Journal of Communications

NK Power-saving Wireless Sensor Network by Distributed Compressed Sensing for Time-series Environmental

2024-01-17T09:19:32+08:00

It is important to understand environmental conditions at various places to address air-pollution problems. Although wireless sensor networks help monitor environmental quality anywhere locally, it has a power supply problem. This study proposes a low-power WSN by distributed compressed sensing for a time-series environmental monitoring system. The proposed method performs data compression at each sensor side to reduce power consumption during data transmission. On the other hand, data restoration is performed on a server with abundant computing resources. This paper examines the power-saving effect of the proposed method and the optimal compression ratio. Experimental results show that the coefficient of determination is 0.9 or higher with a compression ratio = 90%. Our obtained results that our proposed distributed compressed sensing-used WSN is effective for a time-series environment monitoring system is a valuable finding for future research approaches.

GFDM-OQAM Performance Analysis Using Linear Equalization for Audio Transmission

2023-10-19T15:24:42+08:00

In order to minimize inter-carrier interference (ICI), inter-symbol interference (ISI), and out of band (OOB) impacts on orthogonal frequency division multiplexing (OFDM) systems, strong wireless communication performance is required. The use of Generalized Frequency Division Multiplexing (GFDM) is based on the requirement for a block-based multi-carrier technology in which each subcarrier is generated with a filter in the form of non-rectangular pulses known as pulse shaping. Meanwhile, Offset QAM (OQAM) is used to achieve better spectral efficiency than QAM and simultaneously reduce the occurrence of ICI and ISI. In this study, the effect of adjusting the roll-off factor value on the pulse shaping filter utilized is examined in order to detect the original signal supplied by the transmitter using two linear equalizations: Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). In the interim, offset QAM (OQAM) is used to outperform QAM in terms of spectrum efficiency while also reducing the prevalence of ICI and ISI. The performance parameters in this study were measured based on the Signal to Noise Ratio (SNR) parameter to the Bit Error Rate (BER). The results of this study indicate that the GFDM-OQAM system using MMSE equalization has a better BER value than the GFDM-OQAM system using ZF equalization. In addition, the greater the use of the roll-off factor, the lower the performance of the BER system because there is a greater excess bandwidth which is linear with the magnitude of the roll-off factor.

A Piecewise Linear Model for Passive Intermodulation Distortion

2024-01-04T09:24:03+08:00

Passive Intermodulation (PIM) distortion which results from passive components such as antennas, connecters, etc. is a major problem in wireless communications as it limits cell coverage and data rates. In carrier aggregated LTE system, PIM is manifested as intermodulation components that leak from the transmitter to the receiver resulting in significant interference. The primary goal of this paper is to develop a new behavioral model for passive nonlinearities which enables the effects of PIM distortion in the transmit and receive chains of an LTE systems to be predicted. The analysis is based on using a Piecewise Linear (PWL) model to model a passive nonlinearity which is then used to predict PIM in carrier aggregated LTE system. The simulation results show that the proposed model accurately predicts PIM and is more numerically stable and more economical than the polynomial model in terms of the number of parameters which makes it a good candidate for the design of digital cancellation schemes of PIM.

Gain Increase Modification Collinear Dipole Antennas for Secondary Surveillance Radar

2024-02-22T17:01:49+08:00

in this research, an antenna design was developed using the wire-to-micros trip adaptation technique. The aim of developing the antenna is by modifying the change in position and shape of the antenna to get a large gain with a minimum value of 25 dB, for the use of Secondary Radar (SSR). The design of the antenna used in this study is to improve the performance of modifying conventional dipole antennas to become collinear arrays. A collinear array antenna is an antenna technique by modifying the dipole arm to become an array or using it with the addition of a coupling effect on each side of the antenna on both the positive and negative arms and by looking at the comparison of the position of the vertical placement rotated 180 degrees. This aims to increase the resulting gain. In this study, collinear array antennas were carried out using two methods, namely Omni directional and with the addition of reflectors as a technique to change the radiation polarity from Omni to unidirectional. It can be observed that the radiation produced is affected by several changes in the shape and position of the antenna, by conducting primary research on 3 types of antenna arrays of 8 antennas, unidirectional A mode with opposite poles, mode A is not unidirectional with opposite poles, and Mode B Unidirectional and opposite poles. Early research made comparisons of polar differences with polar similarities. For the results of a single polar difference antenna Mode A and B, where Mode A produces S11- 26.884 dB with Gains 3.825 dB, Mode B produces S11-20.408 dB with Gains 2.364 dB, for research on array antennas, it was carried out in stages, with as many as 8 array antennas, without reflectors and with reflectors. for the final antenna with an array of 112 antennas using reflectors to produce s11 for a frequency of 1.03 GHz of- 15.53061 dB with Gain 26.52 dB and an Azimuth beam width of 0.9ᵒ and for the frequency 1.09as big- 20.73117 dB with Gain 25.6 dB and an Azimuth beam width of 0.8ᵒ.

Machine Learning for Channel Coding: A Paradigm Shift from FEC Codes

2024-02-26T15:59:33+08:00

The design of optimal channel codes with computationally efficient Forward Error Correction (FEC) codes remains an open research problem. In this paper, we explore optimal channel codes with computationally efficient Forward Error Correction (FEC) codes, focusing on Turbo and Low-Density Parity-Check (LDPC) codes as near-capacity approaching solutions. We highlight the significance of accurate channel estimation in reliable communication technology design. We further note that the stringent requirements of contemporary communication systems have pushed conventional FEC codes to their limits. To address this, we advocate for a paradigm shift towards emerging Machine Learning (ML) applications in communication. Our review highlights ML's potential to offer solutions to current channel coding and estimation challenges by replacing traditional communication algorithms with adaptable deep neural network architectures. This approach provides competitive performance, flexibility, reduced complexity, and latency, heralding the era of ML-based communication applications as the future of end-to-end efficient communication systems

Efficient and Accurate Indoor Positioning System: A Hybrid Approach Integrating PCA, WKNN, and Linear Regression

2023-10-20T09:42:24+08:00

The high-precision Indoor Positioning System (IPS) is a captivating area of research that has made significant advancements in recent years due to the increasing demand for its applications. Our study proposes an innovative approach to improve indoor positioning accuracy by integrating Principal Component Analysis (PCA), weighted k-Nearest Neighbors (WKNN), and Linear Regression (PCA-WLR). This hybrid strategy enables the system to leverage the unique characteristics of each model, capturing intricate patterns and correlations in the data. Experimental evaluations on a publicly available dataset demonstrate the superiority of our hybrid approach. The Root Mean Squared Error (RMSE) achieved is 1.97 meters, and the mean distance error is 2.23 meters. Remarkably, the ensemble outperforms individual methods in other studies on the same dataset, showing 10.8% to 17.2% improvement in accuracy. Notably, our proposed hybrid approach significantly reduces training time from 581.3599 seconds to 8.8814 seconds, representing an impressive reduction of approximately 98.47%. Similarly, testing time is reduced from 10.1721 seconds to 0.0176 seconds, indicating a substantial decrease of around 99.82%. These significant reductions in training and testing times underscore the efficiency and effectiveness of our proposed ensemble model, making it highly practical for real-time applications.

Channel Estimation Methods for Frequency Hopping System Based on Machine Learning

2024-02-17T21:15:15+08:00

This paper presents a pioneering deep learning strategy for precise estimation of a Narrow Band (NB) multipath channel parameters for a Slow Frequency Hopping (SFH) blindly, leveraging a synthetic dataset rich in intricate temporal variations and channel gain nuances. The Neural Network (NN) architecture, characterized by multiple dense layers, displayed exceptional effectiveness in capturing intricate temporal relationships. Following rigorous training and validation utilizing the mean square error (MSE) loss function, the model achieved a significant reduction in loss, underscoring its proficiency in accurate delay estimation. A verification of a previous work via Subspace based methods through computer simulations are also included. The proposed ML estimator outperforms the classical subspace-based methods and demonstrates effectiveness over several Signal to Noise Ratio (SNR).

Study on Defected Ground Structure Models with Miniaturized Patches for Broadband Wireless

2024-01-11T09:26:54+08:00

This study proposes the defected ground structure mechanism and improves the antenna properties. Three DGS models are discussed: two models of a single antenna and one model of an array microstrip antenna design. The microstrips are designed for broadband wireless frequencies: 2.1 GHz (5G Technology) of a single antenna, and 5.8 GHz (Wireless Local Area Network) of an array antenna. These frequencies are achieved using three shapes of DGS in the simulations and the results are convenient for broadband criteria. The antenna patches are rectangular with two types of DGS, and an array circular with one type of DGS. The results show the DGS gives better bandwidth and gain for a single antenna. Nevertheless, DGS performs gain enhancement for an array antenna and this design gives the miniaturized array antenna of 69.92% and 73.47% in width and length. The three shapes of DGS have different designs, nonetheless, the slot on the DGS with the front side of the patch provides enhancements in antenna performance than other design. Rectangular patch antenna with frame-shaped DGS and array circular antenna with rectangular slots of DGS give bandwidth of 533 MHz and 327 MHz. The single antenna improves the fractional bandwidth of 25.38% with miniaturized patch. Nonetheless, the array circular antenna with DGS obtains a gain enhancement of 16 dB than the initial array without DGS.

Keywords— microstrips antenna, defected ground structure, resonant frequency, impedance bandwidth, gain, miniaturized patch

Improved Improved Extreme Learning Machine Based Hunger Games Search for Automatic IP Configuration and Duplicate Node Detection

2024-01-04T21:14:13+08:00

IP address auto reconfiguration, which ensures theoptimum routing, is individual of the most challengingchallenges in Mobile Ad-hoc Networks (MANET). IP addressreconfiguration protocols are divided into two categories:stateful and stateless. Addresses must be unique, andconflicts between addresses must be avoided. This paperoffers the Hunger Games Search Improved ExtremeLearning Machine (HGS-IELM) Method framework for IPaddress auto reconfiguration in MANET, which is based onthe Hunger Games Search algorithm and the ImprovedExtreme Learning Machine. The HGS-IELM voting enforcesensuring a fresh read depending on each access. Both dataconsistency and message overhead are engineered to worktogether. The suggested HGS-IELM approach is scalable anddoes not need the use of a central server. According to theresults of the experiments, the proposed HGS-IELMframework achieved decreased message overhead andlatency. The suggested HGS-IELM approach exhibitedenhanced address availability while maintaining appropriateredundancy.

A A Reconfigurable Antenna for IoT Applications with Enhanced Performance by Adding Metamaterial

2024-01-19T16:20:52+08:00

In this study, for wireless communication a frequency-reconfigurable antenna is developed and calculated utilizing complementary four split ring resonators (SRR)-based metamaterial. The suggested antenna is printed on a Rogers RT5880 dielectric with the following dimensions (38 × 21 × 1.6) m³ ε_r= 2.2, (with a tangent loss (tan) of 0.0009). Depending on the antenna's switching scenario, Antenna Mode 1 (3.03 GHz) operates in a single-band mode, in Mode 2 a dual-band mode (i.e., 2.34 and 5.06 GHz), and a tri-band mode in Mode 3 (i.e., 1.82, 4.2, and 6.44 GHz). Using a fed microstrip line is possible to use a quarter-wavelength transformer line to get 50 characteristic impedance and good impedance matching. The method for extracting the parameters from the SRR's meta-material property, which is how the existence of negative permeability and the new resonance frequencies are confirmed, is covered in depth. The suggested antenna offers a good number of benefits, such as a straight forward construction, low return loss, and the ability to switch frequencies using a PIN diode (SMP1340-079LF).

Manuscript received xx xx 2023; revised xx xx 2023, accepted xx xx 2023.

Optimizing Downlink Resource Allocation for High-Speed LTE-V Networks Through Intelligent Scheduling

2024-01-05T11:49:21+08:00

Abstract— With the rapid growth of vehicular communication systems, integrating Long-Term Evolution-Vehicle (LTE-V) networks has become essential to support various applications, including enhanced road safety, traffic management, and infotainment services. In such networks, efficient downlink scheduling is pivotal in ensuring reliable and low-latency communication between base stations and vehicles. For high-speed traveling, the rapid and fast-changing channel increases the challenge of downlink scheduling based on latency, throughput, and Bit Error Rate (BER). Available scheduling algorithms suffer from the effect of Doppler shift, interference, and the change of channel characteristics. This paper investigates designing and implementing an Intelligent Downlink Scheduling (IDS) scheme based on an SVM machine learning algorithm tailored for high-speed LTE-V networks. The proposed method aims to optimize resource allocation, enhance spectral efficiency, and provide seamless connectivity in highly dynamic vehicular environments. Leveraging advanced machine learning techniques and network optimization algorithms, the proposed solution addresses the challenges posed by varying vehicle densities, mobility patterns, and quality of service requirements. Through extensive simulations and performance evaluations, results demonstrate the superiority of the proposed algorithm compared to traditional methods, showcasing its potential to significantly enhance the network's overall throughput and reduce the BER.

Joint Time Delay and Frequency Estimation Based on Deep Learning

2024-01-08T15:09:47+08:00

This letter introduces a novel approach for simultaneous estimation of time delay and frequencies in noisy complex sinusoidal signals received at two spatially separated sensors. The proposed method comprises two main components. Firstly, a Convolutional Neural Network (CNN) regression model is employed to estimate frequencies using data from the first sensor. The model is trained on a synthetic dataset specifically designed for this task. Secondly, a deep learning model is developed, incorporating densely connected layers and dropout layers for regularization, to effectively estimate the time delay between the received signal copies at the two sensors. Extensive computer simulations demonstrate the effectiveness of the proposed method, showcasing its accuracy in joint time delay and frequency estimation. This deep learning-based technique offers a promising alternative to classical signal processing approaches, enabling advanced signal analysis in diverse engineering domains.

Impact Of Mobility Model On LoraWan Performance

2024-01-08T15:08:01+08:00

LoRaWan (Long Range Wide Area Network) is a low-power wireless technology with an extended range. It is utilized frequently in Internet of Things (IoT) applications. Consequently, numerous IoT applications and solutions incorporate mobility, However, the increasing number of end devices (ED) and mobility models of nodes impact the network performance of LoRaWAN (packet size, latency, energy consumption, and packet delivery ratio (PDR)). This paper studies the influence of mobility models on the performance of LoRaWAN by using several mobility models under extensive simulations with the NS3 simulator, including the random waypoint model, the Gauss Markov model, and the constant position model. The results
indicate that the manner in which nodes move has a significant impact on network performance; for instance, the Gauss-Markov model maintains a high level of network performance. In order to validate the simulation results, we have conducted numerous experiments with the Lora end device CubeCell HTCC-AB01 model in a variety of scenarios by analyzing the RSSI (Received Signal Strength Indicator) level in urban and rural areas using a large number of trajectories.

A Wide Band Antenna for both S-Band and CBand Satellite Communication Applications

2024-01-23T17:39:21+08:00

For existing and upcoming satellite communications applications on a variety of platforms, including cars, fishing boats, ships, aircraft, and submarines, wide-band antenna technology is currently crucial. These days, platforms are utilized for a variety of tasks, including geographic scanning, mining, depth detection, underwater scanning, and looking for other items like ships, mountains, and erratic geographic areas. It takes effective wide-band antenna technology to carry out such tasks on a variety of platforms. As a wide-band antenna for S-band and C-band satellite communications, a new design of rectangular dual patch antenna with a resistive loading approach has been put forth in this work. The antenna may be made small and compact for high-speed data, voice, and video broadcasts through satellites because it operates in the S-band and C-band frequencies. The simulation results were generated using CST Studio software, and analyzed for four important parameters like axial ratio, 3dB beam width, gain, and VSWR. A broader bandwidth has been attained with the newly developed rectangular twin patch antenna employing a resistive loading method. Finally, it is demonstrated that the recommended rectangular twin patch antenna with resistive loading performs better for both S-band and C-band satellite communication applications.

Spectral-Efficient and Power-Efficient MIMOOFDM System with Time Diversity for Flat Fading Channel with Arbitrary Doppler Frequency Shift

2024-02-14T21:51:05+08:00

In this work, a new method in time-diversity is used with an Orthogonal-Frequency-Division-Multiplexing (OFDM) system to enhance the bit-error-rate (BER) performance without increasing the signal bandwidth or decreasing the transmission rate. The diversity encoder is used to map the modulated symbols to diversity symbols for each OFDM subcarrier. The modulated symbol maps in N diversity symbols and it is transmitted on the same subcarrier through N different OFDM symbols. The diversity gain of the proposed time-diversity method is equal to N. A Multiple-Input-Multiple-Output (MIMO) transmitter that works in spatial multiplexing mode, is used in this system to improve the spectral efficiency of the transmitted signal. Symbols interleaving with programmable widths are used to get independent fading gain at arbitrary Doppler frequency shifts. The proposed system works in a flat fading channel with Doppler frequency shifts from 50 Hz to 7 kHz. In the receiver, a diversity decoder converts the vectors of diversity symbols to vectors of modulated symbols. The performance of the diversity decoder is the same as the performance of the maximal-ratio-combiner (MRC) receiver. The proposed system increases the spectral efficiency of the transmitted signal and increases the signal-to-noise ratio (SNR) of the decision variable by using time diversity however, the transmission bandwidth not increased. The proposed system is simulated and implemented in FBGA.

FT-CSMA: A fine-tuned CSMA protocol for LoRa-based networks.

2024-02-14T21:52:16+08:00

The issue of medium access in wireless networks was the subject of an in-depth study to avoid collisions. Various types and derivatives of the carrier-sense multiple access technique are proposed and exploited. This variety of solutions is due to the different types of networks: WIFI, Bluetooth, ZigBee, and LTE, to name just a few. With the new generation of long-range (LoRa) modulation, LoRa-based networks cannot directly benefit from these solutions because of LoRa's peculiarities. Many researchers have tried to modify existing CSMAs or propose other methods of accessing the medium. The complexity of these techniques prompted us to explore and implement the LoRa-specific Channel Activity Detection functionality as a module in a network simulator such as NS3. This work determines the LoRa network's service quality and scalability performance. In addition, our project highlights the carrier detection techniques presented by other researchers. This module also enabled us to implement and test the CSMA models previously proposed for LoRa. A gradual adjustment of the parameters of these models produced promising results. Following these results, we developed a fine-tuned CSMA (FT-CSMA), a new collision avoidance technique based on a hybrid of CSMA in IEEE 802.15.4 and CSMA in IEEE 802.11. It de facto outperforms other methods in terms of quality of service and energy efficiency.

