This paper presents the application of the improved Self-Organizing Migrating Algorithm (iSOMA) to the synthesis and optimization of quantum circuits. We develop a comprehensive method to evolve candidate quantum circuits with minimal cost by integrating iSOMA with circuit evaluators in Qiskit. Experimental evaluation across 100 independent runs demonstrates a 90% success rate in synthesizing Toffoli gates, with an 80% circuit uniqueness rate indicating diverse solution exploration. The algorithm achieves a median cost of 0.000 and determinism = 8/8 for successful runs, confirming its effectiveness for quantum circuit synthesis. This research is important for future 6G networks and beyond, as quantum computing has the potential to be more efficient, especially in the physical layer of the Radio Access Network (RAN), where quantum-supported optimization mechanisms are able to process a large number of tasks faster. The proposed synthesis of Toffoli gates controlled by iSOMA technology contributes to quantum computing by supporting the efficient design of quantum circuits, which is a prerequisite for the deployment of quantum-native functions, such as the Quantum Fourier Transform, in future communications and wireless infrastructures.

Many time series forecasting methods rely on sliding windows of historical data. The window size is one of the hyperparameters that significantly influences predictive accuracy, yet optimal selection remains challenging in practice. We propose an encoding of time that, when used with generative models, transforms the seasonal time series forecasting problem into a conditional generation problem on a helical representation. This representation allows models to learn position–value relationships rather than sequential dependencies, enabling forecasting using only time-derived conditions, thereby eliminating the need for sliding windows and recent observations during inference while fully exploiting historical patterns during training. We evaluated our approach using conditional Generative Adversarial Networks on taxi demand and influenza-like illness forecasting benchmarks. Our model demonstrated substantial improvements over state-of-the-art baselines in long-term forecasting scenarios. For influenza-like illness forecasting, we achieved a 42.2% mean absolute error reduction (from 1.1378 to 0.6582) and a 35.3% root mean square error reduction (from 1.3063 to 0.8453) compared to baseline models using only historical data for long-term predictions. For taxi demand forecasting, we achieved a 70.7% root mean square error reduction (from 0.7926 to 0.2322) compared to baseline models using recent observations during inference. Our approach provides a specialized solution for seasonal time series forecasting, presenting advantages when long-term predictions are required without waiting for new actual data or in high-frequency applications where continuous re-computation is expensive. • Helical time encoding makes seasonal patterns geometrically explicit in time series. • Paradigm shift from sequential forecasting to conditional generation. • No recent observations required for long-term forecasting. • Implementation of conditional Generative Adversarial Networks. • Edge-ready for real-time, high-frequency seasonal time series forecasting.

As the effectiveness of modern Internet-based anonymization infrastructures grows, law enforcement agencies are experiencing a progressive erosion of their surveillance capabilities. This can severely undermine their efforts to prevent and investigate various types of unlawful activities, potentially increasing the impunity of organized criminal networks. Balancing the legitimate privacy needs of individuals with the imperative to maintain public safety and combat criminal behavior in the digital world remains a complex tradeoff for both policymakers and technologists who need to find a systematic and reliable way to link the traffic traces associated with criminal activities to their anonymized origins. Accordingly, this paper presents a simple but very effective de-anonymization approach capable of associating traffic traces captured at the edge of the overlay infrastructures, in correspondence with the true origins, to those captured in correspondence with the destinations. The approach is based on determining the minimum-distance pairs within a complete bipartite graph in which the traffic traces are the nodes. Experiments with different distance functions, applied in varied ways, show that the resulting framework appears to be a promising solution that is scalable and easily deployable on real-life network equipment. • A framework to de-anonymize traffic based on distances between descriptions of traffic. • An estimation of the confidence in results is provided. • The proposed solution is interpretable and it scales well.

An effective energy management system relies on the accurate prediction of electricity consumption, facilitating energy suppliers to optimise energy distribution, reduce energy waste, and avoid overloading the power system.This paper analyses different methods for the estimation of electricity consumption at the level of an urban area.A statistical model based on Trigonometric seasonality, Box-Cox transformation, Auto-Regressive Moving Average errors, Trend and Seasonal components is first presented.Then a model based on fuzzy logic is also proposed.These methods will be optimised and evaluated on a dataset collected by the electric power supply agency of Sibiu, Romania, with the goal of reducing the forecast error.The models are also compared with a Markov stochastic model and with a Long Short-Term Memory neural model.The experiments have shown that our statistical model using a history length of 200 electricity consumption values and a daily seasonality is the most efficient, with the lowest mean absolute error of 3.6 MWh, thus making it a good candidate for integration into a city-level energy management system.

This paper proposes a prediction model based on fuzzy logic applied to anticipate electricity production and consumption in a building equipped with photovoltaics and connected to the grid. The goal is a smart energy management system able to make decisions and to adapt the consumption to the actual context and to the future electricity levels. The interest is to use as much electricity as possible from own production. The surplus is captured by an energy storage system or is sent to the grid. When no electricity is available from self-production, the grid is used to cover the necessities. The evaluations are performed on a data set collected in a real household. The proposed method is compared in terms of mean absolute error with other existing methods. The method developed based on fuzzy logic has an error of about 67 W, which places it among the most efficient models.

Abstract Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.

