Vulnerabilities in cognitive radio networks: A survey

Cognitive radio networks are envisioned to drive the next generation wireless networks that can dynamically optimize spectrum use. However, the deployment of such networks is hindered by the vulnerabilities that these networks are exposed to. Securing communications while exploiting the flexibilities offered by cognitive radios still remains a daunting challenge. In this survey, we put forward the security concerns and the vulnerabilities that threaten to plague the deployment of cognitive radio networks. We classify various types of vulnerabilities and provide an overview of the research challenges. We also discuss the various techniques that have been devised and analyze the research developments accomplished in this area. Finally, we discuss the open research challenges that must be addressed if cognitive radio networks were to become a commercially viable technology.     

Optimization-based resource allocation in communication networks

The continuously growing number of applications competing for resources in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization Theory can provide the necessary tools to develop such mechanisms that will allocate network resources optimally and fairly among users. The aim of this paper is to provide a starting point for researchers interested in applying optimization techniques in the resource allocation problem for current communication networks. To achieve that we, first, describe the fundamental optimization theory tools necessary to design optimal resource allocation algorithms. Then, we describe the Network Utility Maximization (NUM) framework, a framework that has already found numerous applications in network optimization, along with some recent advancements of the initial NUM framework. Finally, we summarize some of our recent work in the area and discuss some of the remaining research challenges towards the development of a complete optimization-based resource allocation protocol.     

Dynamic reconfiguration of node location in wormhole networks

Several techniques have been developed to increase the performance of parallel computers. Reconfigurable networks can be used as an alternative to increase the performance. Network reconfiguration can be carried out in different ways. Our research has focused on distributed memory systems with dynamic reconfiguration of node location. Briefly, this technique consists of positioning the processors in the network depending on the existing communication pattern among them, to suit the requirements of each computation.In this article, we present a dynamic reconfiguration technique for wormhole networks. We have used both a crossbar and a multistage interconnection network to implement a reconfigurable logical two-dimensional (2-D) torus topology. The reconfiguration mechanism is based on a distributed reconfiguration algorithm. The algorithm is based on a cost function that requires only local information. We discuss reconfiguration features and adjust the different parameters of the reconfiguration algorithm. We have also studied the deadlock problem in reconfigurable wormhole networks, and give details of our solution. Finally, we have evaluated the performance of this technique under several workloads.     

Digital product innovation within four classes of innovation networks

The increased digitization of organizational processes and products poses new challenges for understanding product innovation. It also opens new horizons for information systems research. We analyse how ongoing pervasive digitization of product innovation reshapes knowledge creation and sharing in innovation networks. We argue that advances in digital technologies (1) increase innovation network connectivity by reducing communication costs and increasing its reach and scope and (2) increase the speed and scope of digital convergence, which increases network knowledge heterogeneity and need for integration. These developments, in turn, stretch existing innovation networks by redistributing control and increasing the demand for knowledge coordination across time and space presenting novel challenges for knowledge creation, assimilation and integration. Based on this foundation, we distinguish four types of emerging innovation networks supported by digitalization: (1) project innovation networks; (2) clan innovation networks; (3) federated innovation networks; and (4) anarchic innovation networks. Each network involves different cognitive and social translations – or ways of identifying, sharing and assimilating knowledge. We describe the role of five novel properties of digital infrastructures in supporting each type of innovation network: representational flexibility, semantic coherence, temporal and spatial traceability, knowledge brokering and linguistic calibration. We identify several implications for future innovation research. In particular, we focus on the emergence of anarchic network forms that follow full‐fledged digital convergence founded on richer innovation ontologies and epistemologies calling to critically re‐examine the nature and impact of modularization for innovation.     

Wireless sensor networks for rehabilitation applications: Challenges and opportunities

Rehabilitation supervision has emerged as a new application of wireless sensor networks (WSN), with unique communication, signal processing and hardware design requirements. It is a broad and complex interdisciplinary research area on which more than one hundred papers have been published by several research communities (electronics, bio-mechanical, control and computer science). In this paper, we present WSN for rehabilitation supervision with a focus on key scientific and technical challenges that have been solved as well as interdisciplinary challenges that are still open. We thoroughly review existing projects conducted by several research communities involved in this exciting field. Furthermore, we discuss the open research issues and give directions for future research works. Our aim is to gather information that encourage engineers, clinicians and computer scientists to work together in this field to tackle the arising challenges. We believe that bridging researchers with different scientific backgrounds could have a significant impact on the development of WSN for rehabilitation and could improve the way rehabilitation is provided today.     

Game theory for cognitive radio networks: An overview

Cognitive radio technology, a revolutionary communication paradigm that can utilize the existing wireless spectrum resources more efficiently, has been receiving a growing attention in recent years. As network users need to adapt their operating parameters to the dynamic environment, who may pursue different goals, traditional spectrum sharing approaches based on a fully cooperative, static, and centralized network environment are no longer applicable. Instead, game theory has been recognized as an important tool in studying, modeling, and analyzing the cognitive interaction process. In this tutorial survey, we introduce the most fundamental concepts of game theory, and explain in detail how these concepts can be leveraged in designing spectrum sharing protocols, with an emphasis on state-of-the-art research contributions in cognitive radio networking. Research challenges and future directions in game theoretic modeling approaches are also outlined. This tutorial survey provides a comprehensive treatment of game theory with important applications in cognitive radio networks, and will aid the design of efficient, self-enforcing, and distributed spectrum sharing schemes in future wireless networks.     

Networking issues in wireless sensor networks

The emergence of sensor networks as one of the dominant technology trends in the coming decades (Technol. Rev. (February 2003)) has posed numerous unique challenges to researchers. These networks are likely to be composed of hundreds, and potentially thousands of tiny sensor nodes, functioning autonomously, and in many cases, without access to renewable energy resources. Cost constraints and the need for ubiquitous, invisible deployments will result in small sized, resource-constrained sensor nodes.While the set of challenges in sensor networks are diverse, we focus on fundamental networking challenges in this paper. The key networking challenges in sensor networks that we discuss are: (a) supporting multi-hop communication while limiting radio operation to conserve power, (b) data management, including frameworks that support attribute-based data naming, routing and in-network aggregation, (c) geographic routing challenges in networks where nodes know their locations, and (d) monitoring and maintenance of such dynamic, resource-limited systems. For each of these research areas, we provide an overview of proposed solutions to the problem and discuss in detail one or few representative solutions. Finally, we illustrate how these networking components can be integrated into a complex data storage solution for sensor networks.     

Performance evaluation of an efficient fault tolerant service discovery protocol for vehicular networks

Recently, vehicular networks and inter-vehicle communication have received attention from the research community and the automotive industry. In this context, many services are deployed in vehicular networks that are beneficial to drivers and passengers. Service discovery in vehicular networks is vital to make such services useful. However, due to the lack of communication reliability in vehicular ad hoc networks, it is necessary to implement fault-tolerant techniques during the discovery of service providers in vehicular networks. Very few service discovery protocols for vehicular networks have considered fault tolerant techniques, which are very important for the efficiency of service discovery. Fault tolerant techniques improve service request satisfaction and reduce dropped connections due to faulty components between the service provider and the service requester. In this paper, we propose a fault tolerant location based service discovery protocol for vehicular networks which can work well under service provider failures, communication link failures and roadside router failures. Our protocol permits the discovery of location based services where the requester specifies the region of interest within the request. Our protocol uses an infrastructure support consisting of clusters of roadside routers formed in specific regions in the vehicular network. In addition, service discovery messages are integrated into the network layer and use channel diversity in order to improve service discovery efficiency. We describe our fault tolerant location based vehicular service discovery protocol (FTLocVSDP) and discuss its proof of correctness and performance evaluation. We prove that the success rate is improved in the roadside router failure and in the communication link failure scenarios by 50% and 30%, respectively, compared to the location based service discovery protocol for vehicular networks (LocVSDP) which does not consider fault tolerant techniques.     

5G移动通信网络安全研究

第五代（Fifth Gneration,5G）移动通信网络（简称5G网络或5G）,是为构建网络型社会并实现“万物互联”的宏伟目标而提出的下一代移动网络.随着LTE等第四代移动通信网络进入规模化商用阶段,5G网络研究已成为世界各国的关注焦点.5G网络的实现需要依赖于系统架构和核心技术的变革与创新,目前5G网络还处于技术和标准的初级研究阶段.5G网络的新架构、新业务、新技术对安全提出了新的挑战.本文简述了5G的性能指标、关键技术、应用场景,及标准制定进展,分析了5G网络的安全需求及其所面临的技术挑战,基于目前已有的研究工作和标准研制情况,提炼了5G安全框架,归纳并阐述了若干安全关键问题及其解决方案,展望了5G网络安全的未来研究方向.     

Coordination problem in cognitive wireless mesh networks

Due to their potential to create and extend pervasive communication applications to cognitive environments with distributed control, the emerging technology of cognitive wireless mesh networks is gaining significant attention from a growing research community. However, the major challenge in cognitive networks is the adaptation to time and space variability of the available resources, namely chunks of the frequency spectrum called channels. In particular, this problem is exacerbated in cognitive mesh networks because there exists no direct communication among devices which thereby cannot establish a global (common) control channel to coordinate the entire network. Instead, only local control channels that vary depending on the time instant and location, can be established to coordinate cognitive devices among themselves. This paper first analyzes the underlying challenges and existing approaches to address the absence of a static and global control channel, and then propose a novel Control channel formation protocol, called Connor. Our protocol Connor is a fully distributed coordination scheme where cognitive mesh devices self-organize into clusters based on the similarity of available channels and on topological constraints. Compared with the existing clustering algorithms, which requires synchronization, the proposed Connor performs better in most cases without imposing synchronization.     

