Quantum cryptography offers possibilities to distribute keys securely by relying on the principles of quantum mechanics. This paper presents repeatable simulations of two well-known quantum key distribution protocols, BB84 and E91, and examines how eavesdropping, noise, and transmission losses affect their security. The simulations are carried out using the Qiskit framework. A theoretical analysis of the expected quantum bit error rate (QBER) under ideal conditions and during an intercept–resend attack is included. The Clauser–Horne–Shimony–Holt (CHSH) parameter is also evaluated for the entanglement-based protocol. The results show error rates close to zero without attack, an increase to about 25\% under eavesdropping, and reduced correlations in the presence of attacks or losses. For the E91 protocol, the CHSH parameter exceeds the classical bound (S >2) in the entangled scenario and drops below the classical limit (S <= 2) in the attack case.

The widespread adoption of machine learning, particularly generative models, has revolutionized productivity and capabilities. However, this progress comes with significant security risks, as adversarial attacks can subtly manipulate model predictions through imperceptible perturbations. Multimodal machine learning models, integrating data from modalities such as images and text, further expand the attack surface by enabling cross-modal exploitation. This paper examines the adversarial robustness of multimodal AI systems, focusing on text and image modalities. We analyze security challenges arising from cross-modal interactions and explore adversarial example generation techniques targeting individual modalities and shared embedding spaces. The paper concludes by identifying critical open challenges and promising research directions to enhance the security of multimodal AI systems.

Machine learning (ML) techniques have gained widespread adoption in medical image diagnosis. However, their susceptibility to adversarial attacks raises concerns regarding their reliability in clinical applications. This study investigates the robustness of two convolutional neural network architectures, ResNet50 and VGG16, against adversarial perturbations introduced via the Fast Gradient Signed Method (FGSM) and DeepFool algorithms. An experimental evaluation was conducted using medical imaging data from the Lung Image Database Consortium (LIDC-IDRI), comprising computed tomography (CT) images annotated for lung lesions. Model performance was quantitatively assessed using metrics derived from confusion matrices, including accuracy, precision, sensitivity, specificity, and F1-score. The results demonstrate a significant vulnerability of both the ResNet50 and VGG16 networks to adversarial manipulations, resulting in considerable degradation of the classification accuracy, particularly under higher perturbation magnitudes. To mitigate these vulnerabil- ities, adversarial training employing FGSM-generated perturbations was implemented, notably enhancing model robustness and classification performance in adversarial settings. The findings confirm the efficacy of adversarial training as a defensive approach against adversarial attacks; however, further research into advanced adversarial defense mechanisms and novel model architectures remains essential to ensure the secure and reliable deployment of ML models in medical diagnostics.

Despite the increasingly widespread automation of processes and systems in large-scale cyber infrastructures, human errors such as choosing weak passwords or knowingly or unknowingly exposing oneself to data theft are still the most common factors undermining the security of these infrastructures and systems. However, viewing the human factor as the sole cause of the vulnerability of IT systems to external attacks is a far-reaching oversimplification. Human-centric cybersecurity aims to orient security strategies to the needs, role definitions and behaviours of users of information systems. Such a strategy assumes that when security measures consider users’ experiences, they are more likely to be adopted and less likely to be ignored. This talk highlights the key tenets and trends in human-centric cybersecurity.

This paper presents a new framework for process?ing 3D-volumetric radar data that generates Cartesian products for operational and numerical weather predic?tion purposes. Transformation of a typical polar-radial coordinate system into 3D Cartesian coordinates facil?itates weather radar composition, visualisation, and as?similation. Moreover, using 3D Cartesian coordinates significantly improves the flexibility of the product generation process. Implementation of the framework generated 72 composite products in a reasonable run time.

