A Flexible and Efficient Protocol for Multi-Scope Service Registry Replication

Service registries play an important role in service discovery systems by accepting service registrations and answering service queries; they can serve a wide range of purposes, such as membership services, lookup services, and search services. To provide fault tolerant, and enhance scalability, availability and performance, service registries often need to be replicated. In this paper, we present Swift (Selective anti-entropy WIth FasT update propagation), a flexible and efficient protocol for multi-scope service registry replication. As consistency is a less of concern compared with availability in service registry replication, we choose to build Swift on top of anti-entropy to support high availability replication. Swift makes two contributions as follows. First, it defines a more general and flexible form of anti-entropy called selective anti-entropy, which extends the applicability of anti-entropy from full replication to partial replication by selectively reconciling inconsistent states between two replicas, and improves anti-entropy efficiency by fine controlling update propagation within each subset. Selective anti-entropy is the first that we are aware of in using anti-entropy to support generic partial replication. Secondly, Swift integrates service registry overlay networks with selective anti-entropy. Different topologies, such as full mesh and spanning tree, can be used for constructing service registry overlay networks. These overlay networks are used to propagate new updates quickly so as to minimize inconsistency among replicas. We have implemented Swift for replicating multi-scope Directory Agents in the Service Location Protocol. Our experience shows that Swift is flexible, efficient, and lightweight

The role of homophily in the emergence of opinion controversies

Understanding the emergence of strong controversial issues in modern societies is a key issue in opinion studies. A commonly diffused idea is the fact that the increasing of homophily in social networks, due to the modern ICT, can be a driving force for opinion polariation. In this paper we address the problem with a modelling approach following three basic steps. We first introduce a network morphogenesis model to reconstruct network structures where homophily can be tuned with a parameter. We show that as homophily increases the emergence of marked topological community structures in the networks raises. Secondly, we perform an opinion dynamics process on homophily dependent networks and we show that, contrary to the common idea, homophily helps consensus formation. Finally, we introduce a tunable external media pressure and we show that, actually, the combination of homophily and media makes the media effect less effective and leads to strongly polarized opinion clusters.Comment: 24 pages, 10 figure

Analyzing the impact of storage shortage on data availability in decentralized online social networks

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today’s online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

An Improved Scheme for Interest Mining Based on a Reconfiguration of the Peer-to-Peer Overlay

Tan et al. proposed a scheme to improve the quality of a file search in unstructured Peer-to-Peer systems by focusing on the similarity of interest of the participating peers. Although it certainly improves the cost/performance ratio of a simple flooding-based scheme used in conventional systems, the Tan's method has a serious drawback such that a query cannot reach a target peer if a requesting peer is not connected with the target peer through a path consisting of peers to have similar interest to the given query. In order to overcome such drawback of the Tan's method, we propose a scheme to reconfigure the underlying network in such a way that a requesting peer has a neighbor interested in the given query, before transmitting a query to its neighbors. The performance of the proposed scheme is evaluated by simulation. The result of simulation indicates that it certainly overcomes the drawback of the Tan's method

A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

Monte Carlo optimization of decentralized estimation networks over directed acyclic graphs under communication constraints

Motivated by the vision of sensor networks, we consider decentralized estimation networks over bandwidth–limited communication links, and are particularly interested in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We employ a class of in–network processing strategies that admits directed acyclic graph representations and yields a tractable Bayesian risk that comprises the cost of communications and estimation error penalty. This perspective captures a broad range of possibilities for processing under network constraints and enables a rigorous design problem in the form of constrained optimization. A similar scheme and the structures exhibited by the solutions have been previously studied in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt this framework for estimation, however, the corresponding optimization scheme involves integral operators that cannot be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain particle representations and approximate computational schemes for both the in–network processing strategies and their optimization. The proposed Monte Carlo optimization procedure operates in a scalable and efficient fashion and, owing to the non-parametric nature, can produce results for any distributions provided that samples can be produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically as the communication becomes more costly, through a parameterized Bayesian risk

Replication and availability in decentralised online social networks

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Master of PhilosophyDuring the last few years’ online social networks (OSNs) have become increasingly popular among all age groups and professions but this has raised a number of issues around users’ privacy and security. To address these issues a number of attempts have been made in the literature to create the next generation of OSNs built on decentralised architectures. Maintaining high data availability in decentralised OSNs is a challenging task as users themselves are responsible for keeping their profiles available either by staying online for longer periods of time or by choosing trusted peers that can keep their data available on their behalf. The major findings of this research include algorithmically determining the users’ availability and the minimum number of replicas required to achieve the same availability as all mirror nodes combined. The thesis also investigates how the users’ availability, replication degree and the update propagation delay changes as we alter the number of mirror nodes their online patterns, number of sessions and session duration. We found as we increase the number of mirror nodes the availability increases and becomes stable after a certain point which may vary from node to node as it directly depends on the node’s number of mirror nodes and their online patterns. Moreover, we also found the minimum number of replicas required to achieve the same availability as all mirror nodes combined and update propagation delay directly depends on mirror nodes’ number of sessions and session duration. Furthermore, we also found as we increase the number of sessions with reduced session lengths the update propagation delay between the mirror nodes starts to decrease. Thus resulting in spreading the updates faster as compared to mirror nodes with fewer sessions but of longer durations

DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

Adaptive service discovery on service-oriented and spontaneous sensor systems

Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief