occurs in some situations that require instant intervention. In this context, individuals in need of home care or under surveillance at home are checked with Internet of Healthcare Things equipment in the company of experts. The rapid spread of the IoT ecosystem has also increased data production. Especially sensitive health data is at the beginning of critical data and requires security measures to be taken. In this study, the IoT devices used for home patient care have been evaluated for the sources of data leaks and possible security measures that may be experienced in the process from the data owner to the data storage stage. In order to identify possible risks and threats, 4 different target scenarios were created. These scenarios include home internet connection resources, data transfer, data storage and access. 8 different attacks (Deauth, DDOS, brute force, hashcat, Man-in-the-middle, Injection, Short Address Attack, Smart Contract Overflow) were applied to these possible scenarios where data leakage could occur. In addition, recently, blockchain applications and smart contract transmissions are preferred for data security. Among the attack scenarios, Short Address Attack and Smart Contract Overflow are attack methodologies used for blockchain security. In particular, denial of service was encountered in all attacks on wireless networks. Configuration errors, wrong product selection, use of weak passwords and default configurations in the IOT ecosystem seem to be the main sources of data leaks. As a result, the study includes possible attacker scenarios and possible vulnerabilities have been extracted within the scope of real scenarios. In addition, the measures to be taken against these vulnerabilities were evaluated and recommendations were given to take maximum security measures to prevent data leaks from within the IoT ecosystem.

Application of Blockchain Technology in Electric Vehicle Charging Piles Based on Electricity Internet of Things

With the continuous development of urban intelligence, as traffic, power grids, and electric vehicles are new ideas to solve energy shortages and air control problems, they have received widespread attention from the society and strong support from the government. The charging pile is a key hub for data exchange and has typical characteristics of IoT terminals. However, the guidance of the grid connection of electric vehicles is not standardized, and the security and stability of the power grid will inevitably be affected. The blockchain has the characteristics that data is difficult to tamper with and decentralized. Based on these two characteristics, the information recorded by the blockchain is more authentic and reliable. This paper is aimed at realizing the global unified identification and management of large terminal equipment based on ubiquitous power Internet of Things equipment with the help of blockchain technology and at providing safe, efficient, and stable online management services for a large number of devices in the ubiquitous power Internet of Things. In this paper, we analyze the application possibility of blockchain technology in the current electricity market and apply blockchain technology to the electricity market to solve the drawbacks of the electricity market, as well as adding blockchain analytics to the renewable energy electricity market. The experimental data analysis shows that with the increase of the length of the blockchain, the blocking time of the new area increases correspondingly, but the overall performance declines slightly, so this scheme can meet the practical needs of a large number of concurrent access terminals in the ubiquitous power Internet of Things. It can be seen that the successful application of blockchain technology based on the power Internet of Things in electric vehicle charging piles has greatly improved work efficiency.

Credibility evaluation system for The Internet of Things: Impact and Implications for Health Care Delivery

Due to the imperfection of the technology of speech recognition embedded in the Internet of things devices, the evaluation results of the credibility evaluation system for speech recognition control of the Internet of things devices have errors, so a credibility evaluation system for the Internet of things devices based on speech recognition technology is designed. First of all, the hardware structure of the system is designed, the MCU main control chip is developed, and the system hardware is optimized with the help of MCU. In the software design, the digital filter is used for speech signal preprocessing, the rectangular window and Hamming window are used to frame and add windows to the speech, the function related gate is introduced to divide the fault events and basic events, and the credibility evaluation system of Internet of things equipment based on speech recognition technology is completed. The performance test results show that compared with the traditional system, the average recognition rate of the designed system for different lengths of instructions is improved.

Design and Implementation of Fault Diagnosis System for Power Internet of Things Equipment Based on Neural Network

The design and application of the equipment fault diagnosis system have been improved and upgraded, allowing it to effectively detect the equipment’s operation status and promptly eliminate hidden faults, reducing the occurrence of unexpected accidents and improving the safety index of people’s lives. The purpose of this essay is to design and apply neural network (NN) fault diagnosis system model in power Internet of things (IOT) equipment and explore its accuracy and effectiveness. The BP neural network (BPNN) algorithm was used to construct model of a fault monitoring testing of the power IOT equipment. Neural network is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural network and performs distributed parallel information processing. The network parameters were as follows: there were four input layer nodes, seven hidden layer nodes, and five output layer nodes, the training times were 10000, and the allowable error was 0.002. In this paper, we use the IOT to detect model of a fault monitoring testing of power equipment designed in each sample, the success rate is as high as 97.5%, and the designed network structure and network parameters are reasonable. The trained loss is less than 0.001, and the nontraining set samples may be appropriately identified. It is clear that the NN has a high application for power equipment fault diagnosis in the IOT value.