Intrusion Detection Systems (IDS) should be capable of quickly detecting attacks and network traffic anomalies to reduce the damage to the network components. They may efficiently detect threats based on prior knowledge of attack characteristics and the potential threat impact ('known attacks'). However, IDS cannot recognize threats, and attacks ('unknown attacks') usually occur when using brand-new technologies for system damage. This paper presents two security services -- Net Anomaly Detector (NAD) and a signature-based PGA Filter for detecting attacks and anomalies in TCP/IP networks. Both services are modules of the cloud-based GUARD platform developed in the H2020 GUARD project. Such a platform was the main component of the simulation environment in the work presented in this paper. The provided experiments show that both modules achieved satisfactory results in detecting an unknown type of DoS attacks and signatures of DDoS attacks.

Atmospheric conditions, such as thunderstorms, are significant factors that influence human activity. Harsh weather may severely impact both daily life and professional activities. Severe thunderstorms are a considerable hazard -- they can generate heavy rainfall, high winds, large hail and tornadoes. Tracking of thunderstorms is necessary to gain situational awareness - knowledge of present and future storm-related threats and their significance. Thunderstorms are weather phenomena associated with cumulonimbus clouds. Those clouds are formed in deep, moist convection and are composed of liquid and solid water particles. Weather radars can detect those particles. Cumulonimbus-related particle concentration areas are represented in weather radar data as convective cells, making that measurement technique useful for storm-tracking applications. This paper proposes a new algorithm for storm data tracking in the data fusion process. The algorithm has been tested with real data from the POLRAD weather radar network and upper-air observations. The efficiency of the proposed algorithm has been justified in the empirical analysis. The algorithm projections can be useful in generating weather warnings due to accurate projections of storm movement.

Evolving computing paradigms are progressively introducing new design, development, and operation models for digital services, which increasingly leverage service-oriented architectures and microservices patterns to create data-centric applications. This approach eventually brings more agility in the overall service lifetime management, but also introduces additional security and privacy concerns that cannot be effectively addressed by legacy device- and infrastructure-centric models. The GUARD project developed an extensible platform for building detection and analytics services for advanced assurance and protection of trustworthy and reliable business chains which span multiple administrative domains and heterogeneous infras-tructures. GUARD advocates the implementation of embedded security capabilities in digital services, that can be accessed and orchestrated through API similar to what already happens for management and operation purposes. GUARD features are demonstrated on two challenging use cases, in the Smart Mobility and eHealth domains.

The ultimate goal of Mobile Cloud Computing is to allow users of mobile devices to execute their applications and complex numerical tasks on a broad range of cloud services and resources. One of the most challenging problems in the flow of mobile tasks related to remote cloud services is the security of all aspects of communication, service security and the reliability of cloud resources. In this paper, we developed a new security-aware job flow model for mobile computational clouds. In our model, we defined dedicated algorithm models such as the Filtration Algorithm and Prediction Module to generate an optimal secure system architecture for task and data processing and to ensure optimal cloud resource and service utilization. The robust performance of our model has been demonstrated by experimental analysis. Results of the experiments performed show that our flow model significantly enhances the security level of computations compared to a configuration in which computation time is the major criterion for job processing optimization.

The intensive development and growth in the popularity of mobile cloud computing services bring a critical need to introduce new solutions that increase the level of cloud and users security. One of the critical issues in highly distributed computational systems is a task scheduling process. This process may be exposed to many external and internal security threats, like task injection, machine failure or generation of incorrect schedule. These problems are especially important in mobile environments. It can be even more complicated if we take into consideration the personalization of the services offered. Recently, blockchain has been gaining rapidly in popularity, combining high efficiency with applications in distributed and highly personalized computational environments. In this paper, we developed and described a novel model for security-aware task scheduling in cloud computing based on blockchain technology. Unlike other blockchain-based solutions, the proposed model uses Proof of Stake, which does not have high requirements for computing power. A series of conducted experiments confirmed the high efficiency of the proposed model.

Secure mobile

Summary In this article, we present an original agent‐based adaptive task scheduling system which optimizes the performance of services in the mobile cloud computing environment using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the mobile device and the cloud. Decisions are made taking into account the context (e.g., network connection type, location, security level). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online on a mobile device. To verify the solution proposed, appropriate software has been developed and a series of experiments has been conducted. Results demonstrate that owing to the experience gathered and the learning process performed, the decision module becomes more efficient in assigning the task to either the mobile device or cloud resources. In the face of presented improvements, the security issues inherent in the context of mobile services/applications and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often ruling out the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering performance.

In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that due to the experience gathered and the learning process performed, the decision module has consequently become more efficient in assigning the task to either the mobile device or cloud resources. In face of presented improvements, the security issues inherent within the context of mobile application and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often preventing the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering the performance.

Looking at the rapid development of computer networks, it can be said that the transmission quality assurance is very important issue. In the past there were attempts to implement Quality of Service (QoS) techniques when using various network technologies. However QoS parameters are not always assured. This paper presents a novel concept of transmission quality determination based on Machine Learning (ML) methods. Transmission quality is determined by four parameters - delay, jitter, bandwidth and packet loss ratio. The concept of transmission quality assured network proposed by Pay&Require was presented as a novel multi-agent approach for QoS based computer networks. In this concept the essential part is transmission quality rating which is done based on transmission parameters by ML techniques. Data set was obtained based on the experience of the users test group. For our research we designed a machine learning system for transmission quality assessment. We obtained promising results using four classifiers: Nu-Support Vector Classifier (Nu-SVC), C-Support Vector Classifier (C-SVC), Random Forest Classifier, and K-Nearest Neighbors (kNN) algorithm. Classification results for different methods are presented together with confusion matrices. The best result, 87% sensitivity (overall accuracy), for the test set of data, was achieved by Nu-SVC and Random Forest (13/100 incorrect classifications).

Energy-awareness remians the important problem in today’s cloud computing (CC). Optimization of the energy consumed in cloud data centers and computing servers is usually related to the scheduling prob lems. It is very difficult to define an optimal schedul ing policy without negoative influence into the system performance and task completion time. In this work, we define a general cloud scheduling model based on a Stackelberg game with the workload scheduler and energy-efficiency agent as the main players. In this game, the aim of the scheduler is the minimization of the makespan of the workload, which is achieved by the employ of a genetic scheduling algorithm that maps the workload tasks into the computational nodes. The energy-efficiency agent selects the energy-optimization techniques based on the idea of switchin-off of the idle machines, in response to the scheduler decisions. The efficiency of the proposed model has been tested using a SCORE cloud simmulator. Obtained results show that the proposed model performs better than static energy-optimization strategies, achieving a fair balance between low energy consumption and short queue times and makespan.

Task scheduling in large-scale distributed High Performance Computing (HPC) systems environments remains challenging research and engineering problem. There is a need of development of novel advanced scheduling techniques in order to optimise the resource utilisation. In this work, we develop the Agent Supported Non-Deterministic Meta Scheduler for cloud environments. This scheduling model is a simple combination of intelligent agent-based monitoring model for cloud system and security-aware cloud scheduler. In our model, scheduling, monitoring and reporting are provided in nondeterministic time intervals. An empirical case study using a FastFlow task farm was presented. It has demonstrates the effectiveness of the proposed solution.

This note is a corrigendum of the paper “Control system for reducing energy consumption in backbone computer network” 1. We point out technical errors in mathematical models at pages 1744 and 1747. We apologize for a technical errors in the formulas (6)-(8) and (21)-(23) in Section 5, pages 1744 and 1747, respectively. In (6), (8), (21) and (23) was missing. In (7) and (22) was missing. Additionally, the following sentence “We assume that at a given time instant two ports connected by e-th link are in the same state k.” at page 1743 (Section 5) may be interpreted as a suggestion that links building the network are symmetric, i.e., they should operate in both directions at the same energy state. However, more flexible formulation was intended by the authors of the paper. The symmetric links were not considered. In general, in real networks energy states of both halves of the link can be setup independently. Thus, the mentioned sentence should be removed from the article. Our thanks to Andrzej Karbowski for bringing this matter to our attention.

