Wireless Sensor Research Articles

Research on the Method of Using Wireless Sensing to Detect Indoor People during Fire Based on Unmanned Aerial Vehicle

Research on the Method of Using Wireless Sensing to Detect Indoor People during Fire Based on Unmanned Aerial Vehicle

Application of Fresnel Zone Model in Wireless Sensing Field

As a common model in the field of wireless sensing, the Fresnel zone model performs better than traditional deep learning in generalization ability, computational efficiency, robustness, and so on. It shows significant advantages in breath monitoring, gesture recognition, indoor behavior recognition, and so on. In this paper, the typical applications of the Fresnel band model in fine and coarse-grained wireless sensing tasks in recent ten years are listed. With the progress of the research, the concentric circle model, dynamic Fresnel region model, and through-wall wireless sensing model are developed in combination with other methods, which can well solve the wireless perception problem in non-line-of-sight scenes. Or combined with deep learning to develop new ways to solve specific problems. Over the course of development, the emergence of new methods makes the wireless perception scene more complex, Respiratory monitoring can be changed from one-person monitoring to multi-person detection, and the accuracy of gesture recognition and indoor behavior recognition is getting higher and higher.

Biometric Technology Based on Wireless Sensing: Principles, Applications, and Challenges

With the rapid advancement of computer technology, image processing, and artificial intelligence, biometric identification technology has seen widespread application, gradually permeating areas such as national security, public security, and home security. However, traditional biometric technologies exhibit certain limitations. In contrast, biometric identification based on wireless sensing offers distinct advantages, including non-contact operation and strong concealment, making it a promising alternative in various security applications. This paper provides a comprehensive overview of the history and current state of biometric technology, alongside the development background of wireless sensing technology. It details the principles of wireless sensing-based biometric identification, covering essential characteristics of wireless signals, and related technologies such as RFID, Wi-Fi, Bluetooth, and millimeter wave technology, as well as relevant signal processing algorithms. Furthermore, the paper analyzes the application of this technology in areas like human posture and motion recognition and physiological feature recognition, supported by practical case studies. Finally, it addresses the environmental dependencies, data privacy, and security concerns associated with this technology, while offering insights into future development directions.

The 15th IEEE Topical Conference on Wireless Sensors and Sensor Networks [IEEE Radio & Wireless Week 2025

The 15th IEEE Topical Conference on Wireless Sensors and Sensor Networks [IEEE Radio & Wireless Week 2025

DEWS: A Distributed Measurement Scheme for Efficient Wireless Sensing

DEWS: A Distributed Measurement Scheme for Efficient Wireless Sensing

Parametric Machine Learning-Based Adaptive Sampling Algorithm for Efficient IoT Data Collection in Environmental Monitoring

With the IoT trend, wireless sensors are gaining growing interest. This is due to the possibility of installing them in locations inaccessible to wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize sampling to a sufficient rate to catch important information without excessive energy consumption. One way to achieve sufficient sampling is by using an algorithm for adaptive sampling named dynamic sampling rate algorithm (DSRA); however, this algorithm requires an expert to set and tune its parameters, which might not always be readily available. This study aims to further develop this algorithm to be machine learning based to tune these parameters. To achieve this goal, the algorithm was modified and an optimization strategy that considers a predetermined error threshold was developed. Then the algorithm was implemented using simulated and real data with a set of predetermined errors thresholds to observe its performance. The results showed that the developed algorithm exhibited adaptive sampling behavior, and it could collect data efficiently depending on the predetermined error threshold. Based on the results, it is possible to conclude that the developed algorithm endows sensors with adaptive sampling capabilities based on the signal rate of change.

A hybrid piezoelectric–electromagnetic body energy harvester: design and experiment validation

Abstract The rapid advancement of electronic devices and wireless sensors has heightened the demand for energy sustainability and portable power solutions. Traditional human energy harvesters have limitations in harvesting energy from ultra-low-frequency human motion due to issues related to unstable energy output and wearing comfort. To address this challenge, a piezoelectric–electromagnetic hybrid energy harvesting (HP-EEH) structure designed for the hip joint area. This innovative design employs magnetically coupled frequency boosting alongside electromagnetic energy capture to achieve high output power. Firstly, the structure and principle of the energy capture device are introduced, and the electromechanical coupling model of the energy harvester is derived using Hamilton’s principle. Furthermore, the system is numerically simulated, and the voltage output characteristics of the piezoelectric unit and the electromagnetic unit are analyzed by using the finite element analysis software. Finally, the experimental setup of the (HP-EEH) is constructed, and the voltage output characteristics are tested for different swinging angles and positions. The results show that two parts of energy can be captured simultaneously under ultra-low-frequency motion conditions. At a swing angle of 50 degrees, the piezoelectric and electromagnetic units achieved maximum output power values of 14.96 µW at 0.8 Hz and 10.4 µW at 1.2 Hz, respectively. Incorporating the output power of the electromagnetic unit aims to address the power consumption requirements of low-power devices better.