Comparison of Machine Learning Approaches Based on Multiple Channel Attributes for Authentication and Spoofing Detection at the Physical Layer

2024-02-26T15:57:48+08:00

This paper presents a comprehensive analysis of the utilization of rate-matched 5G New Radio (NR) Low-Density Parity Check (LDPC) codes for decoding faster-than-Nyquist signaled data symbols in the additive white Gaussian noise (AWGN) channel. The rate-matching techniques employing message-bit and parity-bit puncturing are thoroughly explained, showcasing the application of various rates during the coding process. Moreover, in conjunction with the aforementioned rate-matching and puncturing methods, an additional puncturing scheme is employed on the first two columns of the parity-check matrix derived from the 5G NR LDPC base graphs. The performance of the 5G NR LDPC code, integrated as the outer decoder in turbo equalization for faster-than-Nyquist signaled data symbols, is thoroughly investigated. The achieved results are compared against the performance of convolutional codes implemented in the same system setup and operating at identical code rates. By analyzing the employment of rate-matched 5G NR LDPC codes, this study provides valuable insights into their efficacy for decoding faster-than-Nyquist signaled data symbols in the AWGN channel. The comparison with convolutional codes offers a benchmark for performance evaluation, facilitating a deeper understanding of the benefits and trade-offs associated with each coding scheme.

Performance Evaluation of EADQR Across Various Path Loss Models Through Propagation Analysis

2024-02-26T16:01:10+08:00

Abstract—Wireless Sensor Networks (WSNs) occupy a pivotal position within the realm of Internet of Things (IoT) technology, where they act as an indispensable element facilitating the collection and transmission of data utilizing the multitude of small wireless sensors. The propagation paradigm holds significant importance in the development and implementation of WSNs. Path loss, resulting from absorption, reflection, and refraction in the environment, is a pivotal aspect influencing network performance. It significantly impacts communication range and sensor reliability, making it vital for WSN design and optimization. The proposed work aims to evaluate and analyse the performance of a sink-led decentralized routing system, specifically designed to extend network longevity and minimize energy use, under various propagation loss models. The methodology employs an energy-aware model to pick initiator nodes from immediate neighbors, which helps create multiple paths and minimize network redundancy. To improve service quality, the system strategically selects a forward relay node by considering various key factors such as remaining energy, the quality of the radio link between adjacent nodes, and proximity to the sink node. A fuzzy logic-based decision-making process is used to identify the most optimal path among the multitude of possible pathways. The research seeks to demonstrate the impact of path loss on crucial network metrics, such as end-to-end delay, hop count, energy usage, and the number of active nodes in a WSN topology. This analysis will be conducted through simulations, allowing for a comprehensive understanding of how path loss affects these crucial network metrics. The computational results derived from Received Signal Strength Indicator (RSSI) values for near-surface wave propagation demonstrate that the EADQR algorithm performs optimally in scenarios involving Clutter Factor and HATA suburban models. The energy-aware approach can sustain reduced path loss and energy expenditure, thereby extending the operational longevity of the network.

Buffer-Aided Cooperative Relays in OAM Based IOT Networks

2024-01-08T09:54:24+08:00

Orbital Angular Momentum (OAM) is an essential property of electromagnetic transmission. Nowadays, massive part of
research is directed toward OAM due to its capabilities to improve the electromagnetic spectrum efficiency which raising the
effective transmission throughput. However, employing OAM-based solution is costly as it suffers wave divergence mainly at
high OAM orders (modes). This difficulty limits the distance range for successful communication link, particularly in wireless
communication which is essential for realizing IoT applications. One of the available solutions to deal with this limitation is
utilizing a cooperative relaying system. Relays are known for their potential to shorten the link connecting the source to the
destination. In addition, relays provide an alternative link which helps in avoiding deep fading. Nonetheless, traditional relaying
solutions (without buffers) are outperformed by buffer-aided relaying techniques which achieve higher throughput and lower
outage probability. In this paper, we propose employing buffer-aided relays instead of conventional relays in OAM-based networks.
Simulation experiments show that he proposed buffer-aided relay solution assists the OAM-based network in obtaining higher
throughput than their counterpart the traditional relays. The gain of using buffer-aided relays grows as the OAM-based networks
become more restricted when they are transmitting at higher OAM orders. Based on the fact that the received power threshold has
a notable impact on the system throughput. Specifically, at higher thresholds, the rate of successful transmission goes down which
degrades the system throughput. The results shows that the buffer-aided relays outperform traditional relays at all thresholds and
the difference in performance becomes greater at more restricted higher thresholds. In addition, the buffer-aided relays help the
OAM-based networks in achieving lower outage probability than that achieved with traditional relays.

A Semantic-Based Middleware for Supporting Heterogeneity and Context-Awareness Within IoT Applications

2024-01-08T15:09:02+08:00

Internet of things technology or IoT is changing people’s lifestyle. Smart watches, smart cars, smart Homes, smart farm, etc, IoT is already incorporated into a variety of products and services. Smart devices/things around us integrate various sensors to collect real-time data, apply commands through actuators, and exchange data via a communication module, allowing to develop smart application in various domains. In order to efficiently manage interactions between currently deployed smart things and applications, IoT vendors worldwide are coming up each time with different middleware platforms to fulfil the application development requirements. Therefore finding a suitable IoT middleware is the major issue faced by the developers, especially when the system contains heterogeneous smart things and generates huge amount of heterogeneous data. Most of existing IoT middleware models do not satisfy most functional requirements and correspond to a specific system layer. To deal with these issues, this paper proposes a middleware model based on semantic web technologies and context-awareness computing as an enhancement of the MSOAH-IoT middleware previously developed. It uses a low-level ontology to sign up and classify smart things automatically. The developed middleware deals with heterogeneity issues within the smart things, uses ontology to identify all sensors and actuators embedded within detected smart things, and retrieves measures from formatted data.

Internet of Things: Security, Issues, Threats, and Assessment of Different Cryptographic Technologies

2024-02-14T21:53:14+08:00

As a network of objects, data, and the Internet, the Internet of Things can be characterized as a collection of interconnected devices. In the context of the Internet of Things, a thing refers to any object, such as a sensor, that forms a network and can transfer data with other devices. This interconnection of devices leads to the convergence of physical and digital domains, thereby enabling time optimization, cost reduction, and enhanced efficiency in human labor. The Internet of Things enables data exchange to monitor and control interconnected devices, manufacturers and operators. A discernible transition from non-IoT to IoT devices has been evident over the past decade. Projections indicate that by 2030, approximately 75% of all devices will be integrated into the IoT. Consequently, these devices generate a substantial influx of data, commonly called Big Data. This term encompasses the vast magnitude and rapid flow of data and its heterogeneous nature, making it susceptible to potential breaches or unauthorized access. Cryptography offers an efficacious means of bidirectional data transmission while minimizing the susceptibility to unauthorized interventions. This manuscript comprehensively examines the security predicaments of the Internet of Things and illustrates the effectiveness of cryptographic methodologies in ameliorating these concerns.

Design of Microstrip Patch H-Notch Antenna for Vehicle using Array Systems

2024-01-15T09:54:03+08:00

The highly developed antenna technology has a significant impact on wireless technology. The need for micro antennas is growing as Internet of Things (IOT) applications spread quickly in today's communication systems. Due of their interoperability, micro strip patch antennas are frequently employed in Internet of Things applications. The suggested antenna is made on a single side of high-quality Teflon substrate and has a small, 17.19 x 17.8 x 0.933 mm3 dimension; the antenna can be utilized for lOT applications because it is made to operate at a frequency of 5.9 GHz. It is made up of an array of H shapes and is intended to be integrated into an IOT gadget as integrated antenna. The single-sided antenna that is being suggested also has the advantages of being inexpensive, low profile, and compact in addition to being easy to manufacture. Thus, an Array H-notch

Microstrip patch antenna is designed in this study, and its performance is examined. In this study, two different types of antennas: a standard Hnotch micro strip antenna and an optimized micro strip antenna with an array structure were designed. These two antennas' performances in terms of bandwidth, gain, and return loss were compared.