Data-aware scheduling in large-scale heterogeneous computing systems remains a challenging research issue, especially in the era of Big Data. Design of all data-related components of the popular distributed environments, such as Data Clouds (DCs), Data Grids (DGs) and Data Centers supports the processing, analysis and monitoring of the big data generated by various sources at computing centers by the end-users, devices and services. The above facts leave no doubts that data scheduling must be integrated in a single joint process together with the scheduling of computer tasks and applications. Therefore, many of the current optimization issues need to be changed and new requirements have to be considered in the scheduling process. This includes data transmission times, data processing times, availability of the data servers, safety and authentication in the data access processes. This paper presents a new version of the Expected Time to Compute Matrix model (ETC Matrix) for the case of data-aware independent batch scheduling in physical network in DGs and DCs environments. Simple geneticbased schedulers have been developed for experimental justification of the significance of the presented problem.

Summary In today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such as Makespan , is treated as first‐class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi‐objective global minimization problem with Makespan and energy consumption as the main criteria. We apply the dynamic voltage and frequency scaling model for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy‐aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm‐based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). Copyright © 2012 John Wiley & Sons, Ltd.

Task scheduling and resource allocations are the key issues for computational grids. Distributed resources usually work at different autonomous domains with their own access and security policies that impact successful job executions across the domain boundaries. In this paper, we propose an Artificial Neural Network (ANN) approach for supporting the security awareness of evolutionary driven grid schedulers. Making a prior analysis of the trust levels of resources and security demand parameters of tasks, the neural network monitors the scheduling and task execution processes. In the result produce the tasks-machines mapping “suggestions”, which can be then utilized by the scheduler to reduce the makespan or increase the system throughput. In this paper, we report the development of risk-resilient genetic-based schedulers and their integration with an ANN module of the HyperSim-G Grid Simulator to evaluate the proposed model under the heterogeneity and large-scale system dynamics. The simulation results showed a significant impact of the ANN support on enhancing the effectiveness of the genetic-based meta-heuristics in reducing the cost of security awareness in grid scheduling.

Scheduling in traditional distributed systems has been mainly studied for system performance parameters without data transmission requirements. With the emergence of Data Grids (DGs) and Data Centers, data-aware scheduling has become a major research issue. In this work we present two implementations of classical genetic-based data-aware schedulers of independent tasks submitted to the grid environment. The results of a simple. empirical analysis confirm the high effectiveness of the genetic algorithms in solving very complex data intensive combinatorial optimization problems.

