5G IoT Research Articles

Miniaturized tri-band integrated microwave and millimeter-wave MIMO antenna loaded with metamaterial for 5G IoT applications

This study presents a revolutionary tri-band combined microwave (MW) and millimeter wave (MMW) multiple-input-multiple-output (MIMO) antenna. The antenna is built on a Rogers RT-5880 substrate with dimensions of 32 × 32 × 1.6 mm³. This integration increased the isolation from 18 dB to 27 dB in the MW band and 28 dB–40 dB in the MMW band. The effective bandwidths for the MW and MMW bands were initially 3.3–3.7 GHz, 5.45–9.2 GHz, and 24–28 GHz, respectively. The MTM increased these bandwidths to 2.6–4.1 GHz, 5.3–11.3 GHz for the MW band, and 22.5–29.3 GHz for the MMW band. In addition, the realized gain increased significantly, from 1.5 dBi, 3.5 dBi, and 6.5 dBi to 4.5 dBi, 6.1 dBi, and 11 dBi at 3.5 GHz, 6.9 GHz, and 28 GHz. The antenna's overall efficiency increased by 10–12 % across all operational bandwidths. The suggested antenna has excellent diversity performance, with an envelope correlation coefficient of less than 0.0002, a diversity gain greater than 9.998, and a channel capacity loss of less than 0.12 bits/s/Hz. The proposed antenna addresses the increasing demand for IoT devices by efficiently supporting both MW and MMW frequency bands, offering high performance in wireless communication and modern infrastructures. The proposed design not only improves connectivity and efficiency in IoT devices but also supports the Sustainable Development Goals (SDGs) by contributing to the development of smart city infrastructure (SDG 11) and fostering innovation and sustainable industrial practices (SDG 9), thereby promoting sustainable development and industrial innovation.

Investigations on Penta Band High Isolation MIMO Antenna for 5G NR Bands and HIPERLAN Applications Using TCMA

The present article proposes a penta band Multiple Input Multiple Output (MIMO) antenna for 5G New Radio (NR) bands and HIPERLAN applications using Characteristic Mode Analysis (CMA). The design is obtained and optimised in step-by-step procedure using novel CMA approach and by perturbing the conventional rectangular structure with slots on the patch (left and right sides) and the ground plane (wide slot + narrow slots) for enhancing the isolation at multi bands. The MIMO configuration has a total dimension of 0.58 l0 × 0.35 l0 × 0.01 l0 mm3 with an optimum element separation of 0.05 l0 (l0 is the lowest frequency operating wavelength). Multiband resonance is produced at 2.2, 4.2, 7.2, 16, 17.5 GHz. The radiating elements can excite various characteristic modes that support wider bandwidth. The -10 dB impedance bandwidth at working region are 0.2, 0.2, 0.38, 3.6, 2 GHz respectively. The suggested design yields a gain of 2.1, 5.7, 3.5, 3.5, 5.2 dBi and consistent radiation patterns at the working frequencies. The analysis of the diversity performance considers the Diversity Gain (DG) and Envelope Correlation Coefficient (ECC), whose values are near 9.9 dB and 0, respectively. Additionally, the assessed parameters are the CCL and TARC. The structure prototype has been developed, and the observed findings are highly coherent with the simulated results, making it appropriate for use in 5G NR bands, HIPERLAN, and IoT applications.

Environmental Monitoring Data Management and Analysis Based on 5G IoT

As environmental issues become increasingly severe, environmental monitoring has become a crucial measure for protecting and managing ecosystems. The high speed, low latency, and massive connectivity characteristics of 5G technology, combined with the widespread application of IoT technology, provide new solutions for environmental monitoring data management and analysis. This study explores the methods of environmental monitoring data management and analysis based on 5G IoT technology, designs a systematic environmental monitoring framework, and verifies its effectiveness through empirical research. The results show that the environmental monitoring system based on 5G IoT can efficiently, real-time, and reliably collect and analyze environmental data, providing strong support for environmental protection and management.

Development a novel design of miniaturized heptagonal Koch fractal wide band antenna for 5G mm wave and IoT applications

The object of the study is a compact heptagonal broadband antenna specially designed for use in the 5G millimeter band using Koch fractals to improve performance. As a result of the study, the most important problem of achieving higher gain, improving bandwidth and reducing interference at higher frequencies, which is necessary for the effective functioning of 5G networks, was solved. As a result, the maximum realized gain of 5 dB was obtained at a frequency of 27.58 GHz with an impressive bandwidth in the range from 26.5 to 40 GHz. The use of Koch fractal geometry and defective ground planes significantly improves impedance matching and expands bandwidth, which explains the excellent antenna performance compared to traditional designs. The features and distinguishing features of the results obtained, thanks to which they allowed solving the problem under study, are its compact dimensions (only 9 mm by 9 mm) and the ability to maintain VSWR at a level of less than 2 in the entire frequency spectrum. These features make the antenna particularly suitable for millimeter-band integration and flexible applications such as portable devices and wearable home appliances. The field of practical application of the results includes integration into portable and wearable devices, improving the performance and connectivity of Internet of Things applications. The conditions of practical use require compliance with 5G network standards and compatibility with millimeter-wave technologies. This characterizes the antenna as a significant achievement in antenna technology, demonstrating its potential for widespread adoption in next-generation wireless communication systems and paving the way for more reliable and high-performance wireless networks

