Transmission Framework Research Articles

Implications of Asymptomatic Carriers for Tuberculosis Transmission and Control in Thailand: A Modelling Approach

Tuberculosis (TB) is a regional and global bacterial illness that has been expanding and affecting individuals in every generation. An unknown percentage of asymptomatic hosts have TB, and the infection can spread while exhibiting no symptoms. These asymptomatic TB carriers, who contribute to the spread of the illness yet go mostly undetected, may make it more difficult to prevent transmission. In this study, we utilized the concept of symmetry to construct a manageable disease modelling framework for TB transmission and control. We developed a TB model to investigate the potential influence of asymptomatic carriers, symptomatic infections, and the entirety of TB prevalence on different approaches to treatment and prevention in Thailand. Annual TB incidence data from Thailand from 2000 to 2022 were used to calibrate the model parameters. We assessed the potential for reaching conflicting results about the management and spread of tuberculosis in Thailand. Our results showed that some TB strategies that were thought to be effective in reducing transmission may have the opposite impact, or that an intervention’s effectiveness might be overestimated, making it seem unfeasible in certain scenarios. For example, the objective of TB treatment, which attempts to decrease the occurrence of symptomatic TB infections, is to decrease the TB infection and propagation rates if the relative carrier (η) is less than one. Nonetheless, our results indicate that this strategy may increase the frequency of asymptomatic TB patients, symptomatic TB viral infections, and overall TB prevalence if η has been sufficiently understated. We also found that reducing only the progression rate of symptomatic TB infections cannot stop asymptomatic TB carriers and total TB prevalence, even when the relative infection of carriers (η) is less than unity. Our research provides a better understanding of the role of asymptomatic patients in spreading TB and highlights the need to accurately include bearers in models that guide Thailand’s TB control strategy.

Improved migration algorithms for uplink transmission secrecy sum rate maximization in MIMO-NOMA

The most significant technology that could assist future high-speed wireless systems in meeting their growing capacity demands is Non-Orthogonal Multiple Access (NOMA). Nevertheless, NOMA should strive to achieve a harmonious equilibrium between equitable distribution among users and optimal network performance. A wireless communication network is a system that enables connections with limited power resources, such as Internet of Things (IoT) devices. Horizontal multiplexing is a feature of MIMO networks that allows for the concurrent transmission of multiple data streams. Nevertheless, there is a correlation between space multiplexing and the advantages of diversity. The issue of Secrecy Sum Rate (SSR) is complex and challenging. To address this, a first-order Taylor estimate is employed to transform the initial problem into a more manageable convex problem. Striking the right balance to optimize network performance and maximize overall rate can be quite challenging, particularly in time-sensitive scenarios. The total rate that can be achieved is reduced due to interference from different users and the use of multiple transmitting antennas. To optimize performance and achieve the highest possible data transmission rate, it is crucial to employ efficient techniques for managing interference. These may include coding methods, adjustments to positioning, or coordinating multiple users. Thus, this work proposes the utilization of optimization to enhance SSR in the MIMONOMA uplink transmission framework. The Singular Value Decomposition (SVD) method is commonly used for the decomposition of channels in the MIMONOMA system. The primary goal of the developed MIMONOMA uplink transmission framework is to enhance the capacity of the Base Station (BS) by optimizing the power allocation measures of the near user and far user using an Improved Migration Algorithm (IMA). In addition, the SSR is optimized and multiple experimental validations are generated to appraise the suggested model. Ultimately, the suggested outcomes are contracted with conventional Orthogonal Multiple Access (OMA) and other traditional approaches to assess the efficacy of the designed model, and developed model given the SSR to be 30bit/s/Hz.