Security control of unstructured data in the power IoT for isolation network between internal and external

As a key national infrastructure, the safety of the power grid is an overall and strategic issue that affects the development of the national economy and society. With the popularization and development of the Internet of Things in the power industry, power Internet of Things terminal equipment has become a key target of network attacks. Using network attacks to damage power Internet of Things equipment has a strong detrimental power. As the main data storage method of the power Internet of Things, the security management and control of unstructured data has become the focus of the power Internet of Things security. This paper proposes a reformulation of access control policies based on the different attribute states and trust levels of power Internet of Things terminal equipment to reduce its access control authority, and on this basis, proposes a dynamic access control based on the isolation of internal and external networks. The detailed function design of each core security protection component is given, and the component module is explained.

Internet of Things in the Quality Control of Cement Mixing Pile Construction

Cement mixing pile is frequently used in construction, and has a great impact on the overall construction quality. This paper proposes the use of Internet of things equipment in the process of cement mixing pile construction for automatic supervision, so as to achieve the purpose of overall construction quality control. This paper constructs IOT cement mixing pile construction quality control model iotcmp (Internet of Things cement mixing pile) based on BIM. The Internet of things management and control model in this paper is implemented in the form of web. The research shows that the water cement ratio of the cement mixing pile is 0.62 under the control of the Internet of things when the cement dosage is set to fluctuate by 15%. The texture of the mixed cement product is clear, and the average compressive strength is 2.65mpa. In this case, the average number of standard penetration blows is 38. It can be seen that it is feasible to use the Internet of things equipment to monitor the cement mixing pile in real time and evaluate the overall benefit.

CrowdIoT: The Crowd-Sourcing Test System for IoT Devices Based on Blockchain

In view of some problems existing in traditional software crowdsourcing tests and Internet of Things device tests, we developed a CrowdIoT crowdsourcing test system for the Internet of Things devices based on a block chain. CrowdIoT uses the core technology of blockchain to focus its testing on physical devices on the Internet of Things. CrowdIoT uses two different testing methods for different testing needs: online testing and offline testing. Online remote testing is our key function and research object. By opening the testing interface of Internet of Things devices to testers, testers can test the devices in the CrowdIoT system interface without having to get the hardware. At the same time, CrowdIoT introduced multi-threaded parallel testing technology to solve the conflict problem of multiple testers competing for hardware resources in online testing. Offline testing, as a supplement to online testing, is to send Internet of Things devices to testers with high credibility in deposit guarantee, so that testers can fully test the hardware devices and dig out as many vulnerabilities of the devices as possible. CrowdIoT has its own complete and scientific credibility system, combined with relevant incentive mechanism, consensus mechanism and connection mechanism, which not only effectively solves the centralization problem existing in traditional crowdsourcing testing platforms, but also solves the pain point of the lack of remote testing of Internet of Things devices in the market. Then it solves the problems of equipment limitation and testing cost limits in the field of Internet of Things equipment testing, and provides a platform for security testing and use for the Internet of Things participants.

AutoSS: A Deep Learning-Based Soft Sensor for Handling Time-Series Input Data

Soft Sensors are data-driven technologies that allow to have estimations of quantities that are impossible or costly to be measured. Unfortunately, the design of effective soft sensors is heavily impacted by time-consuming feature engineering steps that may lead to sub-optimal information, especially when dealing with time-series input data. While domain knowledge may come into help when handling feature extraction in soft sensing applications, the feature extraction typically limits the adoption of such technologies: in this work, we propose AutoSS, a Deep-Learning based approach that allows to overcome such issue. By exploiting autoencoders, dilated convolutions and an ad-hoc defined architecture, AutoSS allows to develop effective soft sensing modules even with time-series input data. The effectiveness of AutoSS is demonstrated on a real-world case study related to Internet of Things equipment.

