Data Transmission In Networks Research Articles

Multiple Control Policy in Unreliable Two-Phase Bulk Queueing System with Active Bernoulli Feedback and Vacation

In this paper, a bulk arrival and two-phase bulk service with active Bernoulli feedback, vacation, and breakdown is considered. The server provides service in two phases as mandatory according to the general bulk service rule, with minimum bulk size ′a′ and maximum bulk size ′b′. In the first essential service (FES) completion epoch, if the server fails, with probability ′δ′, then the renewal of the service station is considered. On the other hand, if there is no server failure, with a probability ′1−δ′, then the server switches to a second essential service (SES) in succession. A customer who requires further service as feedback is given priority, and they join the head of the queue with probability β. On the contrary, a customer who does not require feedback leaves the system with a probability ′1−β′. If the queue length is less than ′a′ after SES, the server may leave for a single vacation with probability ′1−β′. When the server finds an inadequate number of customers in the queue after vacation completion, the server becomes dormant. After vacation completion, the server requires some time to start service, which is attained by including setup time. The setup time is initiated only when the queue length is at least ′a′. Even after setup time completion, the service process begins only with a queue length ‘N’ (N > b). The novelty of this paper is that it introduces an essential two-phase bulk service, immediate Bernoulli feedback for customers, and renewal service time of the first essential service for the bulk arrival and bulk service queueing model. We aim to develop a model that investigates the probability-generating function of the queue size at any time. Additionally, we analyzed various performance characteristics using numerical examples to demonstrate the model’s effectiveness. An optimum cost analysis was also carried out to minimize the total average cost with appropriate practical applications in existing data transmission and data processing in LTE-A networks using the DRX mechanism.

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

THz Microstrip Antenna for Terabit Wireless Local Area Networks

In order to replace millimetre wave communication for extremely fast terabit wireless local and personal area network connectivity, researchers have been looking into the possibilities of the terahertz band for establishing wireless data communication at terabit rates. The IEEE 802.15 WPAN Terahertz Interest Group (IGTHz) has been created to encourage research in the terahertz bands and set standards for their use, in order to facilitate progress and advancement in this area. The specific objective of this study is to design and analyze a microstrip antenna working at 3.5 THz resonant frequency. The proposed novel antenna includes three layers: a top layer that represents the patch, a second layer that represents the substrate, and a bottom layer that represents the ground plane. It is designed using a 32 nm thin FR-4 substrate with a permittivity of 4.4. Using HFSS simulations, it was found that the proposed antenna has an overall efficiency greater than 85% within the working frequency range of 3.5 THz. Additionally, it exhibited an extremely low reflection coefficient (S11) of -43.61 dB at 3.5 THz, with an efficiency exceeding 80%. This simple and broadband antenna design could have relevance in high-speed data transmission networks.

Feasibility of data transmission under attack: from isolated toughness variant perspective

The graph model is an appreciable tool for data transmission network, where the feasibility of data transmission in site attack circumstances can be described by fractional critical graphs, and the vulnerability of networks can be measured by isolation toughness variant. This paper considers both the stability of the network and the feasibility of data transmission when the sites are destroyed, and determines the isolated toughness variant bound for fractional (a, b, n)-critical graphs, where the parameter n represents the number of damaged sites at a certain moment. A counterexample proves the sharpness of the given isolated toughness variant bound. The main theoretical conclusion provides an equilibrium between performance and cost in network topology designing.

Review and Analysis of the Characteristics of IoT Sensors

Internet of things (IoT) is the concept of data transmission network between physical objects ("things"), equipped with built-in tools and technologies for interaction with each other or with the environment. The work is devoted to the inspection and analysis of sensors, how the IoT systems are associated with environmental objects. IOT uses a wide class of measurement tools, from elementary sensors (eg, temperature, pressure, light), consumption metering devices (such as intellectual meters) to complex integrated measuring systems. The parameters of 30 types of sensors for IoT are given, 2 of them of the type of temperature and humidity sensors, 2 type of pressure sensors, 13 sensors that measure the flow and amount of gas or liquid, 2 type of accelerometers, 10 approach and movement sensors and 1 noise sensor. Among them are 4 sensors for wireless sensory networks (WSM), a single-time medical sensor for internal blood pressure measurement, calorimetric, vortex and electromagnetic flow sensors and gas or fluid, accelerometer, and memes of memes, and e-memometers, and electromagnetic measurement motion, a device of measuring noise level in the range of sound vibrations. For some sensors, there are no metrological characteristics at all, at best there is only a range of measurements and resolution. The resolution ranges from 8 bits to 16 bits with a signal sampling rate from 0.5 Hz to 48 kHz. There is even a LDC1612 digital induction chip with 28 bits. WSM sensors are designed to work with DigiMesh, LoraWan and Arduino, Raspberry. Many sensors use outdated RS 232, RS 432, I2C. Keywords: internet of things, network, review, analysis, sensors, objects, metrology.

Modeling and Analyzing Urban Sensor Network Connectivity Based on Open Data

The optimization of network topology is crucial to achieve efficient data transmission in wireless sensor networks. Recently it has been proven that emerging open data sources can be used for modeling the structures of heterogeneous urban sensor networks. With this, leveraging real location data of various networked and sensing devices became feasible and essential. This approach enables the construction and analysis of more accurate representations based on frequently updated actual network infrastructure topology data, as opposed to using synthetic models or test environments. The presented modeling method serves as the basis for the designed architecture and implemented research environment. This paper introduces a set of algorithms which transform devices’ location data into graph-based wireless network connectivity models. Each algorithm is thoroughly discussed and evaluated. Moreover, static (momentary) and dynamic (time-spanning) network topologies are constructed in four large Polish cities based on publicly available data. Multidimensional simulation-based analysis is conducted to investigate the characteristics of the modeled structures. Directions for further research are suggested as well.

Communication Energy Optimization of UAV-assisted WSN Data Transmission

Unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN) data transmission is a hot topic of research in recent years. This article mainly focuses on the energy consumption optimization of WSN data transmission with the assistance of UAVs. First, we established a communication coverage model for UAV-WSN and derived the energy consumption of data transmission for UAV-WSN, determining the optimal number of cluster heads (CHs) under different UAV flight heights. Second, we built a model for the energy consumption of WSN data transmission and the UAV flight height, and determined the optimal flight height of the UAV, while also dividing the UAV task area into segments. Then, we proposed a model that dynamically optimized the UAV data collection position according to the CH location, which can further reduce the energy consumption of CHs during data transmission. Finally, we used simulation experiments to verify the feasibility and effectiveness of our proposed scheme.

PUDT: Plummeting uncertainties in digital twins for aerospace applications using deep learning algorithms

Identifying objects in aircraft monitoring systems poses significant challenges due to the presence of extreme loading conditions. Despite the presence of several sensor units, the transmission of precise data to multiple data units is hindered by an increase in time intervals. Therefore, the suggested methodology is specifically developed for the purpose of generating digital replicas for aeronautical applications, wherein an aero transfer function is correlated with the digital twins. Mapping functions are utilized in the monitoring of diverse parameters that are associated with the identification of objects inside data transmission networks, with the aim of minimizing uncertainty. The suggested system model is enhanced by incorporating analytical representations and deep learning methods, resulting in the provision of zero point twin functionalities. The present study investigates the aforementioned integrated procedure through the analysis of four different situations. In these settings, an aero communication tool box is employed to transform the device configuration into simulation outputs. The results obtained from the comparison of these scenarios reveal that the projected model significantly enhances the maintenance period while minimizing data errors.

