Improving the Energy Efficiency of Software-Defined Networks through the Prediction of Network Configurations

During the last years, huge efforts have been conducted to reduce the Information and Communication Technology (ICT) sector energy consumption due to its impact on the carbon footprint, in particular, the one coming from networking equipment. Although the irruption of programmable and softwarized networks has opened new perspectives to improve the energy-efficient solutions already defined for traditional IP networks, the centralized control of the Software-Defined Networking (SDN) paradigm entails an increase in the time required to compute a change in the network configuration and the corresponding actions to be carried out (e.g., installing/removing rules, putting links to sleep, etc.). In this paper, a Machine Learning solution based on Logistic Regression is proposed to predict energy-efficient network configurations in SDN. This solution does not require executing optimal or heuristic solutions at the SDN controller, which otherwise would result in higher computation times. Experimental results over a realistic network topology show that our solution is able to predict network configurations with a high feasibility (>95%), hence improving the energy savings achieved by a benchmark heuristic based on Genetic Algorithms. Moreover, the time required for computation is reduced by a factor of more than 500,000 times.

Vulnerability Evaluation and Improvement Method of Civil Aviation Navigation Network

Due to events such as natural disasters and navigation equipment failures, enormous calamity may be caused by the interruption of the navigation network which is a guarantee for the flight safety of civil aviation aircraft. The navigation network consists of the navigation stations as nodes and the routes between them as edges. Different nodes have different effects on the vulnerability of the network due to their different abilities to maintain the stability of the network topology and the normal function of the network. To quantify this difference and identify key nodes that have a greater impact on the vulnerability of the navigation network, an indicator to assess the importance of a navigation station is proposed which combines the structural importance reflected by node topology centrality and functional importance reflected by node weight. The structural importance of a node corresponds to its topology features including local dominance of the node and its global influence, and the important contribution to both adjacent and nonadjacent nodes from this node, while the functional importance is indicated by the flight flow serviced by the node during a fixed period of time. Vulnerability evaluation shows that the navigation network is more vulnerable when subject to the intentional attack of nodes with higher comprehensive node importance than an intentional attack of nodes with a larger value of indicators used in previous literature. Finally, the vulnerability of the navigation network is improved through changing the topology of the most critical node and balancing the node importance of the whole network.

Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology

Mobile health wearables are often embedded with small processors for signal acquisition and analysis. These embedded wearable systems are, however, limited with low available memory and computational power. Advances in machine learning, especially deep neural networks (DNNs), have been adopted for efficient and intelligent applications to overcome constrained computational environments. Herein, evolutionary algorithms are used to find novel DNNs that are accurate in classifying airway symptoms while allowing wearable deployment. As opposed to typical microphone‐acoustic signals, mechano‐acoustic data signals, which did not contain identifiable speech information for better privacy protection, are acquired from laboratory‐generated and publicly available datasets. The optimized DNNs had a low model file size of less than 150 kB and predicted airway symptoms of interest with 81.49% accuracy on unseen data. By performing explainable AI techniques, namely occlusion experiments and class activation maps, mel‐frequency bands up to 8,000 Hz are found as the most important feature for the classification. It is further found that DNN decisions are consistently relying on these specific features, fostering trust and transparency of the proposed DNNs. The proposed efficient and explainable DNN is expected to support edge computing on mechano‐acoustic sensing wearables for remote, long‐term monitoring of airway symptoms.

LINES: muLtImodal traNsportation rEsilience analySis

This study aims to contribute to more sustainable mobility solutions by proposing robust and actionable methods to assess the resilience of a multimodal transport system. Resilience is seen in a dynamic lean setting, looking at aspects in the network topology and user’s flow and demand throughout a parameterizable period. We hypothesize that this network’s appropriate multi-layered and traffic-sensitive modeling can promote the integrated analysis of different transport modes and support an improved resilience analysis. We operationalize the lean resilience conceptual construct with the proposed muLtImodal traNsportation rEsilience aSsessment (LINES) methodological process. Using the city of Lisbon as a study case, we illustrate the relevance of the proposed methodology to detect actionable vulnerabilities in the bus–tram–subway network.