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

Due to the limited computing resources of the mobile edge computing (MEC) server, a massive Internet of things device computing unloading strategy using game theory in mobile edge computing is proposed. First of all, in order to make full use of the massive local Internet of things equipment resources, a new MEC system computing an unloading system model based on device-to-device (D2D) communication is designed and modeled, including communication model, task model, and computing model. Then, by using the utility function, the parameters are substituted into it, and the optimization problem with the goal of maximizing the number of CPU cycles and minimizing the energy consumption is constructed with the unloading strategy and power as constraints. Finally, the game theory is used to solve the problem of computing offload. Based on the proposed beneficial task offload theory, combined with the mobile user device computing offload task amount, transmission rate, idle device performance, and other factors, the computing offload scheme suitable for their own situation is selected. The simulation results show that the proposed scheme has better convergence characteristics, and, compared with other schemes, the proposed scheme significantly improves the amount of data transmission and reduces the energy consumption of the task.

Power transformer fault diagnosis system based on Internet of Things

Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index.ObjectiveTo explore the utility of Internet of Things in power transformer fault diagnosis system.MethodsA total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system.ResultsThe detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

Research on intelligent inspection technology of distribution network based on RFID

As an important part of power system, the safe and stable operation of distribution network affects the power quality of the whole society and the reliability of power grid operation. As a technology widely used in Internet of things equipment, radio frequency identification (RFID) has the characteristics of non-contact reading and wide identification range, which is especially suitable for the working situation of patrol distribution network equipment. Based on radio frequency identification technology, non-contact two-way communication is carried out. According to the task transmitted from the workstation, the inspection results are recorded, and the field data is transmitted to the background management system through the wireless communication network. The intelligent purpose of the distribution network inspection system is realized, so as to improve the inspection efficiency and the reliability of the power grid operation.

Online monitoring method for insulator self-explosion based on edge computing and deep learning

Aiming at the problems of traditional centralized cloud computing occupies large computing resources and high latency, this paper proposes a fault detection scheme for insulators self-explosion based on edge computing and DL (deep learning). In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge, a lightweight SSD (Single Shot MultiBox Detector) target recognition network is designed at the edge, which adopts MobileNets network to replace VGG16 network in the original model to reduce redundant computing. In the cloud, three detection algorithms (Faster-RCNN, Retinanet, YOLOv3) with obvious difference in detection performance are selected to obtain the coordinates and confidence of insulator self-explosion area, and then the self-explosion fault detection of overhead transmission line is realized by a novel multi-model fusion algorithm. The experimental results show that the proposed scheme can effectively reduce the amount of uploaded data, and the average recognition accuracy of cloud is 95.75%. In addition, it only increases the power consumption of edge devices by about 25.6W/h in the working state. Compared with the existing online monitoring technology of insulator self-explosion at home and abroad, the proposed scheme has the advantages of low transmission delay, low communication cost and high diagnostic accuracy, which provides a new idea for online monitoring research of power internet of things equipment.

News Information Platform Optimization Based on the Internet of Things

The Internet of Things device online recommendation system has been applied in some Internet of Things operating companies and has achieved good results. In the process of designing and implementing the Internet of Things equipment online promotion system, this article uses the news information protocol transmission structure language to explain the use case analysis and activity diagram analysis of the Internet of Things equipment online promotion system and uses the Spring Hibernate (SH) integration framework in the field of the news information topology layer under the Internet of Things. Then, design and implement the technical architecture and main functional modules of the Internet of Things device online recommendation system, effectively improving the development efficiency and operating quality of the Internet of Things device online recommendation system. The system design concept and implementation ideas can be used as a reference for related industries when developing enterprise applications. This article uses the news information protocol transmission structure language to explain the demand analysis of the Internet of Things equipment online promotion system and mainly discusses the analysis of the use case of the Internet of Things equipment online promotion system and the analysis of core business activity diagrams. We completed the design of the Internet of Things device online promotion system based on the news information topology layer platform under the Internet of Things, mainly using the integration framework in the field of the news information topology layer under the Internet of Things to design the technical architecture of the Internet of Things device online promotion system, and design the Internet of Things of the function module structure and data table structure of the equipment online recommendation system. At the same time, they complete the realization of the main functions of the Internet of Things device online recommendation system; elaborate on the realization process of core modules such as Internet of Things device category management, Internet of Things device information management, announcement management, and device recommender management; and discuss the system testing process and application effects.