Data fusion based coverage optimization in heterogeneous sensor networks: A survey

Sensor networks, as a promising network paradigm, have been widely applied in a great deal of critical real-world applications. A key challenge in sensor networks is how to improve and optimize coverage quality which is a fundamental metric to characterize how well a point or a region or a barrier can be sensed by the geographically deployed heterogeneous sensors. Because of the resource-limited, battery-powered and type-diverse features of the sensors, maintaining and optimizing coverage quality includes a significant amount of challenges in heterogeneous sensor networks. Many researchers from both academic and industrial communities have performed numerous significant works on coverage optimization problem in the past decades. Some of them also have surveyed the current models, theories and solutions on the problem of coverage optimization. However, most of the existing surveys and analytical studies ignore how to exploit data fusion and cooperation of the deployed sensors to enhance coverage performance. In this paper, we provide an insightful and comprehensive summarization and classification on the data fusion based coverage optimization problem and techniques. Aiming at overcoming the shortcomings existed in current solutions, we also discuss the future issues and challenges in this area and sketch a general research framework in the context of reinforcement learning.     

Rethinking the architecture design of data center networks

In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore’s law. This explosion of data comes from various sources such as mobile phones, video cameras and sensor networks, which often present multidimensional characteristics. The huge amount of data brings many challenges on the management, transportation, and processing IT infrastructures. To address these challenges, the state-of-art large scale data center networks have begun to provide cloud services that are increasingly prevalent. However, how to build a good data center remains an open challenge. Concurrently, the architecture design, which significantly affects the total performance, is of great research interest. This paper surveys advances in data center network design. In this paper we first introduce the upcoming trends in the data center industry. Then we review some popular design principles for today’s data center network architectures. In the third part, we present some up-to-date data center frameworks and make a comprehensive comparison of them. During the comparison, we observe that there is no so-called optimal data center and the design should be different referring to the data placement, replication, processing, and query processing. After that, several existing challenges and limitations are discussed. According to these observations, we point out some possible future research directions.     

认知无线电网络信道交汇研究综述

认知无线电技术被认为是解决目前频谱资源利用率低下问题最有前景的技术,基于该技术,认知无线电网络采用动态频谱接入方式有效地提高了授权频段的利用率.然而,动态变化的信道可用性极大地增加了认知无线电网络组网的难度.信道交汇旨在为用户通信提供公共传输媒介,是实现无线网络组网的基础.介绍了认知无线电网络信道交汇的基本概念和特点,并阐述了信道交汇策略设计面临的挑战以及应考虑的性能指标.提出了信道交汇策略的分类标准和系统模型,根据该分类标准,详细剖析了当前信道交汇策略相关的研究工作.最后,讨论了认知无线电网络信道交汇研究的开放性问题,以期为未来的研究指出可能的方向和重点.     

Certification-based trust models in mobile ad hoc networks: A survey and taxonomy

A mobile ad hoc network is a wireless communication network which does not rely on a pre-existing infrastructure or any centralized management. Securing the exchanges in such network is compulsory to guarantee a widespread development of services for this kind of networks. The deployment of any security policy requires the definition of a trust model that defines who trusts who and how. There is a host of research efforts in trust models framework to securing mobile ad hoc networks. The majority of well-known approaches is based on public-key certificates, and gave birth to miscellaneous trust models ranging from centralized models to web-of-trust and distributed certificate authorities. In this paper, we survey and classify the existing trust models that are based on public-key certificates proposed for mobile ad hoc networks, and then we discuss and compare them with respect to some relevant criteria. Also, we have developed analysis and comparison among trust models using stochastic Petri nets in order to measure the performance of each one with what relates to the certification service availability.     

Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

In wireless networks, context awareness and intelligence are capabilities that enable each host to observe, learn, and respond to its complex and dynamic operating environment in an efficient manner. These capabilities contrast with traditional approaches where each host adheres to a predefined set of rules, and responds accordingly. In recent years, context awareness and intelligence have gained tremendous popularity due to the substantial network-wide performance enhancement they have to offer. In this article, we advocate the use of reinforcement learning (RL) to achieve context awareness and intelligence. The RL approach has been applied in a variety of schemes such as routing, resource management and dynamic channel selection in wireless networks. Examples of wireless networks are mobile ad hoc networks, wireless sensor networks, cellular networks and cognitive radio networks. This article presents an overview of classical RL and three extensions, including events, rules and agent interaction and coordination, to wireless networks. We discuss how several wireless network schemes have been approached using RL to provide network performance enhancement, and also open issues associated with this approach. Throughout the paper, discussions are presented in a tutorial manner, and are related to existing work in order to establish a foundation for further research in this field, specifically, for the improvement of the RL approach in the context of wireless networking, for the improvement of the RL approach through the use of the extensions in existing schemes, as well as for the design and implementation of RL in new schemes.     

