Planar Microwave Sensors
Submission Deadline: 31 May 2025
IEEE Access invites manuscript submissions in the area of Planar Microwave Sensors.
This Special Section of IEEE Access is focused on Planar Microwave Sensors, a topic of growing research interest. Within today’s paradigms of the Internet of Things (IoT), the Fourth Industrial Revolution (also known as Industry 4.0), and the Digital Transformation (or Smart World), there is an increasing demand for cost-effective, small-sized, and smart sensors and sensor networks, to be applied in a wide diversity of scenarios, such as Smart Cities, Smart Health, Smart Agriculture, Civil Engineering, Structural Health Monitoring, Biosensing, Agrifood Industry, Security, Motion Control, Automotive Industry, and Space, etc. There are many sensing technologies (e.g., optics/photonics, acoustics, electrochemical, etc.), but RF/microwaves (extending the spectrum from UHF up to THz frequencies) offer a series of unique advantages aligned with the requirements of the above-cited paradigmatic concepts. Thus, besides their low cost and size, microwave sensors, and particularly planar sensors, can be implemented in flexible substrates, including plastics, organic substrates, and even fabric, by means of subtractive (etching) or additive (printing) processes, and they are also compatible with other technologies of interest for sensing, such as microfluidics, micromachining, 3D-printing, etc. Additionally, microwaves are very sensitive to the electromagnetic properties of the materials with which they interact. Thus, microwave sensors are very useful for the dielectric characterization of materials (solids or liquids), and for the measurement of many physical, chemical, and biological variables related to material permittivity.
Planar microwave sensors can operate by contact or contactless with the material under test (MUT), or analyte, and can be wirelessly connected to the reader (of interest in many IoT applications), in schemes based on the so-called sensing tags (which act as a “smart skin,” able to provide information of the material or sample under study). Another important aspect of planar sensors is that the necessary associated electronics for signal generation, processing, and communication purposes can be seamlessly integrated within the sensor’s substrate, representing a reduction in system costs and complexity. In summary, planar microwave sensors constitute an enabling technology for the deployment of the IoT, Industry 4.0, and Smart World, where sensing is necessary to obtain information of the system under consideration, in order to gain insight on its current state and take appropriate decisions and actions (either through human intervention or autonomously) when necessary.
The main objective of this Special Section of IEEE Access is to publish high-quality papers related to the theory, techniques, technologies, and applications of planar microwave sensors.
The topics of interest include, but are not limited to:
- Sensor Phenomenology, modeling, and evaluation
- Sensitivity, resolution, and selectivity optimization techniques
- Dielectric characterization and permittivity sensors
- Resonant and non-resonant planar sensors
- Contactless, nonintrusive, and non-invasive sensors
- Liquid and microfluidic sensors
- Physical sensors (displacement and proximity, temperature, humidity, pressure, etc.).
- Chemical sensors (gas detection, impurity detection in liquids, etc.).
- Biosensors (bacterial growth, glucose measurements, electrolyte content measurements, cells and organs analysis, etc.) for in vitro and in vivo investigations.
- Microwave spectroscopy
- Wireless sensors, RFID sensors, and sensor networks
- Artificial Intelligence (AI) and related techniques applied to planar microwave sensors
- New materials and technologies for microwave sensing
- Active planar sensors
- “Green” sensors
- Sensor systems and applications
- Sensors for structural health monitoring (SHM)
- Sensors for smart agriculture and agrifood industry
- Sensors for smart Cities
- Sensors for civil engineering
- Sensors for smart industry
- Sensors for smart healthcare and vital signs monitoring
- Sensors for motion control
- Sensors for automotive and space industry
- Wearable sensors
We also highly recommend the submission of a video with each article as it significantly increases the visibility of articles.
Lead Editor: Ferran Martín, Universitat Autònoma de Barcelona, Spain
Guest Editors:
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- Katia Grenier, LAAS-CNRS, Toulouse, France
- Amir Ebrahimi, RMIT University, Melbourne, Australia
- Mohammad Zarifi, University of British Columbia, Canada
- Carlos G. Juan, Universidad Miguel Hernández, Elche, Spain
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Submit manuscripts to: http://ieee.atyponrex.com/journal/ieee-access
For information regarding IEEE Access, including its peer review policies and APC information, please visit the website http://ieeeaccess.ieee.org
For inquiries regarding this Special Section, please contact: Ferran.Martin@uab.cat.