Global optimization in large-scale distributed systems requires massive amounts of computations for complex objective functions. Conventional global optimization based on stochastic algorithms cannot guarantee an actual global optimum with a finite searching iteration. Therefore, scalability is a desirable feature for the optimization techniques in highly distributed dynamic environments, where the storage and computing capabilities can be spread over a wide geographical area. They must dynamically adapt to organizational relationships and real-world uncertainties. Intelligent Networks, such as grids, peer-to-peer, ad hoc networks, constellations, and clouds enable the flexible routing and charging, advanced user interactions and the aggregation and sharing of geographically distributed resources. Collectively owned and managed by distinct organizational bodies, such complex large-scale distributed systems typically encompass computational resources from different institutions, enterprises, and individuals and are governed by heterogeneous administrative policies and regulations. System management techniques must therefore be able to group, predict, and classify different sets of rules, configuration directives, and environmental conditions to impose dissimilar usage policies on various users and resources. They must effectively deal with various optimization criteria, users’ requirements, massive data processing, and, finally, uncertainties in system information that may be incomplete, imprecise, and fragmentary. Next information technology architectures, such as green cloud-to-cloud systems and green mobile clouds, provide elastic and in fact unlimited resources, including storage, as various services to cloud users with possible minimal energy utilization. However, both cloud users and cloud service providers are almost certain to be from different trust domains. Therefore, a secure user-enforced data access control mechanism must be provided before cloud users have the liberty to outsource sensitive data to the cloud for storage and further processing. With the advent of intelligent networks, where efficient interdomain operation and high scalability of the whole system are the most important features, it is arguably required to investigate novel methods and techniques to enable secure access to data and resources, flexible communication, efficient scheduling, self-adaptation, decentralization, and self-organization. This special issue herewith presents six research papers with novel concepts in the analysis, implementation, and evaluation of the next generation of intelligent scalable techniques for data-intensive processing and global optimization problems in large-scale distributed systems. The first three papers discuss novel scalable solutions of data-intensive global optimization problems in well-known large-scale network environments. The presented techniques and their implementations are based on formal mathematical and logical models with the new optimization criteria (energy conservation), semantic rules and ontology, and modern synchronization modules of parallel computational processes. Li et al. in 1 introduced a methodology for improvement of the performance of the dynamic core of Global/Regional Assimilation and Prediction System (GRAPES) – the Numerical Weather Prediction system used by Chinese Meteorology Administration. The system performance is formally modeled as a sequence of large, sparse linear systems formulated by the discretization of global 3D Helmholtz equation. The authors developed a solver that enables an effective synchronization of the numerical processes at the global units of the system. The results of simple empirical analysis show good scalability of the proposed methodology achieved by using up to 6144 active cores in GRAPES. In 2, the authors present a framework for the energy-aware system management in backbone networks. The energy optimization problem is formulated as a general mathematical programming problem with various constraints and control parameters. Dynamic voltage and frequency scaling method is implemented for minimizing the energy utilization at global and local levels of the management system along with a wide range of the resolution methodologies. All possible energy saving decisions of the system units are directly specified, together with decisions concerning traffic assignment to particular links. The results of the experiments show the best performance of the system in the case of concentration of the network traffic on a minimal subset of network components. The problem of massive processing of huge volumes of data in the Internet is discussed in 3. Dong and Hussein propose an ontology-based Web crawler and Web page classifier with an embedded semisupervised learning module. This module enables the continuous enrichment of the definitions of ontological concepts in crawling and Web page classification process. The semantic relevance of crawling topics and Web pages is specified by semantic similarity and probabilistic models. The remaining three papers address the big-data paradigm from various perspective. Bilal et al. 4 benchmark some well-known data center network architectures and categorically state their pros and cons. With this knowledge, the authors propose future advancements pertaining to the network architecture of data centers. In 5, a generic data-structure oriented programming template is discussed for supporting massive remote sensing data. The authors have built the case that the templates provide distributed abstractions for large remote sensing image data with complex data structures. The performance of their technique is improved by developing efficient parallel input/output (I/O) directly to and from the distributed data structures. Zhang et al. 6 have discussed an advanced data center architecture that harness the power of multiple data centers. The key technology that they advocate to manage such a large-scale distributed computing system is to build on both groups of distributed data centers/clusters that are equipped with data center or cluster resource manager. Additional security and access control procedures are put in place to provide a seamless interaction between various domains. In addition to a structure, the domain data centers are organized as collaborative modules, which enables processing of workflow workloads. We believe that all of the papers presented in this Special Issue ought to serve as a reference for students, researchers, and industry practitioners interested or currently working in the evolving and interdisciplinary area of scalable computing and intelligent networking. We hope that the readers will find new inspiration for their research. We are grateful to all the contributors of this issue. We thank the authors for their time and efforts in the presentation of their recent research results. We also would like to express our sincere thanks to the reviewers, who have helped us to ensure the quality of this publication. Our special thanks go to Prof Geoffrey C. Fox (Editor-in-Chief) and all of the editorial and management team of Concurrency and Computation: Practice and Experience Wiley journal for their great support throughout the entire publication process.