The Generalization of Stage-Reduced STPS for Low-Loss Unequal 1 × 4 Phased Array Architecture for 5G IoT Applications

The Generalization of Stage-Reduced STPS for Low-Loss Unequal 1 × 4 Phased Array Architecture for 5G IoT Applications

Managing 5G IOT Network Operations and Safety Using Deep Learning and Attention Methods

Managing 5G IOT Network Operations and Safety Using Deep Learning and Attention Methods

A cloud-based architecture for explainable Big Data analytics using self-structuring Artificial Intelligence

Big Data is steadily expanding beyond the boundaries of its foundational constructs of three primary Vs, Volume, Velocity and Variety, and two secondary Vs, Veracity and Value. The advent of 5G networks, Edge computing and IoT technologies has transformed Big Data into this modern context. With these new manifestations of Big Data, the focus is not only on the data itself but on the context that it applies to its immediate environment as well as the human and societal perception of this context. It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability of such insights in terms of bias, transparency, safety, ethics, and causality. Self-structuring Artificial Intelligence (SSAI) addresses the limitations of conventional AI by adapting to the inherent structure of the data, incrementally learning and abstracting from this structure. SSAI has not been investigated in a cloud-based setting for generating explainable insights from these new types of Big Data. In this paper we propose a cloud-based architecture for explainable Big Data analytics using SSAI in highly-connected 5G and Edge computing environments. The proposed architecture is empirically evaluated on a commercial scale Big Data use case of Smart Grid for Smart Cities. The results of these experiments confirm the functionality and effectiveness of the proposed architecture.

5g-Enabled DevOps: Revolutionizing Integrated Communication And Networking Technologies For Faster Software Delivery

This paper discusses how integrating 5G and IoT technologies can improve the DevOps process regarding communication and networking to foster efficient software delivery. The strengths of 5G are expected to provide interfaces for IoT devices and ensure high-speed, low-latency networks that will bring responses to the existing DevOps challenges, including longer durations of continuous integration and deployment. Exploiting the benefits provided by 5G connectivity and IoT for real-time data processing, one can get more robust, functional, and adaptive DevOps settings. The results suggest that integrating 5G and IoT into DevOps will not only increase the speed of software delivery but also improve overall system performance and open doors to novel solutions for heightened, built-in agile software development and deployment in complex and highly distributed systems. Therefore, this paper aims to demonstrate the possibility of innovation enabled by using 5G and IoT to improve the efficiency and resilience of the DevOps processes, making it possible for technologies and business requirements to advance rapidly in the digital age.

Traffic Fingerprints for Homogeneous IoT Traffic Based on Packet Payload Transition Patterns

Traffic fingerprint was considered an effective security protection mechanism in IoT scenarios because it can be used to automatically identify accessed devices. However, the results of replication experiments show that the classic traffic fingerprints based on simple network traffic attribute features have a significantly lower ability to identify accessed devices in real 5G IoT scenarios compared to what was stated in traditional IoT scenarios. The growing homogenization of IoT traffic caused by the application of 5G is believed to be the reason for the poor ability of traditional traffic fingerprints to identify 5G IoT terminals. Studying an enhanced traffic fingerprint is necessary to accommodate the homogeneous Internet of Things traffic. In addition, during the reproducing experiments, we noticed that the solution of overlap is a key factor that restricts the recognition ability of one-vs-all multi-classifiers, and the efficiency of existing methods still has some room for optimization. Based on targeted improvements to these two issues, we proposed an enhanced IoT terminal traffic fingerprint based on packet payload transition patterns to improve the device recognition ability in homogeneous IoT traffic. Additionally, we designed an improved solution for overlap based on density centers to expedite decision making. According to the experimental results, when compared with the existing traffic fingerprint, the proposed traffic fingerprint in this study demonstrated a Macro-Average Precision of close to 90% for network traffic from real 5G IoT terminals. The proposed overlap solution based on the density centers reduced the decision-making time from hundreds of seconds to tens of seconds while ensuring decision-making accuracy.

Empowering Agricultural Ecosystems: Leveraging 5G IoT for Enhanced Product Integrity and Sustainable Ecological Environments

The study explores the change-making potential of 5G IoT technology in improving the security of agro-products and contributing to conservation of environments in the ecological field. Through the process of a series conducted in expression using the real agricultural settings, the study gives a visible proof that food traceability, quality assurance, resource management and environment conservation all gain the significant improvements. By relying on IoT sensors utilizing the latest 5G connectivity, stakeholders can have real-time data analytics and communication to take well-informed decisions, optimize resource usage, and manage the environmental risks. The analytics showed a noticeable boost in product traceability with real-time tracking, temperature monitoring during transportation, and automatic data logging rendering success rates above 95%. Resource management benefits are demonstrated in H2O and fertilizer reduction, reaching 30%, and up to 20% higher yields of crops than is traditional. Ecological conservation results exhibit the improved ecosystem health and the possibility to detect environmental pollution in real-time allows operational conservation measures. The research emphasizes on the prospect of 5G IoT whose benefits might transform agricultural practices and guarantee the authenticity and sustainability of food in the face of fast-changing environmental conditions.

