Cost-effective Wireless Research Articles

Self-Powered and Cost-Effective Wireless Sensor Node for Air Quality Monitoring With an Optically Transparent Smart Antenna System

Self-Powered and Cost-Effective Wireless Sensor Node for Air Quality Monitoring With an Optically Transparent Smart Antenna System

Enabling cost-effective wireless THz communication through semiconductor optical amplifier’s direct/cross gain modulation for seamless integration of hybrid fiber-coaxial connectivity

We propose a cost-effective THz wireless communication scheme for future femtocell networks that leverages either direct gain modulation (DGM) or cross gain modulation (XGM) of a semiconductor optical amplifier (SOA) to convert coaxially or optically carried data into intensity-modulated THz waves, respectively. This architecture, based on an SOA and a uni-traveling-carrier photodiode (UTC-PD), has advantages over typical THz modulation schemes involving a resonant tunneling diode or an electro-optic modulator combined with a UTC-PD. Consequently, it facilitates the seamless convergence of hybrid fiber-coaxial networks to THz radio access networks with low cost, straightforward monolithic integration, and a more compact footprint. Furthermore, we experimentally verified the possibility of non-return-to-zero on–off keying wireless communication over 0.5 m in the 300 GHz band, achieving real-time transmission of 4 Gbit/s for coaxial access via an SOA’s DGM and 10 Gbit/s for fiber access via the SOA’s XGM. Notably, these data rates were attained by simple non-coherent THz intensity demodulation based on a Fermi-level managed barrier diode while meeting the 7% hard-decision forward error correction (HD-FEC) threshold (3.4×10−3). Finally, we conducted an analysis of the merits and drawbacks of the proposed THz communication scheme based on the combination of an SOA and a UTC-PD, discussed bottlenecks that hinder further rate improvement, and explored possibilities for optimization. In conclusion, this scheme is crucial for realizing future ultra-high-speed wireless networks that seamlessly connect to fiber-coaxial networks.

Wireless-based portable device heart rate measurement as biomedical devices for stress detection

Stress can increase the heart rate, causing dangerous conditions that cause significant harm and even death. Therefore, managing stress well to control the heart rate is vital. Monitoring stress levels using biomedical devices is essential to prevent further damage to the individual. Regular monitoring facilitates the acquisition of individual stress levels through heart rate information. In this research, a MAX30100 sensor can produce Beats Per Minute (BPM) data, which show the stress experienced by a person during four daily activities (sitting, walking, running, and sleeping) and when watching a horror movie. This research underscores the potential of cost-effective wireless solutions to increase portability and facilitate real-time monitoring and remote surveillance. The researchers introduced an innovative methodology by reusing everyday consumer devices, initially intended for routine activities, to measure stress levels in a unique context—precisely in a controlled experiment involving a movie-watching scenario. The ultimate achievement of this research was creating a stationary heart rate monitoring device that is easy to access, easy to use, space-saving, and adept at effectively tracking stress indicators. BPM readings obtained from the MAX30100 sensor have scores similar to those obtained from the heart rate app. The researchers proposed the inclusion of other sensors, such as a photoplethysmograph, an electrocardiogram, and galvanic skin response, to provide a more holistic assessment of stress. Leveraging cloud technology for data storage enables long-term monitoring and in-depth analysis of stress patterns over time.

Bus*: An efficient algorithm for finding Moving K-Nearest Neighbors (MKNNs) with capacity constraints

The large-scale and increasing use of transportation systems in various applications is expected to become an important component of communications networks beyond 5G and 6G in the next decade. To effectively support the massive deployment of transportation systems, reliable, secure, and cost-effective wireless connectivity is required. Communication networks are very important for vehicles that act as mobile user equipment. Although communications networks offer a promising way for cars to stay connected, it isn't easy to make transportation work well. This paper aims to present a new and interesting problem: the finding of the moving K-nearest neighbors (MKNNs), where each neighbor has a capacity limit. Specifically, considering a set of moving objects with different capacity constraints distributed in the road network, query objects with a certain load, find the optimal set of neighbors where the total available capacity is equal to or greater than the load of the query object, and the total travel time of the optimal set to reach the query object is minimized. This problem has significant applications in our lives. For example, it can help bus operating companies find other optimal bus trains in operation to move to the location of the damaged bus and transport its passengers to their destinations. In contrast, the total travel time of the optimal train is minimized. This paper uses previous research methods with a qualitative descriptive approach from sources that researchers found. The results of this research serve as material for proposing new algorithms that are effective for solving problems in real-time when using real data sets

A Wireless Data Acquisition System Based on MEMS Accelerometers for Operational Modal Analysis of Bridges.