Commodity Wi-Fi-Based Wireless Sensing Advancements over the Past Five Years.

With the compelling popularity of integrated sensing and communication (ISAC), Wi-Fi sensing has drawn increasing attention in recent years. Starting from 2010, Wi-Fi channel state information (CSI)-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition, and vital sign monitoring. In this paper, we retrospect the latest achievements of Wi-Fi sensing using commodity-off-the-shelf (COTS) devices from the past 5 years in detail. Specifically, this paper first presents the background of the CSI signal and related sensing models. Then, recent studies are categorized from two perspectives, i.e., according to their application scenario diversity and the corresponding sensing methodology difference, respectively. Next, this paper points out the challenges faced by Wi-Fi sensing, including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical Wi-Fi sensing in real-life applications.

A quantitative comparison of different methods for air conditioning energy consumption prediction in residential buildings

Accurate evaluation of energy consumption of air conditioning (AC) is crucial to energy efficiency in residential buildings, while few studies evaluate quantitatively the differences of the currently used methods. This study aimed to assess two common methods for AC energy consumption evaluation, namely, top-down total energy disaggregation and bottom-up energy simulation, through comparing to field measurement. A longitudinal investigation was conducted in residences in Chongqing, a typical city in the Hot Summer and Cold Winter(HSCW) zone. The yearly daily electricity consumption of 18 residences were collected and wireless sensors and smart sockets were used for onsite measurement. The AC energy consumption was obtained by splitting the total electricity consumption and measurements. Moreover, three typical reference buildings were built for predicting the annual AC energy consumption. Results showed that the simulated AC energy consumption was about 2.76 times of the measured value; while it was about 2.89 times for the top-down disaggregation method. Some key factors influencing AC energy consumption (e.g., indoor temperature distribution, opening duration, AC usage frequency) were identified for different methods. The work quantified the limitations of the top-down and bottom-up methods, contributing to the improvement of AC energy consumption evaluation methods and management of heating and cooling energy in residential buildings.

Movable Antenna Enhanced Wireless Sensing via Antenna Position Optimization

Movable Antenna Enhanced Wireless Sensing via Antenna Position Optimization

Wireless Sensing Network for Implementation of Air Quality Monitoring System and Indoor Air Quality Index Application

Indoor air quality significantly impacts respiratory health and mental activity. This study utilizes a wireless sensing network (WSN) based on the Internet of Things (IoT) to monitor indoor air quality, referred to as an indoor air quality monitoring system. The system was installed and applied on campus at the University of Baghdad. The present study aims to monitor air quality parameters continuously within laboratories. Carbon monoxide, sulfur dioxide, nitrogen dioxide, ammonia, and particulate objects are the pollutants chosen to be monitored by the installed system in this study. These pollutants were selected because they affect indoor facilities' comfort, health, and working conditions. Colored coded data was employed in the monitoring system; defined ranges for each pollutant were also integrated. Sensor nodes, wireless modules that connect to the IoT server, and user applications are the main components of the IAQMS system. LCDs, mobile applications, the ThingSpeak web server, and the LabView platform are examples of techniques used to present data collected by the system. Additionally, the system includes a notification function that alerts students and lab personnel when indoor air quality index IAQI values signal unhealthy indoor air quality. The proactive approach ensures a regulated standard for indoor air quality and pollutants.

5G/GPON: A CONVERGENT ARCHITECTURE FOR WIRELESS AND OPTICAL SENSOR NETWORKS IN BOGOTÁ CITY

This paper presents the design of a convergent optical transport architecture for wireless and photonic sensor networks based on the optical access network deployed in Bogotá city. The proposed architecture is evaluated through the transport of services in subcarrier division multiplexing to enable the seamless transport of different standards such as WiFi, Bluetooth, Zigbee, SigFox, among others. The proposed design demonstrates high scalability given the distribution of the previously deployed network, enabling the incorporation of both optical sensors based on FBG and wireless sensors in locations with greater historical, economic, cultural and social relevance. The proposal presents an optimal response in terms of bit error rate (BER) and signal power in the multi-coverage zone for various interference parameters such as SS-RSRP, SS-RSRQ, and Throughput.

Joint passive beamforming and deployment design for active intelligent reflecting surface-assisted energy harvesting-cognitive radio sensor networks.

To prolong the network lifetime of energy harvesting-cognitive radio sensor networks (EH-CRSNs), this paper integrates active intelligent reflecting surface (IRS) to support both downlink EH and uplink data transmissions and formulates a constrained non-convex optimization problem that maximizes the net energy gain. To solve this problem, a joint passive beamforming and IRS deployment mechanism is proposed to determine the optimal deployment location of the active IRS and optimally configure the IRS reflection coefficient matrices. Specifically, the net energy gain maximization problem at a specified location is divided into two sub-problems according to its characteristics: maximizing the cumulative energy harvested by all CRSNs nodes in the downlink and minimizing the energy consumption of cluster heads in uplink data transmissions, with each sub-problem being solved independently. Furthermore, this paper explores how factors such as the number of active reflecting elements and the amplification power budget influence the optimal deployment location of the active IRS. The findings from this investigation can guide the design of active IRS-assisted EH-CRSNs under specified parameters. Simulation results confirm the aforementioned conclusions and highlight the benefits of the proposed joint mechanism in enhancing net energy gain over various benchmark mechanisms.

