Data Transmission Performance Research Articles

Artificial intelligence enabled Digital Twins for training autonomous cars

This exploration is aimed at the system prediction and safety performance of the Digital Twins (DTs) of autonomous cars based on artificial intelligence technology, and the intelligent development of transportation in the smart city. On the one hand, considering the problem of safe driving of autonomous cars in intelligent transportation systems, it is essential to ensure the transmission safety of vehicle data and realize the load balancing scheduling of data transmission resources. On the other hand, convolution neural network (CNN) of the deep learning algorithm is adopted and improved, and then, the DTs technology is introduced. Finally, an autonomous cars DTs prediction model based on network load balancing and spatial-temporal graph convolution network is constructed. Moreover, through simulation, the performance of this model is analyzed from perspectives of Accuracy, Precision, Recall, and F1-score. The experimental results demonstrate that in comparative analysis, the accuracy of road network prediction of the model reported here is 92.70%, which is at least 2.92% higher than that of the models proposed by other scholars. Through the analysis of the security performance of network data transmission, it is found that this model achieves a lower average delay time than other comparative models. Besides, the message delivery rate is basically stable at 80%, and the message leakage rate is basically stable at about 10%. Therefore, the prediction model for autonomous cars constructed here not only ensures low delay but also has excellent network security performance, so that information can interact more efficiently. The research outcome can provide an experimental basis for intelligent development and safety performance improvement in the transportation field of smart cities.

Organizational Resource Allocation by Mobile Edge Computing in the Context of the Internet of Things

In recent years, the number of electronic devices and users has increased sharply with the rapid development and progress of electronic information technology. The motivation of this paper is to optimize the organization’s resource allocation strategy in the Internet of Things (IoT) environment. The optimal path planning and information processing efficiency are improved through Unmanned Aerial Vehicle (UAV) technology and migration optimization algorithm. The research method is migration optimization algorithm and UAV dynamic network. The information processing capability of traditional wireless communication systems has gradually been unable to meet the actual information processing needs. Therefore, a static network migration algorithm is constructed based on multiple-user multilateral edge computing servers. It migrates each user’s information processing task to the neighboring edge confidence processing server and uses the information processing server to perform auxiliary calculations. The simulation model adds a utility function that simulates energy consumption, delay weighting, and maximum extreme value, combined with the allocation strategy of optimizing each user’s information processing task to achieve the optimization goal. The static network migration algorithm established in this simulation has better results than other benchmark algorithms. Both scenario 1 and scenario 2 in the simulation show very close performance to the optimal solution. Meanwhile, a migration algorithm that can provide wireless charging for UAVs is built by a dynamic edge computing model based on the time associated with the UAV base station and multiple end users. Combined with completing the information processing tasks in each time slot, the energy arrival is also non-directional. The dynamic network migration algorithm can optimize the number of tasks absorbed by the end-user based on the current online status of the system without knowing the global information. The optimized target equation is related to the queue stability, and the parameter V has a linear relationship with the queue backlog length. Here, the problem of computing migration is studied in Mobile Edge Computing (MEC). The results show that the utility function of the weighted sum has an approximately linear relationship with the weights. As the value of the utility function increases, so does the weight function. The optimal data throughput of the proposed model is 70,000 bits, while the optimal data throughput of the state-of-the-art model is 68,000 bits. Therefore, the data transmission performance of the model presented here is better than that of other models. MEC can be significantly improved the efficiency of organizational resource allocation. Combining UAV and wireless charging technology, the computing and communication resource allocation issues of the UAV’s edge computing system are comprehensively discussed to improve the performance and efficiency of the network.

On the performance of overlaid wireless energy harvesting cognitive industrial sensor networks under jamming attacks

Two or more wireless sensor networks coexist in the same space while low energy consuming devices mobiles in a secondary network harvest ambient RF energy from transmissions by nearby active transmitters in the primary network. The channels are allocated to the primary network, while the overlaid secondary network can access the idle channel allocated to perform data transmission opportunistically and operate properly. In this paper, with the jammer implanted, we propose a novel solution in which we execute a deception strategy to exhaust the energy of the jammers. As a result, the energy constraint jammers will be challenging to achieve jamming attacks when the secondary transmitters (STs) transmit information. We formulate the problem first to tackle the issue; that is, we regard throughput optimization issues for ST under jamming attacks as a Markov decision process (MDP). Then, since the focus is mainly on the throughput of the secondary network, a learning algorithm is adopted to maximize it. Through the learning process, the STs can adapt to the dynamics of the primary network while executing proper actions to benefit the overall throughput online. Simulations validate the efficiency and the convergence of the algorithm we proposed.

An Efficient Data Transmission in Cognitive Radio Networks Using Momentum Search Algorithm

Data Transmission plays an important role in the digital world. In here, We are using Cognitive Radio(CR) a concept on Wireless Sensor Networks(WSN) which is being used as an intelligent wireless Communication Technology having unique Capabilities of monitoring spectrum bands and detecting available channels to enable the usage of statically allocated spectrum Furthermore, by dynamically adjusting its operating parameters, it can utilize available channels and to attack the upcoming spectrum crunch issue. Cognitive Radios can be used to find unused licensed spectrum and it can be utilized by secondary users without causing any interference to licensed users. Existing technologies used in cognitive radio include energy sensing, spectrum databases, and spectrum sensing using pilot channels. In small networks, transmission of small packet size can be transmitted with high efficiency without delay, whereas transmission of large data packets can cause data corruption, data packet corruption and may require retransmission over higher frequency channels. To avoid this type of interference, users need higher efficiency and wider bandwidth for efficient transmission. Here we use the technique of momentum search algorithms working on the law of conservation of momentum and the law of conservation of kinetic energy. Data transferred using this method is always unaltered. The transmitted data is split into fixed-size 64-bit packets. And the channel selection will be changed accordingly for higher channel selection efficiency for lossless data transmission. The rules of the Momentum Search algorithm allow users to transmit larger data packets with higher efficiency with the same level of interference as the primary user (PU). This proposal shows how to achieve the highest level of data transmission performance using a cognitive wireless network based on a Momentum search algorithm.

Simulation analysis of a method to improve data-transmission performance of Nanshan 26m Radio Telescope based on Software-Defined Networks

Data Center of Xinjiang Astronomical Observatory (XAO-DC) commenced operating in 2015, and provides services including archiving, releasing and retrieving precious astronomical data collected by the Nanshan 26m Radio Telescope (NSRT) over the years, and realises the open sharing of astronomical observation data. The observation data from NSRT are transmitted to XAO-DC 100 km away through dedicated fiber for long-term storage. With the continuous increase of data, the static architecture of the current network cannot meet NSRT data-transmission requirements due to limited network bandwidth. To get high-speed data-transmission using the existing static network architecture, a method for reconstruction data-transmission network using Software-Defined Networks (SDN) is proposed. Benefit from the SDNʼs data and control plane separation, and open programmable, combined with the Mininet simulation platform for experiments, the TCP throughput (of single thread) was improved by ∼24.7%, the TCP throughput (of multi threads) was improved by ∼9.8%, ∼40.9%, ∼35.5% and ∼11.7%. Compared with the current network architecture, the Latency was reduced by ∼63.2%.

Retracted: Multi constrained network feature approximation based secure routing for improved quality of service in mobile ad‐hoc network

AbstractThe mobile ad‐hoc network is well studied on the routing issues, and the security constraints around achieving higher quality of service (QoS) values are well analyzed. The main task is to establish the path to the target with reliable intermediate nodes based on quality parameters because of the lack of node mobility and central management. In a multi‐constrained QoS issuance, more than the QoS requirements must be satisfied at the end of the application. There are several secure routing protocols available to improve the QoS of Manet by routing packets securely. However, they do not meet the performance requirements. To solve this problem, and efficient Multi‐Constrained Network Feature Approximation (MCNFA) technique is proposed based on safe routing. The method first determines the list of paths between source and destination. According to that, the method approximates the congestion, latency, and hop count values for each route. According to the value obtained in approximation of various parameters, the legitimate weight is computed for all the routes towards the destination. According to the value of the legitimate weight, a single route is selected to perform data transmission. The MCNFA approach improves the routing performance and increases the throughput ratio and other QoS factors. The performance of the proposed method will be assessed using NS2 simulation. The results show that the proposed scheme can maintain a longer network lifecycle in tight scenarios suitable for delay‐tolerant networking. The performance is compared with energy recognition and MCNFA technique‐based energy‐saving routing protocols in various QoS scenarios.

Implementation of ANN-Based Embedded Hybrid Power Filter Using HIL-Topology with Real-Time Data Visualization through Node-RED

Electrical power consumption and distribution and ensuring its quality are important for industries as the power sector mandates a clean and green process with the least possible carbon footprint and to avoid damage of expensive electrical components. The harmonics elimination has emerged as a topic of prime importance for researchers and industry to realize the maintenance of power quality in the light of the 7th Sustainable Development Goals (SDGs). This paper implements a Hybrid Shunt Active Harmonic Power Filter (HSAHPF) to reduce harmonic pollution. An ANN-based control algorithm has been used to implement Hardware in the Loop (HIL) configuration, and the network is trained on the model of pq0 theory. The HIL configuration is applied to integrate a physical processor with the designed filter. In this configuration, an external microprocessor (Raspberry PI 3B+) has been employed as a primary data server for the ANN-based algorithm to provide reference current signals for HSAHPF. The ANN model uses backpropagation and gradient descent to predict output based on seven received inputs, i.e., 3-phase source voltages, 3-phase applied load currents, and the compensated voltage across the DC-link capacitors of the designed filter. Moreover, a real-time data visualization has been provided through an Application Programming Interface (API) of a JAVA script called Node-RED. The Node-RED also performs data transmission between SIMULINK and external processors through serial socket TCP/IP data communication for real-time data transceiving. Furthermore, we have demonstrated a real-time Supervisory Control and Data Acquisition (SCADA) system for testing HSAHPF using the topology based on HIL topology that enables the control algorithms to run on an embedded microprocessor for a physical system. The presented results validate the proposed design of the filter and the implementation of real-time system visualization. The statistical values show a significant decrease in Total Harmonic Distortion (THD) from 35.76% to 3.75%. These values perfectly lie within the set range of IEEE standard with improved stability time while bearing the computational overheads of the microprocessor.

Application of Random Walk Model in Mobile Communication Data Optimization

With the continuous development of society and economy, the development and application of mobile communications have become more and more popular, and more people have begun to use mobile devices for office, study and life. The quality of mobile communications network signals directly affects people’s experience of use. The scope of actual application has gradually increased with the renewal of the technology itself. Based on the random walk model, this paper constructs a mobile communication data optimization plan. Through data storage, transmission delay, and information total control, the optimization form of wireless network mobile communication data transmission performance is studied. Practice shows that this scheme can effectively improve communication quality and transmission efficiency.

Impact of Kerr optical frequency comb linewidth on the performance of NRZ-OOK modulated fiber optical communication system

Optical frequency combs (OFCs) generated in microresonators can substitute for widely employed power-hungry laser arrays, ensuring spectral and energy efficiency in wavelength-division multiplexed passive optical networks (WDM-PONs). Here, we propose a realistic design of a WDM-PON based on a silica microresonator generating an OFC in the dissipative Kerr soliton regime and present a corresponding theoretical study, paying particular attention to the impact of the comb linewidth on the characteristics of the communication system. Using intensive numerical simulation, data transmission performance in an eight-channel 100 GHz spaced WDM-PON is investigated for OFC carrier linewidths of 100 kHz, 1 MHz, 10 MHz, and 100 MHz. We show that microresonator-based OFCs with linewidths of up to 100 MHz can be used for a non-return-to-zero on-off keying modulated data transmission system and give an acceptable bit error rate (BER). Results show that the narrower the linewidth, the lower the BER is. The data transmission results show that error-free data transmission is possible with a BER of 4.4 × 10−12 for 100 kHz OFC carrier linewidth, providing 80 Gbps of total data rate on eight OFC generated carriers.

Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning

The study aims to conduct big data analysis (BDA) on the massive data generated in the smart city Internet of things (IoT), make the smart city change to the direction of fine governance and efficient and safe data processing. Aiming at the multi-source data collected in the smart city, the study introduces the deep learning (DL) algorithm while using BDA, and puts forward the distributed parallelism strategy of convolutional neural network (CNN). Meantime, the digital twins (DTs) and multi-hop transmission technology are introduced to construct the smart city DTs multi-hop transmission IoT-BDA system based on DL, and further simulate and analyze the performance of the system. The results reveal that in the energy efficiency analysis of model data transmission, the energy efficiency first increases and then decrease as the minimum energy collected α0 increases. But a more suitable power diversion factor ρ is crucial to the signal transmission energy efficiency of the IoT-BDA system. The prediction accuracy of the model is analyzed and it suggests that the accuracy of the constructed system reaches 97.80%, which is at least 2.24% higher than the DL algorithm adopted by other scholars. Regarding the data transmission performance of the constructed system, it is found that when the successful transmission probability is 100% and the exponential distribution parameters λ is valued 0.01∼0.05, it is the closest to the actual result, and the data delay is the smallest, which is maintained at the ms level. To sum up, improving the smart city’s IoT-BDA system using the DL approach can reduce data transmission delay, improve data forecasting accuracy, and offer actual efficacy, providing experimental references for the digital development of smart cities in the future.

Modeling of Occupancy-Based Energy Consumption in a Campus Building Using Embedded Devices and IoT Technology

Since occupancy affects energy consumption, it is common to model and simulate occupancy using simulation software. One drawback of simulation software is that it cannot provide data transmission information from the sensors, which is essential for real-time energy monitoring systems. This paper proposes an approach to integrating an occupancy model and a real-time monitoring system for real-time modeling. The integration was performed by implementing a model on embedded devices and employing an IoT-based real-time monitoring application. The experimental results showed that the proposed approach effectively configured and monitored the model using a smartphone. Moreover, the data generated by the model were stored in an IoT cloud server for monitoring and further analysis. The evaluation result showed that the model ran perfectly in real-time embedded devices. The assessment of the IoT data transmission performances yielded a maximum latency of 9.0348 s, jitter of 0.9829 s, inter-arrival time of 5.5085 s, and packet loss of 10.8%, which are adequate for real-time modeling of occupancy-based energy consumption.

Analysis of performance parameters for wireless network using switching multiple access control method

The developments of wireless networks have directed to search for opportunities of a broad diversity of improved and new networking contributions. Wireless Asynchronous Transfer Mode (ATM) is a non-synchronous or random mode of transferring information. The advantages of circuit switching include dedicated connections and guaranteed traffic parameters and the benefits of packet switching are the efficiency at the physical layer and a more cost-effective design. ATM is the only protocol that offers the best of both communication methods. Although the Variable Bit-Rate (VBR) transmission presents a promising prospective of stable data quality, it is usually accompanied by network traffic overload and cell packet loss, which extensively weakens that potential. This work overcomes these concerns by developing a switching-based multiple access control model to improve the data transmission performance of wireless ATM. Therefore, this work discusses the effectiveness of the developed approach to minimize the cell packet losses and network traffic overload in wireless ATM. Three control access is processed; polling, token passing, and reservation algorithms for collision avoidance. The reservation stage reserves the data before sending, which includes two timeline intervals; a fixed-time reservation period, and variable data transmission interval. Using OPNET 10.5, the results show that the presented switching-based multiple access control model can achieve a throughput value of 98.3 %, data transmission delay of about 40.2 ms, and 0.024 % of packet losses during data transmission between the source and destination. It is demonstrated that the introduced method effectively transmits information without creating any network complexity and delay