Retracted: Research on the Use of Task-Based Language Teaching Method in English Language Teaching Based on Big Data 5G IoT Scenario

Retracted: Research on the Use of Task-Based Language Teaching Method in English Language Teaching Based on Big Data 5G IoT Scenario

Use of edge resources for DNN model maintenance in 5G IoT networks

Use of edge resources for DNN model maintenance in 5G IoT networks

Performance Analysis of a Compact Conformal SWB Antenna for 5G and Wearable IoT Applications

Performance Analysis of a Compact Conformal SWB Antenna for 5G and Wearable IoT Applications

Multiband fractal antenna for 5G communications and IoT applications

Multiband fractal antenna for 5G communications and IoT applications

IoT-FKGDL-SL: Anomaly Detection Framework Integrating Knowledge Distillation and a Swarm Learning for 5G IoT

IoT-FKGDL-SL: Anomaly Detection Framework Integrating Knowledge Distillation and a Swarm Learning for 5G IoT

Compact 8-Port 2×2 Array Based on Dual-Polarized Patch Antennas With Modified Cavity Field Distribution for Enhanced Port Isolation for 5G IoT Device MIMO Antennas

Compact 8-Port 2×2 Array Based on Dual-Polarized Patch Antennas With Modified Cavity Field Distribution for Enhanced Port Isolation for 5G IoT Device MIMO Antennas

Time Reduction for SLM OFDM PAPR Based on Adaptive Genetic Algorithm in 5G IoT Networks

In this paper, a new peak average power and time reduction (PAPTR) based on the adaptive genetic algorithm (AGA) strategy is used in order to improve both the time reduction and PAPR value reduction for the SLM OFDM and the conventional genetic algorithm (GA) SLM-OFDM. The simulation results demonstrate that the recommended AGA technique reduces PAPR by about 3.87 dB in comparison to SLM-OFDM. Comparing the suggested AGA SLM-OFDM to the traditional GA SLM-OFDM using the same settings, a significant learning time reduction of roughly 95.56% is achieved. The PAPR of the proposed AGA SLM-OFDM is enhanced by around 3.87 dB in comparison to traditional OFDM. Also, the PAPR of the proposed AGA SLM-OFDM is roughly 0.12 dB worse than that of the conventional GA SLM-OFDM.

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D.

Deep Learning Algorithms in Mobile Edge with Real-Time Abnormal Event Detection for 5G-IoT Devices

IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular and flat localization (3D localization). DRCaG, an acronym for the proposed model, stands for a deep, complicated network with a gated layer on top. The performance of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a variable signal-to-noise ratio (SNR) spanning from 20 dB to + 20 dB, which illustrates the superiority of DRCaG compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings.

Novel Frequency-Reconfigurable Antennas with Ring Resonators and RF Switches: Enhancing Versatility and Adaptability in Wireless Communication Systems

This study introduces innovative designs for frequency-reconfigurable antennas that utilize ring resonators combined with either PIN diodes or RF switches. These designs enhance the versatility, adaptability, and overall performance of the antennas in wireless communication systems. By controlling the switches and ring resonator, the antenna’s resonant frequencies and bandwidths can be adjusted, allowing for compatibility with various communication standards and frequency ranges. The proposed antenna exhibits four distinct operational states, each characterized by different resonance frequencies and operating frequency bands. Return loss, radiation pattern, radiation efficiency, and surface current distribution are analyzed for each state. State-1 (ON-ON) and State-2 (OFF-ON), which are characterized by resonance frequencies of 2.4 GHz and 3.33 GHz respectively, offer ranges suitable for Wi-Fi, Bluetooth, ISM, and IoT applications. State-3 (ON-OFF), with a resonance frequency of 3.0 GHz and bandwidth spanning from 2.59 GHz to 3.643 GHz, complies with Wi-Fi, Wi-Fi 6, and IoT requirements. State-4 (OFF-OFF) covers the band centered around 3.45 GHz. It is compatible with many applications such as 5G mid-band, Wi-Fi 6E, IoT, and cellular systems. The proposed antenna designs are versatile and compact since the overall antenna dimensions are 25 × 18 × 1.6 mm3. The radiation efficiency of the antenna configuration varies depending on operational states. By utilizing the advantages of both ring resonators and RF switches, the proposed antenna configurations offer new solutions that enhance their performance in wireless communication systems. This study compares the effects of using PIN diodes and SPDT switches on the performance of antennas and also examines the DC biasing effect on antenna characteristics. The simulation results are validated by the experimental analysis. The proposed antenna designs offer a new approach for wireless communication systems by using both ring resonators and RF switches.