Amount Of Data Transmission Research Articles

SURF Algorithm-Based Data Aggregation Method and Digital Sharing Economy

Data aggregation technique removes redundant message in original data, reduces the amount of data transmission, reduces network communication overhead, improves message acquisition accuracy and collection efficiency, and prolongs the effective working time of the network. It is an energy-saving technique widely used in wireless sensor networks, one of the techniques. However, with the continuous operation in the wireless sensor network, the data aggregation technique saves the energy cost of the network, but also brings new security risks to the network. Therefore, how to ensure the confidentiality and integrity of data in the process of aggregation, calculation, storage, and forwarding is the core link of wireless sensor network security research. This paper uses SURF algorithm to study this question. The algorithm in this paper not only has good robustness to the data, but also introduces data aggregation and box filter in the calculation, so that the registration time can be better improved. After the research in this paper, the accuracy of the algorithm in this paper is 19% better than the traditional algorithm. At the same time, the accuracy of the aggregated data increases over time as the channel congestion decreases in the traditional process.

Blockchain-Based Privacy Access Control Mechanism and Collaborative Analysis for Medical Images

Medical image analysis technology based on deep learning has played an important role in computer-aided disease diagnosis and treatment. Classification accuracy has always been the primary goal pursued by researchers. However, the image transmission process also faces the problems of limited wireless ad-hoc network (WAN) bandwidth and increased security risks. Moreover, when user data are exposed to unauthorized users, platforms can easily leak personal privacy. Aiming at the abovementioned problems, a system model and an access control scheme for the collaborative analysis of the diagnosis of diabetic retinopathy (DR) are constructed in this paper. The system model includes two stages of data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the model obtained after training into the blockchain, and other private clouds use the best-performing model on the chain to identify the image quality when cleaning data and pass the high-quality image to the lesion classification model for use. In the lesion classification stage, each private cloud trains the classification model separately; uploads its own model parameters to the public cloud for aggregation to obtain a global model; and then sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transmission, and protect personal privacy. Access control schemes include improved role-based access control (RAC) used within the private cloud and blockchain-based access control used during the interaction between the private cloud and the public cloud program (BAC). RAC grants both functional rights and data access rights to roles and takes into account object attributes for fine-grained level control. Based on certificateless public-key encryption technology and blockchain technology, BAC can realize the identity authentication and authority identification of the private cloud while requesting the transmission of model parameters from the private cloud to the public cloud and protect the security of the identity, authority, and model parameters of the private cloud to achieve the effect of lightweight access control. In the experimental part, two retinal datasets are used for DR classification analysis. The results show that data cleaning can effectively remove low-quality images and improve the accuracy of early lesion classification for doctors, with an accuracy rate of 90.2%.

Event-based adaptive horizon nonlinear model predictive control for trajectory tracking of marine surface vessel

This study investigates the trajectory tracking problem of fully actuated marine surface vessels (MSVs). First, an optimal problem for trajectory tracking of MSV is established. Model uncertainty, time-varying environmental disturbances, and actuator saturation are considered in this problem. For the control algorithm, considering that the traditional nonlinear model predictive control (NMPC) algorithm requires large amounts of computational resources, it is not easy to transmit the control signal to the system in real-time in practice. Meanwhile, due to the limited bandwidth of the system, the large amount of data transmission may lead to congestion and loss of signal transmission. Therefore, a novel event-based adaptive horizon nonlinear model predictive control (EAHNMPC) scheme is proposed to relieve the computation burden and reduce the signal transmission frequency for the MSV's trajectory tracking. Moreover, stability proof of adaptive predictive horizon control is given, and simulation experiments under different cases are performed. The results show that the proposed scheme has better control performance and computational performance than other approaches.

Research on intellectual property protection, innovation, and data protection algorithm based on wireless communication network