SUMMARY Network optimization concerned with operational traffic management in existing data networks is typically oriented towards either maximizing throughput in congested networks while providing for adequate transmission quality, or towards balancing the traffic so as to maintain possibly large free capacity for carrying additional (new) traffic. Nowadays, the reduction of power consumption is a new key aspect in the development of modern wired networks. Power management capabilities allow modulating the energy consumption of devices that form a network by putting them into standby state, or by decreasing their performance in case of low incoming traffic volume. This paper presents a framework for backbone network management, which leads to the minimization of the energy used by this network. The policy for dynamic power management of the whole network through energy‐aware routing, traffic engineering, and network equipment activity control is introduced and discussed. The concept of the system is to achieve the desired trade‐off between total power consumption and the network performance according to the current load, incoming traffic, and user requirements. The effectiveness of our framework is illustrated by means of a numerical study. Copyright © 2012 John Wiley & Sons, Ltd.

We are witnessing a paradigm shift in the way mobile devices are being used and operated.What was once a voice network is now predominantly a data network.As a consequence, end-users are now using mobile systems for applications that fall under the data intensive paradigm, such as Skyline queries, streaming information relays, and crowd sourced disaster management.However, this paradigm shift has opened new research directions, such as: (a) Security, as the system now has numerous distributed entry points and the behavior of a malicious entity does not really correlate with any previously known phenomenon (e.g., Internet virus attacks, DOS attacks, etc.).(b) Data interoperability that must cater to the fundamental issue that mobile devices are required to work seamlessly with Internet data, thus requiring revision of protocols, data exchange frameworks to improve data sharing among mobile devices and with the Internet.(c) Sustainable software development that entails the development of software models for mobile devices that have a longer life-cycle and require fewer updates.This also effectively translates into an economically viable mobile system.Privacy and security aspects need to be covered at all layers of mobile networks, from mobile users' devices, to privacy-respecting credentials and mobile identity management.All of the above mentioned research domains are complex on their own, which makes it a very attractive research area for academia and industry.The eventual goal is to make the mobile systems seamless integrate with Inter and Intranet devices without a measurable performance degradation.Is is arguably required to investigate novel methods and techniques to enable secure access to data, network nodes and services, flexible communication, efficient scheduling, self-adaptation, decentralization, and self-organization.This special issue herewith presents six research papers with novel concepts in the analysis, implementation, and evaluation of the next generation of intelligent scalable techniques for data intensive processing and security related problems in modern mobile environments.The first three papers span the fields of key management, power modeling and mobility modeling, but all share a relevance to security aspects.Cryptographic and key management systems in mobile networks must be computationally low-cost because of the limitations of computational and data storage capacities and battery life of most of the network nodes.Wu and Lin present non-interactive authenticated key agreement (NI-AKA) protocols based on the idea of bilinear pairingbased cryptosystem model and Elliptic Curve Encryption (ECE) scheme.The ECE allows the encryption of message multiple times with different keys that can be decrypted in

This paper addresses issues concerned with design and managing of mobile ad hoc networks. We focus on self-organizing, cooperative and coherent networks that enable a continuous communication with a central decision unit and adopt to changes in an unknown environment to achieve a given goal. In general, it is very difficult to model a motion of nodes of a real-life ad hoc network. However, mobility modeling is a critical element that has great influence on the performance characteristics of a cooperative system. In this paper we investigate a novel approach to cooperative and fully connected networks design. We present an algorithm for efficient calculating of motion trajectories of wireless devices. Our computing scheme adopts two techniques, the concept of an artificial potential field and the concept of a particle-based mobility. The utility and efficiency of the proposed approach has been justified through simulation experiments. The results of presented case studies show a wide range of applications of our method starting from simple to more complex ad hoc networks.

ABSTRACT Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small‐sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust‐based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.

Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregate bandwidth requirement is a major bottleneck to data center performance. New Data Center Network (DCN) architectures are proposed to handle different challenges faced by current DCN architecture. In this paper we have implemented and simulated two promising DCN architectural models, namely switch-based and hybrid models, and compared their effectiveness by monitoring the network throughputs and average packet latencies. The presented analysis may be a background for the further studies on the simulation and implementation of the DCN customized topologies, and customized addressing protocols in the large-scale data centers.