Lightweight encryption of enterprise data and secure management of eventual graphical model construction

Abstract In this paper, we use the event map as well as the even map reaction system, combined with the lightweight network data transmission encryption method of matrix tensor product, to establish the even map model for big data security management. The transmission data is compressed and reconstructed by matrix tensor compression perception, and the lightweight AES encryption of network transmission data is encrypted using the symmetric encryption method. In the end, the effectiveness of AES encryption and decryption while transmitting plaintext data is assessed for each department of Enterprise D in the service area of Edge Server Server. The results show that the lightweight AES encryption algorithm encrypts at an average of 85.33 ms less than the AES encryption algorithm, while the decryption time is 48.17 ms less, and the operation speed is faster. The algorithm demonstrates that it ensures the security of data transmission and enhances encryption and decryption performance simultaneously. In this paper, the research realizes the enterprise data transmission encryption college transmission, which provides a reference for data encryption.

Hybrid Multi-Objective-Derived Horse Herd and Dragonfly Algorithm-Based Energy-Efficient Secured Routing in WSN

Energy efficiency and security have become prominent aspects of Wireless Sensor Networks (WSNs) for transmitting data. The major challenge is to increase the “Quality of Service” (QoS) since it is restricted by fewer constrained resources. Although the existing routing protocols acquire efficient transmission, they are still subsisting with the challenges of improving security and energy conservation. It will be helpful in practical implications like the military, weather forecasting and healthcare industry. As the nodes are operated by the energy preservation of the battery, developing an energy-efficient protocol is challenging. Since WSN possesses massive numbers of sensor nodes, it also has to avoid long-term communication, and the clustering mechanism is a prerequisite. Considering the diverse objectives like delay, energy and distance, there are still remarkable challenges to develop the routing protocol. Moreover, security enhancement becomes another challenging issue over the network for data transmission through the sensor nodes. To overcome these issues, a novel energy-efficient routing model is designed in this paper by using a hybrid algorithm and the multi-objective derivatives. Initially, the clustering approach is employed to reduce the complexity and improve the security in an energy-efficient manner. Therefore, the Cluster Head (CH) is selected significantly using the novel Hybrid Horse Herd-Dragonfly Optimisation (HHHDO). The hybrid algorithm is developed by integrating Horse Herd Optimisation (HHO) and Dragonfly Algorithm (DA). Here, the optimal CH selection is achieved by deriving the multi-objective function regarding “distance, delay, energy, load and trust of nodes”. Trust is measured with the Neural Network (NN)-based HHHDO (NN-HHHDO) model. Depending on the trust evaluation and energy efficiency, the proposed model obtains better communication between the nodes. In the end, the experimentation of the recommended model is made using various measures and it’s ensured that the suggested technique enhances trust among the nodes and energy conservation in comparison to the existing algorithms.

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

The increasing usage of several nodes to transfer the massive volume of data to the remotes in wireless sensor networks is a challenging task to reduce the loss. The high volumes of data transmission in wireless sensor networks (WSN) can surpass their capacity, resulting in congestion, latency issues, and packet loss. However, computational intelligence (CI) models can aid in managing and creating intelligent networks in WSN. The WSN congestion issues result in information loss and increased energy usage. CI-based models have been used to resolve this issue, reducing the latency. This paper proposes SoftMax Routing with Deep Neural Network (SRDNN) for efficient routing in WSN. This will route the data packets by choosing the high energy and lower load. It consists of two parts, such as the construction of the routing path, which determines the residual energy of the node. It is analyzed using SoftMax routing to decide whether the node is efficient in energy. The route request and reply established various paths between the source and destination. The path with minimum buffer space and maximum bandwidth is chosen in the optimal routing. The simulation results under the metrics such as energy consumption, data loss rate, throughput, and delay show the proposed model performance.

Research on flow scheduling of train communication based on time-sensitive network

The rapid development of intelligent rail transportation equipment is raising higher requirements for the real-time performance of Train Communication Network (TCN) data transmission. Time-Sensitive Network (TSN) has gained widespread attention in train communication due to its advantages of high transmission rates and deterministic latency. Firstly, for addressing the flow scheduling problem in TSN, a TCN topology model supports TSN is designed. It establishes a multi-objective flow scheduling model based on a priority strategy, aiming to minimize average response time and makespan while ensuring a scheduling success rate. Then, to solve the routing selection problem, a joint scheduling algorithm based on multi-level routing selection is adopted, providing feasible scheduling solutions. Finally, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm is introduced to iteratively and selectively optimize the scheduling solutions. This approach resolves the flow ordering problem while achieving optimization for the scheduling model. The feasibility of this scheduling approach is validated through simulations, and the results demonstrate significant advantages of the proposed model and algorithm in terms of scheduling success rate and real-time performance, providing effective solutions for flow scheduling in TCN.

Prediction-based data reduction with dynamic target node selection in IoT sensor networks

In various applications of IoT sensor networks, special attention is paid to sensor nodes that register higher or lower values of a monitored parameter compared to other nodes. Thus, methods are necessary that enable the selection of target nodes while minimizing the number of data transmissions. The reduced amount of data transmitted during target node selection contributes to energy savings and prolongs the lifetime of battery-powered sensor nodes. This paper introduces a method for reducing data transmissions in sensor networks, where target nodes are selected to perform monitoring tasks. The proposed method allows the IoT gateway to determine when the sensor nodes should report their data readings. The gateway utilizes a prediction algorithm to determine whether a new target node should be selected. In this scheme, data are transmitted only when necessary for the correct selection of the target node. Experiments were conducted for a wireless sensor network consisting of mobile devices, where the target node is the device closest to a given location. The experimental evaluation confirmed that the proposed method significantly reduces the amount of transmitted data and ensures the proper selection of the target node. It was also demonstrated that the introduced approach reduces data transmissions more effectively than the state-of-the-art prediction-based algorithms. During the experiments, the amount of data transmitted from sensor nodes to the IoT gateway was analyzed. The proposed method has reduced the amount of transmissions by 94%, while the highest transmission reduction rate achieved by the existing techniques was 81.4%.

Research on a Blue–Green LED Communication System Based on an Underwater Mobile Robot

Underwater robots have been widely used in ocean exploration, deep-sea observation, seabed operations, marine scientific research, and other fields. Underwater low-latency, efficient, and safe communication modes are key to realizing the application of an underwater robot data transmission system. This paper mainly studies the optical communication between underwater mobile robots, including the large-dispersion-angle light-emitting diode (LED) design, large field of view receiving technology, weak light detector technology, etc. By designing a 120° large divergence angle underwater optical communication system in this study, the receiving field-of-view angle of the receiving end can reach 60°, which is suitable for the optical communication system of an underwater mobile platform. The high-power LED driver circuit is designed to drive the high-power LED and adopt weak light detection technology to ensure its stability and reliability. The experimental results show that, in the case of incomplete alignment between the transmitter and receiver, stable communication of underwater robots in motion is achieved through the design of a large divergence angle and a receiving field-of-view angle and the use of an underwater weak light detection technology. The communication distance is 30 m, and the communication rate remains above 10 Mbps. The information transmission content can include network data transmission, real-time video, high-definition video, high-definition images, and other data types. This equipment provides a solution for cableless data transmission of remotely operated vehicles (ROVs) and substantially enhances the application field of ROVs.

Application of corporate image integrating Zigbee technology in brand APP interface design using IoT technology