Using aging to visually uncover evolutionary processes on networks

Networks are widely used to describe many natural and technological systems. Understanding how these evolve over time poses a challenge for existing visualization techniques originally developed for fixed network structures. We describe a method of incorporating the concept of aging into evolving networks, where nodes and edges store information related to the amount of local evolutionary change they have experienced. This property is used to generate visualizations that ensure stable substructures maintain relatively fixed spatial positions, allowing them to act as visual markers and providing context for evolutionary change elsewhere. By further supplementing these visualizations with color cues, the resultant animations enable a clearer portrayal of the underlying evolutionary process.     

面向图像识别的卷积神经网络鲁棒性研究进展

卷积神经网络是目前人工智能领域在图像识别与处理相关应用中的关键技术之一,广泛的应用使对其鲁棒性研究的重要性不断凸显。以往对于卷积神经网络鲁棒性的研究较为笼统,且多集中在对抗鲁棒性方面。这难以更深入地研究神经网络鲁棒性的发生机制,已经不适应人工智能的发展。引入神经科学的相关研究,提出了视觉鲁棒性的概念,通过研究神经网络模型与人类视觉系统的相似性,揭示了神经网络鲁棒性的内在缺陷。回顾了近年来神经网络鲁棒性的研究现状,并分析了神经网络模型缺乏鲁棒性的原因。神经网络缺乏鲁棒性体现在其对于微小扰动的敏感性,其原因在于神经网络会更倾向于学习人类难以感知的高频信息用于计算和推理。而这部分高频信息很容易被扰动所破坏,最终导致模型出现判断错误。传统鲁棒性的研究大多关注模型的数学性质,无法突破神经网络的天然局限性。视觉鲁棒性在传统鲁棒性的概念上进行拓展。传统鲁棒性概念衡量模型对于失真变形的图像样本的辨识能力,失真样本与原始干净样本在鲁棒模型上都能保持正确的输出。视觉鲁棒性衡量模型与人类判别能力的一致性。这需要将神经科学和心理学的研究方法、成果与人工智能相结合。回顾了神经科学在视觉领域的发展,讨论了认知心理...     

An overview of multi-antenna technologies for space-ground integrated networks

Multi-antenna technologies have already achieved a series of great successes in the development of information networks. For future space-ground integrated networks (SGINs), the traditional various kinds of separated information networks will converge to a whole fully connected information network to provide more flexible and reliable services on a world scale. Regarding their great successes in existing systems, multiantenna technologies will be of critical importance for the realization of SGINs and multi-antenna technologies are definitely one of the most important enabling technologies for future converged SGINs. In this article, a comprehensive overview on multi-antenna technologies is given. We first investigate multi-antenna technologies from a theoretical viewpoint. It is shown that we can understand multi-antenna technologies in a general and unified point of view. This fact has two-fold meanings. First, the research on multi-antennas can help us understand the relationships between different technologies e.g., OFDMA, CDMA, etc. On the other hand, multi-antenna technologies are easy to integrate into various information systems. Following that, we discuss in depth the potentials and challenges of the multi-antenna technologies on different platforms and in different applications case by case. More specifically, we investigate spaceborne multi-antenna technologies, airborne multi-antenna technologies, shipborne multi-antenna technologies, etc. Moreover, the combinations of multiantenna technologies with other advanced wireless technologies e.g., physical layer network coding, cooperative communication, etc., are also elaborated.     

Position-based routing in vehicular networks: A survey

Routing in vehicular network is a challenging task due to network partitioning, high vehicular speed, and city environment characteristics. These characteristics results in degraded performance in traditional routing protocols. Traditional routing protocols, addressing the issues of mobile ad hoc network, are applicable for MANET applications. Position-based routing protocols, which are mostly based on greedy routing, are more suited to highly dynamic and mobile network. In this paper, we survey state of art routing protocols previously used in vehicular networks, present open research challenges and possible future direction. We categorize protocols into two categories based on their communicating mode (vehicle-to-vehicle, vehicle-to-infrastructure) irrespective of their simulating environment (highway, urban). Both vehicle-to-vehicle and vehicle-to-infrastructure communication provides connectivity based on multi-hop paradigm in a seamless way. We discuss pros and cons for routing protocols belonging to each category. By doing qualitative comparison of routing protocols, it is observed that hybrid communication would be the better choice for both communication mode operable in either a city environment or an open environment.     

The State of the Art in Visualizing Dynamic Multivariate Networks

Most real-world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real-world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.