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This study proposes a secondary voltage and frequency control scheme based on the distributed cooperative control of multi-agent systems. The proposed secondary control is implemented through a communication network with one-way communication links. The required communication network is modelled by a directed graph (digraph). The proposed secondary control is fully distributed such that each distributed generator only requires its own information and the information of its neighbours on the communication digraph. Thus, the requirements for a central controller and complex communication network are obviated, and the system reliability is improved. The simulation results verify the effectiveness of the proposed secondary control for a microgrid test system.

Purpose Online social networks (OSNs) are now among the most popular applications on the web offering platforms for people to interact, communicate and collaborate with others. The rapid development of OSNs provides opportunities for people’s daily communication, but also brings problems such as burst network traffic and overload of servers. Studying the population growth pattern in online social networks helps service providers to understand the people communication manners in OSNs and facilitate the management of network resources. In this paper, we propose a population growth model for OSNs based on the study of population distribution and growth in spatiotemporal scale-space. Methods We investigate the population growth in three data sets which are randomly sampled from the popular OSN web sites including Renren, Twitter and Gowalla. We find out that the number of population follows the power-law distribution over different geographic locations, and the population growth of a location fits a power function of time. An aggregated population growth model is conducted by integrating the population growth over geographic locations and time. Results We use the data sets to validate our population growth model. Extensive experiments also show that the proposed model fits the population growth of Facebook and Sina Weibo well. As an application, we use the model to predict the monthly population in three data sets. By comparing the predicted population with ground-truth values, the results show that our model can achieve a prediction accuracy between 86.14 %  and 99.89 % . Conclusions With our proposed population growth model, people can estimate the population size of an online social network in a certain time period and it can also be used for population prediction for a future time.

In previous work [J. D. Skufca and E. Bollt, Mathematical Biosciences and Engineering, 1 (2004), pp. 347-359], empirical evidence indicated that a time-varying network could propagate sufficient information to allow synchronization of the sometimes coupled oscillators, despite an instantaneously disconnected topology. We prove here that if the network of oscillators synchronizes for the static time-average of the topology, then the network will synchronize with the time-varying topology if the time-average is achieved sufficiently fast. Fast switching, fast on the time-scale of the coupled oscillators, overcomes the desynchronizing decoherence suggested by disconnected instantaneous networks. This result agrees in spirit with that of [J. D. Skufca and E. Bollt, Mathematical Biosciences and Engineering, 1 (2004), pp. 347-359] where empirical evidence suggested that a moving averaged graph Laplacian could be used in the master-stability function analysis [L. M. Pecora and T. L. Carroll, Phys. Rev. Lett., 80 (1998), pp. 2109-2112]. A new fast switching stability criterion herein gives sufficiency of a fast switching network leading to synchronization. Although this sufficient condition appears to be very conservative, it provides new insights about the requirements for synchronization when the network topology is time-varying. In particular, it can be shown that networks of oscillators can synchronize even if at every point in time the frozen-time network topology is insufficiently connected to achieve synchronization.

Purpose Cloud computing systems represent one of the most complex computing systems currently in existence. Current applications of Cloud involve extensive use of distributed systems with varying degree of connectivity and usage. With a recent focus on large-scale proliferation of Cloud computing, identity management in Cloud based systems is a critical issue for the sustainability of any Cloud-based service. This area has also received considerable attention from the research community as well as the IT industry. Numerous Cloud Identity Management Systems (IDMSs) have been proposed so far; however, most of those systems are neither widely accepted nor considered highly reliable due to their constraints in terms of scope, applicability and security. In order to achieve reliability and effectiveness in IDMs for Cloud, further extensive research needs to be carried out to critically examine Cloud based IDMSs and their level of security. Methods In this work, we have holistically analyzed Cloud IDMSs to better understand the general as well as the security aspects of this domain. From the security perspective, we present a comprehensive list of attacks that occur frequently in Cloud based IDMSs. In order to alleviate those attacks, we present a well-organized taxonomy tree covering the most desired features essential for any Cloud-based IDMSs. Additionally, we have specified various mechanisms of realization (such as access control polices, encryption, self-service) against each of the features of Cloud IDMSs. We have further used the proposed taxonomy as an assessment criterion for the evaluation of Cloud based IDMSs. Results Our in-depth analysis of various Cloud based IDMSs reveals that most of the systems do not offer support to all the essential features of Cloud IDMS and the ones that do, have their own certain weaknesses. None of the discussed techniques heuristically covers all the security features; moreover, they lack compliance to international standards which, understandably, undermines their credibility. Conclusion Presented work will help Cloud subscribers and providers in understanding the available solutions as well as the involved risks, allowing them to make more knowledgeable decisions while selecting potential Cloud IDMSs that best suits their functional and security requirements.

Wireless sensor and actor networks (WSANs) have various applications in safety and mission critical systems. Sensors are used for sensing the information whereas actors for taking intelligent decisions. Developing and modeling algorithms for WSANs have raised several research issues which have captured attention of the research community. Maintaining inter-actor connectivity or failure recovery is a critical issue in WSANs because these are deployed in harsh and inhospitable environment which may result into physical damage to actors loosing inter-actor connectivity. In case of failure of inter-actor connectivity, the topology of the network may be affected that might be inefficient to recover. Therefore an efficient subnet-based failure recovery algorithm (SFRA) is proposed in this work. It is assumed the partitioning of WSAN into subnets which localizes the failure recovery procedure at subnet level achieving objective of efficiency. Moreover, algorithm is hybrid as it assumes pre-failure planning and post-failure recovery. The proposed model is presented as a graph-based model to represent static part of the network topology. The graph model is transformed into a formal model using Vienna development method-specification language (VDM-SL). The static model is described by defining formal specification of subnets, network topology, sensors, actors and gateways as composite objects. The state space of the WSANs is described in the form of functions and operations as dynamic part of the model. Invariants are defined over the data types in static model for ensuring safety criteria and pre/post conditions are defined in functions and operations for changing state space of the system. The proposed model is validated and verified using VDM-SL Toolbox.

Wireless sensor and actors networks (WSANs) have become an important research area due to its large number of applications in safety, security and mission-critical systems. Natural disasters such as earthquakes and floods have distressing effects on human lives, economy and environment particularly in the developing countries due to their high population and lack of infrastructure. Earthquake is one of the major such disasters which causes a huge loss in terms of deaths, environment damages and loss of property because of its unpredictable nature. There exists much work on earthquake prediction, disaster mitigation and management but mostly is based on simulation and testing techniques which have certain limitations. Formal methods are mathematical approaches which assure correctness of systems to overcome limitations of simulation and testing techniques. That is why a formal system of earthquake disaster mitigation and management using formal methods and WSANs is proposed. Sensors and actors are deployed in the earthquakes vulnerable areas in the form of subnets which increase energy efficiency of the network as the processing becomes localized at a subnet level. Firstly, graph theory is used to represent subnet-based model which is then transformed into a formal model. Vienna Development Method-Specification Language (VDM-SL) is used to describe and prove correctness of the formal specification. The developed specification is then validated and verified through VDM-SL Toolbox facilities by analyzing the pre/post conditions and invariants over the formal system.

Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation , average , maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service.

The growing need of computation and processing has led to the generation of data centers. These data centers are usually comprised of hundreds of thousands of servers and other components. This complicated arrangement of the systems lead to the adoption of complex systems. Complex systems prevail in our society as combination of lots of entities, e.g., immune system, human brain and ecosystems. The adoption and interaction of the entities is possible through nonlinear interactions. The interaction between the components of the complex system is carried out in distributed fashion. Big data which is comprised of thousands of machines is also considered to be a form of complex adaptive systems which makes use of large entities, components and nonlinear interactions with each other. The development of such a complex systems raises certain challenges. Apart from management, energy is the most concerned one which is the core discussion of this research. This paper, surveys the state of the art on modern tools, techniques, architectures and algorithms which has been proposed and deployed to achieve energy efficiency in big data over the period of 2007–2015. We group existing approaches aimed at achieving energy efficiency in the complex paradigm of big data. In this categorization, we aim to provide an easy and concise view of the underlined model adapted by each approach in the context of big data.

From the Internet to networks of friendship, disease transmission, and even terrorism, the concept--and the reality--of networks has come to pervade modern society. But what exactly is a network? What different types of networks are there? Why are they interesting, and what can they tell us? In recent years, scientists from a range of fields--including mathematics, physics, computer science, sociology, and biology--have been pursuing these questions and building a new \"science of networks.\" This book brings together for the first time a set of seminal articles representing research from across these disciplines. It is an ideal sourcebook for the key research in this fast-growing field. The book is organized into four sections, each preceded by an editors' introduction summarizing its contents and general theme. The first section sets the stage by discussing some of the historical antecedents of contemporary research in the area. From there the book moves to the empirical side of the science of networks before turning to the foundational modeling ideas that have been the focus of much subsequent activity. The book closes by taking the reader to the cutting edge of network science--the relationship between network structure and system dynamics. From network robustness to the spread of disease, this section offers a potpourri of topics on this rapidly expanding frontier of the new science.

Large-scale online communities rely on computer-mediated communication between participants, enabling them to sustain interactions and exchange on a scale hitherto unknown. Yet little research has focused on how these online communities sustain themselves and how their interactions are structured. In this paper, we theorize and empirically measure the network exchange patterns of long-duration sustainable online communities. We propose that participation dynamics follow specific forms of social exchange: direct reciprocity, indirect reciprocity, and preferential attachment. We integrate diverse findings about individual participation motivations by identifying how individual behavior manifests in network-level structures of online communities. We studied five online communities over 27 months and analyzed 38,483 interactions using exponential random graph ( p * ) models and mixed-effects analysis of covariance. In a test of competing models, we found that network exchange patterns in online community communication networks are characterized by direct reciprocity and indirect reciprocity patterns and, surprisingly, a tendency away from preferential attachment. Our findings undermine previous explanations that online exchange follows a power law distribution based on people wanting to connect to \"popular\" others in online communities. Our work contributes to theories of new organizational forms by identifying network exchange patterns that regulate participation and sustain online communities.