Novel Algorithms and Protocols for Networks, Volume II

Software-defined networking (SDN) is the fastest growing and most widely deployed network infrastructure due to its adaptability to new networking technologies and intelligent applications. SDN simplifies network management and control by separating the control plane from the data plane. The SDN controller performs the routing process using the traditional shortest path approach to obtain end-to-end paths. This process usually does not consider the nodes’ capacity and may cause network congestion and delays, affecting flow performance. Therefore, we evaluate the most conventional routing criteria in the SDN scenario based on Dijkstra’s algorithm and compare the found paths with our proposal based on a cellular genetic algorithm for multi-objective optimization (MOCell). We compare our proposal with another multi-objective evolutionary algorithm based on decomposition (MOEA/D) for benchmark purposes. We evaluate various network parameters such as bandwidth, delay, and packet loss to find the optimal end-to-end path. We consider a large-scale inter-domain SDN scenario. The simulation results show that our proposed method can improve the performance of data streams with TCP traffic by up to 54% over the traditional routing method of the shortest path and by 33% for the highest bandwidth path. When transmitting a constant data stream using the UDP protocol, the throughput of the MOCell method is more than 1.65% and 9.77% for the respective paths. Full article

In recent years, Active Queue Management (AQM) mechanisms to improve the performance of TCP/IP networks have acquired a relevant role. In this paper, we present a simple and robust RED-type algorithm together with a couple of dynamical variants with the ability to adapt to the specific characteristics of different network environments, as well as to the user’s needs. We first present a basic version called Beta RED (BetaRED), where the parameters can be tuned according to the specific network conditions. The aim is to introduce control parameters that are easy to interpret and provide a good performance over a wide range of values. Secondly, BetaRED is used as a framework to design two dynamic algorithms, which we will call Adaptive Beta RED (ABetaRED) and Dynamic Beta RED (DBetaRED). In those new algorithms, certain parameters are dynamically adjusted so that the queue length remains stable around a predetermined reference value and according to changing network traffic conditions. Finally, we present a battery of simulations using the Network Simulator 3 (ns-3) software with a two-fold objective: to guide the user on how to adjust the parameters of the BetaRED mechanism, and to show a performance comparison of ABetaRED and DBetaRED with other representative algorithms that pursue a similar objective. Full article

Blockchain is an emerging computing platform that provides recording and tracking facilities to substantially increase the security issues in healthcare systems. The evolution of wireless body area networks requires the continuous monitoring of the health parameters of traveling patients while traveling on road. The health parameter data of each patient are sent to the Road Side Units (RSUs) for generating the blocks by computing the required hash functions. A major challenge in such a network is to efficiently exchange the data blocks between mining RSUs and vehicles using a medium access protocol with a reduced number of collisions. The medium access problem becomes more challenging due to the vehicle mobility, high vehicle density and the varying nature of the data generated by the vehicles. In this work, a TDMA-based MAC protocol to meet an Adaptive Patients Data traffic for Vehicular Network ($TAP{D}_{VN}$) is proposed. $TAP{D}_{VN}$ is specifically designed for patients in a vehicular network by considering the frequent entry and exit of vehicles in a mining node’s coverage area. It allows mining nodes to adjust time slots according to the sensitive patient’s data and allows the maximum number of patient vehicular nodes by considering their sensitivity to send their data in a session to compute their hash values accordingly. Simulation results verify that the proposed scheme accommodates the maximum number of high-risk patient data and improves bandwidth utilization by 20%. Full article

As the COVID-19 pandemic emerged, everyone’s attention was brought to the topic of the health and safety of the entire human population. It has been proven that wearing a face mask can help limit the spread of the virus. Despite the enormous efforts of people around the world, there still exists a group of people that wear face masks incorrectly. In order to provide the best level of safety for everyone, face masks must be worn correctly, especially indoors, for example, in shops, cinemas and theaters. As security guards can only handle a limited area of the frequently visited objects, intelligent sensors can be used. In order to mount them on the shelves in the shops or near the cinema cash register queues, they need to be capable of battery operation. This restricts the sensor to be as energy-efficient as possible, in order to prolong the battery life of such devices. The cost is also a factor, as cheaper devices will result in higher accessibility. An interesting and quite novel approach that can answer all these challenges is a TinyML system, that can be defined as a combination of two concepts: Machine Learning (ML) and Internet of Things (IoT). The TinyML approach enables the usage of ML algorithms on boards equipped with low-cost, low-power microcontrollers without sacrificing the classifier quality. The main goal of this paper is to propose a battery-operated TinyML system that can be used for verification whether the face mask is worn properly. To this end, we carefully analyze several ML approaches to find the best method for the considered task. After detailed analysis of computation and memory complexity as well as after some preliminary experiments, we propose to apply the K-means algorithm with carefully designed filters and a sliding window technique, since this method provides high accuracy with the required energy-efficiency for the considered classification problem related to verification of using the face mask. The STM32F411 chip is selected as the best microcontroller for the considered task. Next, we perform wide experiments to verify the proposed ML framework implemented in the selected hardware platform. The obtained results show that the developed ML-system offers satisfactory performance in terms of high accuracy and lower power consumption. It should be underlined that the low-power aspect makes it possible to install the proposed system in places without the access to power, as well as reducing the carbon footprint of AI-focused industry which is not negligible. Our proposed TinyML system solution is able to deliver very high-quality metric values with accuracy, True Positive Ratio (TPR), True Negative Ratio (TNR), precision and recall being over 96% for masked face classification while being able to reach up to 145 days of uptime using a typical 18650 battery with capacity of 2500 mAh and nominal voltage of 3.7 V. The results are obtained using a STM32F411 microcontroller with 100 MHz ARM Cortex M4, which proves that execution of complex computer vision tasks is possible on such low-power devices. It should be noted that the STM32F411 microcontroller draws only 33 mW during operation. Full article

Leaving the current 4th generation of mobile communications behind, 5G will represent a disruptive paradigm shift integrating 5G Radio Access Networks (RANs), ultra-high-capacity access/metro/core optical networks, and intra-datacentre (DC) network and computational resources into a single converged 5G network infrastructure. The present paper overviews the main achievements obtained in the ALLIANCE project. This project ambitiously aims at architecting a converged 5G-enabled network infrastructure satisfying those needs to effectively realise the envisioned upcoming Digital Society. In particular, we present two networking solutions for 5G and beyond 5G (B5G), such as Software Defined Networking/Network Function Virtualisation (SDN/NFV) on top of an ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure, and the clean-slate Recursive Inter-Network Architecture (RINA) over packet networks, including access, metro, core and DC segments. The common umbrella of all these solutions is the Knowledge-Defined Networking (KDN)-based orchestration layer which, by implementing Artificial Intelligence (AI) techniques, enables an optimal end-to-end service provisioning. Finally, the cross-layer manager of the ALLIANCE architecture includes two novel elements, namely the monitoring element providing network and user data in real time to the KDN, and the blockchain-based trust element in charge of exchanging reliable and confident information with external domains. Full article

Vulnerability prioritization is an essential element of the vulnerability management process in data communication networks. Accurate prioritization allows the attention to be focused on the most critical vulnerabilities and their timely elimination; otherwise, organizations may face severe financial consequences or damage to their reputations. In addition, the large amounts of data generated by various components of security systems further impede the process of prioritizing the detected vulnerabilities. Therefore, the detection and elimination of critical vulnerabilities are challenging tasks. The solutions proposed for this problem in the scientific literature so far—e.g., PatchRank, SecureRank, Vulcon, CMS, VDNF, or VEST—are not sufficient because they do not consider the context of the organization. On the other hand, commercial solutions, such as Nessus, F-Secure, or Qualys, do not provide detailed information regarding the prioritization procedure, except for the scale. Therefore, in this paper, the authors present an open-source solution called the Vulnerability Management Center (VMC) in order to assist organizations with the vulnerability prioritization process. The VMC presents all calculated results in a standardized way by using a Common Vulnerability Scoring System (CVSS), which allows security analysts to fully understand environmental components’ influences on the criticality of detected vulnerabilities. In order to demonstrate the benefits of using the the open-source VMC software developed here, selected models of a vulnerability management process using CVSS are studied and compared by using three different, real testing environments. The open-source VMC suite developed here, which integrates information collected from an asset database, is shown to accelerate the process of removal for the critical vulnerabilities that are detected. The results show the practicability and efficacy of the selected models and the open-source VMC software, which can thus reduce organizations’ exposure to potential threats. Full article

Currently, energy efficiency (EE) of wireless communication is essential where many wireless networks with different Radio Access Technologies (RATs) coexist together. The RATs can be effectively selected and managed on a higher level to achieve maximum EE and save energy, e.g., save batteries. The approach to wireless traffic steering in mobile networks with a proof-of-concept solution is presented in this paper, owing to the developed high-level multi-RAT (multi-Radio Access Technology) heterogonous network orchestration approach. Based on the high-level network orchestrator, which traces network indicators, it is possible to decrease the user mobile terminal energy consumption, keeping traffic speed at an adequate level. The solution discussed was implemented in an experimental testbed with Software Defined Radio transmission systems. Downlink and uplink data links were toggled among different RATs according to the decisions that were taken by the end-to-end multi-RAT orchestrator based on the received proper network traffic-related indicators. The authors focused on finding an adequate algorithm that allowed for reduced power usage in the user terminal and made the attempt to verify how to reach the power reduction without introducing RAT-specific rules. The results showed that the proposed orchestration EE reduction was observed (from 11% to 42% for two different scenarios) in relation to the single LTE network deployment. The orchestration compared to the Wi-Fi network does not provide EE gain (−7% and 0%, respectively), but allows the user to achieve a higher data rate (23% and 39% gain, respectively), thus keeping the energy efficiency at almost the same level. Full article

Open RAN (radio access network) movement is perceived as a game changer, having robust potential to introduce shifts in mobile radio access networks towards tailor-made solutions based on the architecture decomposition. It is widely assumed that those changes will affect the approach to network deployments and supply chains of network elements and their further integration and maintenance. First deployments of O-RAN-based networks have already delivered broadband services to end users. In parallel, many proof-of-concept feature evaluations and theoretical studies are being conducted by academia and the industry. In this review, the authors describe the RAN evolution towards open models and make an attempt to indicate potential open RAN benefits and market trends. Full article