Mobile Software Development Kit for Real Time Multivariate Blood Glucose Prediction
In the field of blood glucose prediction, the literature is abounded with algorithms that demonstrate potential in glucose management. However, these propositions face an issue common to many machine learning algorithms: the repeated reuse of datasets (overfitting) and a tendency to develop algorithms in isolation, detached from practical scenarios. Compounding these challenges is that many insulin pump vendors and continuous glucose monitor vendors use closed and proprietary protocols, restricting researchers’ data access and the ability to deploy complex, multivariate optimizers. This study seeks to bridge the gap between theoretical algorithms and their real-world applications by devising a software development kit. This kit collects real-time data from continuous glucose monitors, carbohydrate intake, insulin deliveries from insulin management systems, and metrics like physical activity, stress, and sleep from wearables. Our methodology leverages the open-source insulin management system, Loop, integrated with Apple Health and various wearable devices. Although navigating through diverse communication protocols to link these devices presented challenges, we succeeded in aggregating a comprehensive dataset for blood glucose predictions. To underscore the utility of our software development kit, we executed a technical proof-of-concept on this platform, illustrating real-time, individualized, data-driven multivariate blood glucose predictions. We hope that our platform can contribute to transforming machine learning algorithms from technical developments into actionable tools with real-world benefits in blood glucose management. It provides a foundation for researchers to refine their predictive algorithms and decision support systems within a more dynamic, data-rich environment.
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Enhancing Accuracy in Actigraphic Measurements: A Lightweight Calibration Method for Triaxial Accelerometers
This paper presents a simple, lightweight, automatic calibration method for low-cost triaxial accelerometers, utilizing the Earth’s gravitational constant in various orientations. It can be easily implemented using only fixed-point arithmetic and can run on low-power microcontrollers for real-time measurements, making it practical for scenarios with limited data storage and computational power, such as actigraphy or IoT applications. The method offers ease of use by automatically detecting motionless intervals, eliminating the need for complex positioning techniques. The procedure detects resting states and calculates the corresponding three-dimensional mean acceleration values during the measurement. After appropriately selecting these mean values, a set of calibration points is formed and passed to a gradient-based optimization algorithm for iterative estimation of the calibration coefficients. Different metrics were used for verification and comparison with other methods, which were calculated through simulations and tests based on real measurements. The results show that, despite its lightweight nature, the method performs equally to more complex solutions. This article provides a thorough explanation of a novel method for collecting calibration points, the optimization algorithm, and the methods used for performance evaluation in a reproducible manner.
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Distributed Current Source Method for Modeling of Magnetic and Eddy-Current Fields in Sensing System Design
This paper presents a distributed current source (DCS) method for modeling the eddy current (EC) fields induced in biological or non-ferrous metallic objects in two-dimensional axisymmetric and three-dimensional Cartesian coordinates. The EC fields induced in the objects, magnetic flux density (MFD) in space, and magnetic flux (MF) of the sensing coils are formulated in state-space representation. The harmonic responses of the eddy current fields and electromotive force (EMF) of the sensing coil are formulated in closed-form solutions. The proposed DCS method is applied to design two eddy current sensing systems. The Bio-Differential Eddy Current (BD-EC) sensor distinguishes biological objects, and the Metal-Coaxial Eddy Current (MC-EC) sensor classifies non-ferrous metallic objects. The simulated EC field and EMF are numerically verified by comparing results with finite element analysis. An example is utilized to illustrate the advantage of the DCS method for calculating the MFD, MF, and EMF contributed from the induced ECD in the objects directly, and the EMF generated from each material. The proposed method, along with a prototype of the BD-EC sensor, has been experimentally evaluated on sweep frequency analysis for detecting meat and bone.
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Optimal Sizing of CPP-GMR Read Sensors for Magnetic Recording Densities of 1–4 Tb/in²
Several studies have confirmed that current-perpendicular-to-the-plane giant magnetoresistance (CPP-GMR) technology is appropriate for next-generation read sensors for ultrahigh areal densities (ADs) of data storage applications. Since the physical dimension of the read sensor is a crucial factor for developing the reader to overcome its limitations, this paper proposes an optimal sizing prediction of the CPP-GMR read heads for ADs of 1-4 Tb/in 2 . Micromagnetic modelling was performed in the simulations. The appropriate length of the stripe height (SH) and the read width (RW) of the readers was estimated based on a consideration of sensor outputs including the readback signal, asymmetry parameter, dibit response and power spectral density (PSD) profile. It was found that a variation of SH and RW lengths had an influential impact on the readback signal waveform. Those affectations were further characterized through the echoes of dibit response showing that shortening the SH length or increasing the RW length could improve the resolution and reduce the distortion occurring in the readback signal. Moreover, the PSD profile indicated that the reader operation became more stable at shorter SH lengths or longer RW lengths. The head response spectrum was also examined. In addition, the magnitude of the bias current was studied in relation to the head response. Lastly, the optimal physical dimension (SH × RW) of the CPP-GMR readers for ADs of 1-4 Tb/in 2 was predicted to be ( 40×48 ) nm, ( 28×29 ) nm, ( 25×26 ) nm and ( 19×20 ) nm, respectively. The results can be utilized to design the CPP-GMR sensors at ultrahigh magnetic recording capacities.
*Published in the IEEE Magnetics Society Section within IEEE Access.
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Positioning and Navigation in Challenging Environments
Submission Deadline: 31 July 2022
IEEE Access invites manuscript submissions in the area of Positioning and Navigation in Challenging Environments.
In recent years, positioning and navigation has become a vital part of modern life especially with the continuous performance enhancement and modernization of the four global navigation satellite systems. Positioning and navigation industry has been growing quickly and has played a significant role in the industrial chain. Although great progress and many achievements have been made over the past few decades, there are a range of significant issues to be dealt with, especially in challenging environments.
In complex (e.g. large, multi-floor) indoor environments, it is a challenge to generate a valid positioning and navigation solution by a remote cloud platform with both offline and online data (e.g. WiFi and magnetic fingerprint data) recorded with smartphones The problem may become more complex if a pedestrian goes through different scenarios, such as from one floor to another floor of the same building, or from one building to another connected or neighboring building. There is a preference to avoid any interruption in the provision of valid position information. Thus, in the design of next-generation (beyond 5G) communication systems, the positioning functions need to be enabled and standardization of positioning technology such as in 3GPP should be taken into account.
As natural resources of the earth’s surface and shallow sea are becoming scarce, it is inevitable to acquire resources from deep underground, deep underwater and outer space. Regarding deep underground mining, there are currently a good number of deep mines in the world, including Mponeng Gold Mine and Tau Tona Mine, both located in South Africa with a depth of about 3.9km, and Kidd Creek Copper and Zinc Mine located in Ontario, Canada with a depth of about 2.9km. Deep underground mining is a challenging scenario which usually has high humidity and irregular space distribution, requiring stricter restrictions on the design and building of positioning and navigation systems.
Deep sea mining is promising because of abundant minerals on and under the deep seabed. For instance, a large amount of polymetallic nodules, containing rich concentrations of manganese, nickel, copper, and cobalt, are found in the Clarion-Clipperton Zone, a great abyssal plain as wide as the continental United States that lies 4 to 6 km below the surface of the eastern Pacific Ocean. Abundant naturel gas and oil also exist deep under the sea. Positioning and navigation is important for vehicles and robots to pick up the seabed surface minerals and to perform drilling and extraction of minerals under the seabed.
Space mining is currently a hot topic and should become a reality in the next few decades. , It is crucial to provide accurate and reliable positioning and navigation information for spacecraft and/or space robots which will approach and then usually land on the target planet (e.g. moon) or asteroid, followed by exploration, mining and so on; or simply catch and hold a rather small asteroid and move it back to Earth. For instance, a small Japanese space capsule carrying pristine pieces of the near-Earth asteroid Ryugu touched down on 5 December 2020, northwest of the South Australian capital of Adelaide. This was a successful initial step towards space mining on asteroids.
Positioning and navigation is vital for safe, reliable and effective operations in the scenarios of the frontier applications mentioned above. This Special Section aims to report the recent advances on positioning and navigation in such challenging scenarios. Researchers and engineers are also encouraged to perform more research and development to make advances in this area.
The topics of interest include, but are not limited to:
- Positioning and navigation in complex indoor environments
- Deep underground positioning and navigation
- Positioning and navigation for deep ocean operations and mining
- Positioning and navigation for space exploration and mining
- Cloud computing for positioning and navigation
- High sensitivity GNSS receivers
- Suppression of GNSS jamming and spoofing
- Positioning for communication systems beyond 5G
- Standardization of positioning technology
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Kegen Yu, China University of Mining and Technology, Mainland of China
Guest Editors:
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- Andrew Dempster, University of New South Wales, Australia
- Pau Closas, Northeastern University, USA
- Shih-Hau Fang, Yuan Ze University, Taiwan
- Guenther Retscher, Vienna University of Technology, Austria
- Ali Broumandan, Hexagon Autonomy and Positioning, Canada
Relevant IEEE Access Special Sections:
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: kegenyu@foxmail.com.
Prediction of Re-Occurrences of Spoofed ACK Packets Sent to Deflate a Target Wireless Sensor Network Node by DDOS
The Wireless Sensor Network (WSN) has evolved into a new IoT scheme, and its adoption has no restrictions at present. Sadly, security has an impact on the network of wireless sensors, and Denial-of-Service (DOS) categories of attacks are security concerns. This study therefore focuses on the distributed denial of service (DDOS), especially on DDoS-PSH-ACK (ACK & PUSH ACK Flood) in WSN. An experimental analysis was developed to predict that many spoofed ACK packets were reoccurring in order to deflate the target node. In the proposed approach, several experimental scenarios for the DDOS detection function were established and implemented. The experimental analysis draws traffic flow within the several transmission sessions involving “the normal transmission within sensor nodes and cluster head”, as well as the “transmission and retransmission scenarios within the sensor nodes and cluster head” at same time with different signal sizes. The main contribution of the paper is predicting DDoS attack by variability of transmission behavior with high degree accuracy. It was established that the most ideal delay between transmissions is 23 milliseconds in order to ensure that the receiving end is not overwhelmed. The result of the current study highlighted that when transmission session gets overwhelmed, that influence DDOS success.
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Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring
Development and deployment of sensing technologies is one of the main steps in achieving sustainability in crop production through precision agriculture. Key sensing methodologies developed for monitoring soil moisture and nutrients with recent advances in the sensing devices reported in literature using those techniques are overviewed in this article. The soil moisture determination has been divided into four main sections describing soil moisture measurement metrics and laboratory-based testing, followed by in-situ, remote and proximal sensing techniques. The application, advantages and limitations for each of the mentioned technologies are discussed. The nutrient monitoring methods are reviewed beginning with laboratory-based methods, ion-selective membrane based sensors, bio-sensors, spectroscopy-based methods, and capillary electrophoresis-based systems for inorganic ion detection. Attention has been given to the core principle of detection while reporting recent sensors developed using the mentioned concepts. The latest works reported on the different sensing methodologies point towards the trend of developing low-cost, easy to use, field-deployable or portable sensing systems aimed towards improving technology adoption in crop production leading to efficient site-specific soil and crop management which in turn will bring us closer to reaching sustainability in the practice of agriculture.
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Highly Sensitive Reflective-Mode Phase-Variation Permittivity Sensor Based on a Coplanar Waveguide Terminated With an Open Complementary Split Ring Resonator (OCSRR)
This paper presents a one-port reflective-mode phase-variation microwave sensor consisting of a coplanar waveguide (CPW) transmission line terminated with a grounded open complementary split ring resonator (OCSRR). The sensor is useful for measuring the dielectric constant of the so-called material under test (MUT), which should be placed in contact with the OCSRR, the sensitive element. The output variable is the phase of the reflection coefficient. Design guidelines for the implementation of highly sensitive sensors are derived in the paper, and validated through simulation and experiment. As compared to other reflective-mode phase-variation sensors based on open-ended sensing lines, the designed and fabricated devices exhibit a very small sensitive region by virtue of the use of an electrically small resonant element, the OCSRR. The relevant figure of merit, defined as the ratio between the maximum sensitivity and the size of the sensing area (expressed in terms of the squared wavelength), is as high as FoM
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Towards Smart Cities with IoT Based on Crowdsensing
Submission Deadline: 31 December 2020
IEEE Access invites manuscript submissions in the area of Towards Smart Cities with IoT Based on Crowdsensing.
The proliferation of Internet of Things (IoT) has paved the way for the future of smart cities. The large volume data over IoT can enable decision-making for various applications, such as smart transportation, smart parking and smart lighting. The key to the success of smart cities is data collection and aggregation over IoT. Recently, crowdsensing has become a new data collection paradigm over IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with the microphones on smartphones. Despite the advantages of crowdsensing and IoT, there are many challenges to utilize crowdsensing over IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users. This Special Section aims to solicit original research works that address the challenging problems in utilizing crowdsensing over IoT for smart cities.
The topics of interest include, but are not limited to:
- Collaborative sensing and computing
- Sparse mobile crowdsensing over IoT
- Task allocation/selection in crowdsensing
- Fair/long-term/quality-oriented incentive in crowdsensing
- Data collection/aggregation in crowdsensing
- Truth discovery in crowdsensing
- Security, trust and privacy protection for IoT devices
- Privacy-preserving data collection for crowdsensing
- Fog computing and edge computing
- IoT device monitoring and scheduling in smart city
- Analysis for IoT-based Big Data
- Vehicular crowdsensing networks in smart city
- Resource management of IoT devices for smart city
- Quality measurement of crowdsourced data for smart city
- Crowdsourced data enabled applications in smart city
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor:
Kun Wang, University of California, Los Angeles, USA
Zhibo Wang, Wuhan University, China
Guest Editors:
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- Ye-Qiong Song, University of Lorraine, France
- Dejun Yang, Colorado School of Mines, USA
- Shibo He, Zhejiang University, China
- Wei Wang, Amazon Inc, USA
Relevant IEEE Access Special Sections:
- Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations
- Data Mining for Internet of Things
- Security, Privacy, and Trust Management in Smart Cities
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: zbwang@whu.edu.cn.
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