Self-powered wireless sensing platform for monitoring marine life based on harvesting hydrokinetic energy of water currents

This work presents fully underwater triboelectric nanogenerators (UTENGs) to harvest hydrokinetic energy of water currents towards self-powered marine life sensors and IoT applications.

Wireless Network Sensing of Urban Surface Water Environment Based on Clustering Algorithm

To improve the wireless sensing image extraction technology of urban surface water environment, a regional FCM clustering method combined with water index was proposed in this paper. The normalized water index (NDWI) was obtained by calculating the fusion multispectral wireless sensing image. Through the combination with normalized water index, fuzzy clustering results were obtained by RFCM algorithm proposed in this paper. The optimal threshold was selected to defuzzify the fuzzy clustering results, and finally, the extraction results of urban surface water were obtained. The accuracy of the proposed algorithm was compared with that of the traditional surface water extraction algorithm. The experimental results showed that the size of different neighborhood regions affected the water extraction accuracy. In W city, the kappa coefficient of MFCM16 was 0.41% higher than that of MFCM8, and the overall classification accuracy of MFCM16 was 1.33% higher than that of MFCM. In G city area, the kappa coefficient of MFCM16 was 1.81% higher than that of MFCM8, and the overall classification accuracy of MFCM16 was 1.7% higher than that of MFCM. Comparing the RFCM algorithm with other algorithms, the RFCM algorithm obtained the best experimental results, to reduce the “salt-and-pepper phenomenon” effect.

Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibilities in the design and optimization of wireless networks and the development of new radio services. In this paper, we introduce, formalize, and evaluate a framework for machine learning (ML) based wireless sensing of indoor surface materials in the form of EM properties. We apply the radio-environment (RE) signatures of the wireless link, which inherently contains environmental information due to the interaction of the radio waves with the environment. We specify the content of the RE signature suitable for surface-material classification as a set of multipath components given by the received power, delay, phase shift, and angle of arrival. The proposed framework applies an ML approach to construct a classification model using RE signatures labeled with the environmental information. The ML method exploits the data obtained from measurements or simulations. The performance of the framework in different scenarios is evaluated based on standard ML performance metrics, such as classification accuracy and F-score. The results of the elementary case prove that the proposed approach can be applied for the classification of the surface material for a plain environment, and can be further extended for the classification of wall materials in more complex indoor environments.

Construction of New Intelligent Teaching Scene Based on Multimodal Wireless Sensing Technology

With the continuous development of computer technology and the gradual popularization of information technology application, the construction of intelligent teaching scene based on wireless sensing technology plays a more and more important role in modern information education. Taking a primary school as an example, this paper introduces multimodal wireless sensing technology into the construction of intelligent teaching system. The purpose of this paper is to explore the construction of a new teaching scene. Firstly, this paper deeply analyzes the sensing mechanism of wireless signal and optimizes the sensing mode, deployment structure, and signal processing in practical application, so that the system can run more effectively in the actual environment. Then, based on multimodal wireless sensing technology, this paper designs and optimizes the basic architecture and functions of intelligent teaching scene. The results show that combining the characteristic information of each mode to get the information conducive to identity confirmation, which can get better recognition performance and improve the accuracy. Combining the information of multiple modes can greatly improve the recognition performance. The user interest model combined with dynamic and static is used to optimize the system recommended resources, so that students can obtain high-quality and highly matched learning resources more quickly and accurately, so as to improve students’ learning efficiency in resource acquisition.

Construction of a Wireless Sensing Network System for Leisure Agriculture for Cloud-Based Agricultural Internet of Things

This paper provides an in-depth study and analysis of the construction of a cloud-based agricultural Internet of Things system for a wireless sensing network system for leisure agriculture. Using more mature agricultural sensor technology, compliant economy designed for indoor feeding and planting of distributed and integrated two sensor collection and transmission scheme, analysis of environmental factors selected high-performance various types of sensors and regulation equipment, between nodes based on SI4432 for wireless communication, and controller nodes selected STM32 as a microprocessor, through the W5500-based network port access module or ESP8266-based WiFi module for broadband access. In response to the development of mobile technology and the reality of diversified types of mobile terminals, to make all kinds of terminals accessible to the leisure agriculture system, the server software adopts the SOA software architecture, which makes the system have good openness and scalability. The NoSQL database MongoDB is used for the cloud storage of massive data, and the data structure design is completed after analyzing the database requirements, including collections, documents, and fields. The autosharding technology is used to build a database sharding cluster in the cloud, which realizes the high-speed cloud elastic storage of massive data and rewrites the database access object DAO to ensure that the WEB application is normal. Traditional leisure agriculture is mostly based on field tourism and agritourism methods, and the model is developing slowly and has increasingly failed to attract the interest of urban residents. The introduction of IoT technology in traditional leisure agriculture can increase the interest of leisure agriculture and improve the interest of urban residents in leisure agriculture.