In today's world, with the developing technologies, data protection has become an absolute requirement, many organizations are focusing on the security of the data and information. Because many unauthorized individuals can hack computer system, putting the confidential files of the organization at danger. Therefore, there is need to employ various data security techniques in order to secure information. Furthermore, to resolve the problem of high energy consumption caused by large amount of data transmission in data protection algorithm, a data protection algorithm based on wireless communication network is proposed. Furthermore, we investigated that nowadays the protection of intellectual property is become significant issue. Because intellectual property security is essential for promoting innovation. Without intellectual property protection, individuals and firms would not gain the full advantages of their creations and would devote less time to development. The communication channel and data transmission link are established. Each node calculates the residual energy when clustering and sends it to the cluster head before the link is generated. The random number of nodes is encrypted and sorted according to the order of data link. Based on the wireless communication network, the privacy data within and between clusters are fused, and the data difference of each node is calculated respectively. Each cluster head uses homomorphic transformation to realize data sharing. The data protection algorithm is designed. The node data is partitioned and encrypted and decrypted by hybrid operation. The fusion results are uploaded to the base station to complete data protection. Apart from this, in this research, we proposed various methodologies in order to secure information as much as possible including text, photography, forms, videos and many more. Experimental results show that compared with CPDA algorithm, CMT algorithm and HRCDA algorithm, the data protection algorithm designed in this paper consumes the least total energy in each round. The algorithm can protect the privacy of data, reduce the consumption of data, and extend the life cycle of the whole network.

Performance Analysis of Cognitive SWIPT Ergodic Capacity Based on Different Selection Policies

The advent of 5G era and the increase of terminal’s numbers have led to increasing demand for spectrum resources and energy simultaneously. Therefore, this paper studies how to combine the cognitive radio (CR) and the Simultaneous Wireless Information and Power Transfer (SWIPT) technology effectively in a multiuser scenario, in which the spectrum utilization and energy efficiency are both improved. Firstly, the system model is designed of above cognitive SWIPT system, in which the cognitive users (CU) can choose spectrum access policy based on the amount of data transmission of licensed user. And then, according to cognitive users’ channel conditions and forwarding power, a concept of trust value and different relay selection policies is proposed. Subsequently, the according system ergodic capacity is analyzed. Finally, simulation results of system performance are provided to validate the analytical results. It shows that cognitive SWIPT technology can improve the system ergodic capacity and the spectrum access opportunities of cognitive users effectively, thereby improving the efficiency of energy and spectrum utilization.

Memristor-based signal processing for edge computing

The rapid growth of the Internet of Things (IoTs) has resulted in an explosive increase in data, and thus has raised new challenges for data processing units. Edge computing, which settles signal processing and computing tasks at the edge of networks rather than uploading data to the cloud, can reduce the amount of data for transmission and is a promising solution to address the challenges. One of the potential candidates for edge computing is a memristor, an emerging nonvolatile memory device that has the capability of in-memory computing. In this article, from the perspective of edge computing, we review recent progress on memristor-based signal processing methods, especially on the aspects of signal preprocessing and feature extraction. Then, we describe memristor-based signal classification and regression, and end-to-end signal processing. In all these applications, memristors serve as critical accelerators to greatly improve the overall system performance, such as power efficiency and processing speed. Finally, we discuss existing challenges and future outlooks for memristor-based signal processing systems.

End-to-End Transmission Control for Cross-Regional Industrial Internet of Things in Industry 5.0

Data transmission for the industrial Internet of Things (IoT) is crucial for industrial production, especially in the Industry 5.0 era, where human–machine collaboration is increasingly intensive. To ensure the continuity and robustness of industrial IoT communications in the case of damaged infrastructure communication facilities postdisaster, the industrial IoT can be connected with satellite networks in emergencies. This article presents a cross-regional, end-to-end, transmission control scheme for satellite-supported, multihop industrial IoT. The proposed scheme adjusts the window of data transmission from two phases, slow start and congestion avoidance, to accommodate the low-transmission performance caused by a long delay and high bit error rate in converged networks. The window of data transmission is also adjusted to increase the amount of data transmission for the slow start to fill the high bandwidth-delay product of the converged network, while adjusting the threshold of data transmission based on feedback information to distinguish different data losses during congestion avoidance. The feasibility of the heterogeneous network transmission model is experimentally verified. The results show that the scheme can achieve good performance in heterogeneous networks of industrial IoT and satellite networks. The scheme is effective in ensuring the continuity and stability of intelligent machine production in Industry 5.0 in emergency communication cases.

A CR-5G network based on multi-user for various waveforms detection

The 5G network is promised to provide all the requirements surfacing from the speedily rising number of mobile phone users rather than huge amount of data transmission. Many 5G waveforms can be used by 5G network such as Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM), Universal Filtered Multi-Carrier (UFMC), or Filter Bank Multi-Carrier (FBMC) waveform. To effectively capitalize on the spectrum resources, Internet of Thing (IoT), and multimedia transmission, the Cooperative Spectrum Sensing (CSS) system is being extensively utilized by Cognitive Radio (CR). It is able to meet the requisite communication services, however, the conventional CSS systems have been designed to detect only single kind of 5G waveform and poor detection performance for low signal-to-noise ratio (SNR). In this paper, a hybrid filter based CSS system is proposed for detection of various 5G waveforms by multi-user. The proposed filter includes three cascaded stages: cosine filtering, Welch segmentation, and Hamming windowing. The proposed detection system is applied on different data such as various of waveforms and SNRs. The simulation results exhibit a significant detection performance for the parameters of less than zero dB of SNR, greater than 95% global detection probability, less than 1% global system error probability, and less than 1% global false alarm probability. In addition, the detection performance reveals that the proposed system is outperformed related works as shown in the comparison in terms of the previous parameters.