In modern social APP interface design, how to effectively improve the corporate image and create the connotation of corporate culture is a significant key problem. With the emergence of APP, a growing number of people use them, increasing communication energy usage and slowing network operation. To improve app compatibility and speed, it is necessary to combine it with the most advanced and dependable technology, such as ZigBee, which is regarded as the best solution for wireless sensor networks. The ZigBee protocol is primarily used to incorporate working and data transmission in wireless sensor networks that are based on ZigBee technology. As a result, incorporating ZigBee technology into APP interface design in the Internet of Things (IoT) domain can significantly improve brand APP interface design’s network operation efficiency. This paper presents a novel approach to enhance the performance and corporate image of brand mobile applications (APPs) by integrating ZigBee technology. The primary objective is to improve the operating efficiency and user experience of the brand APPs. The study involves a comparison between 10 brand APPs that have not integrated ZigBee technology and 10 brand APPs that have adopted ZigBee technology. The experimental results indicate that the operating efficiency of the brand APPs incorporating ZigBee technology is 97%, while the efficiency of the brand APPs without ZigBee technology is 85%, resulting in a notable difference of 12%. To assess the effectiveness of ZigBee technology integration, the study conducted experiments with 100 users, randomly assigned to interact with both types of brand APPs. The user feedback and observations revealed that brand APPs integrated with ZigBee technology exhibit significantly higher operating efficiency, contributing to a 12% improvement over their counterparts lacking ZigBee integration. Moreover, 90 out of 100 users reported a preference for the brand APPs integrated with ZigBee technology due to their superior user experience. The integration of ZigBee technology in brand APPs not only enhances the user experience but also contributes to the improvement of the company’s corporate image. Adopting ZigBee technology in brand APPs is a valuable strategy that can facilitate the long-term development and success of the company.

Internet of Things (IoT)-Based Smart Healthcare System for Efficient Diagnostics of Health Parameters of Patients in Emergency Care

The Internet of Things has been instrumental in bringing about several advancements and innovations in the domain of healthcare. Healthcare professionals are essentially life savers when it comes to handling emergency cases such as accidents, heart attacks, etc. Only the patient’s vital parameters generally characterize emergency cases, and the doctors must wait for additional details for a wholesome diagnosis. As a result, the treatment processes and procedures sometimes get hastened and, in turn, put the patients’ lives at risk. It would always be helpful for doctors to be equipped with the medical requirements in advance for deciding the right course of action, thereby increasing the scope and chances of recovery. In this work, Multi-Model IoT (MMIoT) devices are deployed to monitor and collect health data from different body parts simultaneously. The healthcare data comprises signals and imagery captured from the MMIoT devices. Both the U-Net model and LSTM model are used to analyze the data automatically. The data processing is carried out by the server connected to the MMIoT network. All the medical IoT devices experimented with in this work are interconnected using a potential 5G network for optimal data transmission. The output obtained from the U-Net and the LSTM are channelized through a dense layer to classify the health anomalies accurately. It would not only facilitate but also educate medical professionals to handle unseen and typical cases in the future confidently. It can improve the overall quality of treatment and save lives with the best available resources.

Detecting Sybil Attack During Data Transmission Using Concealed Data Sharing Algorithm in Manet

As the mobile users are increasing sharply, the wireless or mobile ad hoc network data transmission is on the rise and gaining lot of attention from the research communities. The most important issue that has to be handled is the detecting the Sybil attack and increase the reliability and confidentiality of the data transmission. The foremost aim of this paper is to develop a mechanism to detect the Sybil attack early and evade the data loss and privacy to ensure credibility to the users. The proposed algorithm employs a concealed data sharing plan during the route discovery phase and detects and alleviates the attack to guarantee attack free data transmission in the MANET. The data sharing plan allows only the nodes that are trustworthy and permit the data to be transmitted across them, thereby evading the malicious nodes during the route discovery process and curtails the wastage of time and energy of the nodes.

Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach

Wireless Sensor Networks (WSNs) are widely studied for their data collection and monitoring capabilities across diverse applications. However, the limited energy resources of sensor nodes present a significant challenge in extending the network's lifespan. To overcome this, we introduce a Deep Learning based Grouping Model Approach (DL-GMA) that optimizes energy usage in WSNs. DL-GMA employs advanced deep learning techniques, particularly Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation, Cluster Head (CH) selection, and CH maintenance. Evaluation using key metrics—Energy Efficiency (88.7 %), Network Stability (90.8 %), Network Scalability (87.1 %), Congestion Level (18.3 %), and Quality of Service (QoS) (93.4 %)—demonstrates the effectiveness of DL-GMA in energy utilization optimization and overall network performance. Incorporating deep learning and intelligent grouping, our approach extends WSN lifespan and improves data transmission efficiency. DL-GMA represents a significant advancement in energy optimization for WSNs, addressing the challenges of limited energy resources and maximizing the network's potential while improving data transmission efficiency.

Enhanced cryptography algorithm and improved butterfly algorithm for secured data transmission in wireless sensor network

Due of their many uses, WSNs (Wireless Sensor Networks) have drawn the greatest interest, but,existing methods do not support reliable data transfer. They face issues in energy consumptions and routing on shortest paths over WSNs. Also, they are incapable to stopping compromised nodes with legitimate identities from launching attacks. This study handled this issue with suggested schema basedon CHs (Cluster Heads) and using IBO (Improved Butterfly Optimization) and DAES (Double key based Advanced Encryption Standard) algorithms. The main phases of this work contain system model, security model, CH node selection and shortest path routing with secured data transmission. Initially,system model is constructed via number of sensor nodes on the given setup. Then, CH nodes areselected using IBO algorithm based on best fitness values. These selections of CHs consider minimum delays and energy consumptions with maximum throughputs for establishing security assurances and energy conservations at sensor nodes along with secure protocol. The security levels for quick datatransfers over multi-path routes in WSNs are improved using DAES. Attack nodes are eliminated for successful transfers. The intermediate layer CHs create routing backbones to gather, combine, and convey data from member nodes. The simulation results demonstrate that the proposed IBO-DAES framework outperforms existing approaches in terms of throughputs, network longevity, data transfer rates, and energy consumptions.

Towards secure IoT system from a smart city perspective: An optimized algorithm and implementation

AbstractSecuring Internet of Things (IoT) systems is crucial for maintaining the resilience and trustworthiness of smart cities. In this context, the routing protocol's efficiency is pivotal for large‐scale IoT applications. However, achieving dependable and energy‐efficient routing in wireless sensor networks (WSNs) poses significant challenges due to limited and non‐rechargeable node energy resources. Also, traditional approaches often fell short of ensuring robust security and efficient data transmission. Hence, the purpose of this research was to address these shortcomings by introducing the Blowfish Honey‐Improved Spotted Hyena Optimization (BONY‐ISHO) protocol aimed at bolstering the security and efficiency of clustering and data routing in WSNs. Firstly, it introduces blockchain technology to establish secure and authenticated clustering, ensuring that only legitimate cluster heads and member nodes can participate. This innovation significantly enhances network security. Secondly, our protocol incorporates a hybrid Blowfish‐Honey cryptographic technique for data protection within the Area of Interest (AOI). This dual‐layer security approach, comprising Blowfish encryption and Honey encryption for key management, guarantees robust privacy and security. Thirdly, we have optimized data routing through the Improved Spotted Hyena Optimization (ISHO) algorithm, extending the network's lifetime and conserving energy resources. Performance evaluations confirm the superiority of the BONY‐ISHO protocol and existing solutions. It achieves a higher packet delivery ratio (99.8%), increased throughput (45 000 kbps), reduced energy consumption (1.68 joules), and minimized latency (0.068 sec) when compared to traditional methods. These performance enhancements make the BONY‐ISHO protocol a compelling choice for securing and optimizing IoT‐based WSNs. Our study concludes that the BONY‐ISHO protocol is a highly effective solution for addressing security and efficiency challenges in IoT‐based WSNs. By combining blockchain‐based authentication, hybrid cryptography, and intelligent routing algorithms, we have achieved a comprehensive enhancement of network security and data transmission efficiency. The protocol's ability to thwart malicious nodes and optimize routing pathways ensures the integrity and reliability of IoT‐based WSNs.