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter.

Towards Embedded Electrochemical Sensors for On-Site Nitrite Detection by Gold Nanoparticles Modified Screen Printed Carbon Electrodes.

The transition of electrochemical sensors from lab-based measurements to real-time analysis requires special attention to different aspects in addition to the classical development of new sensing materials. Several critical challenges need to be addressed including a reproducible fabrication procedure, stability, lifetime, and development of cost-effective sensor electronics. In this paper, we address these aspects exemplarily for a nitrite sensor. An electrochemical sensor has been developed using one-step electrodeposited (Ed) gold nanoparticles (EdAu) for the detection of nitrite in water, which shows a low limit of detection of 0.38 µM and excellent analytical capabilities in groundwater. Experimental investigations with 10 realized sensors show a very high reproducibility enabling mass production. A comprehensive investigation of the sensor drift by calendar and cyclic aging was carried out for 160 cycles to assess the stability of the electrodes. Electrochemical impedance spectroscopy (EIS) shows significant changes with increasing aging inferring the deterioration of the electrode surface. To enable on-site measurements outside the laboratory, a compact and cost-effective wireless potentiostat combining cyclic and square wave voltammetry, and EIS capabilities has been designed and validated. The implemented methodology in this study builds a basis for the development of further on-site distributed electrochemical sensor networks.

Self-Powered Wireless Flexible Ionogel Wearable Devices.

Ionogels are promising soft materials for flexible wearable devices because of their unique features such as ionic conductivity and thermal stability. Ionogels reported to date show excellent sensing sensitivity; however, they suffer from a complicated external power supply. Herein, we report a self-powered wearable device based on an ionogel incorporating poly(vinylidene fluoride) (PVDF). The three-dimensional (3D) printed PVDF-ionogel exhibits amazing stretchability (1500%), high conductivity (0.36 S/m at 105 Hz), and an extremely low glass transition temperature (-84 °C). Moreover, the flexible wearable devices assembled from the PVDF-ionogel can precisely detect physiological signals (e.g., wrist, gesture, running, etc.) with a self-powered supply. Most significantly, a self-powered wireless flexible wearable device based on our PVDF-ionogel achieves monitoring healthcare of a human by transmitting obtained signals with a Bluetooth module timely and accurately. This work provides a facile and efficient method for fabricating cost-effective wireless wearable devices with a self-powered supply, enabling their potential applications for healthcare, motion detection, human-machine interfaces, etc.

Indoor positioning using circle expansion-based adaptive trilateration algorithm

The increasing availability of mobile devices with wireless communications capabilities has stimulated the growth of indoor positioning services. Indoor positioning is used to locate, in real time, devices’ positions for easy access. The indoor positioning, however, is challenging compared to outdoor positioning due to the large number of obstacles. Global positioning system is ideal for outdoor localization but fails in indoor environments with limited space. Recent development of the Internet of Things (IoT) has brought forth portable and cost-effective wireless technologies that can be used for indoor positioning. In this work, an adaptive trilateration algorithm based on received signal strength indicator (RSSI) was proposed. To assess the positioning accuracy of the proposed algorithm, Bluetooth Low Energy (BLE), Wi-Fi (IEEE 802.11n), ZigBee and LoRaWAN IoT technologies were used. Results show that the error performance is improved by 4% in BLE, 17% in ZigBee, 22% in Wi-Fi and 33% in LoRaWAN when compared to the existing related work.

Silica Nanospheres Coated Silver Islands as an Effective Opto-Plasmonic SERS Active Platform for Rapid and Sensitive Detection of Prostate Cancer Biomarkers.

