Improve Network Performance Research Articles

Design and development of a forecasting tool for the identification of new target markets by open time-series data and deep learning methods

Globalization and rapid advancements in the IT sector brought new challenges and intensified competition between companies, strongly highlighting the demand for Business Intelligence and Analytics in decision-making and strategy development planning. Motivated by the analysis and forecasting capabilities offered by time-series data and its limited exploitation in marketing literature, this paper introduces a multidisciplinary framework, called MULTIFOR, which aims to synthesize and deliver profound knowledge about the macroeconomic environment and the attractiveness of new target markets. MULTIFOR main objective is to fully support interested parties (i.e., industries, SMEs, scholars, local and national authorities, etc.) in their decision-making and strategy planning tasks, by providing accurate forecasts and recommendations. The proposed framework along with its Web service offers a unique solution since it encapsulates: marketing fundamentals (PESTEL analysis), open data (time-series data from open Web databases) and deep learning methods (LSTM networks) for time-series analysis and forecasting. MULTIFOR has been tested and validated through a use case on elevator and escalator (E&E) industry, which is a strong industrial sector which has important impact worldwide, with studying its scope in European markets. The research findings revealed that LSTM networks, which according to the existing literature are superior to other forecasting models, when combined with macroeconomic theory can achieve greater forecasting accuracy in identifying new international markets. In the case of MULTIFOR, the improvement of LSTM networks performance was achieved through the selection of appropriate indicators and the pre-processing of time-series data exploiting PESTEL analysis. In particular, MULTIFOR achieved approximately 70% of the 900 time-series reduction of errors in the forecasting process in time-series derived from the PESTEL analysis for European countries. Moreover, the exploitation of MULTIFOR in the E&E industry, revealed the countries of northern Europe as the most attractive markets, of which Sweden and The Netherlands stand out.

IPORM: A Resource Management Scheme for Fog-Enabled Cloud Environments: An Improved Political Optimizer-Based Approach

In recent years, with the rapid growth of the internet of things (IoT), the number of devices connected to the network has increased significantly. The rising trend of mass data that objects send to the network has become a big challenge for cloud networks. The next generation IoT applications require low delay and real-time responses. Hence, IoT applications need a new processing paradigm to be able to access distributed cloud services at the network edge. In recent years, fog computing has emerged as a distributed computing model compatible with heterogeneous environments that can provide cloud services with low delay at the network edge. Meanwhile, services provided by the IoT suffer from varying workload changes over time, and approaches are needed to automatically provision resources and satisfy quality of service (QoS) requirements. With this motivation, this study presents an improved political optimizer-based resource management scheme for fog-enabled cloud environments, which is named IPORM. IPORM seeks to efficiently allocate resources to IoT requests with the purpose of maximizing resource utilization. In addition, other objectives such as energy, bandwidth, and delay are also included in IPORM. Simulation results show that our scheme significantly improves network performance in terms of various metrics compared to its counterparts.

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy.

Multi-Controller Model for Improving the Performance of IoT Networks

Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.

PERENCANAAN BLUEPRINT JARINGAN KOMPUTER BERBASIS INTER-VLAN ROUTING PADA SMKN 3 PUJUT

The lack of access to the internet network in schools is a factor in the slow teaching and learning process, because the lack of wireless signal coverage also causes the school community to only gather at one point. In an effort to deal with this problem, researchers built an inter-vlan network blueprint with wireless hotspots, in order to improve network performance and network security. In the process of building an inter-vlan network blueprint with a mikrotik hotspot, using the ppdioo method, among others, prepare preparation, plan plan, design design, implement, operate, optimize optimization. From the results of this study, in building a blueprint for an inter-vlan network with a mikrotik hotspot, we get the appropriate results from the initial plan. Which can improve network performance and expand access to internet network coverage.

Ergodic Capacity Analysis of Downlink Communication Systems under Covariance Shaping Equalizers

Advances in higher-end spectrum utilization has enabled user equipment to dock multiple antenna elements, and hence make use of selectivity via equalization in new generation of mobile networks. The equalization can exploit channel statistics to shape covariance matrices, and hence improve network performance at the physical layer of these networks by projecting segregated signals to non-overlapping subspaces. We propose to establish the promise of covariance shaping method by incorporating the equalizers in the modelling of a downlink multi-user multiple-input multiple-output (MU-MIMO) systems and thereby characterizing a key performance indicator, namely, the sum ergodic capacity. This is achieved by utilizing a residue theory approach which can account for indefinite eigenvalues. The system modelling is generic in a sense that it requires the base station (BS) to only have second order statistics of the channel rather than instantaneous knowledge. Furthermore, the BS incorporates a transmit beamformer design to enhance the ergodic capacity and feedforward the information of covariance shaping equalizers. Search method for transmit beamforming is also proposed which shows a promising three fold increase in sum ergodic capacity at signal-to-noise ratio of 10 dB for the considered MU-MIMO system. Proposed characterization of the system is authenticated using simulation means, and a comparative analysis of transmit beamformer designs on the sum ergodic rate is showcased.