Underwater point cloud transmission framework: hybrid encoder implementation based on CNN and transformer

Abstract In underwater multi-robot systems, 3D point cloud data generated by sonar and depth sensors facilitates the execution of more complex collaborative tasks among robots, which partly rely on efficient data transmission. In this work, we propose a hybrid encoder framework based on convolutional neural network and Transformer for underwater point cloud transmission, aimed at dealing with large-scale point cloud data. Our encoder allows setting lower compression rate on regions or objects of interest for semantic point clouds, preserving crucial information in the point cloud. For underwater acoustic communication, we employ orthogonal frequency division multiplexing combined with deep joint source-channel coding for transmission to enhance the system’s error-resilience. Compared to state-of-the-art methods in the simulation experiments, our end-to-end framework achieves considerable compression performance while eliminating certain cliff and leveling effects, also demonstrating robustness even with changing channels.

RFID Occupied Advanced Framework for Specialized Vehicle Access, Operating System, and Wireless Data Transmission Without Human Intervention

RFID tags are tiny electronic devices that include a distinctive code that can be detected by a reader that supports RFID. The string of characters is used for recognizing and monitoring the object to which the tag is connected. RFID tags interact with RFID readers using radio frequency. While the tag comes into close proximity to the scanner, that device sends a radio signal that triggers the tag. The tag then responds by sending its unique identity. RFID pinpointing ability is verified experimentally in two different techniques, one utilizing radar and the other a photosensitive transition. The initial phase is intended to test if the continuously changing location provided from RFID tags equals the precise position recorded by sonar. It is also excellent for getting into parking lots, private spaces, professional parking spots, and offices quickly and easily. The implementation of RFID will bring a revolutionary transformation in the authorization, administration, correspondence, computerization, and security systems for specialized vehicle Access, Operating System, and Wireless Data Transmission without Human Intervention using an internet based wireless communication system. RFID vehicular registration is an authentication system which employs RFID readers to give authorization for automobiles. The technique normally consists of installing a reader with RFID technology at the entrance/exit entrance to an enclosed or a limited region. RFID scanning can deliver efficient techniques for transportation administration, making the process straightforward, dependable, as well as secure.

Blockchain Enabled Secure Data Transmission With NB‐IoT Deployment in Smart Agriculture Crop Watch

ABSTRACTThe integration of Narrowband Internet of Things (NB‐IoT) and blockchain technology presents a promising solution to address security and efficiency concerns in data transmission within the Internet of Things (IoT). This research focuses on its application in crop Watch, an agricultural monitoring system. The IoT has revolutionized various industries, including agriculture, by facilitating the collection and transmission of extensive data through sensors and devices. However, ensuring secure and efficient data transmission remains a critical challenge. NB‐IoT boasts advantages such as improved coverage in challenging areas, massive connectivity with up to 100 000 links, low energy usage through prolonged discontinuous receipt (eDRX), and cost‐effectiveness. Despite these benefits, NB‐IoT faces security issues, including authentication, anonymity, and fault tolerance. To enhance data security, public key cryptography is proposed to be incorporated into the NB‐IoT framework. Blockchain technology, with its decentralized and immutable nature, can potentially address these security concerns and enhance data accountability. By creating a trust‐less and transparent infrastructure, blockchain complements NB‐IoT's capabilities and provides a robust mechanism for data verification and integrity. This research paper emphasizes the logical integration of NB‐IoT, public key cryptography, and blockchain technology, offering a secure and efficient data transmission framework for agricultural applications like crop Watch. Bridging the gap between data connectivity and security holds promise for advancing agricultural monitoring and driving IoT innovations.

HF skywave communication for air-to-ground networks: A cooperative transmission framework

High-Frequency (HF) communication is widely used for long-distance transmission in remote and disaster areas. However, the dynamic nature of the ionosphere and multipath propagation in the HF channel pose significant challenges to designing efficient and robust communication systems. In this paper, we propose a Fixed Station (FS) and frequency matching method, as well as a power allocation method, to improve the sum-rate of air-to-ground HF communication networks. We derive optimal power allocation among users that share the same frequency, based on which a modified water-filling algorithm is used to solve the power allocation problem in multi-user scenarios, while a low-complexity algorithm is proposed to solve the integer optimization problem of frequency-FS matching. Simulation results demonstrate that the proposed algorithm outperforms the naive algorithm, indicating its effectiveness.