Dynamic Allocation Method of Economic Information Integrated Data Based on Deep Learning Algorithm.

In view of the low efficiency of traditional data fusion algorithms in wireless sensor networks and the difficulty in processing high-dimensional data, a new algorithm CNNMDA, based on the deep learning model is proposed to realize data fusion. Firstly, the algorithm trains the constructed feature extraction model CNNM at the sink node; then each terminal node extracts the original data features through CNNM and finally sends the fused data to the sink node, so as to reduce the data transmission amount and prolong the network life. Simulation experiments show that compared with similar fusion algorithms, the CNNMDA can greatly reduce network energy consumption under the same data amount, and effectively improve the efficiency and accuracy of data fusion. In order to solve the problem that parameter synchronization takes too long in synchronous parallel, a dynamic training data allocation algorithm in multimachine synchronous parallel is proposed. Based on the computing efficiency of compute nodes, the amount of sample data to be processed by nodes will be dynamically adjusted after each iteration. This mechanism not only enables the model to be synchronized and parallel but also reduces the time of waiting for gradient updates. Finally, a comparative experiment is carried out on the Tianhe-2 supercomputer, and the experimental results show that the proposed optimization mechanism achieves the expected effect.

Dynamic event-triggered average dwell time control for switching linear systems

This paper concerns to an event-triggered control for switching linear systems. For reducing the data transmission amount of the communication network from sensor to controller, a switching-mode independent dynamic event-trigger (DET) is constructed wherein a positive constant and a positive auxiliary variable are simultaneously involved. Based on the received discrete-time state measurements, a mode-dependent feedback control law is proposed. Given some prior activation probabilities of switching signal, an average dwell time (ADT) based controller designed method is presented. It is proved that the closed-loop system achieves Zeno-free mean square bounded exponentially stable (MSBES). Significantly, the minimal event-triggered interval can be accurately given while the maximal event-triggered interval can be approximated. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

Analisis Redaman Kabel Fiber Optic Patchcord Single Core

Fiber Optic attenuation value affects the quality of data transmission. The fiber optic attenuation value can be greater or lower than the standard value. The attenuation value can affect the amount of data transmission sent and received. The attenuation value of single core fiber optic is influenced by various things, one of which is the treatment of the cable itself. Treating the cable by bending and coiling can also affect the damping value. To avoid the high attenuation value of fiber Optic when rolled by doing the rolling process carefully and not twisting during the installation process. The commendation scenario uses Fiber Optic Patchcord, the cable length is 5 meters. From the results of the scenario test on the 3 cables used, the highest attenuation value is in cable C with an attenuation value of 5.39 dB. The highest attenuation is in the 15th winding scenario. Meanwhile, from the 3rd test, the lowest attenuation value is found in cable A with an attenuation value of 1.76 dB. The lowest attenuation at the cable position without a given winding scenario. the more rolls the average damping value will increase the value.

Research on the Correlation between Multisource Big Data Virtual Assisted Preschool Education and the Development of Children's Innovative Ability.

Objective Innovation ability is an important part of children's core literacy and the core goal of science curriculum. As one of the important contents of scientific literacy, innovation ability is the key ability of young children to make informed decisions when facing scientific problems in social and personal life. In order to adapt to the future life, it is very important for children to have the ability to innovate. This research will provide a reference for the cultivation of children's innovative ability. Method In the process of database design, certain design principles need to be followed. In this paper, the system and user experience are greatly optimized by reasonably constructing table structure, allocating storage space, and establishing indexes. In this system, the MySQL database is used to store system data, such as user registration information, subscription information, and system-provided services, and the data uploaded by users that needs to be processed is stored in Hive. Although the GFP algorithm can solve the problem of load balancing, when the largest conditional pattern base of a frequent item is projected to other nodes, a large amount of data transmission will occur, resulting in increased communication between nodes. In order to solve this problem, the FP-growth parallel algorithm based on traffic optimization gives priority to assigning each frequent item to the node that needs the least traffic when grouping it. Results/Discussion. Experiments show that the TFP algorithm not only satisfies the load balance of nodes but also ensures a small amount of communication between nodes, which is more efficient than the traditional FP-growth parallel algorithm. The survey results of the influencing factors of children's innovation ability match the theoretical hypothesis, and different influencing factors have different effects on each dimension of children's innovation ability. Through the basic fit index of the model, the evaluation of the external quality of the model and the test of the internal quality of the model, it is shown that the survey results of the influencing factors of children's innovation ability match the theoretical hypothesis. The three influencing factors of family participation and investment, teacher teaching, and peer collaboration and communication have a positive role in promoting children's innovation ability.

