Network Bandwidth Consumption Research Articles

User experience of english online classroom E-learning based on routing algorithm and data visualization analysis

With the development of the Internet, E-learning has become an important way of education. However, in online English classes, user experience problems still exist, such as network delay and slow video loading speed, which affect the learning effect and user engagement. Therefore, this paper hopes to improve E-learning user experience by studying routing algorithm and data visualization analysis technology. The routing algorithm is used to optimize the network connection. By analyzing the network topology and data transmission path, the optimal route is selected to reduce the network delay and bandwidth consumption. Data visualization technology is used to analyze and display the behavior and learning data of learners in the classroom in real time, so as to help learners better understand their learning situation. The experimental results show that the application of routing algorithm and data visualization analysis technology can significantly improve the user experience of online English classes. The stability of learners’ network connection in the classroom is improved, the video loading speed is accelerated, and the learning effect is also significantly improved. By optimizing network connectivity and providing real-time data analytics, it is possible to meet the individual needs of learners and improve their learning effectiveness and engagement.

IPREDS: Efficient Prediction System for Internet-wide Port and Service Scanning

Internet-wide port and service scanning, a vital tool for network research, is unaffordable in time and network bandwidth consumption. However, scanning only a portion of ports and services may lead to erroneous research conclusions. Previous work has shortened scanning time by predicting potentially active ports and eliminating many invalid scan targets. Still, they suffer from inherent design flaws that compromise their performance in terms of prediction accuracy and efficiency. The vast, unevenly distributed, and noisy nature of active ports presents significant challenges for prediction systems. Meanwhile, service prediction work is still in a shortage state. In this work, we introduce IPREDS, the first efficient prediction system for Internet-wide port and service scanning. IPREDS uses its carefully designed decision model to utilize all input features and predict the scanning reward of each target in parallel, providing high coverage prediction results in minimal time. Our experiment results show that IPREDS can discover 87% of active ports across the entire IPv4 network within two hours, saving at least 87.26% of the total time and 59% of the packets sent compared to existing work. For service scanning, IPREDS finds 91% of all active services using only four handshakes on each active port and saves 85.9% time to find 69% of each active service compared to exhaustive service scanning.

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Due to the rapid development of low earth orbit satellite constellations, e.g., Starlink, OneWeb, etc., integrated satellite-terrestrial networks have been viewed as a promising paradigm to globally provide satellite internet services for users. However, when the contents from ground data centers are provided for users by satellite networks, there will be high capital expenditures in terms of communication delay and bandwidth usage. To this end, in this paper, a cooperative-caching and resource-allocation problem is investigated in integrated satellite–terrestrial networks. Popular contents, which are cached on satellites and ground data centers, can be accessed via inter-satellite and satellite–terrestrial networks in a cooperative way. The optimization problem is formulated to jointly minimize the deployment costs of storage resource usage and network bandwidth consumption. A cooperative caching and resource allocation (CCRA) algorithm based on a neighborhood search is proposed to address the problem. The simulation results demonstrate that the proposed CCRA algorithm outperforms Greedy and BFS in reducing the deployment costs.

Reducing communication in federated learning via efficient client sampling

Federated learning (FL) ameliorates privacy concerns in settings where a central server coordinates learning from data distributed across many clients; rather than sharing the data, the clients train locally and report the models they learn to the server. Aggregation of local models requires communicating massive amounts of information between the clients and the server, consuming network bandwidth. We propose a novel framework for updating the global model in communication-constrained FL systems by requesting input only from the clients with informative updates, and estimating the local updates that are not communicated. Specifically, describing the progression of the model’s weights by an Ornstein–Uhlenbeck process allows us to develop sampling strategy for selecting a subset of clients with significant weight updates; model updates of the clients not selected for communication are replaced by their estimates. We test this policy on realistic federated benchmark datasets and show that the proposed framework provides up to 50% reduction in communication while maintaining competitive or achieving superior performance compared to baselines. The proposed method represents a new line of strategies for communication-efficient FL that is orthogonal to the existing user-driven techniques, such as compression, thus complementing rather than aiming to replace those existing methods.

Enhancing Network Intrusion Recovery in SDN with machine learning: an innovative approach

In modern network environments, the swift recovery of network flow intrusions poses a substantial challenge. Particularly in the context of Software-Defined Networks (SDN), addressing this challenge necessitates the strategic selection of backup paths based on traffic patterns. In response to this critical issue, our paper introduces a groundbreaking approach known as Machine Learning-based Network Intrusion Recovery (MLBNIR) for enhancing intrusion recovery in SDN. We leverage a dedicated SDN dataset to train a flow-based Machine Learning (ML) model, enabling a deeper understanding of traffic dynamics within the SDN framework. Our study, presented in this paper, reveals that the MLBNIR approach significantly reduces intrusion recovery time by up to 90% and concurrently increases network bandwidth consumption by up to 57% when compared to existing methods reviewed in the literature.

Asynchronous Event-Triggered-Based Control for Stochastic Networked Markovian Jump Systems With FDI Attacks

In this article, a new asynchronous event-triggered-based output-feedback control method is proposed for a special type of networked Markovian jump systems susceptible to network delays and attacks. A novel controller is designed to make allowances for four cases that are common in practical situations: 1) a network delay; 2) false data injection (FDI) attacks; 3) asynchrony between system modes and controller modes; and 4) an event-triggered scheme. First, to avoid traditional assumptions about system delays, a novel Lyapunov function is proposed, based on which a new stability theory is formulated to guarantee exponential stability. Second, an FDI attack is represented as a stochastic process, which can be dealt with properly by the designed controller. Third, a new delay-based event-triggered scheme is brought into the controller design to reduce the network bandwidth consumption and save the network transmission resources. Then, an asynchronous controller is engineered with the aid of a hidden-Markov model, which can extend the application scope of the proposed method. Note that we put the above four factors into one framework, which implies a co-design of an anti-attack controller and an event-triggered scheme is developed. Finally, an operational amplifier circuit is simulated to verify the feasibility and superiority of the designed controller.

Adaptive interval type-2 fuzzy fixed-time control for nonlinear MIMO fast-slow coupled systems with user-defined tracking performance

This paper focuses on the adaptive interval type-2 (IT2) fuzzy fixed-time self-triggered control issue for a class of multi-input and multi-output (MIMO) nonlinear fast-slow coupled systems (FSCSs) with preassigned tracking performance requirements. At first, the steady-state error and dynamic response of the investigated system are synthetically weighed by introducing an improved fixed-time prescribed performance function into coordinate transformation. Second, an IT2 fuzzy adaptive self-trigged controller is designed, where the unknown continuous functions are approximated by the interval type-2 fuzzy logic systems (IT2FLSs). In addition, the 1-bit encoding-decoding mechanism (EDM) is embedded in the signal transmission channel between the controller and the actuator, which also provides a feasible solution to further reduce network bandwidth consumption under the framework of the self-triggered protocol. Stability analysis indicates that the developed control method ensures that all states of the closed-loop system are practical fixed-time bounded, and the tracking error evolves to a pre-specified performance envelope within a predetermined time. Finally, a numerical example and an application example are provided to demonstrate the effectiveness and superiority of the proposed method.

An Internet of Electronic-Visual Things Indoor Localization System Using Adaptive Kalman Filter

In recent years, various indoor social security emergencies have brought major changes to people’s lives. However, the state-of-art surveillance technology achieving object tracking alone is insufficient through a single visual tracking method and may cause inaccurate tracking (such as burred faces, loss target, and occlusion) and is time-consuming for a long video sequence. Accurate tracking and localization systems are essential for developing indoor localization systems. This paper designs a fusing communication system called the Internet of Electronic-Visual Things Localization system (IoEVT), which simultaneously tracks targets assisting with an Adaptive Kalman Filter (AKF). The data fusion indoor localization approach using AKF fuses multi-data from electronic (E) and visual (V) data, such as WiFi and cameras. The fusing method significantly improves EV data sensing abilities for solving occluded visual information when only using single V data in occlusion scenarios and reduces the localization error rate. In addition, our real-world experimental validation shows that the proposed IoEVT system achieves better accuracy, scalability, and robustness with low network bandwidth consumption compared with the single E or V data in many scenarios, such as occlusion and occlusion-free.

Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning

In Federated Learning (FL), a common approach for aggregating local solutions across clients is periodic full model averaging. It is, however, known that different layers of neural networks can have a different degree of model discrepancy across the clients. The conventional full aggregation scheme does not consider such a difference and synchronizes the whole model parameters at once, resulting in inefficient network bandwidth consumption. Aggregating the parameters that are similar across the clients does not make meaningful training progress while increasing the communication cost. We propose FedLAMA, a layer-wise adaptive model aggregation scheme for scalable FL. FedLAMA adjusts the aggregation interval in a layer-wise manner, jointly considering the model discrepancy and the communication cost. This fine-grained aggregation strategy enables to reduce the communication cost without significantly harming the model accuracy. Our extensive empirical study shows that, as the aggregation interval increases, FedLAMA shows a remarkably smaller accuracy drop than the periodic full aggregation, while achieving comparable communication efficiency.

Memory sharing for handling memory overload on physical machines in cloud data centers

Over-committing computing resources is a widely adopted strategy for increased cluster utilization in Infrastructure as a Service (IaaS) cloud data centers. A potential consequence of over-committing computing resources is memory overload of physical machines (PMs). Memory overload occurs if memory usage exceeds a defined alarm threshold, exposing running computation tasks at a risk of being terminated by the operating system. A prevailing measure to handle memory overload of a PM is live migration of virtual machines (VMs). However, this not only consumes network bandwidth, CPU, and other resources, but also compels a temporary unavailability of the VMs being migrated. To handle memory overload, we present a memory sharing system in this paper for PMs in cloud data centers. With memory sharing, a PM automatically borrows memory from a remote PM when necessary, and releases the borrowed memory when memory overload disappears. This is implemented through swapping inactive memory pages to remote memory resource. Experimental studies conducted on InfiniBand-networked PMs show that the memory sharing system is fully functional. The measured throughput and latency are around 929 Mbps and 1.3 mus, respectively, on average for remote memory access. They are similar to those from accessing a local-volatile memory express solid-state drive, and thus are promising in real applications.

Computer Vision on the Edge to Reduce Network Bandwidth Consumption and Computing Resources in Multi-view 3D Industrial Inspection without Hidden Surfaces

Industrial inspection industry requires high precision, fast and reliable systems, where images play a central role. These systems are composed by several hardware and also cyber-physical componentes where complexity increases when multiple heterogeneous sensor inputs are combined. Our 3D industrial inspection scanner is able to reconstruct complete objects without occlusion with use of multiple sensors and actuators using a complex software architecture. Our system allows increasing the throughput by removing the bottleneck network issue, decreasing network data transfer using a new edge systems architecture that segments and optimizes image transferring. Also, this work presents the results of applying technology developed during the FitOptiVis European ECSEL project. FitOptiVis will provide a reference architecture supporting composability built on suitable component abstractions and embedded sensing, actuation and processing devices adhering to those abstractions. The reference architecture will support design portability, on-line multi-objective quality and resource management and run-time adaptation guaranteeing system constraints and requirements based on platform virtualization. The FitOptiVis project will be applied to design the new architecture of the new edge components and develop the runtime system monitoring.

A Smart Architecture Leveraging Fog Computing Fusion and Ensemble Learning for Prediction of Gestational Diabetes

Gestational diabetes (GD) is a growing global concern, underscoring the need for early detection and effective management to prevent adverse health consequences. This paper presents an innovative and reliable architecture to predict gestational diabetes in pregnant women. While reducing the frequency of doctor visits by sending the necessary data via Internet of Things (IoT) technology and receiving the prediction results via a mobile application in real time. The proposed architecture is a fusion of fog computing hardware with ensemble machine learning to enable low-latency, energy-efficient solutions for data processing, and cloud computing. The GD_Fog architecture leverages fused fog computing and load balancing techniques to reduce latency, power consumption, Network bandwidth consumption, and response time, and cloud computing is used based on the concept of use on demand for more reliability while harnessing the power of group learning to improve prediction accuracy. In addition, GD_Fog can be configured for different operating modes to ensure optimal quality of service and prediction accuracy in various fog calculation scenarios, which meet different user requirements. Through extensive testing using real-world data from pregnant women, the framework shows promising results, outperforming the latest methods in accuracy and efficiency. Where the percentage of improvement in prediction accuracy was approximately 6.5% when using ensemble learning, and the improvement in energy use, amounted to approximately 87.01% when using fused fog computing instead of cloud computing. These results confirm the potential of the proposed structure as an invaluable tool for the early detection and effective management of gestational diabetes.

Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems

Cloud data centers have started utilizing erasure coding in large-scale storage systems to ensure high reliability with limited overhead compared to replication. However, data recovery in erasure coding incurs high network bandwidth consumption compared to replication. Cloud storage systems also play an important role in the energy consumption of data centers. Heuristic proactive recovery algorithms select all data blocks from failure-predicted disk/machine and perform proactive replication that contributes to huge recovery bandwidth savings. However, they fail to optimize the selection. Optimization can further improve resource savings. To address this issue, we propose a recovery algorithm that applies minimization on data blocks selected for proactive replication by considering the necessary and appropriate constraints that are constructed based on the system’s current network traffic and data duplication information. We evaluate the proposed algorithm using extensive simulations. Experiments show that the recovery algorithm reduces network traffic by 60% and storage overhead by 46% compared to the heuristic proactive recovery approach. Also, the proposed proactive recovery methods reduce the storage system’s energy consumption by up to 52% compared to replication.

Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach

The term “distributed denial of service” (DDoS) refers to one of the most common types of attacks. Sending a huge volume of data packets to the server machine is the target of a DDoS attack. This results in the majority of the consumption of network bandwidth and server, which ultimately leads to an issue with denial of service. In this paper, a majority vote-based ensemble of classifiers is utilized in the Sever technique, which results in improved accuracy and reduced computational overhead, when detecting attacks. For the experiment, the authors have used the CICDDOS2019 dataset. According to the findings of the experiment, a high level of accuracy of 99.98% was attained. In this paper, the classifiers use random forest, decision tree, and naïve bayes for majority voting classifiers, and from the results and performance, it can be seen that majority vote classifiers performed better.

Call Me Maybe: Using Dynamic Protocol Switching to Mitigate Denial-of-Service Attacks on VoIP Systems

Voice over IP is quickly becoming the industry standard voice communication service. While using an IP-based method of communication has many advantages, it also comes with a new set of challenges; voice networks are now accessible to a multitude of internet-based attackers from anywhere in the world. One of the most prevalent threats to a VoIP network are Denial-of-Service attacks, which consume network bandwidth to congest or disable the communication service. This paper looks at the current state of research into the mitigation of these attacks against VoIP networks, to see if the mechanisms in place are enough. A new framework is proposed titled the “Call Me Maybe” framework, combining elements of latency monitoring with dynamic protocol switching to mitigate DoS attacks against VoIP systems. Research conducted around routing VoIP over TCP rather than UDP is integrated into the proposed design, along with a latency monitoring mechanism to detect when the service is under attack. Data gathered from a Cisco Packet Tracer simulation was used to evaluate the effectiveness of the solution. The gathered results have shown that there is a statistically significant improvement in the response times of voice traffic when using the “Call Me Maybe” framework in a network experiencing a DoS attack. The research and findings therefore aim to provide a contribution to the enhancement of the security of VoIP and future IP-based voice communication systems.

Data stream clustering for low-cost machines

Nowadays, the operations performed by the Internet of Things (IoT) systems are no more trivial since they rely on more sophisticated devices than in the past. The IoT system is physically composed of connected computing, digital, mechanical devices such as sensors or actuators. Most of the time, each of them incorporates a logical arithmetic unit that can pre-compute or compute on the device. To extract value from the data produced at the edge, processing power offered by cloud computing is still utilized. However, streaming data to the cloud exposes some limitations related to the increased communication and data transfer, which introduces delays and consumes network bandwidth. Clustering data is one example of a treatment that can be executed in the cloud. In this paper, we propose a methodology for solving the data stream clustering problem at the edge. Data Stream clustering is defined as the clustering of data that arrive continuously, such as telephone records, multimedia data, sensors data, financial transactions, etc. Since we use low-cost and low-capacity devices, the objective is, given a sequence of points, to construct a good clustering of the stream using a small amount of memory and time. We propose a ‘windowing’ scheme, coupled with a sampling scheme to respect the objective. Under the experimental conditions, experiments show that the clustering solutions can be controlled, with difficulties for time-stamped data but not for random data or data with well-delimited clusters. The main advantage of our schema is that we are clustering data “on the fly” with no knowledge or assumption regarding the available data. We do not assume that all the data are known before a treatment batch by batch. Our schema also has the potential to be adapted to other classes of machine learning algorithms.

A novel approach for spam detection using horse herd optimization algorithm

Communication via email has expanded dramatically in recent decades due to its cost-effectiveness, convenience, speed, and utility for a variety of contexts, including social, scientific, cultural, political, authentication, and advertising applications. Spam is an email sent to a large number of individuals or organizations without the recipient's desire or request. It is increasingly becoming a harmful part of email traffic and can negatively affect the usability of email systems. Such emails consume network bandwidth as well as storage space, causing email systems to slow down, wasting time and effort scanning and eliminating enormous amounts of useless information. Spam is also used for distributing offensive and harmful content on the Internet. The objective of the current study was to develop a new method for email spam detection with high accuracy and a low error rate. There are several methods to recognize, detect, filter, categorize, and delete spam emails, and almost the majority of the proposed methods have some extent of error rate. None of the spam detection techniques, despite the optimizations performed, have been effective alone. A step in text mining and message classification is feature selection, and one of the best approaches for feature selection is the use of metaheuristic algorithms. This article introduces a new method for detecting spam using the Horse herd metaheuristic Optimization Algorithm (HOA). First, the continuous HOA was transformed into a discrete algorithm. The inputs of the resulting algorithm then became opposition-based and then converted to multiobjective. Finally, it was used for spam detection, which is a discrete and multiobjective problem. The evaluation results indicate that the proposed method performs better compared to other methods such as K-nearest neighbours-grey wolf optimisation, K-nearest neighbours, multilayer perceptron, support vector machine, and Naive Bayesian. The results show that the new multiobjective opposition-based binary horse herd optimizer, running on the UCI data set, has been more successful in the average selection size and classification accuracy compared with other standard metaheuristic methods. According to the findings, the proposed algorithm is substantially more accurate in detecting spam emails in the data set in comparison with other similar algorithms, and it shows lower computational complexity.

Guest Editor's Introduction: Special Section on Edge AI as a Service

In the last decade there has been a strong move towards mobile computing and the proliferation of the IoT (Internet of Things). A huge number of devices have been connected to the Internet and created zettabytes of data items. To extract value from such massive data volumes, processing power offered by cloud computing is often utilized. However, streaming data to the cloud exposes some limitations related to increased communication and data transfer, which introduces delays and consumes network bandwidth. Another limitation that cloud-based computing for IoT poses is a limited or no network connectivity. Other problems with cloud-based processing of IoT generated data regard the sensitivity of the information, because sending and storing so much information in the cloud involves privacy and security challenges, related to the protection of personally identifiable information, storing it in compliance with privacy laws, securing stored information, and preventing from being stolen, or accessed and shared illegally. The use of AI in edge processing resulted in a new interdisciplinary field that enables distributed intelligence with edge devices and is known as edge AI or edge intelligence. However, research on edge AI is still relatively new, and thus models, techniques, and protocols supporting intelligent management, querying and mining of large-scale amounts of data produced at the edge are required. A lot of challenges related to providing edge intelligence include training edge devices, so they can become more and more smart. There is also a need for the presentation of the most recent outcome of research of distributed intelligence. The papers in this special issue address many of the challenges we have outlined, and are briefly summarized.

COIN: An Efficient Indexing Mechanism for Unstructured Data Sharing Systems

Edge computing promises a dramatic reduction in the network latency and the traffic volume, where many edge servers are placed at the edge of the Internet. Furthermore, those edge servers cache data to provide services for edge users. The data sharing among those edge servers can effectively shorten the latency to retrieve the data and further reduce the network bandwidth consumption. The key challenge is to construct an efficient data indexing mechanism no matter how the data is cached in the edge network. Although this is essential, it is still an open problem. Moreover, existing methods such as the centralized indexing and the DHT indexing in other fields fail to meet the performance demand of edge computing. This paper presents a COordinate-based INdexing (COIN) mechanism for the data sharing in edge computing. COIN maintains a virtual space where switches and data indexes are associated with their coordinates. Then, COIN distributes data indexes to indexing edge servers based on those coordinates. The COIN is effective because any query request from an edge server can be responded when the data has been stored in the edge network. More importantly, COIN is efficient in both routing path lengths and forwarding table sizes for publishing/querying data indexes. We implement COIN in a P4 prototype. Experimental results show that COIN uses 59% shorter path length and 30% less forwarding table entries to retrieve data indexes compared to using Chord, a well-known DHT solution.

Fuzzy Weighted Memory Event-Triggered Control for Networked Control Systems Subject to Deception Attacks

This paper considers the event-triggered control problem for a class of Networked Control Systems (NCS) subject to transmission delays and stochastic deception attacks. A new fuzzy weighted memory event-triggered mechanism is proposed to reduce the network bandwidth consumption and data transmission rates. In this context, a new fuzzy weighted memory state feedback controller is proposed to stabilize the closed-loop NCS. Considering the security problem of NCS, a randomly occurring deception attack model is employed, assuming bounded malicious signals injected by the attacker. Based on a suitable Lyapunov-Krasovskii functional, a design methodology is proposed, in terms of linear matrix inequalities, to synthesize both trigger parameters and NCS controller gains. Finally, a numerical example is considered to illustrated the effectiveness of proposed results, compared to previous ones from the literature.