GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications

Active queue control aims to improve the overall communication network throughput while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in TCP communication networks. The structure of these controllers is optimized using genetic algorithm (GA) and the output weights of ANNs are optimized using particle swarm optimization (PSO) algorithm. The controllers are radial bias function (RBF)-based, but to improve the robustness of RBF controller, an error-integral term is added to RBF equation in the second scheme. Experimental results show that GA- PSO-optimized improved RBF (I-RBF) model controls network congestion effectively in terms of link utilization with a low packet loss rate and outperform Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635

The use of computational intelligence for security in named data networking

Information-Centric Networking (ICN) has recently been considered as a promising paradigm for the next-generation Internet, shifting from the sender-driven end-to-end communication paradigma to a receiver-driven content retrieval paradigm. In ICN, content -rather than hosts, like in IP-based design- plays the central role in the communications. This change from host-centric to content-centric has several significant advantages such as network load reduction, low dissemination latency, scalability, etc. One of the main design requirements for the ICN architectures -since the beginning of their design- has been strong security. Named Data Networking (NDN) (also referred to as Content-Centric Networking (CCN) or Data-Centric Networking (DCN)) is one of these architectures that are the focus of an ongoing research effort that aims to become the way Internet will operate in the future. Existing research into security of NDN is at an early stage and many designs are still incomplete. To make NDN a fully working system at Internet scale, there are still many missing pieces to be filled in. In this dissertation, we study the four most important security issues in NDN in order to defense against new forms of -potentially unknown- attacks, ensure privacy, achieve high availability, and block malicious network traffics belonging to attackers or at least limit their effectiveness, i.e., anomaly detection, DoS/DDoS attacks, congestion control, and cache pollution attacks. In order to protect NDN infrastructure, we need flexible, adaptable and robust defense systems which can make intelligent -and real-time- decisions to enable network entities to behave in an adaptive and intelligent manner. In this context, the characteristics of Computational Intelligence (CI) methods such as adaption, fault tolerance, high computational speed and error resilient against noisy information, make them suitable to be applied to the problem of NDN security, which can highlight promising new research directions. Hence, we suggest new hybrid CI-based methods to make NDN a more reliable and viable architecture for the future Internet.Information-Centric Networking (ICN) ha sido recientemente considerado como un paradigma prometedor parala nueva generación de Internet, pasando del paradigma de la comunicación de extremo a extremo impulsada por el emisora un paradigma de obtención de contenidos impulsada por el receptor. En ICN, el contenido (más que los nodos, como sucede en redes IPactuales) juega el papel central en las comunicaciones. Este cambio de "host-centric" a "content-centric" tiene varias ventajas importantes como la reducción de la carga de red, la baja latencia, escalabilidad, etc. Uno de los principales requisitos de diseño para las arquitecturas ICN (ya desde el principiode su diseño) ha sido una fuerte seguridad. Named Data Networking (NDN) (también conocida como Content-Centric Networking (CCN) o Data-Centric Networking (DCN)) es una de estas arquitecturas que son objetode investigación y que tiene como objetivo convertirse en la forma en que Internet funcionará en el futuro. Laseguridad de NDN está aún en una etapa inicial. Para hacer NDN un sistema totalmente funcional a escala de Internet, todavía hay muchas piezas que faltan por diseñar. Enesta tesis, estudiamos los cuatro problemas de seguridad más importantes de NDN, para defendersecontra nuevas formas de ataques (incluyendo los potencialmente desconocidos), asegurar la privacidad, lograr una alta disponibilidad, y bloquear los tráficos de red maliciosos o al menos limitar su eficacia. Estos cuatro problemas son: detección de anomalías, ataques DoS / DDoS, control de congestión y ataques de contaminación caché. Para solventar tales problemas necesitamos sistemas de defensa flexibles, adaptables y robustos que puedantomar decisiones inteligentes en tiempo real para permitir a las entidades de red que se comporten de manera rápida e inteligente. Es por ello que utilizamos Inteligencia Computacional (IC), ya que sus características (la adaptación, la tolerancia a fallos, alta velocidad de cálculo y funcionamiento adecuado con información con altos niveles de ruido), la hace adecuada para ser aplicada al problema de la seguridad ND

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in the literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NDN (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase – adaptive training – we propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance- we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and high-utilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics

Advanced SDN-Based QoS and Security Solutions for Heterogeneous Networks

This thesis tries to study how SDN can be employed in order to support Quality of Service and how the support of this functionality is fundamental for today networks. Considering, not only the present networks, but also the next generation ones, the importance of the SDN paradigm become manifest as the use of satellite networks, which can be useful considering their broadcasting capabilities. For these reasons, this research focuses its attention on satellite - terrestrial networks and in particular on the use of SDN inside this environment. An important fact to be taken into account is that the growing of the information technologies has pave the way for new possible threats. This research study tries to cover also this problem considering how SDN can be employed for the detection of past and future malware inside networks

Journal of Telecommunications and Information Technology, 2018, nr 4

kwartalni

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Router-based network traffic observation by terminal sliding mode control theory

Since the early days of the Internet, network traffic monitoring (NTM) has always played a strategic role in understanding and characterizing users’ activities. Nowadays, with the increased complexity of the Internet infrastructure, applications, and services, this role has become more crucial than ever. The aims of NTM are mainly focused on the three improvements, which include the quality of service (QoS) of the network, optimization of resource usage, and enhancement of security in computer networks. Specifically speaking, firstly, network conditions can be recognized by the network manager with NTM scheme. It provides the complete details about the QoS of networks, such as bandwidth, throughput, propagation delay, link availability, jitter, server memory, database space and etc. Secondly, with NTM being implemented at network nodes, i.e., network gateways, such as routers, or network links, the network traffic that is traversing the network is under online observation. Thereby, the network utilization can be improved by optimizing the resource usage to avoid the network congestions. Thirdly, unauthenticated service or approaches to the server will be identified by regularly monitoring the traffic. The network convention and statistics about the traffic will be known easily which helps to troubleshoot the network. Security events will also be investigated and the entry of the user will be maintained for responsibility. The work in this thesis focuses on the development of an intelligent real-time dynamic router-based network traffic observation (RNTO) by using the terminal sliding-mode theory. The RNTO technique is applied at network gateways, i.e., routers, to estimate the status of the traffic flows at the router level. The aims of the proposed RNTO technique is to estimate the traffic states, such as queue length (QL)in router buffer, average congestion window size (ACwnd), and the queuing dynamics of the additional traffic flows (ATF). The main contributions of the work can be broadly categorized into four parts. First, the problem of router-based network traffic monitoring is formulated as an observer design by using TSM theory for RNTO applications. The proposed TSM observer in the research is a network-based monitoring, which is implemented into the network gateways, i.e., network routers. Different from the static network traffic monitoring methods, the TSM observer is designed by using control methods based on the fluid-flow mathematical model, which represents the traffic dynamics of the interactions in a set of TCP traffic flows through network routers. By considering the time delay and stochastic properties in the data transmission network, the sliding-mode observation strategy is proposed with its high robustness with system parameter uncertainties as well as the external disturbance rejection. Given the natural weakness of chattering in sliding mode control signal, which can affect the system state, the chattering avoiding technique of the proposed TSM observation was utilized by using a smooth control signal for estimating the abnormal dynamics. It does not need any low-pass filler, which will lead to a phase leg. In addition, for the stochastic dynamics of the network traffics, fast transient convergence at a distance from and within a close range of the equilibrium of the traffic dynamics is essential to quickly capture traffic dynamics in network systems. Thus, a fractional term has been considered in the TSM for faster convergence in system states to efficiently estimate the traffic behaviors. Second, the issue of internal dynamics in network observation system is studied by proposing a novel full-order TSM strategy to speed up the convergence rate of the estimation error. In the RNTO scheme, the precise estimation for ACwnd is needed to estimate the queuing dynamics of ATF. However, the estimation error for ACwnd is not available and it converges to origin asymptotically, which results in a long response time in estimation. The proposed novel TSM observer has been designed to drive the estimation error for ACwnd to a defined known area in the finite-time, which can be calculated. Thereby, the estimation error of ACwnd can converge to origin asymptotically within the defined area. This strategy has shortened the response time and improves the estimation accuracy. This further improves the estimation accuracy for ATF. The comparative studies are conducted to evaluate the performance. Third, the issue of algorithm-efficient RNTO is investigated by considering an event triggered sliding-mode observer to reduce the computational load and the communication burden. Instead of the time-driven observation scheme, the control of the sliding mode observer is formulated under the event triggered scheme. The control of the observer is designed to be smooth and is directly applied to estimate the dynamics of the additional traffic flows. The event triggered observation algorithms is developed to reduce the computational load of the network router and the communication resource of output link in the network. Fourth, the problem of global RNTO is addressed by developing a fuzzy TSM observer by using fuzzy theory to achieve global operation under network uncertainties. The existing RNTO schemes are based on the linearization of a certain network conditions, i.e., a fixed number of TCP connections, which is a constant value N. Given the network suffers from time-varying fading, shadowing and interference and the data rate changes over time, the current methods proposed so far might not effectively and accurately monitor and estimate the traffic dynamics under network uncertainties. The T-S fuzzy models are used to model the traffic dynamics of the time-varying data changes in network link resources, i.e. the time-varying number of TCP sections, N(t) in a mathematical model. Based on the T-S fuzzy models, the fuzzy terminal sliding mode observer is established by using the fuzzy logic theory to estimate the states of the network traffic to achieve the global observation performance under the network uncertainties. In the fuzzy terminal sliding mode observer, the control signal is designed to be continuous for application of estimating the additional traffic flows without the low-pass filter. To evaluate the proposed RNTO technique, the networking simulator tool Network Simulator II (NS-II) has been used. The proposed RNTO algorithms are coded and implemented into network routers in NS-II. Numerous simulation scenarios are considered and performed. The comparative studies are also conducted by analyzing the NS-2 results. The results have demonstrated the effectiveness and efficiency of the proposed RNTO algorithms