In a Delay Tolerant Network (DTN), the nodes have intermittent connectivity and complete path(s) between the source and destination may not exist. The communication takes place opportunistically when any two nodes enter the effective range. One of the major challenges in DTNs is message forwarding when a sender must select a best neighbor that has the highest probability of forwarding the message to the actual destination. However, finding an appropriate route remains an NP-hard problem. This paper presents a concept of Checkpoint (CP) based message forwarding in DTNs. The CPs are autonomous high-end wireless devices with large buffer storage and are responsible for temporarily storing the messages to be forwarded. The CPs are deployed at various places within the city parameter that are covered by bus routes and where human meeting frequencies are higher. For the simulative analysis a synthetic human mobility model in ONE simulator is constructed for the city of Fargo, ND, USA. The model is tested over various DTN routing protocols and the results indicate that using CP overlay over the existing DTN architecture significantly decreases message delivery time as well as buffer usage.

In grid computing, the realization of an enviable fault tolerance ability is linked with the proper utilization of resources and scheduling of jobs. The literature offers two solutions to these two challenging tasks, viz. checkpointing and replication. A checkpointing strategy is being proposed that uses the median of failure intervals of the resources in deciding the checkpoint intervals for the given jobs. The strategy shows improved system throughput, job losses and job execution times while eliminating unnecessary checkpoints.

Global optimization of the energy consumption in heterogeneous environments has been recently an important research issue in wired and wireless networks. This paper presents a general framework for flexible and cognitive backbone network management which leads to the minimization of the energy utilized by the network. The policy for activity control of all the modules and elements that form a network is introduced and discussed. The idea of the system is to achieve the desired trade-off between energy consumption and network performance according to the traffic load.

Analysis of neural signals like electroencephalogram (EEG) is one of the key technologies in detecting and diagnosing various brain disorders. As neural signals are non-stationary and non-linear in nature, it is almost impossible to understand their true physical dynamics until the recent advent of the Ensemble Empirical Mode Decomposition (EEMD) algorithm. The neural signal processing with EEMD is highly compute-intensive due to the high complexity of the EEMD algorithm. It is also data intensive because 1) EEG signals contain massive data sets 2) EEMD has to introduce a large number of trials in processing to ensure precision. The Map Reduce programming mode is a promising parallel computing paradigm for data intensive computing. To increase the efficiency and performance of the neural signal analysis, this research develops parallel EEMD neural signal processing with Map Reduce. In this paper, we implement the parallel EEMD with Hadoop in a modern cyber infrastructure. Test results and performance evaluation show that parallel EEMD can significantly improve the performance of neural signal processing.

Task scheduling and resource allocation remain still challenging problems in Computational Grids (CGs). Traditional computational models and resolution methods cannot effectively tackle the complex nature of Grid, where the resources and users belong to many administrative domains with their own access policies and users' privileges, and security and task abortion awareness are addressed as important scheduling criteria. In this paper we propose a neural network approach for supporting the security awareness of the genetic-based grid schedulers. Making a prior analysis of trust levels of the resources and security demand parameters of tasks, the neural network monitors the scheduling and task execution processes. The network learns patterns in input (tasks and machines initial characteristics) and outputs (information about resource failures and the resulting tasks and machines characteristics) data, and finally sub-optimal schedules are generated, which are then used to modify the initialization procedures of genetic scheduling algorithms. We extended the Hyper Sim-G Grid simulator framework by Neural Network module to evaluate the proposed model under the heterogeneity, the large-scale and dynamics conditions. The relative performance of GA-based and Neural Network GA-based schedulers is measured by the make span and flow time metrics. The obtained results showed the efficacy of the Neural Network approach to enhance the secure GA-based schedulers.