The in vitro diagnostics of cancer are not represented well yet, but the need for early-stage detection is undeniable. In recent decades, surface-enhanced Raman spectroscopy (SERS) has emerged as an efficient, adaptable, and unique technique for the detection of cancer molecules in their early stages. Herein, we demonstrate an opto-plasmonic hybrid structure for sensitive detection of the prostate cancer biomarker sarcosine using silica nanospheres coated silver nano-islands as a facile and efficient SERS active substrate. The SERS active platform has been developed via thin (5-15 nm) deposition of silver islands using a simple and cost-effective Radio Frequency (RF) sputtering technique followed by the synthesis and decoration of silica nanospheres (~500 nm) synthesized via Stober's method. It is anticipated that the coupling of Whispering Gallery Modes and photonic nano-jets in SiO2 nanospheres induce Localized Surface Plasmon Resonance (LSPR) in Ag nano-islands, which is responsible for the SERS enhancement. The as-fabricated SERS active platform shows a linear response in the physiological range (10 nM to 100 μM) and an extremely low limit of detection (LOD) of 1.76 nM with a correlation coefficient of 0.98 and enhancement factor ~2 × 107. The findings suggest that our fabricated SERS platform could be potentially used for the rapid detection of bio-chemical traces with high sensitivity.

Wireless Temperature, Relative Humidity and Occupancy Monitoring System for Investigating Overheating in Buildings.

With the climate change we are experiencing today, the number and intensity of heatwaves are increasing dramatically, significantly impacting our buildings' overheating. The majority of the prefabricated concrete panel buildings in Hungary are considered outdated from an energy point of view. These buildings may be at greater risk from extreme weather events. To examine this, long-term monitoring measurements are needed. Therefore, we developed a unique, reliable, and cost-effective wireless monitoring system, which can track in real time the indoor air quality data (temperature, relative humidity, CO2) of the investigated apartment building, as well as users' habits, such as resident presence, window opening, and blind movement. The data were used to analyse and quantify the summer overheating of the dwelling and user habits. The measurements showed that the average temperature in all rooms was above 26 °C, and there were several occasions when the temperature exceeded 30 °C. Overheating in apartment buildings in summer is a significant problem that needs to be addressed. Further investigation of ventilation habits will help develop favourable ventilation strategies, and using these measurements in dynamic simulations will also help improve the models' validity for further studies.

Low-Cost Nonreversible Electronic-Free Wireless pH Sensor for Spoilage Detection in Packaged Meat Products.

Contamination of meat with pathogenic microorganisms can cause severe illnesses and food waste, which has significant negative impacts on both general health and the economy. In many cases, the expiration date is not a good indicator of meat freshness as there is a high risk of contamination during handling throughout the supply chain. Many biomarkers, including color, odor, pH, temperature, and volatile compounds, are used to determine spoilage. Among these, pH presents a simple and effective biomarker directly linked to the overgrowth of bacteria and degradation of the meat tissue. Low-cost methods for wireless pH monitoring are crucial in detecting spoilage on a large commercial scale. Existing technologies are often limited to short-range detection, with the use of batteries and different electronic components that increases both the manufacturing complexity and cost of the final device. To address these shortcomings, we have developed a cost-effective wireless pH sensor, which uses passive resonant frequency (RF) sensing, combined with a pH-responsive polymer that can be placed within packaged meat products and provide a remote assessment of the risk of microbial spoilage throughout the supply chain. The sensor tag consists of a sensing resonator coated with a pH-sensitive material and a passivated reference resonator operating in a differential frequency configuration. Upon exposure to elevated pH levels >6.8, the coating on the sensing resonator dissolves, which in turn results in a distinct change in the resonant frequency with respect to the reference resonator. Systematic theoretical and experimental results at different pH levels demonstrated that a 20% shift in resonant frequency demarcates the point for spoilage detection. As a proof of concept, the performance of the sensor in remotely detecting the risk of food spoilage was validated in packaged poultry over 10 days. The sensor fabrication process takes advantage of recent developments in the scalable manufacturing of flexible, low-cost devices, including selective laser etching of metalized plastic films and doctor-blade coating of stimuli-responsive polymer films. Furthermore, the biocompatibility of all the materials used in the sensor was confirmed with human intestinal cells (HCT-8 cells).

