Data Transmission Path Research Articles

Analysis of Compressed Sensing for Efficient Signal Acquisition and Reconstruction

In a variety of industries, including image processing, signal processing, and communications, compressed sensing (CS) has emerged as a viable technique for effective signal capture and reconstruction. The core idea of CS is the capacity to collect and represent signals with much fewer measurements than conventional Nyquist-Shannon sampling theory demands. With the help of CS, precise signal reconstruction is possible from a heavily subsampled set of measurements by taking advantage of the inherent sparsity or compressibility of many natural signals.Compressed sensing for effective signal capture and reconstruction is examined in this research. The fundamental ideas and mathematical foundation of computational science are introduced first, with an emphasis on the crucial elements of the sensing, sparse representation, and reconstruction methods. We address several measuring techniques and their effects on signal recovery performance, including random sensing matrices and structured sensing matrices.We explore ideas like the Restricted Isometry Property (RIP), coherence, and incoherence requirements as we delve into the mathematical features and theoretical guarantees of CS. We examine the trade-off between the degree of signal sparsity and the quantity of measurements necessary for precise reconstruction, elucidating the constraints and practical considerations of CS-based systems.The examination of compressed sensing for effective signal capture and reconstruction is detailed in this paper. We highlight the promise of CS as a formidable paradigm for getting beyond the drawbacks of conventional signal capture techniques by carefully examining the underlying concepts, mathematical aspects, reconstruction algorithms, and applications. CS has the potential to revolutionise numerous fields and offer up new paths for effective data processing and transmission by enabling efficient sampling and reconstruction.

Energy-efficient hierarchical routing in wireless sensor networks based on fog computing

Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the twenty-first century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize the energy consumption of sensor nodes to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called energy-efficient hierarchical routing protocol for wireless sensor networks based on fog computing. Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of things applications. In addition, we propose an improved ant colony optimization algorithm that can be used to construct an optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes’ energy consumption, data packet losses, and extends the network lifetime. We are aware that in WSNs, the certainty of the sensed data collected by a sensor node can vary due to many reasons such as environmental factors, drained energy, and hardware failures.

METHOD OF MANAGING ROUTING OF DATA FLOWS IN HETEROGENEOUS NETWORKS UNDER CONFLICT, UNCERTAINTY AND DISTURBANCE

This paper proposes a method of synthesis of data transmission routes in conflicting heterogeneous self-organized wireless data networks under external and internal influences. In this case, routing is understood as the process of determining in a data transmission network one or a set of routes (pathspaths) , that are optimal within the selected criteria between a given pair or set of network nodes. Thus, a route is a sequence of network nodes and data transmission paths that connect a pair of network nodes for communication. The method is based on a mathematical model of the data network operation in a virtual multidimensional parameter space. The model of operation is based on a graph-model of information interaction of network nodes in the process of data transmission between receptor nodes and acceptors in the transmission of data streams. Based on the problem statement, the price function is formally defined as the value of the virtual distance between the nodes of a heterogeneous data network. In contrast to the Floyd-Warshall and Dijkstra algorithms, the synthesis of the data transmission route takes into account both the load of the computing environment of network nodes and the state of information interaction channels of network nodes, as well as possible changes in network parameters during data transmission. The data transmission network is self-organized, has no dedicated nodes, which ensures its reliability under external and internal influences, conflicts in data transmission, as well as variable topology. The proposed method for synthesizing data transmission routes when controlling the routing of data flows in heterogeneous networks is focused on the possibility of implementing the basic model of open systems interaction within the framework of existing protocols - the OSI model, which is a reference network model for communications and the development of network protocols.

Task Parallelism-Aware Deep Neural Network Scheduling on Multiple Hybrid Memory Cube-Based Processing-in-Memory

Processing-in-memory (PIM) comprises computational logic in the memory domain. It is the most promising solution to alleviate the memory bandwidth problem in deep neural network (DNN) processing. The hybrid memory cube (HMC), a 3D stacked memory structure, can efficiently implement the PIM architecture by maximizing the existing legacy hardware. To accelerate DNN inference, multiple HMCs can be connected, and data-independent tasks can be assigned to processing elements (PEs) within each HMC. However, owing to the packet-switched network structure, inter-HMC interconnects exhibit variable and unpredictable latencies depending on the data transmission path and link contention. A well-designed task schedule using context switching can effectively hide communication latency and improve PE utilization. Nevertheless, as the number of HMC increases, the variability of a wide range of inter-HMC communication latencies causes frequent context switching, degrading overall performance. This paper proposes a DNN task scheduling that can effectively utilize task parallelism by reducing the communication latency variance owing to HMC interconnect characteristics. Task partitions are generated to exploit parallelism while providing inter-HMC traffic within the sustainable link bandwidth. Task-to-HMC mapping is performed to hide the average communication latency of intermediate DNN processing results. A task schedule is generated using retiming to accelerate DNN inference while maximizing resource utilization. The effectiveness of the proposed method was verified through simulations using various realistic DNN applications performed on a ZSim x86-64 simulator. The simulations revealed that DNN processing with the proposed scheduling improved the DNN processing speed by reducing the processing time by 18.19% over conventional methods where each HMC operated independently.

Cyber Resilient Platform for Internet of Things (IIOT/IOT)ED Systems: Survey of Architecture Patterns

Purpose of the article: development of tools for building the cyber resilient platform for Internet of things (IIoT/IoT). The urgency of development the cyber resilient platform for Internet of things (IIoT/IoT) is to provide the required security and resilience of critical information infrastructure of the Russian Federation in the face of rising security threats, and imperfection of the known models, methods and means for data collection and processing in IIoT/IoT networks, based on the wireless technology Sigfox, LoRaWaN, “Strij/”Vaviot” (XNB/Nb-Fi), NBIoT. Research methods: It uses the author’s models and methods of similarity and dimensions theory of the distributed computing, as well as the domestic technology of wireless communication Logic Inter Node Connection (LINC) (http:// aura360.ru/), as well as the domestic FenixOS operating system (https://fenix.link/kontakty/), designed for collecting and processing the telemetry data. Results: Developed tools for building the cyber resilient platform for Internet of things (IIoT/IoT). The article presents the main scientific and technical results of solving this problem. The research was carried out within the framework of the Federal project “Information security” of the national program “Digital economy of the Russian Federation”. It is important to note that the results allowed designing a prototype of the domestic Internet of things (IIoT/IoT) platform with self-healing data reception and transmission paths between smart devices

Attribute‐Associated Neuron Modeling and Missing Value Imputation for Incomplete Data

The imputation of missing values is an important research content in incomplete data analysis. Based on the auto associative neural network (AANN), this paper conducts regression modeling for incomplete data and imputes missing values. Since the AANN can estimate missing values in multiple missingness patterns efficiently, we introduce incomplete records into the modeling process and propose an attribute cross fitting model (ACFM) based on AANN. ACFM reconstructs the path of data transmission between output and input neurons and optimizes the model parameters by training errors of existing data, thereby improving its own ability to fit relations between attributes of incomplete data. Besides, for the problem of incomplete model input, this paper proposes a model training scheme, which sets missing values as variables and makes missing value variables update with model parameters iteratively. The method of local learning and global approximation increases the precision of model fitting and the imputation accuracy of missing values. Finally, experiments based on several datasets verify the effectiveness of the proposed method.

Failure-Tolerant and Low-Latency Telecommand in Mega-Constellations: The Redundant Multi-Path Routing

In recent years, in order to provide worldwide broadband Internet access services, many mega-constellation projects have been proposed. Traditional ground-based and space-based telecommand systems only rely on a single path for data transmission, which is vulnerable once the path fails. This paper utilizes inter-satellite-links (ISLs) in the constellation network to reduce the risk of data transmission failure, and proposes the failure-tolerant and low-latency redundant multi-path routing algorithm (RMPR). RMPR transmits multiple data copies on multiple shortest edge-disjoint paths according to the optimal proportions with the minimum delay. Compared with traditional single-path routing and backup multi-path routing, RMPR ensures both reliability and timeliness in telecommand system.

Research on Distributed In‐Vehicle Wireless Self‐Organized Routing Protocol Distribution Mechanism

In recent years, the application of intelligent transportation systems has gradually made the transportation industry safe, efficient, and environmentally friendly and has led to a broader research prospect of vehicle wireless communication technology. Distributed vehicular self‐organizing networks are mobile self‐organizing networks in realistic traffic situations. Data interaction and transmission between nodes are achieved through the establishment of a vehicular self‐organizing network. In this paper, a multipath routing protocol considering path stability and load balancing is proposed to address the shortcomings of existing distributed vehicular wireless self‐organizing routing protocols. This protocol establishes three loop‐free paths in the route discovery phase and uses the path stability parameter and load level parameter together to measure the total transmission cost. The one with the lowest total transmission cost is selected as the highest priority path for data transmission in the route selection phase, and the other two are used as alternate paths, and when the primary path breaks, the higher priority of the remaining path will continue to transmit data as the primary route. In this paper, to improve the content distribution performance of target vehicles in scenarios where communication blind zones exist between adjacent roadside units, an assisted download distribution mechanism for video‐like large file content is designed in the V2V and V2I cooperative communication regime. That is, considering a two‐way lane scenario, we use the same direction driving vehicles to build clusters, reverse driving vehicles to carry prefetched data, and build clusters to forward prefetched data to improve the data download volume of target vehicles in nonhot scenarios such as highways with the sparse deployment of roadside units, to meet the data volume download demand of in‐vehicle users for large files and give guidance for efficient distribution of large file content in highway scenarios.

Optimization of energy-efficient dynamic task assignment for wireless sensor networks based on particle swarm algorithm

This paper provides an in-depth analysis of the optimization of energy-efficient dynamic task allocation in wireless sensor networks through an improved particle swarm optimization algorithm, and introduces the idea of software-defined networking into wireless sensor network to propose a software-defined wireless sensor network non-uniform cluster routing protocol. The protocol decouples the data layer from the control layer, and the base station performs the cluster head election, network clustering, and routing control operations. The base station optimizes the cluster head election process by electing cluster head nodes using an improved particle cluster algorithm. Based on the elected cluster head nodes, the base station calculates their corresponding contention radius and plans the data transmission path. The results of the calculation are sent to the corresponding nodes for cluster creation and data transmission. The simulation results fully show that the use of this protocol can achieve the purpose of significantly extending the service life of the network. This paper comprehensively analyses the whole process of mobile charging of UAVs under improved conditions and proposes a path planning algorithm. The multi-level weighted charging path planning proposed in this paper considers both fairness and timeliness. Finally, the paper verifies the effectiveness of the algorithm.

Research on adaptive prediction of multimedia data transmission path based on machine learning

Aiming at the problems of high probability of data packet loss and long data receiving delay in current methods, an adaptive prediction method of multimedia data transmission path based on machine ...

S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks

Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and generates heavy memory traffic, which impose a severe challenge on computing system design. Through optimizing parallel executions and data reuse in convolution, systolic architecture demonstrates great advantages in accelerating CNN computations. However, regular internal data transmission path in traditional systolic architecture prevents the systolic architecture from completely leveraging the benefits introduced by neural network sparsity.Deployment of fine-grained sparsity on the existing systolic architectures is greatly hindered by the incurred computational overheads.In this work, we propose S2Engine a novel systolic architecture that can fully exploit the sparsity in CNNs with maximized data reuse. S2Engine transmits compressed data internally and allows each processing element to dynamically select an aligned data from the compressed dataflow in convolution. Compared to the naive systolic array, S2Engine achieves about 3.2 and about 3.0 improvements on speed and power efficiency, respectively.

Research on adaptive prediction of multimedia data transmission path based on machine learning

Research on adaptive prediction of multimedia data transmission path based on machine learning

Security in IoT-Enabled Digital Twins of Maritime Transportation Systems

The purposes are to explore the safety performance of the Maritime Transportation System (MTS) based on Digital Twins (DTs) Internet of Things (IoT) and develop maritime transportation towards intelligence and digitalization. Because the comprehensive operational security of modern MTS is not yet mature, historical transportation data of the Maritime Silk Road are acquired and preprocessed. Afterward, DTs are introduced, and relay nodes are added to data transmission paths to construct a maritime transportation DTs model based on relay cooperation IoT. Eventually, this model's security performance is validated through simulation experiments. Relay security analysis suggests that interference information is a vital guarantee to assist in information non-disclosure, from which the constructed model can harvest energy to increase the data transmission power, thereby improving communication performance and secrecy rate. Outage probability analysis reveals that the simulated and the theoretical results are almost the same; moreover, given the system's multi-hop paths in the same environment, the more the relays and the greater the fading index, the better the system performance and the lower the outage probability. Once the iterations reach a particular number, the node secrecy rate becomes optimal and cannot cause excessive burden to the system; besides, the power distribution can establish a new equilibrium when the nodes are in different locations, so that system security performance gets improved. The simulated value is closest to the actual result under 100% successful transmission probability and 0.01~0.05 λ value. To sum up, the constructed maritime transportation DTs model presents extraordinary transmission and security performance, providing an experimental basis for intelligent and secure maritime transportation in the future.

Разработка алгоритма многопутевой маршрутизации в программно-конфигурируемых сетях связи

The paper describes the development of routing algorithm in software-defined communication networks using the principle of multipath message delivery. The use of the OpenFlow protocol as the main one for connecting data- and control-plane devices between each other, that is, programmable switches with the controller, allows us to take the network topology presented in undirected weighted graph form as the initial data for the algorithm. There are known solutions to the problem of finding ways to transmit data in a communication network, as a rule, using the network resource reservation protocol, but additional restrictions are imposed on the network, since RSVP has a low degree of scalability, respectively, inappropriate consumption of computing resources and storage system resources of individual routers. In view of the above, an algorithm has been developed for finding a set of paths on a graph with the construction of an auxiliary graph based on the original one. Conditions are given under which an auxiliary graph can be constructed from the initial one. The algorithm takes into account the possibility of constructing several paths passing through one vertex, while meeting the requirements for the delay of the input data stream. To expand the functionality and possible areas of application of the algorithm for finding a set of paths, a criterion for the required total throughput by a set of data transmission paths is introduced. Conditions for constructing paths from a vertex to set of vertices are given. The algorithm presented in the work has an order of magnitude less time complexity, which allows you to quickly respond to changes in the data transmission network, while the most significant differences in the time spent on building a set of paths are noticeable with an increase in nodes in the data transmission network and the number of possible paths.

Trusted Sensing Model for Mobile Ad HoC Network Using Differential Evolution Algorithm

Mobile Ad Hoc Network (MANET) has set of mobile nodes that are allowed to communicate with each other through wireless links. The nodes are deployed spontaneously without any infrastructure in a geographical area. Due to the lack of centralized administration and prior organization, MANETs are vulnerable to different attacks of malicious nodes. To overcome the problem of black hole attack in MANETs, a trust model using Differential Evolution (DE) algorithm has been proposed. It identifies the malicious node and inhibits them to become the member of data transmission path. The proposed work consists of two phases; one is to obtain the optimized path and the other deals with the penalty factor for malicious nodes. Moreover, the Differential Evolution is one of the most promising optimization to enhance security with increased network density. The proposed algorithm is compared with AOMDV, DSR, Genetic algorithm and ACO.

Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT

IoT integrates and connects intelligent devices or objects with varied architectures and resources. The number of IoT devices is growing exponentially. Due to the massive wave of IoT objects, their diversity and heterogeneity among their architectures, the existing communication protocols for wireless networks become ineffective in the context of IoT. Wireless Sensor Network (WSN) has the potential to be integrated to the internet of things (IoT). The issues of the routing of WSNs impose nearly similar prerequisites for IoT routing technique. Most of the traditional routing protocols are not appropriate for WSNs and IoT because of resource constraints, computational overhead and environmental interference and do not take into account the different factors affecting energy parameter and do not accommodate node mobility. Routing algorithms must ensure the data transmission in an efficient way, having proper knowledge of the IoT system. For this reason, many intelligent systems have been utilized to design routing algorithms to handle the network's dynamic state. In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. The proposed routing algorithm is simulated using MATLAB for performance evaluations. The evaluation results have recorded an improvement in conservation of energy, of almost 50% less consumed energy even with an increase in the number of nodes, by comparing with an existing routing technique based on ant system, a current routing protocol for IoT and the conventional ACO algorithm. © 2020 ASTES Publishers. All rights reserved.

Dynamic Clustering-Genetic Secure Energy Awareness Routing To Improve the Performance of Energy Efficient In IoT Cloud

The Internet of Things (IoT) is one of the emerging technologies that has attracted the attention of researchers in the field of education and industry. The idea behind the Internet of Things is that things and devices are operated on the Internet to achieve some common goals with humans. It serves as a platform for monitoring the collection process, controlling the cyber-global fire world, and collecting data and analyzing data using IoT sensor nodes. Power saving is important for battery-powered devices. The amount of previous work is small, although it has a large tip capacity, especially in terms of battery life and energy efficiency. This regulates the development of IoT a simple and energy efficient routing scheme for war sensor networks. The nodes require an increased lifespan of intelligently transmitted data communications. To conserve the energy of IoT nodes, the Dynamic Clustering-Genetic Secure Energy Awareness Routing Protocol (DC-GSEARP) utilizes clustering, cluster head selection and energy and efficient path calculation for efficient and real-time routing. To accomplish the efficient cluster head selection, to have used Dynamic Clustering algorithm. The proposed DC-GSEARP protocol achieve an improved that can be used to construct an optimal path for energy efficient data transmission for IoT sensor nodes. DC-GSEARP, which uses the integration of clustering supported by short path resolution to provide energy efficient and advanced routing capabilities for IoT, ensures entering a path with lower power consumption and enhanced QoS measurements. The results of the simulations show that the proposed approach can reduce the power consumption of sensor nodes, data packet losses and extend network life.

Performance Analysis of Data Transmission on a Wireless Sensor Network Using the XBee Pro Series 2B RF Module

Wireless Sensor Network (WSN) is a wireless network technology that is capable of scanning and can be used to monitor environmental conditions, both indoors and outdoors. One of the devices used in data communication at WSN is XBee. XBee is a device in WSN that uses radio frequency as a data transmission path from one device to another. In sending data, Xbee can be affected by the distance and signal strength between devices. In this research, Xbee communication distance analysis is done outdoors and indoors so as to get data in the form of maximum distance vulnerable from Xbee. The results of this study indicate that the Xbee Pro Series 2B that is measured outside the room is capable of sending data up to a distance of 110 meters, while the Xbee Pro Series 2B device that is measured indoors is capable of sending data up to a distance of 20 meters.

Optimal path strategy for the web computing under deep reinforcement learning

Purpose With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation. Design/methodology/approach The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion. Findings Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput. Originality/value This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

An Analysis Scheme of Balancing Energy Consumption with Mobile Velocity Control Strategy for Wireless Rechargeable Sensor Networks.

Wireless Rechargeable Sensor Networks (WRSN) are not yet fully functional and robust due to the fact that their setting parameters assume fixed control velocity and location. This study proposes a novel scheme of the WRSN with mobile sink (MS) velocity control strategies for charging nodes and collecting its data in WRSN. Strip space of the deployed network area is divided into sub-locations for variant corresponding velocities based on nodes energy expenditure demands. The points of consumed energy bottleneck nodes in sub-locations are determined based on gathering data of residual energy and expenditure of nodes. A minimum reliable energy balanced spanning tree is constructed based on data collection to optimize the data transmission paths, balance energy consumption, and reduce data loss during transmission. Experimental results are compared with the other methods in the literature that show that the proposed scheme offers a more effective alternative in reducing the network packet loss rate, balancing the nodes’ energy consumption, and charging capacity of the nodes than the competitors.