In advanced data processing systems, one of the most important paradigms for distributed data analysis is innovative and transformative computing approaches. Such solutions allow not only data analytics tasks to be facilitated, but also intelligent and secure information analysis oriented especially for applications of new technologies and computational intelligence techniques. Nowadays there is a great demand to efficiently store and analyze a huge amount of information, originated from distributed sources or sensors, as well as an expectation to manage such information in a secure manner for applications in ubiquitous and mobile computing. The possibility of creation and development of such computation technologies will be connected with the introduction of new transformative computing procedures dedicated to security, big data analysis and cloud computing technologies. These subjects, as well as others, connected with innovative computational models for transformative computing technologies, data security, security protocols, and distributed data analysis will form the subject of this Transformative 2020 special issue. The main topics of transformative computing in security, big data analysis and cloud computing applications presented at Transformative 2020 are primarily oriented at new computational approaches for big data and cloud security, transformative computing applications, personalized cryptography and biometric security, ambient intelligence, innovative security and privacy protocols, security of cognitive information systems, cryptography and secret data management, computational intelligence in data and services management, security and privacy for mobile and distributed systems, visual and cognitive CAPTCHA, cognitive approaches for big data analytics, advanced cognitive steganography systems, behavioral features in data analysis and security solutions, and transformative approaches for big data analytics. This special issue features 12 papers, which present high quality scientific research, and interesting cutting-edge topics. The first paper entitled “Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy-analytic hierarchy process” by Park et al.1 proposes a new system that integrates a multi advisory function. The proposed solution starts from problem definition and continues to define the required solving task. Such technology supports customized information on defining problems and enables the definition of the requirements to characterize user features. The second paper “Time-based legality of information flow in the capability-based access control model for the Internet of Things” by Nakamura et al.2 introduces a new idea of time based legality of information flow. In the capability-based access control (CBAC) model, each subject has a capability token with which users can effectively manage a device. The authors propose a time-based operation interruption protocol (TBOI) to prevent illegal information—at any given time and at a later date. The third paper entitled “Transformative and Cognitive Approaches to Information Retrieval and Security Procedures” by Ogiela3 describes cognitive approaches in data security and a transformative computing paradigm dedicated to creation of human-centered security protocols. Such transformative computing applications focus on data exploration in distributed systems. The fourth paper “A Machine Learning-Based Memory Forensics Methodology for TOR Browser Artifacts” by Pizzolante et al.4 presents a bottom-up formal investigation model for the memory forensics of the Tor Browser. This methodology was developed based on a bottom-up logical approach for collecting information from different abstraction levels. The fifth paper entitled “Healthcare Fraud Detection Using Primitive Sub Peer Group Analysis” by Settipalli and Gangadharan5 presents new algorithms for identifying suspicious behaviors in health insurance. In this paper, a primitive sub peer group analysis (PSPGA) based on peer group analysis (PGA) and pattern interpretation and analysis is proposed for identifying user behaviors. The PSPGA recognizes drifts and classifies them as correct or fraudulent. The sixth paper “New Cognitive Sharing Algorithms for Cloud Service Management” by Ogiela and Ogiela6 presents new algorithms based on the meaning description dedicated to data analysis and security processes. The authors describe the possibility of applying such algorithms, depending on the structure of the target system, especially in cloud computing. This paper also discusses examples of two-stage secret protection algorithms—the simple data protection and the information set with semantics. The seventh paper entitled “Protocol Fuzzing to Find Security Vulnerabilities of RabbitMQ” by Kwon et al.7 shows a new fuzzy protocol for systems and service communications. A message broker named RabbitMQ is developed to find unknown vulnerabilities inherent in software. Their simulations demonstrate that the proposed algorithm is able to solve tasks by using the RabbitMQ especially in data security processes. The eighth paper “A deep learning-based indoor-positioning approach using received strength signal indication and carrying mode information” by Lin et al.8 describes a new indoor positioning scheme—learning-based indoor positioning system (LEIPS) which is used for identification of smartphone users by using inertial sensors and deep learning algorithms. Their experimental results demonstrate that the LEIPS has reached 96% of positioning accuracy. The ninth paper entitled “Optimizing Resource Scheduling Based on Extended Particle Swarm Optimization in Fog Computing Environments” by Narayana et al.9 introduces the extended particle swarm optimization (EPSO) algorithm which was developed with additional gradient method for optimizing scheduling tasks in cloud-fog environments. It also improves the efficiency of data analysis and minimizes the time of their implementation. The tenth paper “Population Data Mobility Retrieval at Territory of Czechia in Pandemic Covid-19 Period” by Platos et al.10 addresses the selection and collection steps of data analysis on mobile phones at the Czech Republic during the Covid pandemic. A data collection architecture is then proposed for spatial temporal mobility analysis. The analysis precision including the pandemic and non-pandemic periods is also shown. The eleventh paper entitled “Design and Analysis of Efficient Neural Intrusion Detection for Wireless Sensor Networks” by Batiha and Krömer11 analyses the acceleration of a neural intrusion detection model. The model was developed to detect intrusion/malicious behaviors for wireless sensor networks. The authors present their computational experiments with classification accuracy and training efficiency on different devices. In the last paper “Improved Publicly Verifiable Auditing Protocol for Cloud Storage” by Zhang et al.,12 the authors describe an outsourcing data protocol for cloud systems which is one of the new full integrity cloud auditing protocols. In addition to identifying its weaknesses, authors also analyze this protocol and present its security issues. This special issue introduces new solutions of transformative computing paradigms on different important topics. A part of them focuses on security and system protection. Some solve urgent network problems. All these techniques are oriented from information flow processes and machine learning in transformative computing, cognitive and semantic description of data management and security, fuzzy protocols and deep learning positioning, intrusion detection, fog-cloud applications, and big data collection and analysis processes. The wide range and impacts of the presented papers indicate an extraordinary variety of transformative computing paradigms, applications, and approaches. The authors also raise important open topics and their solutions. Following that, authors also show the possibility of further development on the discussed aspects. We would like to specially thank the Editors in Chief—Professor Geoffrey Fox, who first gave the opportunity of publishing this issue, and Professor David W. Walker who led this work at all times to the end. We are especially grateful for the opportunity to present this Special Issue and for their great kindness and help, as well as the unique opportunity to present new and interesting scientific works in Concurrency and Computation: Practice and Experience. We would also like to thank all the authors who have submitted their papers to this Special Issue. We congratulate all authors whose works have been accepted and positively evaluated. These works bring a great contribution to the development of the computer science, show new directions for research, as well as an innovative view of the previously developed experience and science.

Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.