5G/SDR-Assisted Cognitive Communication in UAV Swarms: Architecture and Applications

Unmanned aerial vehicles (UAVs) are in general faster to deploy and easier to operate due to many off-the-shelf guidance, navigation, and control solutions. Such solutions usually support short-range line-of-sight operations. However, operating a swarm of UAVs for surveillance purposes in diverse terrains requires cost-effective wireless connectivity, infrastructure-less operation, adaptive mobility models, multiclass routing solutions, and a tailored ground control station . In this article, we address the challenge of UAV swarm communications by presenting a framework that offers an open-interface communication and networking solution for surveillance operations in urban/outreach areas. It is based on a hybrid connectivity module that can enable the coexistence of 5G infrastructures, adaptive multiband SDR waveforms empowered with cooperative communication capacities, and satellite communications for continuous swarm operation in any demographic area. In addition, we also discuss some of the current and futuristic applications and scenarios that can benefit from the provided solution.

Performance evaluation of SDWNs in cloud systems

The bursty traffic conditions and fast mobility of consumers induce significant delays in wireless networks. Taking the aforementioned factors into account, designing cost-effective wireless solutions with efficient resource usage and manageability is significantly challenging. Although cloud providers undeniably offer high-performance scalable wireless networks, delays may occur due to frequent packet losses as a result of weak signal strength, high mobility, and congestion. There is a slight difference between a wireless networking environment and typical wired networks, however, in practice, various variables exist, making it impossible to fully implement the core advantages of SDN such as rapid data processing, among others. To cope with such situations, we propose software-defined wireless networking (SDWN), a new means for wireless network virtualisation and programming capabilities. Moreover, to evaluate the proposed system, Mininet-WiFi in SDWN is used and conduct an experimental study in terms of packet transmission time and mobility visualisation over distance.

Conformal Transmitarrays for Unmanned Aerial Vehicles Aided 6G Networks

Unmanned aerial vehicles (UAVs) aided wireless communications promise to provide high-speed cost-effective wireless connectivity without needing fixed infrastructure coverage. They are a key technology enabler for sixth generation (6G) wireless networks, where a three-dimensional coverage including space, aero and terrestrial networks are to be deployed to guarantee seamless service continuity and reliability. Owing to the aerodynamic requirements, it is highly desirable to employ conformal antennas that can follow the shapes of the UAVs to reduce the extra drag and fuel consumption. To enable hundred giga-bits-per-second (Gb/s) data rates and massive connectivity for 6G networks, conformal antenna arrays featured with high gains and beam scanning/multiple beams are demanded for millimeter-wave and higher-frequency-range communications. However, new challenges exist in designing and implementing high-gain conformal arrays for UAV platforms. In this article, we overview the recent advances in conformal transmitarrays for UAV-based wireless communications, introducing new design methodologies and high-lighting new opportunities to be exploited.

An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge, and expensive devices. However, the high price of energy-harvesting devices hinders the use of this technology in large and dense networks, as such networks require multiple WMCs to improve the quality of service to the sensor nodes. To solve this problem, multiple power banks can be employed instead of utilizing WMCs. Furthermore, the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works. However, the sensor nodes are equipped with radio frequency (RF) technology, which allows them to exchange energy wirelessly. Consequently, critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes. Therefore, in this paper, an energy-efficient and cost-effective wireless power transmission (ECWPT) scheme is presented to improve the network lifetime and performance in forest fire detection-based systems. Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time (12.6%); network throughput (60.7%); data delivery ratio (20.9%); and network overhead (35%) as compared to previous related schemes. In conclusion, the proposed scheme significantly improves network energy efficiency for WSN.

Toward 6G with Connected Sky: UAVs and Beyond

The large-scale and ever-growing use of unmanned aerial vehicles (UAVs) in a wide range of applications is foreseen to be a major part of beyond 5G and 6G wireless networks in the next decade. The effective support of such massive deployment of UAVs requires offering reliable, secure, and cost-effective wireless connectivity. Cellular networks play essential roles in serving UAVs acting as flying user equipments. While the cellular networks provide promising connectivity solutions for UAVs, enabling robust UAV operations faces several challenges. In this article, an overview of key barriers and design considerations of widespread commercial use of flying UAVs are presented along with their potential solutions. In addition, we discuss how cellular networks can support UAVs by relying on their advanced features, network intelligence, key enabling technologies for beyond 5G and 6G, and exploiting new tools from machine learning. Finally, we shed light on offering wireless services to high altitudes and the integration of non-terrestrial networks with terrestrial networks toward limitless connectivity in 6G.

