Reliable Data Transmission Research Articles

Integrated Sensing and Communication-Assisted User State Refinement for OTFS Systems

Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of communications strongly relies on timely and accurate tracking of the relevant user state parameters. In this context, the problem of integrated sensing and communication (ISAC) assisted user state refinement is addressed in the framework of OTFS systems. In particular, by exploiting the initial yet coarse angle estimate provided by the typical codebook-based user state sensing algorithm, we judiciously design a hybrid digital-analog architecture to output the nested array structured low dimensional observations. In this way, the corresponding nested array based technique is employed to perform angle refinement by fully utilizing the degrees of freedom provided by the measurements. Next, based on the refined angle estimate, we develop a two-stage joint delay and Doppler shifts estimation scheme to update the corresponding coarse estimates. Numerical results validate the effectiveness of the proposed algorithm in various scenarios, showing that our well designed user state refinement scheme is able to improve the performance of the considered ISAC-assisted OTFS systems in term of both radar and communication metrics.

Use of QUIC for Mobile-Oriented Future Internet (Q-MOFI)

With the proliferation of mobile devices and various mobile services, ensuring smooth mobility for users has become a major challenge. The future internet is expected to be more mobile-friendly, with advancing technologies that will transform internet management in the coming decades. These technological advancements will help address mobility issues and provide a better internet experience for mobile devices and users. The transport layer plays a crucial role in the internet protocol suite by enabling communication between applications running on different servers. However, the widely used protocols, TCP and UDP, have several limitations, such as unreliability and slow performance due to three-way handshakes. To tackle these issues, Google introduced quick UDP internet connections (QUIC). QUIC aims to enhance latency, delay, and data transmission reliability. Q-MOFI, a future internet architecture focused on mobile devices and based on QUIC, strives to achieve these goals. Moreover, it enhances throughput by implementing multiplexing. Q-MOFI outperforms traditional UDP-based MOFI in terms of throughput gains, minimizing packet loss, and reducing binding operation latency, even when the number of hosts increases. The efficiency of this model has been validated through experimental testing.

Research on Power Service Route Planning Scheme Based on SDN Architecture and Reinforcement Learning Algorithm

The power communication network carries various power services to ensure the safe operation of the power network, among which, the relay protection service is the most important service. Reasonable planning of the service route can improve the effectiveness and reliability of data transmission in the power communication network, thereby ensuring the reliable operation of the power grid. This paper constructs a route planning architecture for the power communication network based on a software-defined network. On this basis, parameters such as the power service and network-carrying service status are defined. With the goal of minimizing network risk variance and considering link bandwidth utilization and overload constraints of relay protection services, a service route allocation problem has been raised. To solve this problem, a power service route planning scheme based on a reinforcement learning algorithm is proposed. This algorithm uses the state–action–reward–state–action (SARSA) algorithm to complete service route planning. The simulation results show that using the route planning scheme proposed in this paper can avoid the overload of relay protection services, reduce network risk variance, and effectively balance network risk.

Multi-Criteria Decision-Making Framework with Fuzzy Queries for Multimedia Data Fusion

Multimedia Internet of Things (MIoT) is widely explored in many smart applications for connectivity with wireless communication. Such networks are not like ordinary networks because it has to collect a massive amount of data and are further forwarded to processing systems. As MIoT is very limited in terms of resources for healthcare, smart homes, etc., therefore, energy efficiency with reliable data transmission is a significant research challenge. As smart applications rely on bounded constraints, therefore duplicate and unnecessary data transmission should be minimized. In addition, the timely delivery of data in crucial circumstances has a significant impact on any proposed system. Consequently, this paper presents a fuzzy logic-based edge computing framework to provide cooperative decision-making while avoiding inefficient use of the sensing power of smart devices. The proposed framework can be applied to critical applications to improve response time and processing cost. It consists of the following two functional components: Firstly, it provides the automated routing process with a natural language interface at the sink node. Secondly, to ensure reasonable performance, it also transmits semantic data between sensors using fuzzy queries and security. According to the performance evaluation, the proposed framework significantly outperformed related studies in terms of energy consumption, packet overhead, network throughput, and end-to-end delay.

A hybrid-trust-based emergency message dissemination model for vehicular ad hoc networks

The security of data transmission is one of the focus issues in vehicular ad hoc networks (VANETs). The timely and effective dissemination of emergency messages can help drivers make intelligent decisions and improve road safety and traffic efficiency. However, malicious nodes in the network seriously interfere with the proper judgement of vehicles via data tampering or spreading false messages, which brings significant risks to traffic safety. In order to guarantee the security and reliability of data transmission, this paper proposes a hybrid-trust-based emergency message dissemination (HTEMD) model that effectively integrates entity trust into the data consistency evaluation. In the proposed model, for emergency messages from different sources, the receiver first calculates the probability of the event based on the sender’s entity trust and event status. Then, it uses the principle of information entropy to conduct consistent analysis of the results to help the vehicle make correct decisions. Afterwards, a series of simulations are conducted in four typical scenarios. The results show that the HTEMD model has strong environmental adaptability and robustness. Moreover, compared with other classical message dissemination models, the correct decision rate of the HTEMD model always remains above 80%, while other models drop to 75.5%, 63.2%, and 40.2%. The model proposed in this paper significantly outperforms the baseline model with a higher correct decision rate and resistance to attack.

Analysis of PAPR reduction of optical-OTFS for 256-QAM using companding and clipping–filtering algorithms

Abstract The article presents an investigation into Peak-to-Average Power Ratio (PAPR) reduction techniques in Optical Orthogonal Time Frequency Space (O-OTFS) modulation. Focusing on clipping and filtering as well as companding methods, the study explores their efficacy in mitigating PAPR challenges inherent in O-OTFS waveforms. The research evaluates the impact of these techniques on signal quality, particularly in the context of a Rician channel. Clipping and filtering (C&F) are examined for their ability to control amplitude peaks, while companding is analyzed for its role in optimizing dynamic range. The study conducts a comprehensive analysis of Bit Error Rate (BER) and power spectrum density (PSD) under varying conditions, shedding light on how these methods influence the reliability and robustness of OTFS communication. The investigation considers the interplay of these PAPR reduction methods with the unique characteristics of the Rician and Rayleigh channel, which includes a dominant line-of-sight component. The findings contribute valuable insights into designing efficient OTFS modulation systems for real-world scenarios. Ultimately, this research aims to provide a deeper understanding of PAPR reduction strategies in OTFS, offering guidance for optimizing signal processing techniques in communication systems where mitigating PAPR is crucial for achieving high performance and reliable data transmission. It is noted that the proposed C&F and companding algorithms outperform the conventional methods and achieved a PAPR gain of 1–3 dB and BER gain of 10.6–2 dB.

Energy Efficient Routing Technique Using Enthalpy Ant Net Routing for Zone-Based MANETS

Routes discovery that can provide reliable data transmission in Mobile Ad-hoc Networks is challenging due to its wireless channel characteristics and dynamic transmission environment. Ad-hoc networks frequently experience link failure because nodes are mobile and their positions are not fixed. The cellular ad hoc community's dynamic nature makes it possible to analyze multi-route routing protocols specifically. One of the most trustworthy and environment-friendly routing methods is the Zone Routing Protocol used in Mobile Ad Hoc Networks. Offering timely and trustworthy communication services, however, depends critically on maintaining the Quality of Service, electricity performance, and outstanding resource management. In this research, we suggested an energy-efficient routing method for zone-based MANETs called enthalpy and net routing. First, a completely fuzzy quarter clustering based on an Energy guide is used to perform the clustering. With the help of fuzzy memberships, the AFCM set of rules permits the input of statistics for each elegance. In the second, the best route is selected using Enthalpy Ant Net Routing (EANR) while taking the following factors into account: community disconnection, channel error, buffer overflow, and contention at the link layer. The results of the experiment show that the collection of rules performs better than many existing algorithms in terms of community longevity, electricity consumption, and other metrics.

Reputation-based Raft-Poa layered consensus protocol converging UAV network

Integration of blockchain technology offers a robust solution for ensuring reliable data transmission within unmanned aerial vehicle (UAV) networks. Nevertheless, it is crucial to address challenges associated with UAV networks, including their dynamic nature, high latency, limited scalability, and algorithmic inefficiencies. Hence, our proposal involves integrating UAV networks with blockchain technology to enhance their performance by optimizing the consensus algorithm based on network efficiency. We first propose a comprehensive framework for optimizing network performance based on network tomography, incorporating a systematic analysis of network performance and fault avoidance strategies. To improve analysis of the core network’s real-time performance, we also propose a topology prediction method based on an effective probability matrix. Additionally, we establish a calculation method for optimally placing network monitoring points in the core network for enhanced efficiency. We then introduce an adjustable reputation-based Raft-Poa layered consensus protocol that improves consensus protocol scalability and efficiency. To tackle data and node security concerns, we further propose a trust management mechanism based on group signatures. This mechanism verifies the authenticity of node data and identifies malicious nodes. Finally, we validate the protocol’s effectiveness and compare the algorithm’s effectiveness with MMP, Raft, Poa, etc. The simulation results of this protocol show remarkable improvements in network communications and consensus process efficiency, and reductions in communication latency and node utilization rates.

An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs.

In the fields of industrial production or safety monitoring, wireless sensor networks are often content with unreliable and time-varying channels that are susceptible to interference. Consequently, ensuring both transmission reliability and data accuracy has garnered substantial attention in recent years. Although multipath routing-based schemes can provide transmission reliability for wireless sensor networks, achieving high data accuracy simultaneously remains challenging. To address this issue, an Energy-efficient Multipath Routing algorithm balancing data Accuracy and transmission Reliability (EMRAR) is proposed to balance the reliability and accuracy of data transmission. The multipath routing problem is formulated into a multi-objective programming problem aimed at optimizing both reliability and power consumption while adhering to data accuracy constraints. To obtain the solution of the multi-objective programming, an adaptive artificial immune algorithm is employed, in which the antibody initialization method, antibody incentive calculation method, and immune operation are improved, especially for the multipath routing scheme. Simulation results show that the EMRAR algorithm effectively balances data accuracy and transmission reliability while also saving energy when compared to existing algorithms.

LIGHTING UP DATA: THE FUTURE OF WIRELESS DATA TRANSFER WITH LIGHT FIDELITY TECHNOLOGY

This paper presents an exploration of light fidelity (Li-Fi) technology for data transfer. The project focuses on developing a Li-Fi system consisting of a transmitter (which includes an Arduino Uno and a laser module) and a receiver, an Arduino Uno, a light-dependent resistor (LDR), and a light-emitting diode (LED) display. Li-Fi technology, utilizing light waves for data transmission, offers several advantages over traditional wireless communication systems. This paper showcases the potential of Li-Fi by implementing a practical system using readily available components. The transmitter circuit, controlled by the Arduino Uno, modulates the laser module to encode the data to be transmitted. The Arduino Uno's programmability enables precise control over the timing and modulation of the laser, ensuring accurate and reliable data transmission. On the receiver side, the Arduino Uno interfaces with the LDR to detect and convert the modulated light signals into electrical signals. The Arduino Uno reads the analog values from the LDR and performs necessary calculations and decoding algorithms to extract the transmitted data. The decoded data is then displayed on the LED module for visualization. The project leverages the capabilities of the Arduino Uno microcontrollers to control and synchronize the transmitter and receiver circuits. The use of a laser module enables high-speed data transfer, and the LDR facilitates the reception and decoding of the transmitted light signals.

Design of Enhance Co-operative Balanced Route Protocol (CBRP) in WSNs

Wireless Sensor Networks (WSNs) are instrumental in various applications, including environmental monitoring, healthcare, and smart infrastructure. However, ensuring efficient and reliable data transmission in WSNs remains a challenge due to the dynamic nature of the network topology and limited resources of sensor nodes. This paper proposes an enhanced Cooperative Balanced Route Protocol (CBRP) for WSNs to improve routing efficiency and energy consumption. The proposed protocol introduces a cooperative approach where sensor nodes collaborate to relay data packets, balancing energy consumption and prolonging network lifetime. Simulation results demonstrate that the enhanced CBRP outperforms existing protocols in terms of network throughput, end-to-end delay, and energy efficiency, making it a promising solution for WSN applications.

Modeling of Dung Beetle Optimization-based Sink Node Localization Approach for Wireless Sensor Networks

Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques

UI-MoCap: An Integrated UWB-IMU Circuit Enables 3D Positioning and Enhances IMU Data Transmission.

While inertial measurement unit (IMU)-based motion capture (MoCap) systems have been gaining popularity for human movement analysis, they still suffer from long-term positioning errors due to accumulated drift and inefficient data transmission via Wi-Fi or Bluetooth. To address this problem, this study introduces an integrated ultrawideband (UWB)-IMU system, named UI-MoCap, designed for simultaneous 3D positioning as well as wireless IMU data transmission through UWB pulses. The UI-MoCap comprises mobile UWB tags and hardware-synchronized UWB base stations. Each UWB tag, a compact circular PCB with a 3.4cm diameter, houses a nine-axis IMU unit and a UWB transceiver for data transmission. The base stations are equipped with a UWB transceiver and an Ethernet controller, ensuring efficient reception and management of messages from multiple tags. Experiments were conducted to evaluate the system's validity and reliability of 3D positioning and IMU data transmission. The results demonstrate that UI-MoCap achieves centimeter-level 3D positioning accuracy and maintains consistent positioning performance over time. Moreover, UI-MoCap exhibits high update rates and a minimal packet loss rate for IMU data transmission, significantly outperforming Wi-Fi-based transmission techniques. Future work will explore the fusion of UWB and IMU technologies to further enhance positioning performance, with a focus on human movement analysis and rehabilitation applications.

Optimization of False Alarm Rate and Misdetection Rate for a Desired Threshold Voltage in Cooperative Communication

Cooperative communication system (CCS) involves collaboration among sensor nodes to transmit data more effectively, especially in scenarios with limited resources or challenging environmental conditions. Optimizing the total error rate (TER) for cooperative communication in wireless sensor networks (WSN) is a critical task to enhance the reliability and efficiency of data transmission. The link quality of a WSN can be improved by cooperative relaying with a relatively low TER. In this paper, real-coded genetic algorithm (RGA) and particle swarm optimization (PSO) are used in WSN to reduce TER. The number of nodes are varied from 1 to 16, SNR is varied from 0 dB to 20 dB, threshold is varied from 25 mV to 35 mV and mutation rate is 0.1. Minimum TER is obtained for a threshold of 25 mV to 35 mV compared to TER obtained without optimization. The optimization method provides significant improvements to achieve the desired threshold voltage with minimum false alarm rate and misdetection rate which enhances the overall performance of the CCS in WSNs.

Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks

Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival, or angle of arrival from adjacent SNs. The accuracy of sink node localization directly influences the efficiency of data aggregation, routing procedures, and complete performance of the network in tasks like environmental monitoring, target tracking, and event recognition. As WSNs are frequently used in remote environments where physical involvement is unusable, an effective and accurate sink node localization model plays a vital part in certifying the network's longevity and reliability. This study develops an Efficient Sink Node Position Estimation using the Harris Hawks Optimization (SNPE-HHO) Algorithm in WSN. The main intention of the SNPE-HHO technique is to recognize the optimal position of the sink node in the network. To achieve this, the SNPE-HHO technique employs the HHO system which gets inspiration from the hunting tactics of Harris Hawk. Moreover, the SNPE-HHO technique computes a fitness function that can drive the searching direction of the HHO algorithm and enhance the node estimation performance. The performance analysis of the SNPE-HHO method is verified by utilizing distinct metrics. The experimentation values confirmed the improved estimation performance of the SNPE-HHO technique over other existing methods

AUTOMATIC DETECTION AND RECOGNITION OF DIGITAL MODULATED SIGNALS USING A NEURAL NETWORK

Issues on automatic detection and recognition of digital modulated signals are becoming increasingly relevant in modern communication systems. These processes play a key role in ensuring reliable and efficient communication in a wide range of areas, from mobile communication to broadcasting and satellite systems. The importance of these processes continues to grow with the increase in the amount of data transmitted through these systems and with the development of new communication technologies. Accurate detection and classification of signals allow communication systems to correctly interpret the transmitted information, which, in turn, ensures the reliability and efficiency of data transmission. This is especially important in conditions when signals are subject to various types of interference and distortions. Therefore, the development and improvement of the methods for automatic detection and recognition are becoming increasingly important for ensuring high-quality communication. The present article presents a neural network-based system capable of automatically detecting signals with a single carrier frequency in the range up to 6 GHz and classifying four types of digitally modulated signals: BPSK, QPSK, 16-QAM, and 64-QAM. The system is capable of operating in the presence of additive white Gaussian noise, phase and frequency offset in the received signal. The algorithms for detection and recognition of signals, developed in the LabVIEW software environment are presented. The architecture and hyperparameters of the neural network allowing quick and efficient training of the network are described. These algorithms and training methods can be applied and adapted for other systems and applications, making them very useful for a wide range of tasks. The system was tested both on simulated signals and in a real communication channel. The results obtained confirm the effectiveness of the proposed methods and emphasise their potential for further application and research.

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Simple Calculator Application with Client-Server Connection Using Java Socket

This research aims to develop a simple calculator application using a client-server connection based on Java Socket. The research method involves the software development process that integrates a client-server architecture to enable users to perform basic mathematical calculations over a network. The main findings of this research are the successful implementation that enables effective communication between the client and server, facilitating the exchange of data for simple mathematical operations. The conclusion of this research indicates that the use of Java Socket in a calculator application can provide reliable and efficient data transmission performance. The contribution of this research lies in the development of a practical application that demonstrates the benefits of implementing a client-server connection using Java Socket in the context of mathematical calculations.

An Energy Efficient Tree-Based Routing Protocol for Wireless Multimedia Sensor Networks

The recent technological advances in micro electromechanical systems have promoted the development of a powerful class of sensor-based distributed intelligent systems capable of retrieving multimedia information, namely Wireless Multimedia Sensor Networks (WMSNs). WMSNs are gaining more popularity day by day as they are envisioned to support a large number of both non-real time and real-time multimedia applications. Routing in WMSN is very challenging task. Multipath establishes several path for data transmission rather than single path. In this paper we proposed An Energy Efficient Tree Based Multipath Routing for WMSN’s (EETMRP) technique that will intend to provide a reliable transmission of data for data synchronization at the destination node with low energy consumption. This is done by efficiently utilizing the energy availability and the received signal strength of the nodes to identify multiple routes to the destination. EETMRP discusses that all of the packets of audio, image and scalar data are spread over the nodes lying on different possible paths between the source and the sink, in proportion to their residual energy and received signal strength. The objective of EETMRP is to assign more loads to highest energetic paths and fewer loads to lower energetic paths so that uniform resource utilization of all available paths can be ensured. It uses multiple paths between source and the sink which is intended to provide a reliable transmission environment with low energy consumption, by efficiently utilizing the energy availability of the nodes to identify multiple routes to the destination and efficiently balanced the loads of all sensor nodes. We also focused on node failure and it recovery process during packet transmission and during the start of packet transmission. Finally we proposed an algorithm based on our proposal to simulate and analysis our proposed routing protocol.

A Parity-Based Dual Modular Redundancy Approach for the Reliability of Data Transmission in Nanosatellite’s Onboard Processing

A Parity-Based Dual Modular Redundancy Approach for the Reliability of Data Transmission in Nanosatellite’s Onboard Processing