Robust and adaptive techniques in self-organizing neural networks

Robust and adaptive training algorithms aiming at enhancing the capabilities of self-organizing and Radial Basis Function (RBF) neural networks are reviewed in this paper. The following robust variants of Learning Vector Quantizer (LVQ) are described: the order statistics LVQ, the L ₂ LVQ and the split-merge LVQ. Successful application of the marginal median LVQ that belongs to the class of order statistics LVQs in the self-organized selection of the centers in RBF neural networks is reported. Moreover, the use of the median absolute deviation in the estimation of the covariance matrix of the observations assigned to each hidden unit in RBF neural networks is proposed. Applications that prove the superiority of the proposed variants of LVQ and RBF neural networks in noisy color image segmentation, color-based image recognition, segmentation of ultrasonic images, motion-field smoothing and moving object segmentation are outlined.     

A survey on routing techniques in underwater wireless sensor networks

Underwater Wireless Sensor Networks (UWSNs) are finding different applications for offshore exploration and ocean monitoring. In most of these applications, the network consists of significant number of sensor nodes deployed at different depths throughout the area of interest. The sensor nodes located at the sea bed cannot communicate directly with the nodes near the surface level; they require multi-hop communication assisted by appropriate routing scheme. However, this appropriateness depends not only on network resources and application requirements but also on environmental constraints. All these factors provide a platform where a resource-aware routing strategy plays a vital role to fulfill the different application requirements with dynamic environmental conditions. Realizing the fact, significant attention has been given to construct a reliable scheme, and many routing protocols have been proposed in order to provide an efficient route discovery between the sources and the sink. In this paper, we present a review and comparison of different algorithms, proposed recently in order to fulfill this requirement. The main purpose of this study is to address the issues like data forwarding, deployment and localization in UWSNs under different conditions. Later on, all of these are classified into different groups according to their characteristics and functionalities.     

Topology management techniques for tolerating node failures in wireless sensor networks: A survey

In wireless sensor networks (WSNs) nodes often operate unattended in a collaborative manner to perform some tasks. In many applications, the network is deployed in harsh environments such as battlefield where the nodes are susceptible to damage. In addition, nodes may fail due to energy depletion and breakdown in the onboard electronics. The failure of nodes may leave some areas uncovered and degrade the fidelity of the collected data. However, the most serious consequence is when the network gets partitioned into disjoint segments. Losing network connectivity has a very negative effect on the applications since it prevents data exchange and hinders coordination among some nodes. Therefore, restoring the overall network connectivity is very crucial. Given the resource-constrained setup, the recovery should impose the least overhead and performance impact. This paper focuses on network topology management techniques for tolerating/handling node failures in WSNs. Two broad categories based on reactive and proactive methods have been identified for classifying the existing techniques. Considering these categories, a thorough analysis and comparison of all the recent works have been provided. Finally, the paper is concluded by outlining open issues that warrant additional research.     

A survey of graph-modification techniques for privacy-preserving on networks

Recently, a huge amount of social networks have been made publicly available. In parallel, several definitions and methods have been proposed to protect users’ privacy when publicly releasing these data. Some of them were picked out from relational dataset anonymization techniques, which are riper than network anonymization techniques. In this paper we summarize privacy-preserving techniques, focusing on graph-modification methods which alter graph’s structure and release the entire anonymous network. These methods allow researchers and third-parties to apply all graph-mining processes on anonymous data, from local to global knowledge extraction.     

A survey on resource allocation techniques in OFDM(A) networks

In this contribution, we present a survey on the radio resource allocation techniques in orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) systems. This problem goes back to 1960s and that is related to properly and efficiently allocate the radio resources, namely subcarriers and power. We start by overviewing the main open issues in OFDM. Then, we describe the problem formulation in OFDMA, and we review the existing solutions to allocate the radio resources. The goal is to discuss the fundamental concepts and relevant features of different radio resource management criteria, including water-filling, max–min fairness, proportional fairness, cross-layer optimization, utility maximization, and game theory, also including a toy example with two terminals to compare the performance of the different schemes. We conclude the survey with a review of the state-of-the-art in resource allocation for next-generation wireless networks, including multicellular systems, cognitive radio, and relay-assisted communications, and we summarize advantages and common problems of the existing solutions available in the literature. The distinguishing feature of this contribution is a tutorial-style introduction to the fundamental problems in this area of research, intended for beginners on this topic.     