Data Transmission Control of Vehicle Ad Hoc Network in Intelligent Transportation Systems for Smart Cities

The superposition of various application data streams in smart cities can intensify the load of vehicular networks in intelligent transportation systems, which can have an impact on the popularity of smart cities. To improve the performance of large amount of data transmission in telematics, this study proposes a scheme to determine the network state using congestion parameters and routing parameters and matches different data transmission amounts according to different states of the network. The scheme first considers the possibility of the network congestion state. Once congestion is judged to occur, the amount of data sent is reduced, and reducing the data backlog can further mitigate the possibility of congestion formation on the network. Secondly, after rejecting the possibility of congestion, the routing situation of the network needs to be judged, in such cases, whether a change in vehicles in the multi-hop path leads to a path change or an interruption of the data transmission path. Congestion parameters and routing parameters evaluate the state of the network, and the size of the congestion window is appropriately limited by the routing parameters to prevent excessive data volume from causing backlogs in the vehicular network. Experimental simulations show that the proposed scheme exhibits good performance in both linear and crossover vehicular networking scenarios. The research results provide a useful reference for the data transmission of telematics in smart cities.

Beamspace Scene Classification Algorithm for Low-Angle Estimation in MIMO Radar

This paper discusses the issue of low-angle estimation in multiple-input, multiple-output (MIMO) radar. Due to the low amount of data transmission, storage, and computation required for beamspace super-resolution algorithms in low-angle estimation, the method has gained considerable interest in recent years. This paper develops a beamspace scene classification (BSC) algorithm to enhance the performance of low-angle estimation in MIMO radar. The proposed BSC algorithm solves an initial angle estimate and the multipath coefficient estimate using the 3D beamspace data, and further constructs the beamspace data required for transmitting and receiving sides to reduce the data dimensions. It is additionally used to provide three estimation schemes with closed-form solutions for three multipath scenes (obtaining two estimations for transmitting and receiving sides). Finally, it fuses the estimates of the two sides by the minimum variance criterion. As a result, the proposed method achieves high estimation accuracy while requiring few processing resources. Moreover, the computational complexity of the proposed algorithm is examined in this study, with the results demonstrating that the proposed method is superior for engineering applications.

Impact of 5G on IOT Implemented Devices

As the IOT is gaining popularity there is a requirement for a technology which can support large amounts of data transmission efficiently and at very high bandwidth. The near-term future i.e. the next generation of IOT devices one of the primary needs or goals that need to be met are greater capacity, a higher speed of data transfer, and decreased latency. The development of next generation wireless mobile communication technology namely, 5G which promises to fulfil the needs of complex IOT architectures. This paper focuses on the requirements which can be fulfilled by 5G and enlightens architecture, merits and demerits of 5G network. A detailed survey on 5G enabled IOT devices, the research on this type of network have been conducted in various places around the globe.

Wireless power transfer and energy harvesting in distributed sensor networks: Survey, opportunities, and challenges

Distributed sensor networks have emerged as part of the advancements in sensing and wireless technologies and currently support several applications, including continuous environmental monitoring, surveillance, tracking, and so on which are running in wireless sensor network environments, and large-scale wireless sensor network multimedia applications that require large amounts of data transmission to an access point. However, these applications are often hampered because sensor nodes are energy-constrained, low-powered, with limited operational lifetime and low processing and limited power-storage capabilities. Current research shows that sensors deployed for distributed sensor network applications are low-power and low-cost devices characterized with multifunctional abilities. This contributes to their quick battery drainage, if they are to operate for long time durations. Owing to the associated cost implications and mode of deployments of the sensor nodes, battery recharging/replacements have significant disadvantages. Energy harvesting and wireless power transfer have therefore become very critical for applications running for longer time durations. This survey focuses on presenting a comprehensive review of the current literature on several wireless power transfer and energy harvesting technologies and highlights their opportunities and challenges in distributed sensor networks. This review highlights updated studies which are specific to wireless power transfer and energy harvesting technologies, including their opportunities, potential applications, limitations and challenges, classifications and comparisons. The final section presents some practical considerations and real-time implementation of a radio frequency–based energy harvesting wireless power transfer technique using Powercast™ power harvesters, and performance analysis of the two radio frequency–based power harvesters is discussed. Experimental results show both short-range and long-range applications of the two radio frequency–based energy harvesters with high power transfer efficiency.