A deep learning-based edge-fog-cloud framework for driving behavior management

Among the various reasons behind vehicle accidents, drivers' aggressiveness and distractions play a significant role. Deep learning (DL) algorithms inside a car mobile edge (CME) have been used for driver monitoring and to perform automated decision-making processes. Training and retraining the DL models in resource-constrained CME devices come with several challenges, especially regarding computational and memory space costs. Moreover, training the DL models periodically on representative data nearest to CME without imposing communication overheads on the cloud improves the quality of service (QoS) parameters, such as memory demand, processing time, power consumption, and bandwidth. This paper investigates the deployment of a deep neural network (DNN) model on a cloud-fog-edge computing framework for aggressive driver behavior detection and monitoring. To reach this goal, our framework proposes utilizing effective systems and databases of sensor-based metrics and data, cost-effective wireless networks, cloud-and fog-edge computing technologies, and the Internet. Experimental results of the DNN model showed that the accuracy of detection is improved by 1.84% compared with the current related work without any pre-processing window on data points that come from bio-signal sensors. Moreover, the experimental results of the networking part prove the efficiency and effectiveness of the proposed framework.

Towards Accurate, Cost-Effective, Ultra-Low-Power and Non-Invasive Respiration Monitoring: A Reusable Wireless Wearable Sensor for an Off-the-Shelf KN95 Mask.

Respiratory rate is a critical vital sign that indicates health condition, sleep quality, and exercise intensity. This paper presents a non-invasive, ultra-low-power, and cost-effective wireless wearable sensor, which is installed on an off-the-shelf KN95 mask to facilitate respiration monitoring. The sensing principle is based on the periodic airflow temperature variations caused by exhaled hot air and inhaled cool air in respiratory cycles. By measuring the periodic temperature variations at the exhalation valve of mask, the respiratory parameters can be accurately and reliably detected, regardless of body movements and breathing pathways through nose or mouth. Specifically, we propose a voltage divider with controllable resistors and corresponding selection criteria to improve the sensitivity of temperature measurement, a peak detection algorithm with spline interpolation to increase sampling period without reducing the detection accuracy, and effective low-power optimization measures to prolong the battery life. The experimental results have demonstrated the effectiveness of the proposed sensor, showing a small mean absolute error (MAE) of 0.449 bpm and a very low power consumption of 131.4 W. As a high accuracy, low cost, low power, and reusable miniature wearing device for convenient respiration monitoring in daily life, the proposed sensor holds promise in real-world feasibility.

Hybrid SDN Performance: Switching between Centralized and Distributed Modes under Unreliable Control Communication Channels

Software-defined networking generally assumes ideal control channels between controller and network nodes. This may not be the case in challenged environments that are becoming more common due to dense and reduced-coverage 5G deployments and use cases requiring cost-effective wireless transport networks. In this paper, we evaluate the impact on network performance of unreliable controller-to-node communication channels, propose a hybrid SDN (hSDN) solution that switches between centralized and distributed operational modes depending on network conditions, and evaluate this solution under a variety of network scenarios (e.g., link impairments or packet loss ratios) designed to assess its operational limits. The results show that the proposed solution substantially improved the aggregated throughput, particularly when control channel packet loss ratios increased, while only showing a slight increase in average latency (e.g., 28% throughput improvement for 20% control packet losses). This enables network operation in hard conditions under which a canonical centralized SDN control would result in a nonoperational network.

Design and Implementation of a Cost-Effective Wireless Charger for an Electric Bicycle

Wireless charging is a convenient method of charging Electric Vehicles (EVs). Its application has been widely studied for electric cars but so far there have been limited experiments for electric bicycles (e-bikes). This paper addresses the design process and implementation details related to a wireless charger for a specific e-bike. Some issues such as the position of the secondary coil or the control algorithm prove to be crucial to the performance of the wireless transfer. Concerning the placement of the secondary coil, the paper proposes and validates a theoretical model to quantify the effects of the surrounding interfering materials. A CC-CV (Constant Current - Constant Voltage) control algorithm has been implemented to validate the theoretical results in these two charging phases. The control, the coils and the compensation networks have been designed to be simple and robust. The theoretical model, the charger and its control have proven to be effective in a real 84-W e-bike.