A survey of wormhole routing techniques in direct networks

Several research contributions and commercial ventures related to wormhole routing, a switching technique used in direct networks, are discussed. The properties of direct networks are reviewed, and the operation and characteristics of wormhole routing are discussed in detail. By its nature, wormhole routing is particularly susceptible to deadlock situations, in which two or more packets may block one another indefinitely. Several approaches to deadlock-free. routing, along with a technique that allows multiple virtual channels to share the same physical channel, are described. In addition, several open issues related to wormhole routing are discussed     

An interference-aware virtual clustering paradigm for resource management in cognitive femtocell networks

Femtocells represent a promising alternative solution for high quality wireless access in indoor scenarios where conventional cellular system coverage can be poor. They are randomly deployed by the end user, so only post-deployment network planning is possible. Furthermore, this uncoordinated deployment creates severe interference to co-located femtocells, especially in dense deployments. This paper presents a new architecture using a generalised virtual cluster femtocell (GVCF) paradigm, which groups together FAP into logical clusters. It guarantees severely interfering and overlapping femtocells are assigned to different clusters. Since each cluster operates on different band of frequencies, the corresponding virtual cluster controller only has to manage its own FAPs, so the overall system complexity is low. The performance of the GVCF algorithm is analysed from both a resource availability and cluster number perspective. Simulation results conclusively corroborate the superior performance of the GVCF model in interference mitigation, particularly in high density FAP scenarios.     

A survey of traffic-based routing metrics in family of expected transmission count for self-organizing networks

Self-Organizing Networks (SONs) are attractive for applications where flexibility, resilience, and a large network service area are required. In order to meet the requirements of those applications, the principal issue is to guarantee efficient routing in SONs. Hence, the design and selection of appropriate routing metrics is important. A mass of metrics have been proposed in the past several decades. Among all the metrics, Expected Transmission Count (ETX) has drawn the most attention. Since the proposal of ETX, many ETX-based or extended metrics have been proposed. ETX and its descendants compose the ETX family. This paper is an attempt to analyze, compare and summarize traffic-based routing metrics in the ETX family. Details of each routing metric are presented and analyzed. Some of our viewpoints on the principal for designing metrics in SONs are presented.     

A survey of memory management techniques in virtualized systems

Virtualization technology allows multiple operating systems to share hardware resources of a computer system in an isolated manner. Traditionally, memory is shared by an operating system using segmentation and paging techniques. With virtualization, memory partitioning and management has several new challenges. For isolated and safe execution, hypervisors do not provide direct access to hardware resources. Lack of direct access to the memory management hardware like page tables disqualifies direct usage of virtual memory solutions used on native (non-virtualized) setups. Further, aspects of dual control of the memory resource (by the guest OS and the hypervisor) and lack of semantics regarding memory usage in virtual machines present additional challenges for memory management. This paper surveys different techniques of memory partitioning and management across multiple guest OSs in a virtualized environment.An important goal of virtualization is to increase the physical machine utilization in order to save costs. With varying application demand for memory and diverse memory management policies of the guest OSs, ensuring optimal usage of memory is non-trivial. In this survey, challenges of memory management in virtualized systems, different memory management techniques with their implications, and optimizations to increase memory utilization are discussed in detail.     

Real-time data management on wireless sensor networks: A survey

In the recent past, search in sensor systems focused on node hardware constraints and very limited energy resources. But nowadays, that new applications need data processing with temporal constraints in their tasks; then one of the new challenges faced by wireless sensor networks (WSNs) is handling real-time storage and querying the data they process. Two main approaches to storage and querying data are generally considered warehousing and distributed. The warehousing approach stores data in a central database and then queries may be performed to it. In a distributed approach, sensor devices are considered as local databases and data are managed locally. The data collected by sensors must represent the current state of the environment; for this reason they are subject to logic and time constraints. Then, this paper identifies the main specifications of real-time data management and presents the available real-time data management solutions for WSNs, in order to discuss them and identify some open issues and provide guidelines for further contributions.     

A survey of key management schemes in wireless sensor networks

Wireless sensor networks have many applications, vary in size, and are deployed in a wide variety of areas. They are often deployed in potentially adverse or even hostile environment so that there are concerns on security issues in these networks. Sensor nodes used to form these networks are resource-constrained, which make security applications a challenging problem. Efficient key distribution and management mechanisms are needed besides lightweight ciphers. Many key establishment techniques have been designed to address the tradeoff between limited memory and security, but which scheme is the most effective is still debatable. In this paper, we provide a survey of key management schemes in wireless sensor networks. We notice that no key distribution technique is ideal to all the scenarios where sensor networks are used; therefore the techniques employed must depend upon the requirements of target applications and resources of each individual sensor network.     

Dynamic key management in wireless sensor networks: A survey

Wireless sensor networks (WSNs) have a vast field of applications, including environment monitoring, battlefield surveillance and target tracking systems. As WSNs are usually deployed in remote or even hostile environments and sensor nodes are prone to node compromise attacks, the adoption of dynamic key management is extremely important. However, the resource-constrained nature of sensor nodes hinders the use of dynamic key management solutions designed for wired and ad hoc networks. Hence, many dynamic key management schemes have been proposed for WSNs recently. This paper investigates the special requirements of dynamic key management in sensor network environments, and introduces several basic evaluation metrics. In this work, the state of the art dynamic key management schemes are classified into different groups and summarized based on the evaluation metrics. Finally, several possible future research directions for dynamic key management are provided.     

A dynamic programming approximation for downlink channel allocation in cognitive femtocell networks

Both femtocells and cognitive radio (CR) are envisioned as promising technologies for the NeXt Generation (xG) cellular networks. Cognitive femtocell networks (CogFem) incorporate CR technology into femtocell deployment to reduce its demand for more spectrum bands, thereby improving the spectrum utilization. In this paper, we focus on the channel allocation problem in CogFem, and formulate it as a stochastic dynamic programming (SDP) problem aiming at optimizing the long-term cumulative system throughput of individual femtocells. However, the multi-dimensional state variables resulted from complex exogenous stochastic information make the SDP problem computationally intractable using standard value iteration algorithms. To address this issue, we propose an approximate dynamic programming (ADP) algorithm in pursuit of an approximate solution to the SDP problem. The proposed ADP algorithm relies on an efficient value function approximation (VFA) architecture that we design and a stochastic gradient learning strategy to function, enabling each femtocell to learn and improve its own channel allocation policy. The algorithm is computationally attractive for large-scale downlink channel allocation problems in CogFem since its time complexity does not grow exponentially with the number of femtocells. Simulation results have shown that the proposed ADP algorithm exhibits great advantages: (1) it is feasible for online implementation with a fair rate of convergence and adaptability to both long-term and short-term network dynamics; and (2) it produces high-quality solutions fast, reaching approximately 80% of the upper bounds provided by optimal backward dynamic programming (DP) solutions to a set of deterministic counterparts of the formulated SDP problem.     

A Stackelberg game-based spectrum allocation scheme in macro/femtocell hierarchical networks

Femtocells widely deployed in a macrocell form hierarchical cell networks, which can improve indoor coverage and network capacity, and have been regarded as one of the most promising approaches. However owing to the absence of coordination between the macro and femtocells, and among femtocells, decentralized spectrum allocation between macro and femtocell users become technically challenging. In this paper, a dynamic spectrum allocation scheme based on Stackelberg game is proposed, in which macrocell base stations as leaders and femtocell base stations as followers are players, and the same spectrum is the resource that players will choose assigning to users for minimizing the affected interference among each other. The Stackelberg equilibrium is defined and proved to be existed, which is also the optimal spectrum allocation manner. Simulations were conducted to study the impact of femtocells on the macrocells regarding throughput, outage probability and spectrum efficiency. And the comparison results show that the proposed scheme might be a solution for efficiently allocating the spectrum in hierarchical cell networks, as the improvement in terms of throughput, outage probability and spectrum efficiency had been achieved.     