Summary Learning on imbalanced datasets, where one class is underrepresented, is problematic and important at the same time. On the one hand, a limited number of positive examples restricts the generalization ability of classifiers. On the other hand, often, the class of interest is such exactly because it is rare. The Synthetic Minority Oversampling TEchnique (SMOTE) is a preprocessing method that creates new synthetic examples by interpolating between neighboring instances. In this work, an enhancement to SMOTE is proposed, which characterizes synthetic instances as solutions of attraction‐repulsion problems among the neighboring data points. Experimental evaluation shows an improvement in the positive predictive power of classification.

The adoption of digital and communication technologies (DCTs) is a critical success factor in port industry. Several pieces of empirical evidence are demonstrating that advanced knowledge infrastructures support port efficiency and competitiveness, for example, through intelligent transport systems, such as sensors, actuators, and platforms. In this perspective, this paper evaluates the impact of investments in DCTs-i.e., interactive websites and social media marketing solutions-on port efficiency. To this end, we perform the nonparametric method of data envelopment analysis, both in its crisp and fuzzy approaches, in order to account for the vague and imprecise nature of some data. Findings pinpoint that port efficiency is generally supported by DCT solutions, and for some ports, the effect is particularly relevant. The outcomes provide managerial suggestions for port authorities, policy makers, and industrial practitioners to identify critical investments for improving port competitiveness.

Differently from attacks aimed at gaining control of the resources of a mobile device, energy-related attacks have the essential goal of significantly raising the energy demand on the victim side, without apparently affecting its activities. It is a fundamental point to highlight how such a goal can possibly be accomplished by mounting well-known canonical attacks and waiting for the system defenses to detect and stop them. In such an endeavor, defenses require additional amounts of energy which eventually render the mobile device completely useless. In the System on Chip (SoC) architecture, many components, each with a separate function, are integrated. As the total energy adsorption is the composition of the energy consumptions of individual components, each component may be the target of an energy-based attack. This work analyzes and discusses the effects and implication of new energy-based Denial of Service attacks based on the proper solicitation of hardware-layer encode/decode capabilities by using specifically crafted multimedia resources, in order to introduce an anomalous battery drain, and hence significantly shorten the overall battery lifetime in mobile smart devices. These attacks do not require physical access nor compromise of the target device, and they take advantage of new HTML5 functionalities that can be properly triggered during normal browsing activity. The more significant result is that the Digital Signal Processor (DSP) offers an exploitable attack surface to be kept into consideration early in the design process. Countermeasures include special filtering rules that prevent “irrelevant” content from reaching the DSP or, in a more far-reached perspective, the introduction of a power-draw controller on the SoC with the purpose of monitoring energy consumption and raising alerts.

People using smartphones to connect to the Internet for day-life activities has overtaken the number of people using canonical PCs. This lead to a huge quantity of security threats that usually tend to penetrate the defenses of a smartphone in order to gain control of its resources. Differently, energy-based attacks have the objective of increasing the energy consumption of the victim device. It is important to highlight that this objective could be possibly achieved by just activating the system's defenses as a consequence of canonical attacks and letting the system defenses detect and (try to) defeat them. These activities consume additional energy and could led the mobile device to its complete uselessness. In this paper, an energy-based attack based on soliciting hardware-level encoding/decoding functions through properly crafted multimedia files is analyzed and its impact evaluated. Such kind of attacks are performed without accessing the device by taking advantage of the new HTML5 functionalities. A series of experiments have been performed in order to understand which are the codecs that have a more relevant impact on energy consumption, and, as a consequence, that make the attack more effective.

SUMMARY Network anomalies, circumstances in which the network behavior deviates from its normal operational baseline, can be due to various factors such as network overload conditions, malicious/hostile activities, denial of service attacks, and network intrusions. New detection schemes based on machine learning principles are therefore desirable as they can learn the nature of normal traffic behavior and autonomously adapt to variations in the structure of ‘normality’ as well as recognize the significant deviations as suspicious or anomalous events. The main advantages of these techniques are that, in principle, they are not restricted to any specific environment and that they can provide a way of detecting unknown attacks. Detection performance is directly correlated with the traffic model quality, in terms of ability of representing the traffic behavior from its most characterizing internal dynamics. Starting from these ideas, we developed a two‐stage anomaly detection strategy based on multiple distributed sensors located throughout the network. By using Independent Component Analysis , the first step, modeled as a Blind Source Separation problem, extracts the fundamental traffic components (the ‘source’ signals), corresponding to the independent traffic dynamics, from the multidimensional time series incoming from the sensors, corresponding to the perceived ‘mixed/aggregate’ effect of traffic on their interfaces. These components will be used to build the baseline traffic profiles needed in the second supervised phase, based on a binary classification scheme (detection is casted into an anomalous/normal classification problem) driven by machine learning‐inferred decision trees. Copyright © 2013 John Wiley & Sons, Ltd.

The growing intelligence and popularity of smartphones and the advances in Mobile Ubiquitous Computing have resulted in rapid proliferation of data-sharing applications. Instances of these applications include pervasive social networking, games, file sharing and so on. In such scenarios, users are usually involved in selecting the peers with whom communication should take place, continuously facing trust issues. Unfortunately, providing trust support in a pervasive world is challenging due to peer mobility and lack in central control. We propose a novel approach that establishes trust leveraging users' profiles: humans today produce rich strings of unique data twenty-four hours a day. These information enables a task-aware trust model, namely a finer-grained model in which users are classified as trusted or not depending on the intended business activity. However, simply collecting user's interests may be insufficient to provide a reasonable trust management system. In order to enable the system to recognize malicious users, we include a recommendation subsystem based on the Wilson score confidence interval. It has been designed to be lightweight, minimizing battery depletion. It also protects user privacy. To make our approach fully deployable, it supports two modalities: a TPM-based one and a TPM-less one. The former gives more security guarantees and ensures a fully distributed approach. The latter, requires a Trusted Authority to avoid feedbacks to get tampered and is no more fully distributed.

Energy consumption and the concomitant Green House Gases (GHG) emissions of network infrastructures are becoming major issues in the Information and Communication Society (ICS). Current optical network infrastructures (routers, switches, line cards, signal regenerators, optical amplifiers, etc.) have reached huge bandwidth capacity but the development has not been compensated adequately as for their energy consumption. Renewable energy sources (e.g. solar, wind, tide, etc.) are emerging as a promising solution both to achieve drastically reduction in GHG emissions and to cope with the growing power requirements of network infrastructures. The main contribution of this paper is the formulation and the comparison of several energy-aware static routing and wavelength assignment (RWA) strategies for wavelength division multiplexed (WDM) networks where optical devices can be powered either by renewable or legacy energy sources. The objectives of such formulations are the minimization of either the GHG emissions or the overall network power consumption. The solutions of all these formulations, based on integer linear programming (ILP), have been observed to obtain a complete perspective and estimate a lower bound for the energy consumption and the GHG emissions attainable through any feasible dynamic energy-aware RWA strategy and hence can be considered as a reference for evaluating optimal energy consumption and GHG emissions within the RWA context. Optimal results of the ILP formulations show remarkable savings both on the overall power consumption and on the GHG emissions with just 25% of green energy sources.

The enormous growth in popularity of peer-to-peer applications has recently introduced great interest in understanding the associated traffic workload and behavior. The goal of this work is determining the fundamental dynamics characterizing such traffic that can be used to develop simple and effective prediction models and to illustrate and describe fundamental performance issues. The discovery of nonlinear traffic dynamics, due to the very complex characteristics of the involved time series, led us to use several nonlinear analysis techniques and tools evidencing the presence of chaos-related structures together with self-similarity and long-range dependence features.

Simulation seems to be the best available alternative to the deployment of expensive and complex testbed infrastructures for the activities of testing, validating and evaluating optical network control protocols and algorithms. In this paper we present SimulNet, a specialized optical network simulation environment providing the foundation for the study and analysis of the key control plane characteristics of wavelength-routed networks. Such an environment would provide researchers with an open framework for easily exploring the evolving characteristics of WDM-routed technologies which includes developing new protocol suites or performing rapid evaluation and easier comparison of results across research efforts.