Computer Aided Research on the Physical Security of Ubiquitous Power Internet of Things Equipment

Through the analysis of the microgrid architecture under the access of the ubiquitous power Internet of Things and the current construction of the peak shaving auxiliary service market, the feasibility, necessity and economy of the microgrid participating in the peak shaving auxiliary service market are demonstrated. The thesis makes full use of information technology such as Internet of Things technology, computers, big data analysis, distributed applications, and mobile technology. It focuses on the safety management and control of the work site of power grid enterprises, and uses intelligent monitoring terminal equipment to access, such as access control (barrier), Authorize locks, intelligent safety monitoring robots and electronic fences to supervise and control production sites and operating equipment, standardize operating procedures, and improve on-site safety management and control capabilities and terminal operating equipment real-time supervision capabilities.

Voice-Transfer Attacking on Industrial Voice Control Systems in 5G-Aided IIoT Domain

At present, specific voice control has gradually become an important means for 5G-Internet-of-Things-aided industrial control systems, such as controlling the operation and adjustment of industrial Internet of Things equipment through telephone voice of the controller. However, the security of specific voice control system needs to be improved, because the voice cloning technology based on transfer learning can easily simulate the voice of the controller, which may lead to industrial accidents and other potential security risks. Therefore, this article mainly aims to study and understand the principle of voice cloning attack technology, putting forward a voice clone attack method, in order to prepare for the construction of a specific voice recognition system in the future. At present, the key technology of voice cloning attack is how to solve the problem that the target speaker's personalized speech with high quality cannot be synthesized under small samples. In fact, voice cloning is a very challenging problem because speech is more difficult to be represented in the hidden space of the model. We propose a transductive voice transfer learning method to learn the predictive function from the source domain and fine-tune in the target domain adaptively. The target learning task and the source learning task are both synthesizing speech signals from the given audio, while the datasets of both domains are different. By adding different penalty values to each instances and minimizing the expected risk, an optimal precise model can be learned. In addition, an evaluation method to verify the audio similarity of the target speaker was given to show the similarity between the synthesized audio and the original audio. Many details of the experimental results show that our method can effectively synthesize the speech of the target speaker with small samples.

Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing

Due to the improvement of network infrastructure and the application of Internet of Things equipment, a large number of sensors are deployed in the industrial pipeline production, and the large size of data is generated. The most typical case in the production line is product inspection, that is, defect inspection. To implement an efficient and robust detection system, in this study, we propose a classification computing model based on Lie Group Machine Learning, which can find the possible defective products in production. Usually, a workshop has a lot of assembly lines. How to process large data on so many production lines in real-time and accurately is a difficult problem. To solve this problem, we use the concept of fog computing to design the system. By offloading the computation burden from the cloud server center to the fog nodes, the system obtains the ability to deal with extremely data. Our system has two obvious advantages. The first one is to apply Lie Group Machine Learning to fog computing environment to improve the computational efficiency and robustness of the system. The other is that without increasing any production costs, it can quickly detect products, reduce network latency, and reduce the load on bandwidth. The simulations prove that, compared with the existing methods, the proposed method has an average running efficiency increase of 52.57%, an average delay reduction of 42.13%, and an average accuracy increase of 27.86%.

Two-stage multi-classification algorithm for Internet of Things equipment identification

Two-stage multi-classification algorithm for Internet of Things equipment identification

Design and Data Analysis of Sports Information Acquisition System Based on Internet of Medical Things

The Internet of medical things is an emerging information network technology, which can realize the automatic identification, monitoring and management of personnel, medical equipment, medicines, etc. through this network, and is an effective means to reduce medical errors and improve work efficiency. This article first studies the theory and method of health information and sports information collection, that is, the temperature sensor and the acceleration sensor are used to collect human body temperature and exercise steps, respectively, and then estimate the human health and sports. Second, the prototype system of health and sports information collection system is realized the system is divided into two parts: terminal node and client information management system. Finally, a component collaborative modeling and data analysis method for Internet of medical things is proposed. This method constructs different types of components according to different functions of the Internet of Things equipment, and designs a set of communication mechanisms between the components based on the Internet of Things network communication characteristics, and uses visual methods to model. The experimental results verify that the method of collecting and analyzing human motion information using a motion information collection system is feasible. Multiple methods should be integrated to obtain as much information as possible to make the human motion analysis more scientific and reasonable.