A Multi-Agent-Based Simulation Model for the Spreading of Diseases Through Social Interactions During Pandemics

Epidemiological models have a vital and consolidated role in aiding decision-making during crises such as the Coronavirus Disease 2019 (COVID-19) pandemic. However, the influence of social interactions in the spreading of communicable diseases is left aside from the main models in the literature. The main contribution of this work is the introduction of a probabilistic simulation model based on a multi-agent approach that is capable of predicting the spreading of diseases. Our proposal has a simple model for the main source of infections in pandemics of respiratory viruses: social interactions. This simplicity is key for incorporating complex networks topology into the model, which is a more accurate representation for real-world interactions. This flexibility in network structure allows the evaluation of specific phenomena, such as the presence of super-spreaders. We provide the modeling for the dynamical network topology in two different simulation scenarios. Another contribution is the generic microscopic model for infection evolution that enables the evaluation of impact from more specific behaviors and interventions on the overall spreading of the disease. It also enables a more intuitive process for going from data to model parameters. This ease of changing the infection evolution model is key for performing more complete analyses than would be possible in other models from the literature. Further, we give specific parameters for a controlled scenario with quick testing and tracing. We present computational results that illustrate the model utilization for predicting the spreading of COVID-19 in a city. Also, we show the results of applying the model for assessing the risk of resuming on-site activities at a collective use facility.

Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network

The Unmanned Aerial Vehicle (UAV) has been used for the delivery of medical supplies in urban logistical distribution, due to its ability to reduce human contact during the global fight against COVID-19. However, due to the reliability of the UAV system and the complex and changeable operation scene and population distribution in the urban environment, a few ground-impact accidents have occurred and generated enormous risks to ground personnel. In order to reduce the risk of UAV ground-impact accidents in the urban logistical scene, failure causal factors, and failure modes were classified and summarized in the process of UAV operation based on the accumulated operation data of more than 20,000 flight hours. The risk assessment model based on the Bayesian network was built. According to the established network and the probability of failure causal factors, the probabilities of ground impact accidents and intermediate events under different working conditions were calculated, respectively. The posterior probability was carried out based on the network topology to deduce the main failure inducement of the accidents. Mitigation measures were established to achieve the equivalent safety level of manned aviation, aiming at the main causes of accidents. The results show that the safety risk of the UAV was reduced to 3.84 × 10−8 under the action of risk-mitigation measures.

Green Smart City Intelligent and Cyber-Security-Based IoT Transportation Solutions for Combating the Pandemic COVID-19

This chapter presents the fruit of our research by merging smart transportation and smart health to provide IoT transportation solutions based on green smart city intelligence and safety to fight against the COVID-19 pandemic. For this, we have realized a model that allows transporting citizens via a means of transportation based on electric mobility to reduce energy consumption, reduce CO 2 emission and cost by searching the optimal path that this vehicle will be used. And to transport people, we need a system that allows us to check the health situation of citizens to avoid and prevent the spread of the COVID-19 pandemic. In order to find solutions to this work, we have proposed approaches to calculate the most optimal path that meets our needs, as well as to propose scenarios that allow checking the situation of the citizens. And to complete this work with minimum consumption of memory and time, we made a comparative study on the nodes used for the different IoT network topologies to choose the best one for our platform. Concerning the communication, we chose to use the CoAP protocol to ensure the communication between the nodes, and we used the AES-SHA256 encryption algorithm to compare it with RSA-SHA256 to ensure the elements of security and protection the data from any intrusion. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks

A flying ad hoc network (FANETs), also known as a swarm of unmanned aerial vehicles (UAVs), can be deployed in a wide range of applications including surveillance, monitoring, and emergency communications. UAVs must perform real-time communication among themselves and the base station via an efficient routing protocol. However, designing an efficient multihop routing protocol for FANETs is challenging due to high mobility, dynamic topology, limited energy, and short transmission range. Recently, owing to the advantages of multi-objective optimization, Q-learning (QL)-based position-aware routing protocols have improved the performance of routing in FANETs. In his article, we provide a comprehensive review of existing QL-based position-aware routing protocols for FANETs. We rigorously address dynamic topology, mobility models, and the relationship between QL and routing in FANETs, and extensively review the existing QL-based position-aware routing protocols along with their advantages and limitations. Then, we compare the reviewed protocols qualitatively in terms of operational features, characteristics, and performance metrics. We also discuss important open issues and research challenges with potential research directions.

HP-SFC: Hybrid Protection Mechanism Using Source Routing for Service Function Chaining

Service Function Chaining (SFC) is an emerging paradigm aiming to provide flexible service deployment, lifecycle management, and scaling in a micro-service architecture. SFC is defined as a logically connected list of ordered Service Functions (SFs) that require high availability to maintain user experience. The SFC protection mechanism is one way to ensure high availability, and it is achieved by proactively deploying backup SFs and installing backup paths in the network. Recent studies focused on ensuring the availability of backup SFs, but overlooked SFC unavailability due to network failures. This paper extends our previous work to propose a Hybrid Protection mechanism for SFC (HP-SFC) that divides SFC into segments and combines the merits of local and global failure recovery approaches to define an installation policy for backup paths. A novel labeling technique labels SFs instead of SFC, and they are stacked as per the order of SFs in a particular SFC before being inserted into a packet header for traffic steering through segment routing. The emulation results showed that HP-SFC recovered SFC from failure within 20–25 ms depending on the topology and reduced backup paths’ flow entries by at least 8.9% and 64.5% at most. Moreover, the results confirmed that the segmentation approach made HP-SFC less susceptible to changes in network topology than other protection schemes.

Walkability Compass—A Space Syntax Solution for Comparative Studies

The ongoing discourse on air quality and climate changes positions walkability as a pivotal point of sustainable urban planning. Urban studies examine a city’s walkability in terms of pedestrian flows, design qualities, and street network topology, leaving walkability comparative frameworks under development. Building on the space syntax theory, this research introduces a “walkability compass”, a four spatial indicator-designed tool for city walkability assessment and comparison. The tools are being tested on eight Baltic region cities: Vilnius, Kaunas (LT), Malmö (SE), Riga (LV), Tallinn (ES), Gdansk, Bialystok, Lublin (PL). The nine-step method framework integrates four indexes: Gravity (Gr), Reach (Re), Straightness (St), and Population density (Pop). The “walkability compass” results reveal significant Re and St correlations;thus, visual and cultural aspects become the main factors in pedestrian-friendly cities. The spatial pattern typology has matched similar cities (Malmö and Kaunas) to work closely on sustainable urban planning development. In all case studies, specific walkability zones were mapped, but the Gr zones turned out to be the most compact ones (the Z-score of Gr was ranged from 355.4 to 584;other indexes oscillated between 209.4 and 542.6). The walkability mapping results are publicly shared via WebMap to stimulate the participatory discussion on case studies cities further development.

Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser

Cloud computing has been a dominant computing paradigm for many years. It provides applications with computing, storage, and networking capabilities. Furthermore, it enhances the scalability and quality of service (QoS) of applications and offers the better utilization of resources. Recently, these advantages of cloud computing have deteriorated in quality. Cloud services have been affected in terms of latency and QoS due to the high streams of data produced by many Internet of Things (IoT) devices, smart machines, and other computing devices joining the network, which in turn affects network capabilities. Content delivery networks (CDNs) previously provided a partial solution for content retrieval, availability, and resource download time. CDNs rely on the geographic distribution of cloud servers to provide better content reachability. CDNs are perceived as a network layer near cloud data centers. Recently, CDNs began to perceive the same degradations of QoS due to the same factors. Fog computing fills the gap between cloud services and consumers by bringing cloud capabilities close to end devices. Fog computing is perceived as another network layer near end devices. The adoption of the CDN model in fog computing is a promising approach to providing better QoS and latency for cloud services. Therefore, a fog-based CDN framework capable of reducing the load time of web services was proposed in this paper. To evaluate our proposed framework and provide a complete set of tools for its use, a fog-based browser was developed. We showed that our proposed fog-based CDN framework improved the load time of web pages compared to the results attained through the use of the traditional CDN. Different experiments were conducted with a simple network topology against six websites with different content sizes along with a different number of fog nodes at different network distances. The results of these experiments show that with a fog-based CDN framework offloading autonomy, latency can be reduced by 85% and enhance the user experience of websites.

A New Tool to Assess Maximum Permissible Solar PV Penetration in a Power System

With diminishing fossil fuel resources and increasing environmental concerns, large-scale deployment of Renewable Energy Sources (RES) has accelerated the transition towards clean energy systems, leading to significant RES generation share in power systems worldwide. Among different RES, solar PV is receiving major focus as it is most abundant in nature compared to others, complimented by falling prices of PV technology. However, variable, intermittent and non-synchronous nature of PV power generation technology introduces several technical challenges, ranging from short-term issues, such as low inertia, frequency stability, voltage stability and small signal stability, to long-term issues, such as unit commitment and scheduling issues. Therefore, such technical issues often limit the amount of non-synchronous instantaneous power that can be securely accommodated by a grid. In this backdrop, this research work proposes a tool to estimate maximum PV penetration level that a given power system can securely accommodate for a given unit commitment interval. The proposed tool will consider voltage and frequency while estimating maximum PV power penetration of a system. The tool will be useful to a system operator in assessing grid stability and security under a given generation mix, network topology and PV penetration level. Besides estimating maximum PV penetration, the proposed tool provides useful inputs to the system operator which will allow the operator to take necessary actions to handle high PV penetration in a secure and stable manner.

Modeling Multi-Dimensional Public Opinion Process Based on Complex Network Dynamics Model in the Context of Derived Topics

With the rapid development of the Internet, the speed with which information can be updated and propagated has accelerated, resulting in wide variations in public opinion. Usually, after the occurrence of some newsworthy event, discussion topics are generated in networks that influence the formation of initial public opinion. After a period of propagation, some of these topics are further derived into new subtopics, which intertwine with the initial public opinion to form a multidimensional public opinion. This paper is concerned with the formation process of multi-dimensional public opinion in the context of derived topics. Firstly, the initial public opinion variation mechanism is introduced to reveal the formation process of derived subtopics, then Brownian motion is used to determine the subtopic propagation parameters and their propagation is studied based on complex network dynamics according to the principle of evolution. The formula of basic reproductive number is introduced to determine whether derived subtopics can form derived public opinion, thereby revealing the whole process of multi-dimensional public opinion formation. Secondly, through simulation experiments, the influences of various factors, such as the degree of information alienation, environmental forces, topic correlation coefficients, the amount of information contained in subtopics, and network topology on the formation of multi-dimensional public opinion are studied. The simulation results show that: (1) Environmental forces and the amount of information contained in subtopics are key factors affecting the formation of multi-dimensional public opinion. Among them, environmental forces have a greater impact on the number of subtopics, and the amount of information contained in subtopics determines whether the subtopic can be the key factor that forms the derived public opinion. (2) Only when the degree of information alienation reaches a certain level, will derived subtopics emerge. At the same time, the degree of information alienation has a greater impact on the number of derived subtopics, but it has a small impact on the dimensions of the final public opinion. (3) The network topology does not have much impact on the number of derived subtopics but has a greater impact on the number of individuals participating in the discussion of subtopics. The multidimensional public opinion dimension formed by the network topology with a high aggregation coefficient and small average path length is higher. Finally, a practical case verifies the rationality and effectiveness of the model proposed in this paper.