Optimal Data Transmission for WSNs with Data-Location Integration

Wireless sensor networks (WSNs) have good performance for data transmission, and the data transmission of sensor nodes has the function of symmetry. However, the wireless sensor nodes are facing great pressure in data transmission due to the increasing amount and types of data that easily cause premature energy consumption of some nodes and, thus, affects data transmission. Clustering algorithm is a common method to balance energy consumption, but the existing algorithms fail to balance the network oad effectively for big data transmission. Therefore, an optimal data transmission with data-location integration (ODTD-LI) is proposed for WSNs in this paper. For optimal data transmission, we update the network topology once for one round. In the proposed algorithm, we perform calculations of the optimal cluster heads, clustering and data transmission routing through three steps. We first deploy N homogeneous and symmetry nodes in a square area randomly and calculate the optimal number of cluster heads according to the node ocations. then, the optimal number of cluster heads, energy consumption, the distances and degrees of the nodes are taken into consideration during the clustering phase. Direct communication is carried out within a cluster, and the member nodes of the cluster pass the information directly to the cluster head. Lastly, an optimal hybrid routing from each cluster node to Sink is constructed for data transmission after clustering. The simulations verify the good performance of the proposed algorithm in view of the ifetime, average delay, coverage rate (CR) and oad balance of the network compared with the existing algorithms. Through the research conducted in this paper, we find that our work has good performance for selecting the hybrid routing in the network with the nodes randomly arranged.

Data transmission system of networked digital voice room based on Internet of Things technology

In order to solve the problem of poor transmission performance caused by data congestion in network digital language lab, its data transmission system is designed based on the technology of internet of things. In the hardware part, the main control circuit and transmission interface circuit are designed to reduce the signal reflection caused by the change of line width and angle. In the software part, the data transmission compression algorithm is designed to reduce the communication overhead. The internet of things technology is used to set data transmission nodes, and the weighted fairness data transmission protocol is set to control the energy efficiency of transmission. The random key pre-distribution scheme based on the hash key chain is used to construct the data security transmission model. The experimental results show that, compared with the data transmission system based on data mining and cloud computing, the designed system achieves the lowest word error rate and sentence error rate, and has better data transmission performance.

Distributed asynchronous learning for multipath data transmission based on P-DDQN

Adaptive packet scheduling can efficiently enhance the performance of multipath Data Transmission. However, realizing precise packet scheduling is challenging due to the nature of high dynamics and unpredictability of network link states. To this end, this paper proposes a distributed asynchronous deep reinforcement learning framework to intensify the dynamics and prediction of adaptive packet scheduling. Our framework contains two parts: local asynchronous packet scheduling and distributed cooperative control center. In local asynchronous packet scheduling, an asynchronous prioritized replay double deep Q-learning packets scheduling algorithm is proposed for dynamic adaptive packet scheduling learning, which makes a combination of prioritized replay double deep Q-learning network (P-DDQN) to make the fitting analysis. In distributed cooperative control center, a distributed scheduling learning and neural fitting acceleration algorithm to adaptively update neural network parameters of P-DDQN for more precise packet scheduling. Experimental results show that our solution has a better performance than Random weight algorithm and Round-Robin algorithm in throughput and loss ratio. Further, our solution has 1.32 times and 1.54 times better than Random weight algorithm and Round-Robin algorithm on the stability of multipath data transmission, respectively.

Performance modeling of multipath mobile data offloading in cellular/WiFi networks with bandwidth uncertainty

Mobile data offloading is one of the most effective approaches for improving the Quality of Service (QoS) of mobile users. Through this method, the mobile data traffic load over a cellular network is mitigated by offloading some of the traffic to an alternative path; usually a WiFi network. Most previous studies on WiFi offloading have assumed that the data traffic is offloaded to a path with constant bandwidth. However, in real-world applications, it is generally impossible to perform data transmission at a constant rate through WiFi over the entire offloading process, Hence, one of the most challenging issues in analyzing the performance of mobile data offloading is that of evaluating the effects of bandwidth uncertainty on the actual transmission rate. Accordingly, the present study proposes an analytical model for predicting the performance of multipath data offloading over WiFi network with bandwidth uncertainty. The theoretical results are validated by means of numerical simulations. The effectiveness of the proposed model is then further investigated by comparing the QoS metrics of the data offloading process with those obtained in a previous study on mobile data offloading under the assumption of a constant bandwidth. Overall, as the WiFi connection times are highly larger than the WiFi disconnection times, our model removes estimated errors in the baseline model by 8.7% and 6.7% in deadline miss ratio, by 23.2% and 14.9% in average offloading volume, when we consider two and three varying WiFi bandwidth respectively. The results show that the proposed method provides mobile operators with an effective framework for maintaining the QoS level of mobile data offloading in real-world networks characterized by WiFi bandwidth uncertainty.

Research on Campus Network Equipment Environment Monitoring Based on Internet of Things

The working environment of equipment has a great impact on the performance of equipment; therefore, it needs to be monitored and managed effectively. Based on the Internet of things (IoT), this study analyzed the monitoring of the campus network equipment environment. A monitoring system was designed, the design methods of software and hardware were analyzed, and the system was tested. The test results showed that the sensor used in the system had high accuracy, the temperature error was ± 0.5 ℃, the humidity error was within 0.5% RH, the data transmission performance was good, the transmission delay was between 8 ms and 9 ms, and the average packet loss rate was 0.7%. The test results verify the effectiveness of the designed system, and the system can be popularized and applied in the actual campus network.

Two-Level Congestion Control Mechanism (2LCCM) for Information-Centric Networking

As an emerging network architecture, Information-Centric Networking (ICN) is considered to have the potential to meet the new requirements of the Fifth Generation (5G) networks. ICN uses a name decoupled from location to identify content, supports the in-network caching technology, and adopts a receiver-driven model for data transmission. Existing ICN congestion control mechanisms usually first select a nearby replica by opportunistic cache-hits and then insist on adjusting the transmission rate regardless of the congestion state, which cannot fully utilize the characteristics of ICN to improve the performance of data transmission. To solve this problem, this paper proposes a two-level congestion control mechanism, called 2LCCM. It switches the replica location based on a node state table to avoid congestion paths when heavy congestion happens. This 2LCCM mechanism also uses a receiver-driven congestion control algorithm to adjust the request sending rate, in order to avoid link congestion under light congestion. In this paper, the design and implementation of the proposed mechanism are described in detail, and the experimental results show that 2LCCM can effectively reduce the transmission delay when heavy congestion occurs, and the bandwidth-delay product-based congestion control algorithm has better transmission performance compared with a loss-based algorithm.

The Design of Automatic Carton Packing System of Tobacco Based on STM32

This article is aimed at the requirements of a cigarette factory’s off-site technical transformation task, and it needs to transfer part of the stem and expanded silk produced by the new factory to the old factory. Therefore, it is necessary to construct a set of fully automatic carton packing system for shredded tobacco to improve production efficiency and reduce labor intensity. The system is divided into mechanical equipment layer, equipment control layer and production monitoring layer. The mechanical equipment layer is the design of the process flow between the various machinery and equipment of the tobacco packing production line; the equipment control layer is based on the STMicroelectronics32(STM32) single-chip microcomputer as the control core, through Modbus The protocol controls each device and performs data transmission, and communicates with the upper computer through the industrial Ethernet and Modbus Transmission Control Protocol(Modbu TCP) protocol; the production monitoring layer is based on the Qt programming tool for interface design and data transmission with the lower computer, and the relevant data is stored in My Structured Query Language (MySQL) In the database. This solution realizes the automation, networking and intelligence of industrial production lines, liberating manpower and improving efficiency.

The Evaluation of Twisted Pair Cable Performance in Circuits with DS18B20 Temperature Sensors

The transmission of data and energy has been widely used over the past few centuries, but in a complex manner and at high cost. Gradually in order to simplify this process and reduce costs, important network cables have appeared, namely: twisted pair cables, coaxial cables, and fiberoptic cables. Thus, instead of various wires, a single cable has become necessary. Within this context, this study seeks to evaluate data transmission performance using two types of Ethernet cables, CAT E5 and CAT E6, in circuits with DS18B20 temperature sensors. The evaluation of the two cables was made by reducing their lengths by 10 meters every two weeks to find the length of cable which would ensure a reliable reading of the sensors for use in the research project entitled “The Evaluation of the Potential Use of Surface Geothermal Energy in the City of Dourados, MS”. The obtained data indicates that CAT E5 cables with lengths less than 70 meters ensure reliable readings close to the real value, while for CAT E6 Ethernet cables this takes place at lengths of less than 80 meters.