Efficient Wireless Research Articles

Smart Zone-Based Vehicle Speed Retarding System Utilizing Radio Frequency (RF) in Electronic Vehicles

The proposed system aims in designing an efficient automatic wireless vehicle speed control in low-speed zones using RF communication. The advent of new high-speed technology and the growing computer capacity provided realistic opportunity for new robot controls and realization of new methods of control theory. This technical improvement together with the need for high performance robots created faster, more accurate and more intelligent robots using new robots control devices, new drivers and advanced control algorithms. The proposed method uses RF transmitter and receiver modules for establishing wireless communication, DC motors for the vehicle movement controller. The status of the project will display on LCD display. The controlling device of the whole system is a Microcontroller to which RF receiver module, DC motors are interfaced through a motor driver. Whenever the vehicle enters into a low-speed zone, the data from RF transmitter of low-speed zone will be received by the vehicle system. The microcontroller in the vehicle system automatically slows down the speed to the speed limit of that zone. The microcontroller checks the data with the program embedded in it and performs appropriate actions on the electric motors. The microcontroller is programmed using Embedded C language.

Efficient Wireless Federated Learning With Partial Model Aggregation

Efficient Wireless Federated Learning With Partial Model Aggregation

A Universal Model for Ultrasonic Energy Transmission in Various Media.

This study presents a comprehensive model for ultrasonic energy transfer (UET) using a 33-mode piezoelectric transducer to advance wireless sensor powering in challenging environments. One of the advantages of UET is that it is not stoppable by electromagnetic shielding and can penetrate metal. Existing models focus on feasibility and numerical analysis but lack an effective link between input and output power in different media applications. The proposed model fills this gap by incorporating key factors of link loss, including resonant frequency, impedance matching, acoustic coupling, and boundary conditions, to predict energy transfer efficiency more accurately. The model is validated through numerical simulations and experimental tests in air, metal, and underwater environments. An error analysis has shown that the maximum error between theoretical and experimental responses is 3.11% (air), 27.37% (water), and 1.76% (aluminum). This research provides valuable insights into UET dynamics and offers practical guidelines for developing efficient wireless powering solutions for sensors in difficult-to-access or electromagnetically shielded conditions.

Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.

Low-Cost Efficient Wireless Intelligent Sensor (LEWIS) for Research and Education.

Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology's complexity, costs, fabrication and implementation challenges often hinder engineers' exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication.

Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study

Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study

An adaptive compressive sensing method on hybrid-field channel estimation for a massive MIMO system

Massive MIMO (Multiple-input-multiple output), crucial for 5 G and Beyond 5 G (B5G) networks, faces challenges with terahertz frequencies in B5G as the communication shifts from far-field to near-field. This shift disrupts traditional Massive MIMO channel estimation, leading to increased pilot overhead and limited performance. The current study used the hybrid-field orthogonal matching pursuit (HF-OMP) algorithm from the compressive sensing (CS) framework to estimate the channel with scatters from both far-field and near-field. The method, however, needs more flexibility regarding scatter location in the field of interest. Also, the usage of HF-OMP under a single measurement vector (SMV) scheme limits the overall performance of the existing method. To address these, we have proposed an adaptive hybrid-field simultaneous-OMP algorithm under multiple measurement vector (MMV) of the CS framework which selects multiple atoms having common support within a single iteration. This innovative approach tackles channel estimation for both far-field and near-field scatters while adapting to their varying distributions. Compared to classical methods, simulations show the new algorithm achieves up to a 14 % improvement in normalized mean square error within a 0 dB to 10 dB signal-to-noise ratio range. This translates to significant reductions in pilot overhead and enhanced channel reliability, paving the way for more efficient and robust wireless networks in the future.