Cloud–Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach

Driven by numerous emerging mobile devices and various Quality-of-Service (QoS) requirements, mobile-edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource; 2) simple or nonintelligent resource management; and 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing, and avoid excessive consumption of system resources. Based on the designed network model, a cloud–edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval, and transmission power, it aims to minimize the consumption overheads of system energy and latency. Then, the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.

Performance of Narrow Band Wide Area Networks with Gateway Diversity.

This paper quantifies the coverage area of Low-Power Wide-Area Networks (LPWAN) for Packet Success Rates (PSR) above 85%, where acceptable Quality of Service (QoS) can be achieved. The network consists of battery-operated end-nodes (ENs) and multiple stationary gateways (GWs). We consider asynchronous communication that uses ALOHA-based random channel access. Each transmission from the ENs can be received by multiple GWs. Such spatial diversity results in favorable Signal-to-Noise ratios (SNR). The LoRa modulation is assumed and its specific features, such as IQ inversion, further contribute to decreasing the impact of interference. An increase in the GW density improves network performance, which allows support for a larger density of end-nodes as well as increasing the coverage area. Our simulation results show that a suburban area of up to 1.44 km2 can be covered with five GWs with up to fifty end-nodes with a PSR greater than 86%.

Blockchain-Based Internet of Things Security and Reliability via SDN-Enabled 5G-VANETs

New opportunities for vehicular Internet of Things (IoT) services using current and modern rational transportation system, along with newer challenges for vehicular ad-hoc networks (VANETs). Along with improved network performance, a workable and trustworthy security system is required to handle trust management while maintaining user privacy. A key technology for extremely dependable, low-latency wireless communication services is the newly developed 5G mobile communication system. Additionally, the software-defined network (SDN) design allows for network control and worldwide data collection within the 5G-VANET. As a result, real-time IoT services for monitoring and reporting on transportation can be effectively supported. Both open the door for a cutting-edge automobile security system.This paper investigates the security and privacy issue in the transportation system and the vehicular IoT environment in SDN-enabled 5G-VANET. A blockchain-based security framework is created to support the vehicular IoT services, such as real-time cloud-based video reporting and trust management on vehicular messaging, due to the decentralised and immutable nature of blockchain. The 5G-VANET model with SDN support and the framework's scheduling processes are specifically illustrated in this study. The outcomes of the numerical simulation also demonstrate that it is possible to detect malicious vehicular nodes or communications while maintaining acceptable overhead and network performance impacts in large-scale scenarios. We show how our design considerably ensures a secure and reliable vehicle IoT environment with user privacy preserved through case studies and theoretical analysis.

Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks

Utilizing the millimeter-wave (mmWave) frequency is a promising solution to meet fast-growing traffic demand over wireless networks. However, mmWave communications are sensitive to physical obstructions on signal propagation. In this paper, the reconfigurable intelligent surfaces (RISs) are investigated to overcome the limitations of mmWave communications. Particularly, an RIS is deployed to reflect the mmWave signals towards vehicular users who experience direct link blockages that may occur due to static or dynamic obstacles. To this end, a risk-averse optimization problem is designed to optimize the Base Station (BS) precoding matrix and the RIS phase shifts under stochastic link blockages. A solution approach is developed in two phases: the BS precoding optimization and the RIS phase shift control phases. In the first phase, a Decomposition and Relaxation-based Precoding Optimization (DRPO) algorithm is developed to obtain the optimal precoding matrix. In the second phase, a learning-based method is introduced to dynamically adjust the direction of reflected signals under channel uncertainty. Extensive simulations are presented to validate the efficacy of the developed algorithms. The obtained results show that the developed algorithms can ensure reliable transmissions to users in non-LoS areas and improve network performance.

Improvement of distribution networks performance using renewable energy sources based hybrid optimization techniques

A very critical and integral part of the power system is the distribution networks. The final component of the power system, including transmission systems or consumers, is the distribution system (DS). This leads to the highest energy loss that happens. So improving the performance of distribution networks is required not only to provide the reliability of power supply but also to achieve the most economic cost. By optimizing the power flow and simultaneously minimizing the total emission cost and generation cost and taking into account the power losses, these objectives can be achieved. In recent years, heuristic methods are widely employed for solving such complex problems, and The main modern optimization techniques are genetic algorithm(GA). The most important issue in Evolutionary Algorithms is exploration vs. exploitation. Maybe GA is restricted for exploration features, what causes slow convergence and poor robustness Therefore, using the hybridization strategy, the main reason behind this is that such a hybrid approach is expected to create swape between the exploration and exploitation. This work presents performance improvement of a radial distribution networks using a new hybrid optimization technique of Genetic Algorithms (GA) with Equilibrium optimizer (EO) algorithm called Hybrid Genetic Algorithm Equilibrium optimizer (GAEO). It is used for optimum location and size of Renewable Energy Sources (wind energy, photovoltaic, fuel cell) on distribution systems. DG source locations and capacity have strongly influenced the improvement of the distribution network performance by reducing the entire system's power loss, enhancing the voltage profile, reducing fuel costs and emissions of contaminants.

Modulated relay based stable election protocol for large‐scale wireless sensor networks

SummaryInternet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy‐efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R‐SEP (MR‐SEP) for large‐scale WSN‐based IoT applications. MR‐SEP is based on the well‐known stable election protocol (SEP). MR‐SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi‐hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR‐SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR‐SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.

Incorporating the image formation process into deep learning improves network performance

We present Richardson–Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson–Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson–Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN’s performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

Network Traffic Prediction: Apply the Transformer to Time Series Forecasting

Along with the development of technology and social progress, the Internet is increasingly widely used in life. Mobile communication, fiber optic broadband, and other essential Internet networks have gradually become indispensable in everyday life. The task of further improving and optimizing the quality of Internet network links and improving the efficiency of Internet networks has been on the agenda. This paper proposed a deep learning-based network traffic prediction model, which can capture the characteristics of network traffic information changes by inputting past network traffic data to achieve the effect of future network traffic prediction. The model structure is flexible and variable, which improves the problems of other methods that cannot capture long time series prediction features and cannot parallelize the output. It also has apparent advantages in time complexity and model convergence speed without the evident disadvantage of time lag. Based on this network traffic prediction model, it can help Internet service providers optimize network resource allocation, improve network performance, and allow Internet data centers to provide abnormal network warnings and improve user service level agreements.

EVALUATION OF SEVERAL FULLY CONVOLUTIONAL NETWORKS IN SAR IMAGE CHANGE DETECTION

Abstract. In recent years, the world is suffering from frequent natural disasters. Change detection (CD) technology can quickly identify the change information on the ground and has developed into an important means of disaster monitoring and assessment. Synthetic aperture radar (SAR) has the characteristics of periodic observation and wide coverage. Moreover, SAR has the advantages of penetrating, all-day and all-weather observation, which plays an important role in disaster monitoring. Due to the rapid development of satellite sensors, the available CD data has been greatly enriched. This situation provides an opportunity for deep learning change detection (DLCD) techniques. However, SAR data are affected by speckle noise and lack of available labeled samples, it remains challenging to precisely locate the change information with high efficiency. This paper focuses on several commonly used and outstanding networks in the DLCD field to evaluate their performance and develop them to SAR data. In addition, Transfer learning experiments are designed to evaluate the generalization performance of each network for the CD task. The experimental results show that the Siamese CD network encoding multi-temporal data separately has the best ability to detect changes and generalization performance. In addition, adding high quality explicit difference guidance information to the network is more specific for the CD task, which can further improve network performance and refine the boundaries of changed ground objects on change map.

Two-stage training strategy combined with neural network for segmentation of internal mammary artery graft

Cardiovascular disease is a major health condition affecting the global population, leading to 420 million diagnoses and nearly 20 million deaths annually. Coronary heart disease is one of the most prevalent cardiovascular diseases, and its diagnosis and treatment creates a large social and economic burden, necessitating a large number of imaging evaluations and repetitive manual assessments by expert radiologists. Recently, deep learning-based methods have demonstrated excellent performance in coronary artery recognition (coronary artery segmentation and centerline extraction, etc.). However, current research focuses on CTA and MRI data types, and for inexperienced cardiologists, even if the neural network gives accurate segmentation results, it is impossible to determine its specific location in the heart. In response to the above problems, we constructed a novel dataset, CardiacVessel, which contains 2D slices from CTA images through 3D reconstruction techniques, and performed pixel-level annotations of vessels. We then propose a simple and effective two-stage training strategy for internal mammary artery graft segmentation. We conduct extensive experiments to compare the performance of various CNN- and transformer-based network architectures. The proposed two-stage training strategy can significantly improve network performance.

The Role of Information in the U.K. Passenger Rail Industry

In 1993, the U.K. passenger rail industry was privatized with expectations of greater investment, increased efficiency, and improved network performance. To date, progress has been mixed and the industry has been subject to a critical national press and passenger complaints that have reached record levels. The industry is continuing to develop a service that can do justice to its privatization. Passenger information is an important aspect of these improvements and national rail journey planning services are now heavily used. However, relatively little consideration has been given to understanding the role that information might play in assisting passengers who have already planned their journey but who encounter problems when they travel by train.Failure to execute a journey as planned can be severely disruptive to rail passengers in terms of lost time, expense, anxiety, and frustration. This article charts the development of the privatized rail industry and defines a set of journey breakdown situations that can be encountered by passengers. Insights are gained from passenger complaint letters. Such letters typically provide detailed accounts of journey breakdowns, attempts to recover the situations, and the use made of available information. Inaccurate or misunderstood pre-trip information is found to be a factor in many journey breakdowns. Accessible, timely, and appropriate provision of en-route information can improve passengers’ satisfaction by enabling completion of their immediate journey and might also be decisive in ensuring they have the confidence to use the rail network again in the future.

Application of Image Super-Resolution Reconstruction in Gymnastics Training by Using Internet of Things Technology.

Image super-resolution reconstruction, in short, is to restore some not too clear images, so that the image is more convenient to identify. It is generally used in intelligent surveillance systems and medical imaging systems in hospitals. This kind of technology is applied in many traditional algorithms. Today, the ultra-high-resolution algorithm has been revised. This paper analyzes in detail the convergence characteristics of two widely used pixel-by-pixel loss functions from theoretical and experimental perspectives, fully considers each convergence characteristic, and studies the combination of MAE and network. The MSE advantage learning method uses a single loss function to train the network. This training method can improve network performance. The level of Chinese gymnasts is relatively high, and it can even be said that they represent the most advanced level in the world. For many gymnasts, training for sharp turns is the focus of training, as well as training. Chinese gymnasts face similar problems. How to help Chinese gymnasts to improve their training quality has become a major issue in gymnastics training at this stage.

Reliability Optimization for Channel Resource Allocation in Multihop Wireless Network: A Multigranularity Deep Reinforcement Learning Approach

This article investigates the high-reliable data transmission for multihop and multichannel wireless sensor networks (WSNs), which jointly optimizes the channel allocation and channel access mechanisms. We propose a novel wireless paradigm empowered by mobile-edge computing (MEC) and deep reinforcement learning (DRL) to improve the data process ability of WSNs and formulate the joint resource allocation problem for reliability maximization as a partially observable Markov decision process (POMDP). Meanwhile, we introduce the distributed decision-making (DDM) framework to decouple channel optimization into two subproblems: 1) channel allocation and 2) channel access. Correspondingly, we present an asynchronous channel allocation algorithm for multiagent scenario and enable the neighbor cooperation to tackle the nonstationary problem, which can significantly improve the network convergence speed. Besides, we present a collision-free channel access algorithm including three submodules that can simultaneously eliminate vanishing nodes, hidden terminal, and exposed terminal problems in large-scale WSNs. Simulation results demonstrate that the proposed algorithm significantly improves network performance in terms of convergence, throughput, collision, and packet delivery ratio (PDR).

An Energy-Efficient Distributed Congestion Control Protocol for Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks (WMSNs) generate a huge amount of multimedia data. Congestion is one of the most challenging open issues in WMSNs. Congestion causes low throughput, high packet loss and low energy efficiency. Congestion happens when the data carried by the network surpasses the available capacity. This article presents an energy-efficient distributed congestion control protocol (DCCP) to mitigate congestion and improve end-to-end delay. Compared to the other protocols, the DCCP protocol proposed in this article can alleviate congestion by intelligently selecting the best path. First, congestion is detected by using two congestion indicators. Second, each node aggregates the received data and builds a traffic congestion map. The traffic congestion map is used to calculate the best path. Therefore, the traffic is balanced on different routes, which reduces the end-to-end delay. Finally, a rate controller is designed to prevent congestion in the network by sending a congestion notification message to a source node. After receiving a congestion notification message, the source node immediately adjusts its transmission rate. Experimental results based on raspberry pi sensor nodes show that the proposed DCCP protocol significantly improves network performance and is superior to existing modern congestion control protocols.