Internet Of Things Applications Research Articles

Awareness and Perception of Students Toward Execution of Internet of Things in Library Services: A study of Indian Institute Technologies of Northern India

Abstract This study aimed to investigate students’ awareness and perception toward the execution of Internet of Things (IoT) in library services of Indian Institute Technologies (IITs) of Northern India. This study consists of a quantitative research design in which a survey was conducted utilizing a stratified random sampling technique to survey 813 students from five IITs. A total of 780 correctly filled-out surveys were received, yielding a response rate of 95.94%. Statistical tools, including Microsoft Excel and the Statistical Package for the Social Sciences, were used to analyze the data. To assess the significance of the results at the 0.05% level, statistical analysis was carried out utilizing percentages and analysis of variance. This study will communicate the attitude, awareness, and perception of students regarding the implementation of IoT in libraries of IITs. It is conducted to understand the motive behind new technology acceptance in academic libraries. The findings reveal that most of the students got familiar with IoT from browsing on the Web/Internet, social networking sites, research projects, conferences, workshops, webinars and faculty development programs, professional discussions, and meetings, as part of the curriculum. Respondents were in favor of implementing IoT in library services. They believed that IoT will ensure better and more efficient utilization of library resources, followed by real-time information accessibility, recommend IoT-based library services, system content quality will be updated, and will increase the quality of the library; the efficiency of library services will increase with implementation of IoT. It will increase the productivity of library services, be useful in an emergency, and promote information literacy. The study’s findings would be beneficial to libraries and academic institutions interested in implementing IoT applications in their libraries.

A Survey Study on IoT Application and its Attacks

The Internet of Things (IoT) has witnessed exponential growth in recent years, with billions of interconnected devices seamlessly communicating and exchanging data. While IoT offers unprecedented opportunities for innovation and convenience, it also introduces a plethora of security challenges. This abstract provides an overview of the key security concerns in IoT and explores emerging solutions to address them. Security in IoT is paramount due to the potential consequences of breaches. IoT devices are often resource-constrained and lack robust security features. Malicious actors can exploit this vulnerability to gain unauthorized access, compromise privacy, launch cyberattacks, and disrupt critical services. Therefore, securing IoT ecosystems is imperative for the continued growth and adoption of IoT technologies.

Toward a Real-Time TCP SYN Flood DDoS Mitigation Using Adaptive Neuro-Fuzzy Classifier and SDN Assistance in Fog Computing

The growth of the Internet of Things (IoT) has recently impacted our daily lives in many ways. As a result, a massive volume of data are generated and need to be processed in a short period of time. Therefore, a combination of computing models such as cloud computing is necessary. The main disadvantage of the cloud platform is its high latency due to the centralized mainframe. Fortunately, a distributed paradigm known as fog computing has emerged to overcome this problem, offering cloud services with low latency and high-access bandwidth to support many IoT application scenarios. However, attacks against fog servers can take many forms, such as distributed denial of service (DDoS) attacks that severely affect the reliability and availability of fog services. To address these challenges, we propose mitigation of fog computing-based SYN Flood DDoS attacks using an adaptive neuro-fuzzy inference system (ANFIS) and software defined networking (SDN) assistance (FASA). The simulation results show that the FASA system outperforms other algorithms in terms of accuracy, precision, recall, and F1-score. This shows how crucial our system is for detecting and mitigating TCP-SYN floods and DDoS attacks.

Buckling-driven piezoelectric defect-induced energy localization and harvesting using a Rubik’s cube-inspired phononic crystal structure

Wireless sensor networks that enable advanced internet of things (IoT) applications have experienced significant development. However, low-power electronics are limited by battery lifetime. Energy harvesting presents a solution for self-powered technologies. Vibration-based energy harvesting technology is one of the effective approaches to convert ambient mechanical energy into electrical energy. Various dynamic oscillating systems have been proposed to investigate the effectiveness of energizing low-power electronic sensor devices for supporting various IoT applications across engineering disciplines. Phononic crystal structures have been implemented in vibrational energy harvesters due to their unique bandgap and wave propagation properties. This work proposes a Rubik’s cube-inspired defective-state locally resonant three-dimensional (3D) phononic crystal with a 5 × 5 × 5 perfect supercell that contains 3D piezoelectric energy harvesting units. The advantage of defect-induced energy localization is utilized to harness vibrational energy. The 3D piezoelectric energy harvesting units are constructed by the buckling-driven assembling principle. Adapting to the low-frequency and broadband characteristics of ambient vibration sources, soft silicone gel is used to encapsulate the buckled 3D piezoelectric units, which are embedded in the 3D cubic phononic crystal to assemble an entire system. The energy harvesting performance of various defective layouts and their defect modes is discussed. The results demonstrate that the harvester functions well under multidirectional, multimodal, and low-frequency conditions. The proposed methodology also offers a new perspective on vibrational energy harvesters for defective phononic crystals with superior working performance.

IOT Rancang Bangun Alat Pengusir Hama Burung pada Padi Sawah Petani Berbasis Internet of Things (IoT)

This research aims to develop an effective and efficient bird pest repellent tool in farmers' rice fields by utilizing Internet of Things (IoT) technology. Bird pests often pose a serious threat to agricultural products, especially in rice fields. Through the application of IoT, this tool can monitor real-time bird activity around agricultural areas and take appropriate preventive measures. This tool is designed to utilize motion sensors or pir sensors, surveillance cameras, and a sound or visual signal delivery system to chase away birds. approaching agricultural areas. PIR sensors are widely used in a variety of applications, including home security systems, motion sensor lighting, and building automation. The main advantages of PIR sensors are their low power consumption and ability to provide fast response to movement, making them an efficient and effective choice for many detection applications. motion, data collected from sensors and cameras will be sent to the IoT platform with the Telegram application and which is connected via an internet connection, allowing farmers to monitor and control tools remotely via smart devices such as smartphones or computers. The main advantage of this tool is its ability to provide a quick response to the presence of bird pests, reduce agricultural yield losses and increase crop productivity. Additionally, integration with IoT provides high flexibility and connectivity, enabling continuous system optimization. designing this tool involves the hardware and software design stages. The hardware consists of a pir sensor, an esp 32 cam microcontroller, and a speaker and servo. While the software consists of Arduino Ide, Blynk and Telegram. After design, the tool is field tested for performance. Test results show that this tool is able to detect the presence of birds with good accuracy, provides a fast response in chasing them away, Telegram is able to communicate by giving commands and receiving images, Blynk can provide information on the condition of the tool when it is online and give commands to activate the servo, sound or both of them. With a significant removal success rate, this tool is successful in reducing losses caused by bird strikes in agriculture. Additionally, it has low power consumption and can be integrated with existing IoT platforms for remote monitoring and data analysis. It is hoped that this research can make a positive contribution to efforts to develop sustainable agricultural technology and improve farmers' welfare by protecting agricultural products from bird pest attacks. Thus, it is hoped that the implementation of this tool in the field can provide an innovative and efficient solution in controlling bird pests in farmers' rice fields.

LAAKA: Lightweight Anonymous Authentication and Key Agreement Scheme for Secure Fog-Driven IoT Systems

Fog computing is a new computing model with limited resources located near the Internet of Things (IoT) devices to provide low transmission delay, mobility, and location awareness services. However, fog technology not only extends the cloud resources but also inherits its properties and security problems, such as device capture, man-in-the-middle, secret key compromise, identity impersonation, and message replay attacks. These vulnerabilities pose significant risks to the integrity of IoT-fog communications. Nevertheless, the existing mutual authentication approaches either suffer from high computational overheads or fail to provide secure mutual authentication. To address this gap, this paper introduces LAAKA, a Lightweight Anonymous Authentication and Key Agreement scheme. Considering the constrained resources of IoT and fog devices, LAAKA utilizes lightweight operations such as hash function and bitwise XOR. Its main objective is to facilitate mutual authentication and establish secure session keys between IoT devices and fog servers, making it useful for various IoT applications. We validate the robustness of LAAKA against various security threats by conducting comprehensive formal security analysis, including Burrows-Abadi-Needham (BAN) logic and Random Oracle Model (ROM), as well as informal analysis. The efficacy of our scheme is further demonstrated through evaluation with the Scyther tool. Compared to other proposed authentication approaches, the results illustrate the superior performance and efficiency of our approach in enhancing the security features, minimizing the computational cost, and optimizing storage utilization.

A high-efficiency optimized bias-flip energy harvesting system for IoT applications

This paper presents a high-efficiency optimized bias-flip energy harvesting system for Internet of Things (IoT) applications. The harvester consists of a piezoelectric transducer and an interface circuit. Unlike previous bias-flip harvesters, a piezoelectric transducer uses an optimized electrode structure instead of a monolithic electrode to reduce the internal capacitance of the piezoelectric transducer and increase the voltage flip efficiency and maximum output power. The optimized bias-flip harvester does not increase the inductor value or aspect ratio of flip switches to increase efficiency and does save chip area and system volume. While the interface circuit uses a switched-capacitor controller to control the gate-overdrive of the flip switches, employing the adiabatic technique reduces the power consumption of the switching in the control circuit by about 10%. Theoretical analysis of the electrode length effect on the voltage flip efficiency and output power has been performed. Theoretical analysis is confirmed by COMSOL and HSPICE simulations. The maximum output power of the proposed harvester is about 7.4 times the FBR and has superior performance compared to previously reported interface circuits.

Frequency-adjustable OAM antenna with co-divergent beams for IoT applications

A frequency-tunable Orbital Angular Momentum (OAM)-generating antenna array system is introduced for the first time to provide high-data-rate and secure communication for Internet of Things (IoT) infrastructures and devices. Propagation of simultaneous OAM modes brings about the high-data-rate and frequency tunability leads to interference suppression for secure communication. Regarding the design evolution of the proposed structure, the novel Multiple-Input-Multiple-Output (MIMO) antenna consisting of two input ports creates a 2 × 2 UCA, resulting in two 2 × 2 sub-UCA. Feeding each 2 × 2 sub-array with [0°, 90°, 180°, 270°] phase gradients in clockwise and counterclockwise states gives rise to dual-mode OAM-carrying waves with simultaneous excitation capability. It should be mentioned that each MIMO element owns frequency tunability, resulting in a frequency-adjustable dual-mode OAM antenna array. A prototypes of the suggested MIMO antenna as well as the OAM array are fabricated and measured. Changing the applied DC-voltage to the varactor diodes embedded in capacitive stubs of the antenna elements shifts the operating frequency in the MIMO antenna and consequently in the OAM array. Concerning the unique radiation characteristics of the OAM antenna, vortex beams having helical phase fronts are observed, confirming the generation of expected OAM waves. Therefore, it is asserted that the introduced structure, for the first time, proposes new features of frequency-tunability, and co-divergent simultaneous OAM propagation using the MIMO-UCA technique to provide high-data-rate and secure communication in IoT applications.

PRICELESS: Privacy enhanced AI‐driven scalable framework for IoT applications in serverless edge computing environments

AbstractServerless edge computing has emerged as a new paradigm that integrates the serverless and edge computing. By bringing processing power closer to the edge of the network, it provides advantages such as low latency by quickly processing data for time‐sensitive Internet of Things (IoT) applications. Additionally, serverless edge computing also brings inherent problems of edge and serverless computing such as cold start, security and privacy that are still waiting to be solved. In this paper, we propose a new Blockchain‐based AI‐driven scalable framework called PRICELESS, to offer security and privacy in serverless edge computing environments while performing cold start prediction. In PRICELESS framework, we used deep reinforcement learning for the cold start latency prediction. For experiments, a cold start dataset is created using a heart disease risk‐based IoT application and deployed using Google Cloud Functions. Experimental results show the additional delay that the blockchain module brings to cold start latency and its impact on cold start prediction performance. Additionally, the performance of PRICELESS is compared with the current state‐of‐the‐art method based on energy cost, computation time and cold start prediction. Specifically, it has been observed that PRICELESS causes 19 ms of external latency, 358.2 watts for training, and 3.6 watts for prediction operations, resulting in additional energy consumption at the expense of security and privacy.

Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.

Optimizing Medical IoT Disaster Management with Data Compression

In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce "Beyond Orion," a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology.

