Changing Network Conditions Research Articles

Real-Time Dynamic Route Optimization Based on Predictive Control Principle

The principle of predictive control is applied to research on real-time dynamic route optimization of traffic travel, and a real-time dynamic route optimization model based on predictive control is proposed. Taking the driving time as the controlled variable, the driving speed as the manipulated variable, some traffic conditions as disturbance factors, the shortest driving time as the set value, the static shortest route as the reference route of the control system, and the objective function with the shortest driving time, which is defined as the control performance is established. According to the change in the road network state and the optimal solution result of the objective function, the real-time dynamic route selection based on the shortest driving time is realized by switching among different static shortest routes, and the rolling optimization and combination of dynamic and static routes are implemented in the process. A unique method is also used to obtain the optimal solution of the objective function in this study, which is scientific, reasonable, fast, and convenient. The optimization model overcomes the shortcomings of determining the dynamic shortest route by depending on traffic flow prediction and speed prediction. The simulation results and case study prove that the predictive control model algorithm of real-time dynamic route optimization is correct and better. The most important feature of the model algorithm is that it takes the static driving route and desired driving time as the control goals, and it can achieve the global optimal solution of the shortest path and shortest time. The proposed model algorithm has good innovation and practical applications.

A Review on Spam Detection Techniques for IOT Devices

Abstract: The number of Internet of Things (IoT) devices in smart homes is rapidly increasing, generating massive volumes of data that is mostly transmitted over wireless communication channels. The amount of data released by these gadgets increased as well. Aside from the increasing volume, the IoT gadget generates a significant amount of data. Having varied data quality characterised by its speed in terms of time and position in various different modalities dependence. However, different IoT devices are vulnerable to different dangers, such as cyber-attacks and changing network conditions. Data leakage, connections, and so on. The properties of IoT nodes, on the other hand, make the current solutions obsolete. inadequate to cover the entire security range of IoT networks Machine learning can help in this situation. Algorithms can be useful in detecting irregularities in data, which improves the security of IoT systems. The methods are aimed at the data anomalies that exist in the smart Internet in general.

A Comparative Study of Services Placement Algorithms in Future Networks

The study aimed to analyze and compare several algorithms in the context of networks services placement, and then proposed a self-organized dynamic heuristic algorithm adaptable to continually changing network conditions in order to achieve the ideal placement of services replicas in future networks. It is known that future networks demand a high degree of self-organization to keep pace with ongoing changes while maintaining performance optimized. One of the important challenges in this context is the services placement problem. Service placement issue refers to the selection of the most appropriate network node for hosting a service. The ideal placement of services replicas reduces the cost of serving customers, improves connectivity between clients and servers as well as the use of available resources. The study summarized the results of qualitative comparison between several placement algorithms and refers to the most important requirements to be taken into account when implementing the placement algorithm. Generally, each service has its own placement technique, and the action taken by a specific service may affect other services decisions and force them to adapt. There is an urgent need to a management service for managing services replicas to make the optimal placement decision. This service should work in a distributed manner and does not require comprehensive knowledge about the network. It is also characterized by its ability to adapt to changing network conditions in terms of load and topology. Other services coordinate with the management service about replicating or migrating actions, thus services will be offered at a minimized cost.

Quality Evaluation of Online Mental Health Education Based on Reinforcement Learning in the Pandemic

The COVID-19 pandemic has become one of the biggest major health crises reported due its massive impact on many countries. From mental health experts, we know that we cannot lose sight of an equally alarming issue which is the long-term mental health impact the pandemic is going to leave on the society. The rapid spread of the pandemic gives little chance to prepare for or even process all that has happened in terms of job losses and the complete uprooting of everyday life and relationships. It is understandable that students may feel irritable, frustrated, or sad sometimes. Loneliness, confusion, and anxiety are also common, but the issue is how we can know if students’ emotions are a normal reaction to an abnormal situation. Therefore, online mental health education has become pretty important for students during the pandemic. Furthermore, it is important to evaluate the quality of online mental health education through microlessons. In this paper, based on Q-learning algorithm, the real-time adaptive bitrate (ABR) configuration parameters mechanism is proposed to detect the changes of network state constantly and select the optimal precalculated configuration according to the current network state. The simulation results show that the proposed algorithm based on Q-learning outperforms other baselines in average latency, average bitrate, and Mean Opinion Score (MOS) on Chrome DevTools and Clumsy. Meanwhile, the experimental results also reveal that the average number of identified mental health problems of the proposed mechanism has always been the best with the bandwidth from 10 Mbit/s to 500 Mbit/s.

Microgrid distributed energy resources planning based on a long-term dynamic microsimulation

In this paper, we study a DERs simulation platform that can adapt to dynamic infrastructure changes. We present a long-term dynamic-planning simulation platform focused on DERs based on MATLAB and TCP/IP network communication via GridLAB-D. This platform can capture critical features of DERs by combining MATLAB’s ability to optimize control with GridLAB-D’s capacity for accurate and discrete event-based microsimulations. The proposed platform architecture is divided into inner and outer layers. The outer layer simulates changes in the external environment (i.e., changes in load level as well as the price of diesel fuel and planned equipment components) and equipment characteristics (i.e., the running lives of batteries and diesel generators). These features provide dynamic scenarios and re-planning boundary conditions that can be used to inform the inner layer simulation. The inner layer presents an operational simulation of the planned microgrid that leverages day-ahead scheduling and the real-time adjustments associated with uncertainties in DER output. Simulation results have shown our proposed planning scheme will result in increased profits and improved resource utilization efficiency during the entire 10-year cycle. Compared to traditional planning scheme, our proposed planning scheme can perform dynamic adjustments to the configuration of various DERs according to changing external factors and network conditions.

Methodology of Adaptive Instantaneous Overcurrent Protection Setting

This paper proposed a methodology of adaptive instantaneous overcurrent protection (AIOCP) setting that ensures that the protection coverage remains unchanged regardless of the operating condition of the electrical network. The methodology calculates the protection setting parameters based on the real-time estimation of the Thevenin equivalent circuit (TEC). The estimation algorithm uses the positive-sequence voltage and current values and a system of non-linear equations, which is solved iteratively by the Gauss–Newton method. The proposed methodology calculates the IOCP settings in real time by using the real-time estimation of the TEC; therefore, any change in the electric network conditions is represented in the TEC, and the calculated setting keeps the desired protection coverage constant. Simulation results demonstrate that the proposed AIOCP can keep constant the protection coverage overcoming the classical problems of classical IOCP: sub-allocation and over-coverage.

Fault-Resilience for Bandwidth Management in Industrial Software-Defined Networks

Industrial Cyber-Physical Systems (ICPS) expect assurances of timely delivery of data even during the occurrence of distinct faults. It is a challenge to manage the required bandwidth by providing resilience to link failures and dynamically changing bandwidth requirements. In this paper, we address the aforementioned challenge by exploring Software-Defined Networks (SDN). We present a framework coined SDN-RMbw (Software-Defined Networking Resilience Management for Bandwidth), which is a contract-based framework, where the components are bound to bandwidth contracts and a resilience manager. The bandwidth contracts state the bandwidth requirements of traffic flows. With each such contract, a monitor is associated, which is responsible to detect two events, run-time changes and link failures. Directly after receiving the event trigger reports from the monitor, new routes are calculated by a path-finding algorithm. Based on newly calculated routes, an observer detects whether the contract requirements are still satisfied, or the contract gets violated (termed as fault). To provide resilience to such faults in the network, a resilience manager integrated with control logic decides and executes a suitable response strategy. The proposed SDN-based framework aims at providing fault-resilience as well as adapting to different network-state changes. The proposed framework is evaluated using a Ryu SDN controller on a hardware testbed. Our results show that the proposed framework provides enhanced network resilience as compared to baseline mechanisms and improves the success rate up to 21% and bandwidth up to 111 Mbps under distinct network scenarios. Furthermore, extensive experimental emulations on the Mininet tool depicts the scalability of the proposed framework.

Towards Stability Analysis of Data Transport Mechanisms: A Fluid Model and Its Applications

The Transmission Control Protocol (TCP) utilizes congestion avoidance and control mechanisms as a preventive measure against congestive collapse and as an adaptive measure in the presence of changing network conditions. The set of available congestion control algorithms is diverse, and while many have been studied from empirical and simulation perspectives, there is a notable lack of analytical work for some variants. To gain more insight into the dynamics of these algorithms, we: (1) propose a general modeling scheme consisting of a set of functional differential equations of retarded type (RFDEs) and of the congestion window as a function of time; (2) apply this scheme to TCP Reno and demonstrate its equivalence to a previous, well known model for TCP Reno; (3) show an application of the new framework to the widely-deployed congestion control algorithm TCP CUBIC, for which analytical models are few and limited; and (4) validate the model using simulations. Our modeling framework yields a fluid model for TCP CUBIC. From a theoretical analysis of this model, we discover that TCP CUBIC is locally uniformly asymptotically stable -- a property of the algorithm previously unknown.

Content Dependent Representation Selection Model for Systems Based on MPEG DASH

A standard called MPEG Dynamic Adaptive Streaming over HTTP (MPEG DASH) ensures the interoperability between different streaming services and the highest possible video quality in changing network conditions. The solutions described in the available literature that focus on video segmentation are mostly proprietary, use a high amount of computational power, lack the methodology, model notation, information needed for reproduction, or do not consider the spatial and temporal activity of video sequences. This paper presents a new model for selecting optimal parameters and number of representations for video encoding and segmentation, based on a measure of the spatial and temporal activity of the video content. The model was developed for the H.264 encoder, using Structural Similarity Index Measure (SSIM) objective metrics as well as Spatial Information (SI) and Temporal Information (TI) as measures of video spatial and temporal activity. The methodology that we used to develop the mathematical model is also presented in detail so that it can be applied to adapt the mathematical model to another type of an encoder or a set of encoding parameters. The efficiency of the segmentation made by the proposed model was tested using the Basic Adaptation algorithm (BAA) and Segment Aware Rate Adaptation (SARA) algorithm as well as two different network scenarios. In comparison to the segmentation available in the relevant literature, the segmentation based on the proposed model obtains better SSIM values in 92% of cases and subjective testing showed that it achieves better results in 83.3% of cases.

Cooperative routing in wireless multimedia sensor network with deep learning based decision making system

Wireless Multimedia Sensor Networks (WMSNs) provide great attention on various attractive applications and their scientific researches due to their potential impact. For the past few years, the most active area related to the research field is routing in WMSNs. Moreover, the multimedia data is transmitted based on the routing protocol for determining the efficient resources and stable path, and also provides different levels of Quality-of-Experience (QoE)/Quality-of-Service (QoS) assurance. Still, it is the difficult aspect based on the complicated operations of multimedia applications, dynamic changes of network condition and limited network resources. This paper intends to introduce a Deep Belief Network (DBN) oriented decision making system to select the suitable cooperative nodes. Moreover, constraints like (i) Tri-level energy consumption of nodes-Level 1: Low energy level for text information, Level 2: Medium energy level for text, image and video information, Level 3: High energy level for high definition pictures (ii) Reliability, and (iii) Delay are examined while transmitting the multimedia information in the network. To make the precise decision, it is planned to tune the optimal weight of DBN by a new hybrid Dragon Integrated Firefly (DIFF) algorithm that combines the concept of Firefly Optimization (FF) and Dragonfly Optimization (DA) algorithm. At last the performance of the adopted scheme is computed over other traditional schemes with respect to alive nodes, delay, residual energy, network lifetime and reliability analysis.

I2BN: Intelligent Intent Based Networks

Intent based network management reduces the complexity of network programming from a growing set of deeply technical APIs to context-free high-level objectives that the network should autonomously achieve and keep. The practical implementation of an intent based network requires substantial automation technology embedded in the network. Automation should cover the entire lifecycle of intents, from their ingestion to fulfillment and assurance. This article investigates the feasibility of automatically assembling interworking implementation units into intent specific automation pipelines, where units are reusable self-learning closed loop micro-services with self-declared capabilities. Each closed loop may gain knowledge and respond to dynamically changing network conditions, thereby enabling network autonomy in reaching the declared intent objectives. The human-network intent interface for expressing intents is proposed to be based on the aggregation of the deployed network and service automation capabilities, rather than a formalism decoupled from the actual network implementation. This principle removes the ambiguity and compatibility gap between human intent definition and machine intent fulfillment, while retaining the flexibility and extendibility of the intents offered by any specific system via onboarding additional micro-services with novel capabilities. The concepts discussed by the article fit into the architecture and closed loop work items already defined by ETSI ZSM and provides considerations towards new areas such as intent driven autonomous networks and enablers for automation.

Reliable and scalable Kafka-based framework for optical network telemetry

Telemetry data acquisition is becoming crucial for efficient detection and timely reaction in the case of network status changes, such as failures. Streaming telemetry data to many collectors might be hindered by scalability issues, causing delay in localization and detection procedures. Providing efficient mechanisms for managing the massive telemetry traffic coming from network devices can pave the way to novel procedures, speeding up failure detection and thus minimizing response time. This paper proposes a novel Kafka-based monitoring framework leveraging the telemetry service. The proposed framework exploits the built-in scalability and reliability of Kafka to go beyond traditional monitoring systems. The framework allows a continuous monitoring of optical system data and their distribution through simple compressed text messages to a large number of consumers. Moreover, the proposed framework keeps a limited history of the monitored data, easing, for example, root cause failure analysis. The implemented monitoring platform is experimentally validated, considering the disaggregated paradigm, in terms of functional assessment, scalability, resiliency, and end-to-end message latency. Obtained results show that the framework is highly scalable, supporting up to around 4000 messages per second (and potentially more) with low CPU load, and is capable of achieving an end-to-end (i.e., producer–consumer) latency of about 50 ms. Moreover, the considered architecture is capable of overcoming the failure of a monitoring framework core component without losing any message.

A Unified Framework for Flexible Playback Latency Control in Live Video Streaming

Live video streaming has seen tremendous growth in the past decade. An important fact in live streaming is that the demand for low playback-latency inherently conflicts with the desire for high QoE. This requires different types of live services to seek different latency-QoE tradeoffs according to their service-requirements. However, our investigations revealed that it is fundamentally difficult for existing streaming algorithms to keep consistent latency in changing network conditions, let alone achieve the service-desired latency-QoE tradeoff. To tackle the challenge, this article develops a novel framework called Flexible Latency Aware Streaming (FLAS) that not only can achieve consistent low latency, but also control the latency-QoE tradeoff flexibly. Specifically, FLAS generates a set of adaptation logics offline, each optimized for a candidate tradeoff point, then selects the most appropriate one to run online. We first show how FLAS can be applied to optimizing the existing algorithms, then developed a novel Genetic Programming approach to fully exploit FLAS's potential. Extensive evaluations show that FLAS can precisely control latency all the way down to 1s and achieve substantially higher QoE than state-of-the-arts. FLAS can be readily implemented into real streaming platforms, offering a practical and reliable solution for live-streaming services.

A low-complexity, energy-efficient data securing model for wireless sensor network based on linearly complex voice encryption mechanism of GSM technology

A wireless sensor network is the formation of a temporary network of sensor nodes equipped with limited resources working in an ad hoc environment. Routing protocol is one of the key challenges while designing a wireless sensor network, which requires optimum use of limited resources of a sensor node, such as power and so on. Similarly, data security and integrity is another open issue that has emerged as a flash point in research community in the last decade. This article proposes a secure model for routing data from source to destination named as secure and energy-efficient routing. The proposed secure and energy-efficient routing is inherited from authentication and voice encryption scheme developed for Global System for Mobile Communications. Necessary modifications have been carried out in order to fit the Global System for Mobile Communications technology in a wireless sensor network ad hoc environment. Due to its low complexity, the secure and energy-efficient routing consumes lesser battery power both during encryption/decryption and for routing purposes. It is due to the XoR operation used in the proposed scheme which is considered as the most inexpensive process with respect to time and space complexity. It is observed through simulations that secure and energy-efficient routing can work effectively even in critical power level in a sensor network. The article also presents a simulation-based comparative analysis of the proposed secure and energy-efficient routing with two notable existing secure routing protocols. We proved that the proposed secure and energy-efficient routing helps to achieve the desired performance under dynamically changing network conditions with various numbers of malicious nodes. Moreover, in Global System for Mobile Communications, generally three linear feedback shift registers are used to fragment the key in data encryption mechanism. In this article, a mathematical model is proposed to increase the number of possible combinations of shift register in order to make the data encryption mechanism more secure which has never been done before. Due to its liner complexity, lesser power consumption, and more dynamic route updating, the secure and energy-efficient routing can easily find its use in the emerging Internet-of-Things systems.

Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

With the development of Video on Demand applications due to the availability of high-speed internet access, adaptive streaming algorithms have been developing and improving. The focus is on improving user’s Quality of Experience (QoE) and taking it into account as one of the parameters for the adaptation algorithm. Users often experience changing network conditions, so the goal is to ensure stable video playback with satisfying QoE level. Although subjective Video Quality Assessment (VQA) methods provide more accurate results regarding user’s QoE, objective VQA methods cost less and are less time-consuming. In this article, nine different objective VQA methods are compared on a large set of video sequences with various spatial and temporal activities. VQA methods used in this analysis are: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), MultiScale Structural Similarity Index (MS-SSIM), Video Quality Metric (VQM), Mean Sum of Differences (DELTA), Mean Sum of Absolute Differences (MSAD), Mean Squared Error (MSE), Netflix Video Multimethod Assessment Fusion (Netflix VMAF) and Visual Signal-to-Noise Ratio (VSNR). The video sequences used for testing purposes were encoded according to H.264/AVC with twelve different target coding bitrates, at three different spatial resolutions (resulting in a total of 190 sequences). In addition to objective quality assessment, subjective quality assessment was performed for these sequences. All results acquired by objective VQA methods have been compared with subjective Mean Opinion Score (MOS) results using Pearson Linear Correlation Coefficient (PLCC). Measurement results obtained on a large set of video sequences with different spatial resolutions show that VQA methods like SSIM and VQM correlate better with MOS results compared to PSNR, SSIM, VSNR, DELTA, MSE, VMAF and MSAD. However, the PLCC results for SSIM and VQM are too low (0.7799 and 0.7734, respectively), for the usage of these methods in streaming services instead of subjective testing. These results suggest that more efficient VQA methods should be developed to be used in streaming testing procedures as well as to support the video segmentation process. Furthermore, when comparing results obtained for different spatial resolutions, it can be concluded that the quality of video sequences encoded at lower spatial resolutions in cases of lower target coding bitrate is higher compared to the quality of video sequences encoded at higher spatial resolutions at the same target coding bitrate, particularly when video sequences with higher spatial and temporal information are used.

Research on New Generation of Power Line Carrier Communication Trusted Networking Algorithm

With the safe and stable operation of the new generation of power line carrier communication, it is of great significance to the rapid development of smart power grid. However, due to its own physical characteristics and the impact of the external environment, communication cables usually have some security risks, such as aging, damage, theft. In this paper, a new generation of power line carrier communication trusted access network is studied. In this paper, the current situation of the new generation of power line carrier trusted access and network requirements are analyzed, then the principle and characteristics of basic ant colony algorithm are briefly introduced, then the characteristics of power line carrier communication system network and network requirements are analyzed, the improved ant colony algorithm is applied to network optimization of the new generation of PLC communication system. The algorithm makes the power line carrier communication system have certain destruction resistance and recombination ability when the network state changes, it can guarantee the reliable operation of power network communication system.

Genetic Improvement of Routing Protocols for Delay Tolerant Networks

Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of, e.g., (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes’ mobility. In these contexts, routing is NP-hard and is usually solved by heuristic “store and forward” replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely, Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.

S-DPS: An SDN-Based DDoS Protection System for Smart Grids

Information Communication Technology (ICT) environment in traditional power grids makes detection and mitigation of DDoS attacks more challenging. Existing security technologies, besides their efficiency, are not adequate to cater to DDoS security in Smart Grids (SGs) due to highly distributed and dynamic network environments. Recently, emerging Software Defined Networking- (SDN-) based approaches are proposed by researchers for SG’s DDoS protection; however, they are only able to protect against flooding attacks and are dependent on static thresholds. The proposed approach, i.e., Software Defined Networking-based DDoS Protection System (S-DPS), is efficiently addressing these issues by employing light-weight Tsallis entropy-based defense mechanisms using SDN environment. It provides early detection mechanism with mitigation of anomaly in real time. The approach offers the best deployment location of defense mechanism due to the centralized control of network. Moreover, the employment of a dynamic threshold mechanism is making detection process adaptive to the changing network conditions. S-DPS has demonstrated its effectiveness and efficiency in terms of Detection Rate (DR) and minimal CPU/RAM utilization, considering DDoS protection focusing smurf attacks, socket stress attacks, and SYN flood attacks.

EVeREst: Bitrate Adaptation for Cloud VR

Cloud Virtual Reality (VR) technology is expected to promote VR by providing a higher Quality of Experience (QoE) and energy efficiency at lower prices for the consumer. In cloud VR, the virtual environment is rendered on the remote server and transmitted to the headset as a video stream. To guarantee real-time experience, networks need to transfer huge amounts of data with much stricter delays than imposed by the state-of-the-art live video streaming applications. To reduce the burden imposed on the networks, cloud VR applications shall adequately react to the changing network conditions, including the wireless channel fluctuations and highly variable user activity. For that, they need to adjust the quality of the video stream adaptively. This paper studies video quality adaptation for cloud VR and improves the QoE for cloud VR users. It develops a distributed, i.e., with no assistance from the network, bitrate adaptation algorithm for cloud VR, called the Enhanced VR bitrate Estimator (EVeREst). The algorithm aims to optimize the average bitrate of cloud VR video flows subject to video frame delay and loss constraints. For that, the algorithm estimates both the current network load and the delay experienced by separate frames. It anticipates the changes in the users’ activity and limits the bitrate accordingly, which helps prevent excess interruptions of the playback. With simulations, the paper shows that the developed algorithm significantly improves the QoE for the end-users compared to the state-of-the-art adaptation algorithms developed for MPEG DASH live streaming, e.g., BOLA. Unlike these algorithms, the developed algorithm satisfies the frame loss requirements of multiple VR sessions and increases the network goodput by up to 10 times.

Optimizing Video QoE for Mobile eMBMS Users in Cellular Networks

Evolved Multimedia Broadcast Multicast Service (eMBMS) is used in cellular networks to improve the utilization of scarce wireless resources in high user density service areas. However, eMBMS configuration involves interwoven decisions including which base stations (eNB) to synchronize to form Single Frequency Networks (SFN), which video qualities to be serviced, and how to distribute resources among different videos. These decisions should accommodate disparate channel conditions for eMBMS users, and the impact of eNB's unicast-load in the service area. In this paper, we formulate eMBMS configuration as an optimization problem that maximizes the video QoE for users. Additionally, we present NIMBLE as an eMBMS configuration heuristic, guided by our optimization framework, to solve the problem in realtime. Furthermore, NIMBLE's design integrates elements to accommodate the dynamic nature of cellular networks resulting from changes in both user, and network state over time. We developed a simulation testbed, and performed extensive experiments to show that, in comparison to state-of-the-art schemes, NIMBLE can increase the average user throughput by 150%, and reduce the bitrate switches by 75%.