A mechanics and electromagnetic scaling law for highly stretchable radio frequency electronics

Many classes of flexible and stretchable bio-integrated electronic systems rely on mechanically sensitive electromagnetic components, such as various forms of antennas for wireless communication and for harvesting energy through coupling with external power sources. This efficient wireless functionality can be important for body area network technologies and can enable operation without the weight and bulky size of batteries for power supply. Recently, antenna designs have received increased attention because their mechanical and electromagnetic properties significantly influence the wireless performance of bio-integrated electronics, particularly under excessive mechanical loads. These mechanical factors are critical for skin-integrated electronics during human motion, as complex skin deformations can damage the conductive traces of antennas, such as those used for near-field communication (NFC), leading to yield or fracture and affecting their electromagnetic stability. Serpentine interconnects have been proposed as a geometric alternative to in-plane circular or rectangular spiral antenna designs to improve the elastic stretchability of the metallic traces in NFC antennas and prevent mechanical fractures. Despite the use of serpentine interconnects within the physiologically relevant strain range for skin (<20 %), the electromagnetic stability of the antennas decreases. This instability, reflected by shifts in resonance frequency and scattering parameters due to inductance changes, reduces the antennas' wireless power transfer efficiency and readout range. Therefore, maintaining the electromagnetic stability of antennas, specifically NFC antennas, under various mechanical deformations has become a critical challenge in practical wireless skin-integrated applications, such as sensing and physiological monitoring. Here, we establish a new mechanics and electromagnetic scaling law that quantifies the inductance changes under strain in a rectangular-loop serpentine structure typically used for NFC wireless communication in stretchable electronics. We present a systematic analysis of the antenna's geometric parameters, material properties of the antenna and substrate, and the applied strain on the inductance change. Our findings demonstrate that the relative change of inductance is solely influenced by the serpentine structure's width-radius ratio, arc angle, aspect ratio of the NFC antennas, and the applied strain. Additionally, under physiological strain conditions for the skin, the relative change of inductance can be minimized to preserve the NFC antenna's performance and prevent mechanical fracture and electromagnetic stability loss.

Adaptive Model Pruning for Communication and Computation Efficient Wireless Federated Learning

Adaptive Model Pruning for Communication and Computation Efficient Wireless Federated Learning

Communication and Energy Efficient Wireless Federated Learning With Intrinsic Privacy

Communication and Energy Efficient Wireless Federated Learning With Intrinsic Privacy

TOWARD ROBUST IN-CAR CONNECTIVITY: SIMULATIVE INSIGHTS INTO THE BEHAVIOUR OF SINGLE PATCH AND 2x2 ARRAY ANTENNAS FOR 2.4 GHZ WI-FI APPLICATIONS

The incessant demand for reliable and robust in-car connectivity drives the evolution of automotive wireless communication. In this pivotal research, we ventured into the realm of 2.4 GHz Wi-Fi applications, emphasising refining antenna designs that focused specifically on the Bio-composite material single patch and the 2x2 array configurations. Our simulations, executed meticulously in CST Microwave Studio, yielded noteworthy findings. The single patch antenna, fortified with a quarter-wave feeding line, delivered an efficiency of 51% and a gain of 2.83 dBi. While promising, its bandwidth limitations beckoned a deeper dive into alternative configurations. Subsequently, our exploration of the 2 x 2 array antenna, enriched with a power divider, stood out as a beacon of improvement. Notably, it exhibited a significant gain elevation, registering at 5.79 dBi. This uptick underscores the 2 x 2 array's superiority over its single-patch counterpart in amplifying in-car Wi-Fi strength and hints at its potential to set a new benchmark in automotive wireless solutions. In synthesis, this research does more than just present empirical data; it offers a roadmap for automotive manufacturers and WiFi solution providers, leading them towards more efficient and robust in-car wireless ecosystems. Beyond its immediate implications, our study lays a strong foundation for future explorations, potentially steering the discourse towards more intricate array antenna designs tailored for vehicular environments.

An Efficient Soft-Switching Wireless Battery Charger With Low-Loss Auxiliary Circuit

An Efficient Soft-Switching Wireless Battery Charger With Low-Loss Auxiliary Circuit

Research and Application of Visible Light Communication Technology

In recent years, the rapid growth of mobile devices and wireless services has created a significant demand for RF-based technologies. Among the various emerging wireless communication forms, Visible Light Communications (VLC) stands out as a potentially transformative technology that not only complements RF communications but also introduces innovative possibilities for mobile wireless device applications. VLC utilizes visible light for communication, offering high-speed data transmission, improved energy efficiency, and enhanced communication security and privacy. This article thoroughly explores VLC, discussing its core concepts, fundamental principles, and essential technologies that define this rapidly developing field. By providing insights into the intricacies of VLC, readers can gain a better understanding of its mechanisms and capabilities. The exploration also extends to elucidating the diverse application scenarios where VLC can be effectively applied, showcasing its versatility and potential impact on various sectors. This comprehensive survey not only presents the current state of VLC but also predicts future development trends, providing a forward-looking perspective on the evolution of this technology. As the demand for efficient and secure wireless communications continues to rise, VLC emerges as a promising frontier, poised to play a pivotal role in shaping the landscape of communication technologies in the years to come.

Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach

AbstractThe efficient development of Mobile Wireless Sensor Networks (MWSNs) relies heavily on optimizing two key parameters: Throughput and Energy Consumption. The proposed work investigates network connectivity issues with MWSN and proposes two routing algorithms, namely Self‐Organising Maps based‐Optimised Link State Routing (SOM‐OLSR) and Deep Reinforcement Learning based‐Optimised Link State Routing (DRL‐OLSR) for MWSNs. The primary objective of the proposed algorithms is to achieve energy‐efficient routing while maximizing throughput. The proposed algorithms are evaluated through simulations by considering various performance metrics, including connection probability (CP), end‐to‐end delay, overhead, network throughput, and energy consumption. The simulation analysis is discussed under three scenarios. The first scenario undertakes ‘no optimisation’, the second considers SOM‐OLSR, and the third undertakes DRL‐OLSR. A comparison between DRL‐OLSR and SOM‐OLSR reveals that the former surpasses the latter in terms of low latency and prolonged network lifetime. Specifically, DRL‐OLSR demonstrates a 47% increase in throughput, a 67% reduction in energy consumption, and a CP three times higher than SOM‐OLSR. Furthermore, when contrasted with the ‘no optimisation’ scenario, DRL‐OLSR achieves a remarkable 69.7% higher throughput and nearly 89% lower energy consumption. These findings highlight the effectiveness of the DRL‐OLSR approach in wireless sensor networks.

New energy vehicle technology research - Charging technology

In recent years, Chinas economic development is becoming more and more rapid, leading to the increasingly serious pollution emission problem of traditional fuel vehicles. In order to solve the environmental problems, the transformation of automobiles has become an urgent problem. At the present stage, electric vehicles are also widely concerned by all sectors of society because of their low carbon emissions, energy saving and low use cost. With the rapid growth of electric vehicle sales, the demand for charging piles is rising, and the mainstream charging facilities for new energy vehicles are still in short supply. Therefore, we need to focus on efficient wireless charging to solve the major problems in the current electric vehicle industry. This paper analyzes the wireless dynamic energy transmission technology, extending the service life of the battery, how to reduce the cost, the optimization of coil parameters design, the offset of transmission coil and receiving coil, foreign body detection and overheating problems.

Implementation and Analysis of an Efficient Soft-Switching Battery Wireless Charger with Re-Configurable Rectifier

To significantly reduce switching loss of the high frequency inverter in battery wireless charger (BWC), improve the soft-switching stability and system reliability, a novel soft-switching BWC with low loss auxiliary network and re-configurable rectifier is presented. The presented soft-switching BWC can implement the smooth switching between constant current mode (CCM) and constant voltage mode (CVM) without frequency switching and relays, which is beneficial to enhancing the system stability. With the help of a novel low energy consumption auxiliary circuit, the power switch tubes in the H-bridge inverter can implement zero-voltage soft switch-ON and zero-current soft switch-OFF in the full range, completely eliminating switching loss of the inverter. The proposed scheme can enhance the system efficiency while refraining from the soft-switching range limitation and soft-switching failure caused by the system parameter perturbation. Finally, a 1-kW soft-switching BWC principle prototype is developed to validate the effectiveness and correctness of the proposed scheme.

Multioutput Wireless Charger for Drone Swarms With Reduced Switch Requirements and Independent Regulation Capability

This paper develops a cost-effective and efficient wireless charger with multiple outputs based on newly proposed H3 bridge inverter, which has the benefits of reduced switch requirements and independent regulation capability. The output power of each channel is regulated by asymmetric duty cycle control. With the help of auxiliary LC branch, soft switching is ensured in spite of load variations. The sequential logic of driver signals is analyzed in detail to explore the independent regulation characteristic. For arbitrary 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> (or 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> +1) outputs, the total number of required switches is only 3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> (or 3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> +2) and the voltage stress of switches is the input voltage, which can reduce switch requirements. Simulation and experimental results are in good agreement with each other, proving the proposed wireless charger as a feasible solution for drone swarms.

Evolution of Energy Efficient Wireless Sensor Network: A Survey on Energy and Clustering Issues

In different fields of human endeavor, wireless sensor networks (WSNs) are being developed to collect, aggregate, fuse, and send data collected by a wide range of sensors in real-time. When developing a wireless network's data transmission routing system, energy consumption is among the most essential components. Designing an energy-efficient (WSN) routing framework is challenging. Several energyefficient routing algorithms exist for effectively transferring data packets between cluster members (CM) and cluster heads (CH). Some suggest the change of state of the nodes that made up the network from active to sleep in the event where the nodes are not sensing of sending data as a method of energy optimisation while others reduce the distance between the nodes because the distance between nodes is directly proportional to the amount of energy use during data transmission. Whatever the method may be, achieving reduced energy consumption in a WSN environment is an enormous task due to the need for both long network life and network efficiency. This paper reviews some research and energy saving techniques that had been carried out on WSN over a period of time.

On Design and Verification of an Efficient Microwave Wireless Power Transmission System

We report on the theoretical design and experimental verification of a high efficiency microwave wireless power transmission (MWPT) system operating in the Fresnel region. To achieve high conversion efficiency over a transmit distance of 11 meters, the transmit reflector antenna was optimized to locate the focal point at 11 m. The size of the receive antenna was decided by calculating the field distribution and the received power at different positions in the receive antenna aperture. Furthermore, an accurate model of the diode is presented, which was imported into the ADS software for high-precision rectifying circuit design. As a result, an overall DC-DC conversion efficiency of 20% was achieved, as measured in an anechoic chamber at a given distance of 11 m. The experimental results validated the proposed method.

Secure and Efficient Wireless Sensor Network and Machine Learning -based Monitoring System for Student Physical and Mental Health

In this research we have reviewed the performance of machine learning algorithms and WSNs for the monitoring student physical and mental health. We have found that logistic regression is a stronger alternative after an exhaustive evaluation of machine learning algorithms that consistently produce superior performance metrics than Gaussian mixture models (GMM). This has demonstrated average accuracy of 87.1% across 10 experiments, besting the average accuracy of 83.8% of GMM. It also illustrates greater efficacy for assessing system security, producing average accuracy of 86.6%, with 91.9% for GMM. The findings demonstrate the significance of select a machine learning algorithms that meets the specific needs of monitoring student health systems. They are also an illustration of logistic regressions dependability, and adeptness at identifying subtle changes in student health indicators that allow for proactive initiation of interventions and support mechanisms tailored to individual students’ needs. Going forward, monitoring systems should next be further refined and optimised so that they can more effectively support both student well-being and academic success in the complex and diverse educational settings of Indian universities.