A foremost objective in wireless networks is to facilitate the communication of mobile users and the widespread tracking and prediction of their mobility regardless of their point of attachment to the network. In indoor environments the effective users' motion prediction system and wireless localization technology play an important role in all aspects of people's daily lives, including e.g. living assistant, navigation, emergency detection, surveillance/tracking of target-of-interest, evacuation purposes, and many other location-based services. Prediction techniques that are currently used do not consider the motivation behind the movement of mobile nodes and incur huge overheads to manage and manipulate the information required to make predictions. In this paper we propose an activity-based continuous-time Markov model to define and predict the human movement patterns. Then we demonstrate the utility of Nonparametric Belief Propagation (NBP) technique in particle filtering, for both estimating the node locations and representing location uncertainties, and for prediction of the areas that would be visited and those that would not in the future. NBP method admits a wide variety of statistical models, and can represent multi-modal uncertainty. This prediction system may be used as an additional input into intelligent building automation systems.

There has been a lot of research recently devoted to scheduling and resource allocation in Grid systems. Research efforts have been done in particular to the use of heuristic and meta-heuristic approaches in the design of efficient Grid schedulers. In this paper we present a comprehensive study on the performance of different population-based heuristic methods, namely Genetic Algorithms, Memetic Algorithms and Cellular Memetic Algorithms for the problem. The aim is to shed light on the advantages and limitations of different population based methods as well as their hybridization with local search methods, such as Tabu Search, when solving the multi-objective version of the problem under execution time restrictions of Grid schedulers. We considered a set of scenarios that represent a high variation regarding the size of entries and static/dynamic features aiming to judge on the robustness with regard to the quality of the solutions obtained by the considered methods. These scenarios are divided into static, which provides a single set of tasks and resources for each entry, and dynamic, using a grid simulator used to observe the behavior of heuristics in Grid environments in real time.

systems We present a simulation model designed for evaluation of dependability in distributed systems. The model is a modification of the MONARC simulation model by adding new capabilities for capturing the reliability, safety, availability, security, and maintainability requirements. It includes components for failures injection, and it provides evaluation mechanisms for different replication strategies, redundancy procedures, and security enforcement mechanisms. The model is implemented as an extension of the multi-threaded, process oriented simulator MONARC, which allows the realistic simulation of a wide-range of distributed system technologies, with respect to their specific components and characteristics. The experimental results show that the application of the discrete-event simulators in the design and development of the dependable distributed systems is appealing due to their efficiency and scalability

Scheduling independent tasks in Computational Grids commonly arises in many Grid-enabled large scale applications. Much of current research in this domain is focused on the improvement of the efficiency of the Grid schedulers, both at global and local levels, which is the basis for Grid systems to leverage large computing capacities. However, unlike traditional scheduling, in Grid systems security requirements are very important to scheduling tasks/applications to Grid resources. The objective is thus to achieve efficient and secure allocation of tasks to machines. In this paper we propose a new model for secure scheduling at the Grid sites by combining game-theoretic and genetic-based meta-heuristic approaches. The game-theoretic model takes into account the realistic feature that Grid users usually perform independently of each other. The scheduling problem is then formalized as a noncooperative non-zero sum game with Nash equilibria as the solutions. The game cost function is minimized, at global and user levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model through a static benchmark of instances, for which we have measured two basic metrics, namely the makespan and flowtime. The obtained results suggest that it is more resilient for the Grid users (and local schedulers) to tolerate some job delays defined as additional scheduling cost due to security requirements instead of taking a risk of allocating at unreliable resources.

In this work we present an implementation of Hierarchic Genetic Strategy (HGS) for Independent Job Scheduling on Computational Grids. In our formulation of the scheduling problem, makespan and flowtime parameters are simultaneously optimized. The efficient assignment of jobs to machines that optimizes both objectives is crucial for many Grid systems. The objective of this work is to examine several variations of HGS operators in order to identify a configuration of operators and parameters that works best for the problem. Differently from classical GA algorithms, which maintain only an unstructured population of individuals, HGS performs by many small populations enabling a concurrent search in the optimization domain. From the experimental study we observed that HGS implementation outperforms existing classical GA schedulers for most of considered instances of a static benchmark for the problem.