Design of a Dual-Band Printed Antenna for Modern Wireless Communications and Internet of Things Applications

Wireless technology is increasingly influenced by the advanced technological development of antennas. With the rapid expansion of Internet of Things ( IoT) application in the contemporary communication networks, the demand for small printed antennas is increasing, due to their small size and ease of design and manufacture to suit their various application. In this paper, the antenna is designed which is operating at two different frequency bands 2.40GHz and 5.78GHz. The design parameters of the antenna have been calculated using the transmission line model, and an antenna design program has been used for the simulation process is HFSS ( High frequency simulator structure ) tool, which is based on Finite Element Method (FEM). In this work, the proposed slot dual band patch antenna is implemented using FR4 substrate material with a thickness 1.6mm and relative permittivity 4.4. The size of the proposed antenna is just (28 × 27.7mm). The proposed antenna has Omni-directional radiation patterns on most of the operating band. The dual band antenna is designed by adding a slot to the top of the radiated patch. Therefore, the proposed dual- band antenna shows good performance, which is suitable for modern wireless communications and Internet of Things (IoT) applications. The measurements are found to be in good accordance with the simulated results.

Development of Circuits for Antilogarithmic Converters with Efficient Error–Area–Delay Product Using the Fractional-Bit Compensation Scheme for Digital Signal Processing Applications

Digital signal processing (DSP) has been widely adopted in sensor systems, communication systems, digital image processing, artificial intelligence, and Internet of Things applications. However, these applications require circuits for complex arithmetic computation. The logarithmic number system is a method to reduce the implementation area and transmission delay for arithmetic computation in DSP. In this study, we propose antilogarithmic converters with efficient error–area–delay products (eADPs) based on the fractional-bit compensation scheme. We propose three mathematical approximations—case 1, case 2, and case 3—to approximate the accurate antilogarithmic curve with different DSP requirements. The maximum percentage errors of conversion for case 1, case 2, and case 3 are 1.9089%, 1.7330%, and 1.2063%, respectively. Case 1, case 2, and case 3 can achieve eADP savings of 15.66%, 80.80%, and 84.61% compared with other methods reported in the literature. The proposed eADP-efficient antilogarithmic converters can achieve lower eADP and digitalized circuit implementation. The hardware implementation utilizes Verilog Hardware Description Language and the digital circuits are created via very-large-scale integration by the Taiwan Semiconductor Manufacturing Company with 0.18 µm CMOS technology. This proposed antilogarithmic converter can be efficiently applied in DSP.

Building a Smart Water City: IoT Smart Water Technologies, Applications, and Future Directions

Water is an essential service for the sustainable development and economic competitiveness of any country. The global water demand has increased substantially due to economic development, climate change, and rising population. The Internet of Things (IoT) and Information and Communication Technologies (ICT) can help conserve available water resources. Smart cities apply IoT to boost the performance and efficiency of urban facilities. Smart cities are towns created to use IoT and ICT (innovative technologies) such as smart water applications. Several studies on smart water technology have been conducted, but there is a need to review current research that leverages the IoT as a communication technology to design effective smart water applications. This review paper is aimed at presenting evidence on the current design of smart water applications. The study also covers publication statistics to increase collaboration between stakeholders. Findings show that various technologies such as microcontrollers, embedded programming languages, sensors, communication modules, and protocols are used by researchers to accomplish their aim of designing IoT-based smart water solutions. None of the publications employed the 5G mobile networks as a communication module for their smart water application development. Findings further show that the integration of 3D printing and solar energy into IoT-based smart water applications is revolutionary and can increase the sustainability of the systems. Future directions required to ensure that developed smart water applications are widely adopted to help conserve and manage water resources are suggested.

Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology

Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development.

Design and Analysis of a Quad-Band Antenna for IoT and Wearable RFID Applications

The role of antennas in wireless communication is critical for enabling efficient signal transmission and reception across various frequency bands, including those associated with IoT (Internet of Things), X-band, S-band, and RFID (radio-frequency identification) systems. This paper presents a small quadruple-band antenna with 25 × 40 × 1.5 mm3 dimensions designed for diverse wireless applications. It is adept at operating in the S-band (2.2 GHz), wireless local area network (WLAN) (5.7 GHz), microwave RFID frequency band (5.8 GHz), and X-band (7.7 GHz and 8.3 GHz). While the majority of existing research focuses on antennas covering two or three bands, our work stands out by achieving quad-band operation in the proposed antenna design. This antenna is constructed on a semiflexible Rogers RT5880 substrate, making it well-suited for wearable applications. Computer Simulation Technology (CST) Microwave studio (2019) simulation package software is chosen for design and analysis. The antenna design features a comb-shaped radiating structure, where each “tooth” is responsible for resonating at a distinct frequency with an appropriate bandwidth. The antenna retains stability in both free space and on-body wearability scenarios. It achieves a low specific absorption rate (SAR), meeting wearable criteria with SAR values below 1.6 W/Kg for all resonating frequencies. The proposed antenna demonstrates suitable radiation efficiency, reaching a maximum of 82.6% and a peak gain of 6.3 dBi. It exhibits a bidirectional pattern in the elevation plane and omnidirectional behavior in the azimuth plane. The antenna finds applications across multiple frequencies and shows close agreement between simulated and measured results, validating its effectiveness.

Dynamic load balancing in IoT-enabled WSNs using fuzzy logic-based control

The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications.

Novel Meta-Fractal Wearable Sensors and Antennas for Medical, Communication, 5G, and IoT Applications

Future communication, 5G, medical, and IoT systems need compact, green, efficient wideband sensors, and antennas. Novel linear and dual-polarized antennas for 5G, 6G, medical devices, Internet of Things (IoT) systems, and healthcare monitoring sensors are presented in this paper. One of the major goals in the evaluation of medical, 5G, and smart wireless communication devices is the development of efficient, compact, low-cost antennas and sensors. Moreover, passive and active sensors may be self-powered by connecting an energy-harvesting unit to the antenna to collect electromagnetic radiation and charge the wearable sensor battery. Wearable sensors and antennas can be employed in smart grid applications that provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. A low-cost wearable antenna may be developed by etching the printed feed and matching the network on the same substrate in the printed antenna. Active modules may be placed on the same dielectric board. The antenna design parameters and a comparison between the computation and measured electrical performance of the antennas are presented in this paper. The electrical characteristics of the new compact antennas in the vicinity of the patient’s body were simulated by using electromagnetic simulation techniques. Fractal and metamaterial efficient antennas and sensors were evaluated to maximize the electrical characteristics of smart communication and medical devices. The dual- and circularly polarized antennas developed in this paper are crucial to the evaluation of wideband and multiband compact 5G, 6G, and IoT advanced systems. The new efficient sensors and antennas maximize the system’s dynamic range and electrical characteristics. The new efficient wearable antennas and sensors are compact, wideband, and low-cost. The operating resonant frequency of the metamaterial antennas with circular split-ring resonators (CSRRs) may be 5% to 9% lower than the resonant frequency of the sensor without CSRRs. The directivity and gain of the metamaterial fractal antennas with CSRRs may be up to 3 dB higher than the antennas without CSRRs. The directivity and gain of the metamaterial fractal passive sensors with CSRRs may be up to 8.5 dBi. This study presents new wideband active meta-fractal antennas and sensors. The bandwidth of the new sensors is around 9% to 20%. At 2.83 GHz, the receiving active sensor gain is 13.5 dB and drops to 8 dB at 3.2 GHz. The receiving module noise figure with TAV541 LNA is around 1dB.

A survey of millimeter wave backscatter communication systems

Backscatter communication is an emerging low-power communication technology that has been integrated into the Internet of Things (IoT) due to its exceptional self-sustainability. However, conventional backscatter communication (BackCom) technologies often encounter limitations in data transmission rates, making it challenging to meet the growing bandwidth demands of IoT applications. In contrast, millimeter wave (mmWave) BackCom exhibits significant potential for high-speed data transmission owing to its abundant spectrum resources. Therefore, this paper presents a comprehensive survey of the existing literature on mmWave BackCom. We start by introducing the fundamental principles of mmWave BackCom, encompassing its architecture, basic techniques, and foundational knowledge. Subsequently, we review various mmWave backscatter communication techniques proposed in the current literature. Finally, we discuss several applications based on these techniques before finally addressing challenges and open issues associated with mmWave backscatter.