An Improved Protocol for Verifiable Polynomial Cloud Outsourcing Computation

The cloud computing industry is booming, which involves a large amount of data transmission and calculation, and polynomial calculation is the most basic calculation. Since polynomial calculations have extremely high requirements on the computing power of computing equipment, in most cases, the computing equipment in the user’s hands cannot meet the computing requirements. The user needs to calculate but has no way to do it by himself. In this case, the computing task must be handed over to the cloud server. Aiming at the untrustworthy problem of the cloud server environment, an improved verifiable polynomial cloud outsourcing computing protocol scheme based on the previous outsourcing computing protocol is proposed to ensure the security of the computing process, the privacy of the computing content, and the verifiability of the computing results. This solution hides all the calculation parameters behind the deformed encrypted parameters, so the cloud server cannot obtain the user’s true parameters during the calculation process, and the calculation results can be verified by the user on the client with minimal computing power.

Dual-Chain Blockchain in Agricultural E-Commerce Information Traceability Considering the Viniar Algorithm

In this paper, we consider the Vennia algorithm to conduct in-depth research and analysis on the traceability of dual-chain blockchain agricultural products' E-commerce information. This paper adds a collaborative verification module to the traceability system and carries out a detailed design of information storage, traceability consensus algorithm, and smart contract for agricultural products according to the characteristics of the agricultural products supply chain, among which the collaborative verification module adopts dynamic data storage technology; the ConsiderVinia consensus algorithm is improved by introducing the way of integral penalty mechanism to ensure the block data validity. After a comparative study of the features and differences of the three major blockchain technology platforms, this paper selects the super ledger to implement the agricultural traceability system based on blockchain technology, introduces the partitioning and credit mechanism into the ConsiderVinia algorithm, and elaborates the improvement process of the algorithm. The improved algorithm reduces the malicious behavior of nodes and maintains the system security through a credit mechanism while maintaining the consistency of blockchain. In the event of a transaction dispute, the third-party platform will determine the party at fault based on the transaction records and other evidence and make corresponding punishments and compensations. The experiment proves that the algorithm proposed in this paper can reduce the amount of network data transmission in the process of node consensus, which is better than the ConsiderVinia algorithm in terms of both throughput and latency, improves the consensus efficiency, and alleviates the communication bottleneck caused by the increase of users in blockchain applications, and the solution of applying the blockchain technology to the agricultural products traceability system is practical and feasible. The blockchain-based agricultural products information traceability system solves the problems of information asymmetry, difficult sharing, easy tampering, and storage centralization in the traditional IoT-based agricultural products traceability system and truly realizes the credible and reliable traceability of the whole chain of agricultural products information. The research content and results of this paper have certain theoretical and practical values.

Efficient stereo matching on embedded GPUs with zero-means cross correlation

Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile platforms. To resolve this trade-off, we herein propose a novel acceleration approach for the well-known zero-means normalized cross correlation (ZNCC) matching cost calculation algorithm on a Jetson Tx2 embedded GPU. In our method for accelerating ZNCC, target images are scanned in a zigzag fashion to efficiently reuse one pixel’s computation for its neighboring pixels; this reduces the amount of data transmission and increases the utilization of on-chip registers, thus increasing the processing speed. As a result, our method is 2X faster than the traditional image scanning method, and 26% faster than the latest NCC method. By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1280x384 pixel images with a maximum disparity of 128. Additionally, the evaluation results on the KITTI 2015 benchmark show that our combined system is more accurate than the same algorithm combined with census by 7.26%, while maintaining almost the same processing speed. • Introduce an effective GPU-based calculation method for ZNCC. • Propose a strategy to achieve higher parallelism of GPU threads driven by GPU cores. • Design two parallel summation methods used in our Z 2 -ZNCC. • Create FastDT, an upgraded version of the GPU-based domain transformation method.

A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks

Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes.