Data Transmission Delay Research Articles

IoT based traffic congestion control for environmental applications

Recently, the demands of the Internet of Things (IoT) have been steadily growing .The main idea of the Internet of Things (IoT) is to revolutionize the current Internet by enriching it with a large number of intelligent objects that communicate with each other. Heterogeneous communication technologies are integrated into the Internet to achieve the IoT application. The function of these objects is to collect data from sensors, process it, and communicate it. Therefore, sensors are main features of IoT. The Heterogeneous Wireless sensor network (HWSN) is a key technology element of the Internet of Things (IoT), which is considered as the future evolution of the Internet. The integration of HWSNs into IoT makes communication with any type of object possible and opens the gates to a multitude of application areas. We have proposed: (Load-Balancing based Clustering Multipath routing protocol for Heterogeneous sensor networks). After having detailed the functioning of our protocol, we present the realization of the simulations by the NS3 simulator, whose objective is to allow us to evaluate the performances of the proposed (Load-Balancing based Clustering Multipath routing protocol for HWSN) by comparing the results with the two protocols: (Weight based clustering in wireless sensor networks) and (Distributed Energy efficient Adaptive Clustering Protocol). We thus take into account several metrics for the performance of evaluation. We also note that the proposed protocol is 47% better than DEACP with respect to fist node die, and 35% better than WBCHN with respect to last node die while maintaining the average data transmission delay. We were able to show through the simulation results obtained that the objectives have been reached.

Recent Advances in New Materials for 6G Communications

Abstract5G communication has begun commercial deployment. To maintain the sustainability and competitiveness of wireless communication systems, various industries and academic institutions around the globe are designing the framework for 6G communications. Compared with the previous generations of communication systems, 6G will be revolutionary, with unprecedented requirements in data transmission rates, delays, and global coverage. Many promising technologies in 6G systems, including terahertz communication, ultra‐large‐scale antenna arrays, large‐scale intelligent surfaces, quantum communication, and computing, have differing material requirements compared with previous systems. With the development of materials science, an increasing number of new materials will play important roles in 6G communications. Herein, the high demand of 6G systems is introduced first in this review, and recent advances of new materials having a broad variety of applications with great potential in 6G systems are systematically summarized. Perspectives on the research areas that require special attention are also presented.

A novel gateway node reconfiguration method of IOT based on hierarchical coding particle swarm optimization

In order to overcome the problems of low channel utilization, low transmission success rate and high data transmission delay in current gateway node reconfiguration methods of IOT, this paper proposes a novel gateway node reconfiguration method of IOT based on hierarchical coding particle swarm optimization. Based on the IOT network model, this paper analyzes the delay characteristics of the IOT, and constructs the object function of the gateway node reconfiguration of IOT. By monotone decreasing inertia weight strategy, the coding particle swarm optimization is optimized, and the reconfiguration objective function of the gateway node of IOT by using the optimized particle swarm optimization algorithm is solved. Experimental results show that the channel utilization ratio of the proposed method is higher than 90%, the success rate of information transmission is more than 80%, and the data transmission delay is less than 0.5 s, which indicates that the proposed method has high channel utilization, high transmission success rate and low data transmission delay.

ENERGY EFFICIENT BASED SECURE DATA TRANSMISSION FOR MULTI HOP TRUST MANAGEMENT TECHNIQUE USING WIRELESS SENSOR NETWORK

Secure data transfer is intended to keep data safe from illegal access, damage, or disruption. In this proposed technique, an intrusion prevention system is built to counter the rapidly growing threats offered by the current generation of malware, software, and exploits. As the number of intruders has grown, the network environment has become more complicated, making threat mitigation more difficult. Modern wireless sensor networks have emerged for the aim of transmitting important information and services to an ever-growing set of users. Security is the most important issues in wireless network. Using this proposed Multi Hop Trust Management (MHTM) approach; trust management technique is used to identify the trusted nodes with malicious node. Then secured and efficient way of data transmission are directed and communicated to any kind of networks re confirmed here. The result attained shows MHTM technique attains better performance than TSRP in stipulations of energy efficiency, data transmission delay, communication overhead, throughput, malicious sensor device misclassification rate and identification.

A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment

Garbage classification is a social issue related to people’s livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. Aiming at the problems of complex systems with data source and cloud service center data transmission delay and untimely response, at the same time, in order to realize the perception, storage, and analysis of massive multisource heterogeneous data, a garbage detection and classification method based on visual scene understanding is proposed. This method uses knowledge graphs to store and model items in the scene in the form of images, videos, texts, and other multimodal forms. The ESA attention mechanism is added to the backbone network part of the YOLOv5 network, aiming to improve the feature extraction ability of the network, combining with the built multimodal knowledge graph to form the YOLOv5-Attention-KG model, and deploying it to the service robot to perform real-time perception on the items in the scene. Finally, collaborative training is carried out on the cloud server side and deployed to the edge device side to reason and analyze the data in real time. The test results show that, compared with the original YOLOv5 model, the detection and classification accuracy of the proposed model is higher, and the real-time performance can also meet the actual use requirements. The model proposed in this paper can realize the intelligent decision-making of garbage classification for big data in the scene in a complex system and has certain conditions for promotion and landing.

Forward Design Method of Avionics System Based on Communication

With the rapid development of computer technology, electronic technology, and control technology, communication-based avionics systems can provide greater potential for the improvement of aircraft combat capabilities. Moreover, during the flight, passengers have an increasing demand for space communications. How to provide passengers with a communication-based aerospace electronic system that is indistinguishable from the ground during the flight is a problem that needs to be solved urgently. The research on the forward design method of this system has been highly valued by experts at home and abroad. This article is a communication-based forward design method for avionics systems. Based on the development of aerospace data communication technology, this article discusses the advantages of the application of the CCSDS (Consultative Committee for Space Data Systems) protocol in the design of aerospace electronic communication systems. This research designs the technical scheme of the entire aerospace communication electronic system and refers to the relevant CCSDS protocol to complete the design of the distributor’s fast transmission telemetry package and the distributor’s slow transmission telemetry package. This article introduces the two existing architectures of the integrated avionics system, compares and analyzes them, and then elaborates the integrated avionics system architecture based on trusted computing and the trust chain transfer mechanism. The experimental results show that the method designed in this paper uses the NBSP basic protocol in an aeronautical communication environment with a nesting depth of 2 s. When the data transmission delay is 2 s, the NBSP scheme is 0.9 and the NERON scheme is 0.4. This shows that the design method of this study is able to meet the requirements of the system and technical indicators.

A fault-tolerant clustering algorithm for processing data from multiple streams

Nowadays often multiple sensors provide real-time data about the same process. However, most data stream analysis algorithms work over a single feature vector. A common approach to overcome this issue is to combine the features extracted from each sensor into a single vector. The more sensors monitor a process, the more likely there is a malfunction or delay in data transmission. Hence the interest in developing algorithms capable of handling missing data or incorporating delayed data when it becomes available. This work presents a dynamic ensemble clustering algorithm based on evidence accumulation that can process multiple data streams. Each algorithm of the ensemble only processes one data stream, and obtaining the final partition does not require that data from all the streams is available. Therefore, final results can be provided even when there are sensor malfunctions or network delays. Furthermore, if delayed data arrives it can be used to extract evidence from the moment of arrival onwards. The algorithm was applied to identify arrhythmias over 71 standard 12-lead electrocardiogram recordings, achieving a 0.314% ± 0.010 error rate. The strategies of combining all the leads in a single feature vector or working with each lead independently were compared, obtaining better results in the second case.

Peripheral Computations: The Basis for Data Processing in Internet of Things

Internet of Things (ІоТ) is a global mainstream trend of today, which determines the importance of studies dealing with problems related with data processing by use of ІоТ. 
 A definition to the notion “Internet of Things” is given, with stressing that peripheral computing is a new trend for connected devices of IoT. The main applications of IoT are described. Peripheral computing is compared with cloud and fog computing. The need for peripheral computing in the conditions of “smart” life is demonstrated. Various forms of the launched peripheral computing infrastructure are discussed. The need for peripheral computing in the software based on IoT technology is justified. It is shown that IoT pushes up the demand for peripheral computing. Real examples of successful applications of peripheral computing and IoT across industries are shown. 
 It is stressed that the strongest impediment for the massive dissemination of IoT is that its infrastructure involves infinite numbers of options for connection, which may be quite often fully incompatible with each other. It is argued that peripheral computing is needed to remove drawbacks in software design and services on the basis of cloud technologies with consideration to performance and normative requirements. The forms of peripheral computing infrastructure are shown and described: special local devices, localized or regional data processing centers. The purpose of IoT technology is mentioned, which is the collection of large scopes of data from great many censors and smart devices, to be employed for business performance enhancement. The main reasons for the necessity of peripheral computing in the software based on IoT technology are outlined: the need to increase the throughput, the possibility to minimize the delay in data transmission and processing, and normative requirements. The main advantages of digital technology introduction are highlighted: better comfort of clients, higher exploitation efficiency, creation of new income flows.

Research on Secure Data Transmission Algorithm Based on Confidence of Wireless Opportunistic Networks for Energy Internet

The Energy Internet is a leading development direction for the modernized and intelligent transformation of the power grid, and a new type of infrastructure that supports the high-quality development of the economy and society. With the access of a variety of mobile terminals, security is one of the most important challenges faced in the construction of Energy Internet. Research on reliable networking and data forwarding strategies on the edge is of great significance to its development. On the basis of analyzing the requirements of typical application scenarios, we introduce in this paper a secure data transmission algorithm based on confidence of wireless opportunistic networks for Energy Internet to resist the influence of malicious behaviours. The simulations of real scenes confirm the advantages of introducing our algorithm on parameters such as the success rate and the delay of data transmission.

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.

Multi-objective task assignment for maximizing social welfare in spatio-temporal crowdsourcing

With the development of the Internet of Things (IoT), spatio-temporal crowdsourcing (mobile crowdsourcing) has become an emerging paradigm for addressing location-based sensing tasks. However, the delay caused by network transmission has led to low data processing efficiency. Fortunately, edge computing can solve this problem, effectively reduce the delay of data transmission, and improve data processing capacity, so that the crowdsourcing platform can make better decisions faster. Therefore, this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment (MOO-TA) problem in the edge computing environment. The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area. In this paper, the Weighted and Multi-Objective Particle Swarm Combination (WAMOPSC) algorithm is proposed to maximize both platform's and crowd workers' utility, so as to maximize social welfare. The algorithm combines the traditional Linear Weighted Summation (LWS) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose. Through comparison experiments on real data sets, the effectiveness and feasibility of the proposed method are evaluated.

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.

Task-Space Trajectory Tracking Control for Coordinated Manipulation Using Sampled Coupling Data

This letter studies the task-space synchronization control problem of networked manipulators which are commanded to track the desired task-space trajectories to achieve the coordinated manipulation transportation tasks in industrial and logistic applications. To guarantee the practical applicability and increase information transmission efficiency, sampled data-coupling strategy is utilized and the transmission delays as well as dynamics uncertainties are modelled explicitly. Firstly, the discrete-time-style synchronization error with sampled data is transferred into an equivalent continuous-time version with time-varying communication delays. Secondly, a distributed task-space tracking control law is presented based on the dynamic model by introducing the task-space end-position synchronization error for coordinated manipulation tasks of networked manipulators. Thirdly, a sufficient condition is derived from the Lyapunov-Krasovskii stability analysis approach to ensure the convergence of the dynamic control of each manipulator. By solving the proposed condition, the upper bound of the acceptable sampling period can be calculated. Simulational experiment results on a group of PUMA560 manipulators validate the task-space trajectory tracking performance, end-position synchronization performance and robustness to external force interference of the multiple-manipulator systems. To the best knowledge of the authors, this is the first letter which resolves the task-space tracking control of coordinated manipulators in the presence of coupling data sampling, transmission delays and dynamics model uncertainties.

Deep learning for autonomous vehicle and pedestrian interaction safety

The present work aims to study how deep learning approaches solve the safety problems in the interaction between autonomous vehicles and pedestrians. A Vehicle-Pedestrian Detection (VPD) algorithm based on Convolutional Neural Network (CNN) is proposed regarding the massive amounts of parameters during feature extraction of traditional vehicle–pedestrian interaction algorithms. Furthermore, the Squeezenet algorithm is applied to extract traffic characteristics with fewer parameters. The performance of the proposed algorithm is analyzed through simulation experiments. Results demonstrate that when the successful transmission probability reaches 100% and the λ value is 0.01–0.05, the proposed algorithm can provide the result closest to the actual value, with the smallest data delay and the highest data transmission security. In different categories, the proposed algorithm can provide the highest accuracy as the number of iterations increases compared with other algorithms (AlexNet, DenseNet, VGGNet, IGCNet, and ResNet), which can accurately forecast traffic safety accidents. The proposed algorithm can provide an accuracy of 81.98%, an improvement of at least 1.94% over other advanced CNNs, and at least 3.3% over algorithms included in comparative simulations. Hence, it can recognize and identify safe interactions between autonomous vehicles and pedestrians. Through experiments, the constructed algorithm can significantly reduce the data transmission delay, improve the prediction accuracy of the safe interaction between autonomous vehicles and pedestrians, and increase the recognition accuracy remarkably, which can provide experimental references for the intelligent development of the transportation industry in the future.

ENHANCED LION SWARM OPTIMIZATION ALGORITHM WITH CENTRALIZED AUTHENTICATION APPROACH FOR SECURED DATA TRANSMISSION OVER WSN

Securing data accuracy in WSNs (Wireless Sensor Networks) is a major problem. Aggregation techniques for improving accuracy in data processing have been gaining attention of scholars, recently. Existing security systems using singular paths for transmission of data have delays in transmissions while being open to intrusions. Moreover, increased computational overheads and processing time increases the delay of data transmission in the given networks. To overcome these issues, this work proposes ELSOA-CA (Enhanced Lion Swarm Optimization Algorithm and Centralized Authentication) method. This method focuses on an optimal, faster and energy efficient data transmissions while ensuring accurate decisions on tomato crops. Multipath routing introduced in the proposed method ensures faster data transmissions by selecting optimal forwarder nodes which satisfy the constraints of delay and energy. Optimal forwarder node selection is done using ELOSA algorithm. Data transmissions are secured by centralized authentication involving third party nodes. Each and every node in a region gets registered with the authentication node where a node’s genuineness is checked the node is allowed into the forwarder node list, used for data routing. CA (Centralized Authentication) enhances the security level of data transmissions over WSN multi path routing. ELSOA-CA framework’s simulation results provide higher throughputs, reduced energy consumptions, improved network lifetimes PDRs (Packet Drop Ratios) and lesser delay times.

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

A Robust Scheme for Coherency Detection in Power Systems

This paper presents an approach for online generator coherency identification based on windowed dynamic time warping (DTW). Generator rotor speed deviations measured by phasor measurement units (PMUs) are used as input data to compute a DTW dissimilarity matrix. Using the dissimilarity matrix together with Agglomerative Hierarchical Clustering (AHC) and Hubert-Levin index (C-index), generators are optimally grouped into coherent clusters. In addition to the clustering of generators, an index for characterizing the transmission delay of a Wide Area Measurement System (WAMS) is presented. A data delay factor that can indicate whether there is an inconsistent PMU data transmission delay is also proposed. The coherency identification technique and indices were tested using simulations carried out on the IEEE 39-bus system. The test results indicate that the proposed scheme accurately clusters generators into coherent groups. The suggested indices were also found to be valid. Keywords : coherence identification, dynamic time warping, speed deviation, phasor measurement unit, dissimilarity matrix

Effective path prediction and data transmission in opportunistic social networks

With the development of social media, social networks have become an important platform for people to share and communicate. In social network communication, when people carry mobile devices for data transmission, they need to find a definite transmission destination to ensure the normal conduct of information exchange activities. This is manifested in the process of data transmission by nodes, which requires analysis and judgment of surrounding areas, and finds suitable nodes for effective data classification and transmission. However, the node cache space in social networks is limited, and the process of waiting for the target node will cause end-to-end transmission delay. In order to improve such a transmission environment, this paper proposes a node trajectory prediction method named EDPPM algorithm. The EDPPM algorithm guarantees that nodes with high probability are given priority to obtain data information, which realized an effective data transmission mechanism. Through experiments and comparison of opportunistic transmission algorithms in social networks, such as Epidemic algorithm, Spray and Wait algorithm, and PRoPHET algorithm, the proposed scheme outperforms ProPhet by 47% in terms of cache utilization of nodes, 55% in terms of data transmission delay, and 32% in terms of network efficiency; compared with Epidemic by 53% in terms of cache utilization of nodes, 62% in terms of data transmission delay, and 47% in terms of network efficiency; same thing with regard to the Spray and wait by 27% in terms of cache utilization of nodes, 31% in terms of data transmission delay, and 17% in terms of network efficiency.

Design of Smart Classroom System Based on Internet of Things Technology and Smart Classroom

Smart classroom teaching is one of the new teaching methods. With the support of technology, teaching is carried out with the help of smart teaching tools to enhance teacher-student communication, enhance students’ learning autonomy, and provide new ideas for the realization of students’ deep learning. How to promote the overall intelligence of the teaching environment so that the teaching equipment can be used more efficiently and managed more effectively has become the main concern of schools. This article mainly studies the smart classroom system based on the Internet of Things technology and smart classroom. For temperature detection, we mainly use the DS18B20 chip to detect the temperature in the classroom. For the light intensity of the classroom, we use a photoresistor to collect the light data, and after amplification by the amplifier, the A/D sampling process of the single-chip microcomputer is used to obtain the light intensity, combined with the clock module to distinguish the influence of the classroom light. The data collection adopts the method of directly observing the source data, and the data format has not undergone secondary conversion, which ensures the accuracy of the source data. This test uses the USR-TCP232 network debugging assistant to debug the data collection. To optimize the safety and reliability of the system, dual-computer backup switching is adopted on the hardware, and process monitoring and management strategies are adopted on the software. At the same time, the amount of data interaction in the smart classroom is relatively large, so it is necessary to build a highly available cluster server, so that the system not only has a certain degree of stability, but also can quickly respond to users’ access requests. We can calculate that the average transmission time is about 10 ms, and 99.9% of the data transmission delay is less than 30 ms. The results show that the Internet of Things and smart classroom provide great convenience for future smart campus construction, daily teaching, and campus management and can also provide reference for the construction of smart classrooms in other universities.

Developing sensor signal-based digital twins for intelligent machine tools

Digital twins can assist machine tools in performing their monitoring and troubleshooting tasks autonomously from the context of smart manufacturing. For this, a special type of twin denoted as sensor signal-based twin must be constructed and adapted into the cyber-physical systems. The twin must (1) machine-learn the required knowledge from the historical sensor signal datasets, (2) seamlessly interact with the real-time sensor signals, (3) handle the semantically annotated datasets stored in clouds, and (4) accommodate the data transmission delay. The development of such twins has not yet been studied in detail. This study fills this gap by addressing sensor signal-based digital twin development for intelligent machine tools. Two computerized systems denoted as Digital Twin Construction System (DTCS) and Digital Twin Adaptation System (DTAS) are proposed to construct and adapt the twin, respectively. The modular architectures of the proposed DTCS and DTAS are presented in detail. The real-time responses and delay-related computational arrangements are also elucidated for both systems. The systems are also developed using a Java™-based platform. Milling torque signals are used as an example to demonstrate the efficacy of DTCS and DTAS. This study thus contributes toward the advancement of intelligent machine tools from the context of smart manufacturing.