Design and implementation of an electronic voting system for optimal electoral process

Electronic voting is a tool for increasing the credibility and efficiency of elections in several democracies around the world. In Nigeria, many elections have conducted sub-optimally due to the manual methods employed in voting and transmission of election results. In such cases, electoral officials have been accused of colluding with politicians to alter election results while the results are being transmitted from polling units to results collation centres. To address this problem, this paper proposes and presents a framework for electronic voting and transmission of election results. The proposed system is designed and implemented using a C++-based Visual Studio Integrated Development Environment (IDE) and a MATLAB/Simulink application. This setup allowed for the simulation of the voting process, encryption of voting data, transmission of voting results and decryption of election results. The simulation-based results obtained for the proposed system was compared with that of the existing system. The results indicate that for sample sizes ranging from 2,000 to 7,000 voters, the proposed system accurately accredited an average of 85% of voters, compared to 73% for the existing system. Specifically, for 2,500, 4,000, 5,500, and 7,000 accredited voters, the existing system had 684, 1,095, 1,505, and 1,916 failed voters respectively. In comparison, there was a significant reduction in the number of failed voters for the proposed system and this stood at 364, 582, 801, and 1,019 respectively. Additionally, the quality of verified feedback for 3,000, 4,500, 6,000, and 7,000 accredited voters showed that the proposed system had quality scores of 0.362, 0.138, 0.019, and 0.000, compared to 0.549, 0.197, 0.024, and 0.001 for the existing system. In conclusion, the proposed system has proven to be highly effective, credible, secure, and transparent in safeguarding the integrity of the electoral process and it is therefore recommended for hardware implementation in subsequent general elections.

Optimizing Opportunistic Routing between Communities Using Decision Trees and Apriori Algorithm

This study proposes a novel data transmission framework aimed at optimizing node data transmission between different communities. We have designed an intelligent routing strategy based on node characteristics for situations where the source node and destination node have different communities. Through the decision tree algorithm, we trained a model to identify "messenger nodes" that are specifically responsible for cross community data transmission. In addition, the association rule model constructed using the apriori algorithm can calculate the probability of establishing a connection between the "messenger node" and the destination community node, thereby selecting the optimal path for data transmission. This method effectively reduces transmission delay and improves the efficiency and reliability of data transmission.

Blockchain-Enabled Technique for Privacy Preserved Medical Recommender System

With the proliferation of privacy issues surrounding the Internet of Medical (IoMT) recommender system data, this study presents a Secure Recommendation and Training Technique (SERTT) which is contingent on a combination of both federated learning and blockchain approaches. Firstly, the study presents a new framework for recording, sorting, and transmission of IoMT data while incorporating blockchain to ensure that the IoMT data transmitted to cloud servers is not made vulnerable by the sharing of the original data. Secondly, by utilizing medical data, the study designs a Recommender Data Management Neural Architecture (REDMANA) which is based on federated learning and model searching training framework. The proposed technique guarantees that the model gradients which are trained by each node are not disclosed all through the universal training and modelling procedure. This makes the raw data inaccessible to either the IoMT data provider or the user. Considering that the model ensures that users can only obtain their necessary inquiries, neither medical data suppliers nor users can obtain access to raw data. Thus, it reduces the issues of safeguarding medical data sets to the issues of securing data processing. Using numerical analysis and experiments the proposed technique is compared with other existing techniques, the result shows that the proposed SERTT system is efficient and secures recommender data management training and modelling technique and that it performs previously designed techniques as compared.