Experimental Demonstration of Integrated Optical Wireless Sensing and Communication

Experimental Demonstration of Integrated Optical Wireless Sensing and Communication

Discrepancies between Promised and Actual AI Capabilities in the Continuous Vital Sign Monitoring of In-Hospital Patients: A Review of the Current Evidence.

Continuous vital sign monitoring (CVSM) with wireless sensors in general hospital wards can enhance patient care. An artificial intelligence (AI) layer is crucial to allow sensor data to be managed by clinical staff without over alerting from the sensors. With the aim of summarizing peer-reviewed evidence for AI support in CVSM sensors, we searched PubMed and Embase for studies on adult patients monitored with CVSM sensors in general wards. Peer-reviewed evidence and white papers on the official websites of CVSM solutions were also included. AI classification was based on standard definitions of simple AI, as systems with no memory or learning capabilities, and advanced AI, as systems with the ability to learn from past data to make decisions. Only studies evaluating CVSM algorithms for improving or predicting clinical outcomes (e.g., adverse events, intensive care unit admission, mortality) or optimizing alarm thresholds were included. We assessed the promised level of AI for each CVSM solution based on statements from the official product websites. In total, 467 studies were assessed; 113 were retrieved for full-text review, and 26 studies on four different CVSM solutions were included. Advanced AI levels were indicated on the websites of all four CVSM solutions. Five studies assessed algorithms with potential for applications as advanced AI algorithms in two of the CVSM solutions (50%), while 21 studies assessed algorithms with potential as simple AI in all four CVSM solutions (100%). Evidence on algorithms for advanced AI in CVSM is limited, revealing a discrepancy between promised AI levels and current algorithm capabilities.

Chemical Sensor Technologies for Sustainable Development: Recent Advances, Classification, and Environmental Monitoring

AbstractOne of the significant issues of concern in recent times is detecting, quantifying, and monitoring various chemical contaminants in the environment. Numerous inorganic and organic substances are discharged into the environment from various anthropogenic activities, thereby polluting the ecosystems' air, water, soil, and other components. These contaminants have been documented to have deleterious effects on humans, animals, and the environment. Therefore, the need for monitoring and continual quantification of the contents of these contaminants in various environmental matrices becomes paramount. Many diverse sensors are fabricated for multiple utilizations, including liquids and gas sensors, wireless sensors, electronic tongues, and noses for detecting volatile airborne substances, pollutants, and effluents. This review focuses on cutting‐edge technology used in chemical sensors for environmental protection and sustainable development. It also discusses the fabrication and development of chemical sensors and their working principles. In addition, highlights about the applications of chemical sensors in environmental monitoring are also elaborated. This review article discusses emerging sensor technologies and their application in ecological monitoring. Further, the recognition of chemical warfare agents and homeland security applications with the help of bioelectronic nose and tongue is discussed. Finally, some challenges associated with chemical sensors and future trends are also attempted.

Coordinated Ship Welding with Optimal Lazy Robot Ratio and Energy Consumption via Reinforcement Learning

Ship welding is a crucial part of ship building, requiring higher levels of robot coordination and working efficiency than ever before. To this end, this paper studies the coordinated ship-welding task, which involves multi-robot welding of multiple weld lines consisting of synchronous ones to be executed by a pair of robots and normal ones that can be executed by one robot. To evaluate working efficiency, the objectives of optimal lazy robot ratio and energy consumption were considered, which are tackled by the proposed dynamic Kuhn–Munkres-based model-free policy gradient (DKM-MFPG) reinforcement learning algorithm. In DKM-MFPG, a dynamic Kuhn–Munkres (DKM) dispatcher is designed based on weld line and co-welding robot position information obtained by the wireless sensors, such that robots always have dispatched weld lines in real-time and the lazy robot ratio is 0. Simultaneously, a model-free policy gradient (MFPG) based on reinforcement learning is designed to achieve the energy-optimal motion control for all robots. The optimal lazy robot ratio of the DKM dispatcher and the network convergence of MFPG are theoretically analyzed. Furthermore, the performance of DKM-MFPG is simulated with variant settings of welding scenarios and compared with baseline optimization methods. Compared to the four baselines, DKM-MFPG owns a slight performance advantage within 1% on energy consumption and reduces the average lazy robot ratio by 11.30%, 10.99%, 8.27%, and 10.39%.

Wi-Loop SLAM: Loop Closures With Wireless Sensing in Multipath SLAM

Wi-Loop SLAM: Loop Closures With Wireless Sensing in Multipath SLAM

Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

TEG-Self-Powered System for Wireless Sensing Node Operating in Aqua-Greenhouse

TEG-Self-Powered System for Wireless Sensing Node Operating in Aqua-Greenhouse