Power management techniques in smartphone-based mobility sensing systems: A survey

The rapidly enhancing sensing capabilities of smartphones are enabling the development of a wide range of innovative mobile sensing applications that are impacting on everyday life of mobile users. However, supporting long-term sensing applications is challenging because of their key requirements for continuous access to embedded sensors for gathering raw data, which can deplete the device’s battery in a few hours. This problem is expected to remain in the near future because the improvements on the capacity of batteries are coming at a slower pace than those advances in computing and sensing capabilities. The research community has highlighted the need for power-aware and context-aware sensing techniques deployed at different levels of mobile platforms for making a more efficient use of energy resources. Previous studies have analyzed the optimization of power consumption in mobile devices over different critical axes, like data transmission, computing, and hardware design. However, a comprehensive study focused in the challenges of power-aware smartphone-based sensing and strategies for addressing them has not been produced yet. This survey aims to fill this void with a particular focus on mobility sensing systems (e.g., human activity recognition, location-based services), presenting a comprehensive review of relevant strategies aimed at solving this issue. Also, this survey defines a taxonomy for such solutions, highlighting their strengths and limitations. Finally, most relevant open challenges and trends are discussed for providing insights for future research in the field.     

A survey of techniques for remote access to home networks and resources

Consumer Electronics devices are becoming network enabled, and along with smart phones and personal computers, they are all interconnected in home networks with broadband Internet connectivity. This sets the opportunity of making the home network, its devices and content accessible from the Internet, allowing the home owners to remotely access their connected home any time, any place, using any device. This paper provides a holistic overview of the “Remote Acces” topic, presenting all the problems and issues that make it challenging in different contexts, and most importantly analyzing six techniques and methods for enabling the remote access scenarios. It is a guide created by the combination of scientific research, extensive industrial experiences and first hand participation in relevant standardization activities.     

Femtocell网络中基于联合传输的分簇与功率分配算法*

针对家庭基站(femtocell)密集部署时的下行干扰问题,提出一种基于多点协作联合传输的分簇与功率分配相结合的算法。首先根据受到的干扰程度进行分簇,将干扰强的家庭基站分到同一个簇,簇内所有家庭基站通过寻找对自身最有利的基站进行协作,将干扰信号转变有用信号,联合传输为用户提供服务,提升用户的信干噪比来保证用户性能,簇间则复用相同的频谱资源。然后通过给用户分配合适的功率使得总传输速率最大。仿真结果表示,所提算法能在抑制系统干扰的同时提高系统的吞吐量。     

A survey on expert finding techniques

Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.     

面向大数据流式计算的任务管理技术综述

流式计算是大数据的一种重要计算模式,大数据流式计算已成为研究热点。任务管理是大数据流式计算的核心功能之一,负责对流式计算的任务进行资源调度及全生命周期管理。目前对于大数据流式计算的技术调研工作主要集中于流式计算应用需求、体系结构及整体技术,缺乏对大数据流式计算任务管理技术的精细化调研分析。首先给出流式计算任务管理的抽象功能模型,其次基于该模型对任务管理的关键技术进行了分类和综述,最后对既有主流的大数据流式计算系统对上述关键技术的应用、集成和优化进行了调研分析。     

有效的基于软频率复用长期演进femtocell网络的小区间干扰协调方案

家庭基站是一种小体积、低发射功率的基站,它为长期演进(LTE) femtocell双层网络提供更好室内覆盖的同时也增加了整个系统的容量。然而,femtocell和宏基站(MeNB)之间的干扰不容小觑。针对二者间的干扰问题,提出一种基于软频率复用(SFR)的有效的LTE femtocell网络的小区间干扰协调(ICIC)方案。该方案中,为避免共信道干扰,先对宏小区进行SFR频域资源分配,然后让femtocell用户使用宏基站未占用的频域资源;并且当femtocell位于宏小区中心区域时,不使用同一扇区边缘区域占用的频带。仿真结果表明,此方案减小了不同类型用户间的干扰,整个网络的吞吐量比没有使用ICIC技术的情况提高了14%,同时小区边缘用户的平均吞吐量至少提高了34%。