Review on Pregnancy Progress Monitoring

This project outlines the creation of an innovative pregnancy progress monitoring system leveraging cutting-edge sensor technology and artificial intelligence (AI) models. This project presents an integrated system designed to revolutionize the monitoring of pregnancy progression through a sophisticated fusion of sensor technology and artificial intelligence (AI). The collaborative functionality of DS18B20 temperature sensors, heart beat sensor, and GSM modules establishes a seamless data collection and transmission framework. These sensors continuously capture vital parameters including body temperature, heart rate, and blood oxygen saturation, while the GSM module ensures secure and real-time transmission of this data to designated servers or caregivers’ devices. Leveraging AI-driven algorithms, the system interprets these multifaceted datasets, employing pattern recognition and anomaly detection to discern potential health irregularities. The intuitive user interface grants caregivers comprehensive access to visualized data, facilitating proactive monitoring and swift response through customizable alerts for critical health indicators. This innovative solution undergoes iterative development and validation, seeking to redefine prenatal care by enabling early identification of deviations and fostering informed decision-making among medical practitioners and expectant parents. By amalgamating sensor technology with AI capabilities, this system aspires to enhance the safety and well-being of both mother and fetus, marking a paradigm shift in prenatal health monitoring and intervention strategies.

Rate-Distortion-Perception Optimized Neural Speech Transmission System for High-Fidelity Semantic Communications.

We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate-distortion-perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source-channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication.

Polar code-based secure transmission with higher message rate combining channel entropy and computational entropy

The existing physical layer security schemes, which are based on the key generation model and the wire-tap channel model, achieve security by utilizing channel reciprocity entropy and noise entropy, respectively. In contrast, we propose a novel secure transmission framework that combines noise entropy with reciprocity entropy, achieved by inserting reciprocity entropy into the frozen bits of polar codes. Note that in real-world scenarios, when eavesdroppers employ polynomial-time attacks, the bit error rate (BER) increases due to the introduction of computational entropy. To achieve indistinguishability security, we convert the practical physical layer security metric, BER, into the average min-entropy, a widely accepted concept in cryptography. The simulation results demonstrate that the eavesdropper’s BER can be significantly increased without compromising the communication performance of the legitimate receiver. Under concrete parameters we selected, when compared to the joint scheme of physical layer key generation and one time pad, the modular semantically-secure scheme based on the wire-tap channel model, and the simple channel entropy combination scheme, our scheme achieves a message rate approximately 1.2 times, 3.8 times, and 1.4 times better, respectively. Experimental testing validates the feasibility of our scheme.

Blockchain-based secure communication of internet of things in space–air–ground integrated network

Under the Internet of Things (IoT) era, the cooperation of multiple service providers has facilitated the development of large-scale distributed IoT with a strong demand for reliable and real-time communications. The Space–Air–Ground Integration Network (SAGIN) can be seen as an extension of the traditional terrestrial network with open and ubiquitous connectivity that can be used to support the future IoT in the 6G era. However, the deployment of SAGIN is fraught with numerous challenges, particularly in terms of security. As an emerging technology, blockchain has received academic attention in mitigating the trust problem of SAGIN communication systems. In this paper, we introduce a decentralized data transmission framework that combines the characteristics of SAGIN and blockchain. This framework leverages the increasing computational and communication capabilities of low Earth orbit satellites. Subsequently, we provide an overview of the three-layer structure of this framework and detail the four working steps of the transmission mechanism. Simulation results show that our proposed scheme has excellent performance in terms of latency and throughput while ensuring the security of data transmission.

An Optimized Link State Routing Protocol with a Blockchain Framework for Efficient Video-Packet Transmission and Security over Mobile Ad-Hoc Networks

A mobile ad-hoc network (MANET) necessitates appropriate routing techniques to enable optimal data transfer. The selection of appropriate routing protocols while utilizing the default settings is required to solve the existing problems. To enable effective video streaming in MANETs, this study proposes a novel optimized link state routing (OLSR) protocol that incorporates a deep-learning model. Initially, the input videos are collected from the Kaggle dataset. Then, the black-hole node is detected using a novel twin-attention-based dense convolutional bidirectional gated network (SA_ DCBiGNet) model. Next, the neighboring nodes are analyzed using trust values, and routing is performed using the extended osprey-aided optimized link state routing protocol (EO_OLSRP) technique. Similarly, the extended osprey optimization algorithm (EOOA) selects the optimal feature based on parameters such as node stability and link stability. Finally, blockchain storage is included to improve the security of MANET data using interplanetary file system (IPFS) technology. Additionally, the proposed blockchain system is validated utilizing a consensus technique based on delegated proof-of-stake (DPoS). The proposed method utilizes Python and it is evaluated using data acquired from various mobile simulator models accompanied by the NS3 simulator. The proposed model performs better with a packet-delivery ratio (PDR) of 91.6%, average end delay (AED) of 23.6 s, and throughput of 2110 bytes when compared with the existing methods which have a PDR of 89.1%, AED of 22 s, and throughput of 1780 bytes, respectively.

A blockchain privacy-conserving framework for secure medical data transmission in the internet of medical things

The Internet of Medical Things (IoMT) has transformed healthcare, collecting and transmitting vast medical data. This study proposes an innovative solution, integrating blockchain into IoMT within a fog-cloud computing framework for secure medical data transmission. The blockchain-based zero-trust system ensures reliable data auditing and Electronic Health Repository (EHR) protection. New blockchain entries atop prior blocks deter tampering, with Quad Merkle tree and zero-knowledge proof encryption ensuring data integrity and privacy. The multi-critic deep deterministic policy gradient algorithm optimizes task-offloading decisions in the fog-cloud layer. Security analyses validate its effectiveness, improving computing efficiency and ensuring data fidelity and privacy. These findings position the system as a promising solution for enhancing medical data security and integrity in edge-fog cloud environments, aligning with evolving healthcare technology demands.

A Statistical Analysis of Wireless Body Area Network using Clustering Based Routing Protocols

Recently, research on wireless body-area sensor networks (WBASN) or wireless body area networks (WBANs) has gained great importance in medical applications, and now plays an important role in patient monitoring. Clustering is the most important task in wireless sensor networks (WSNs) by data aggregation through each cluster head (CH). The development of technology has seen comfort in the domestic and professional life of an individual. However, such survival was not able to meet medical emergencies during the pandemic COVID-19 and other health surveillance scenarios. As a result, it is important to design an energy-efficient routing system for WBAN. Existing routing algorithms focus more on energy efficiency than security. A Secure Optimal Path-Routing (SOPR) protocol is proposed to identify and discover routes in WBAN that are secure against black-hole attacks. In this paper the sensor's wireless capability has been tested with a simulator to detect and transmit signals across a wide range of frequencies and to ensure that data is transferred successfully. CARF reduces the maximum work done by relay nodes in case of overloaded nodes and thereby increases the energy distribution in the network. The proposed CARF design reduces packet loss and time delay, thereby increasing network lifetime compared to other fuzzy and heuristic methods used for routing in WSNs. The proposed algorithm maintains more than 75% of the residual energy in the network at a minimum initial energy level of 1 J to enable data syncing. A SAC-TA approach for data aggregation in WSNs is presented. False injection attacks are identified based on traffic analysis at the time of the route discovery process. One-time key generation algorithm is introduced to eliminate malicious nodes from the network. The presented paper proposes a congestion-free routing framework for data transmission from source to sink nodes in WSNs. Congestion-aware routing mechanisms modeled using fuzzy sets (CARF) include operations such as establishing non-localized nodes, by which more paths can be created for traffic distribution, and predicting an optimal congestion.

Physical Layer Security of the MIMO-NOMA Systems under Near-Field Scenario

In this paper, we propose a secure transmission framework for near-field MIMO-NOMA systems. This architecture integrates beamforming mechanisms for both transmission and reception, allowing the base station to send encrypted information to authorized users, effectively countering eavesdropping attempts in a near-field environment. To optimize the secrecy communication capability in the near field, a two-phase alternating optimization algorithm is introduced. In the first phase, the semidefinite relaxation (SDR) method is used to relax constraints in the problem and convert it into a semidefinite programming (SDP) problem. In the second phase, the successive convex approximation (SCA) algorithm is employed to transform the original non-convex problem into a convex optimization problem, obtaining a locally optimal solution through multiple iterations. Simulation results validate that the proposed near-field communication strategy exhibits superior secrecy communication capabilities under various parameter settings compared to far-field communication strategies.

Optimization of Image Transmission in Cooperative Semantic Communication Networks

In this paper, a semantic communication framework for image data transmission is developed. In the investigated framework, a set of servers cooperatively transmit image data to a set of users utilizing semantic communication techniques, which enable servers to transmit only the semantic information that accurately captures the meaning of images. To evaluate the performance of studied semantic communication system, a multimodal metric called image-to-graph semantic similarity (ISS) is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. Due to the co-channel interference among users associated with different servers, each server must cooperate with other servers to find a globally optimal semantic oriented RB allocation. We formulate this problem as an optimization problem whose goal is to minimize the sum of the average transmission latency of each server while reaching the ISS requirement. To solve this problem, we propose a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) algorithm. The proposed algorithm enables each server to coordinate with other servers in training stage and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL algorithms, the proposed RL framework improves the exploration of valuable action of servers and the probability of finding a globally optimal RB allocation policy based on local observation of wireless and semantic communication environments. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% and improve the convergence speed by up to 100% compared to the traditional multi-agent RL algorithms.

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

In the present era, the number of Internet of Health Things (IoHT) devices and applications has drastically expanded. Security and attack are major issues in the IoHT domain because of the nature of its architecture and sorts of devices. Over the recent few years, network attacks have dramatically increased. Many detection and encryption techniques are existing however they lack accuracy, training stability, insecurity, delay etc. By the above concerns, this manuscript introduces a novel deep learning technique called Agnostic Spiking Binarized neural network with Improved Billiards optimization for accurate detection of network attacks and Light Weight integrated Puzzle War Elliptic Curve Cryptographic framework for secure data transmission with high security and minimal delay. Optimal features from the datasets are selected by volcano eruption optimization algorithm with better convergence for reducing the overall processing time. Wilcoxon Rank Sum and Mc Neymar’s tests are performed for proving the statistical analyses. The outcomes show that the introduced approach performs with an overall accuracy of 99.93% which is better than the previous techniques demonstrating the effectiveness.

Voice Fence Wall: User-optional voice privacy transmission

Sensors are widely applied in the collection of voice data. Since many attributes of voice data are sensitive such as user emotions, identity, raw voice collection may lead serious privacy threat. In the past, traditional feature extraction obtains and encrypts voice features that are then transmitted to upstream servers. In order to avoid sensitive attribute disclosure, it is necessary to separate the sensitive attributes from non-sensitive attributes of voice data. Motivated by this, user-optional privacy transmission framework for voice data (called: Voice Fence Wall) is proposed. Firstly, we provide user-optional, which means users can choose the attributes (sensitive attributes) they want to be protected. Secondly, Voice Fence Wall utilizes minimum mutual information (MI) to reduce the correlation between sensitive and non-sensitive attributes, thereby separating these attributes. Finally, only the separated non-sensitive attributes are transmitted to the upstream server, the quality of voice services is satisfied without leaking sensitive attributes. To verify the reliability and practicability, three voice datasets are used to evaluate the model, the experiments demonstrate that Voice Fence Wall not only effectively separates attributes to resist attribute inference attacks, but also outperforms related work in terms of classification performance. Specifically, our framework achieves 89.84 ​% accuracy in sentiment recognition and 6.01 ​% equal error rate in voice authentication.