Top-k query processing for replicated data in mobile peer to peer networks

In mobile ad hoc peer to peer (M-P2P) networks, since nodes are highly resource constrained, it is effective to retrieve data items using a top-k query, in which data items are ordered by the score of a particular attribute and the query-issuing node acquires data items with the k highest scores. However, when network partitioning occurs, the query-issuing node cannot connect to some nodes having data items included in the top-k query result, and thus, the accuracy of the query result decreases. To solve this problem, data replication is a promising approach. However, if each node sends back its own data items (replicas) responding to a query without considering replicas held by others, same data items are sent back to the query-issuing node more than once through long paths, which results in increase of traffic. In this paper, we propose a top-k query processing method considering data replication in M-P2P networks. This method suppresses duplicate transmissions of same data items through long paths. Moreover, an intermediate node stops transmitting a query message on-demand.     

Peer to peer video streaming in Bluetooth overlays

As Bluetooth is available in most personal and portable terminals (eg, cellular phone, PDA, videocamera, laptop, etc) Peer-to-peer video streaming through Bluetooth networks is now a reality. Camera equipped Bluetooth phones capture video and broadcast it to other Bluetooth devices and to the infrastructure. Tra ditionally, large scale Bluetooth networks were designed using scatternet concepts. However, many Bluetooth devices do not support Scatternet connections and, even if they support it, they provide only very limited features suitable mostly for static environments. In high mobility situations, a traditional Scatternet design is not useful because of frequent disconnections and reconnections. To overcome these problems, we propose overlaid Bluetooth Piconets (OBP) and simplified overlaid Bluetooth Piconets (SOBP) that interconnect Piconets forming virtual Scatternets. In OBP, every Piconet dynamically reconfigures to collect metadata from neighboring Piconets. If metadata shows the existence of useful data to transfer, an inter-Piconet connection is made to carry out the transfer. SOBP can be used instead of OBP once neighbor Piconets have already discovered each other. In this paper, we compare via analysis and simulation the throughput and efficiency of OBP, SOBP and Scatternet for video applications. We demonstrate the feasibility of video over OBP and SOBP for a representative application.

Adaptive reconfiguration of data networks using genetic algorithms

Genetic algorithms are applied to an important, but little investigated, network design problem, that of reconfiguring the topology and link capacities of an operational network to adapt to changes in its operating conditions. These conditions include: which nodes and links are unavailable; the traffic patterns; and the quality of service (QoS) requirements and priorities of different users and applications. Dynamic reconfiguration is possible in networks that contain links whose endpoints can be easily changed, such as satellite channels, terrestrial wireless connections, and certain types of optical connections. We report preliminary results that demonstrate the feasibility of performing genetic search quickly enough for online adaptation.     

Robust control in cloud assisted peer to peer live streaming systems

Existing live video streaming systems can be classified as server (cloud) based or as peer-to-peer (P2P). The client–server approach promises stability and (Quality of Service) QoS by incurring expensive bandwidth provision cost on the server. On the other hand, P2P architecture is scalable with low bandwidth and maintenance cost. Here we propose a cloud assisted P2P live streaming architecture which is scalable and stable. In order to achieve this we have developed: (i) a scalable gossip protocol that monitors dynamically the total available bandwidth resources of the participating peers, (ii) a control strategy that dynamically allocates the bandwidth that is required.The first step towards this direction is to create a theoretical model that captures the dynamic relationship between the total bandwidth surplus/deficit and peers’ bandwidth utilization in order to be able to apply a control theoretical approach. Moreover, we quantify the impact of monitoring inaccuracies and peers’ dynamic bandwidth changes and we calculate analytically, as a function of them, the minimum amount of bandwidth overprovision that ensures the undisturbed distribution of the stream. System is evaluated through a detailed simulator of a complete P2P live streaming system and testified the uninterrupted and complete stream delivery even in very adverse bandwidth changes.     

基于对等组的P2P安全通信机制

当前对P2P网络系统安全的研究大多着眼于构造信任和名誉模型,而对通信过程的安全问题研究较少。提出了一种基于对等组实现的P2P安全通信机制——P2PSCM。P2PSCM首先为通信节点构建安全对等组。组内节点相互通信时,先通过交换公私密钥和对称密钥建立节点间安全通信渠道;然后采用对称密钥加密传输数据;最后对接收数据进行身份确认与完整性验证,确保相互间安全通信。     

A review of genetic algorithms applied to training radial basis function networks

The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.     

基于遗传算法的人工神经网络的应用综述

本文介绍了一种新型的、随机性的全局优化方法－遗传算法,系统地讨论了基于遗传算法的人工神经网络的主要应用,并给出了大量实验数据,实验结果表明遗传算法具有快速学习网络权重的能力,并且能够摆脱局部极点的困扰。     

Using genetic algorithms to design mesh networks

Designs for mesh communication networks must meet conflicting, interdependent requirements. This sets the stage for a complex problem with a solution that targets optimal topological connections, routing, and link capacity assignments. These assignments must minimize cost while satisfying traffic requirements and keeping network delays within permissible values. Since such a problem is NP-complete, developers must use heuristic techniques to handle the complexity and solve practical problems with a modest number of nodes. One heuristic technique, genetic algorithms, appears to be ideal to handle the design of mesh networks with capability of handling discrete values, multiobjective functions, and multiconstraint problems. Existing applications of genetic algorithms to this problem, however, have only optimized the network topology. They ignore the difficult subproblems of routing and capacity assignment, a crucial determiner of network quality and cost. This article presents a total solution to mesh network design using a genetic algorithm approach. The application is a 10-city network that links Hong Kong and nine other cities in China. The development demonstrates that this method can be used for networks of reasonable size with realistic topology and traffic requirements     

无线传感器网络中基于遗传算法的优化覆盖机制

覆盖作为无线传感器网络应用的一个基本问题．反映了网络监测和实现目标跟踪的质量效果．针对传感器节点的高密度部署情况,研究了工作节点集选取问题．提出两种基于加权遗传算法和基于约束遗传算法的优化覆盖机制．根据生成的适值函数进行遗传算法操作．并计算传感器网络充分覆盖区域所需的近似最优工作节点集．仿真结果表明该算法能快速收敛于最优解．完成工作节点集的优化选取,从而降低网络冗余,延长网络生存时间．     

Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors

Motivated by recent developments in wireless sensor networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm is proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently, and therefore help provide useful benchmarks for various centralized and distributed clustering scheme designs.     

井下分布式对等通信技术研究

针对现有井下通信装备应急通信能力薄弱的问题,通过分析分布式通信技术在井下应用的要求以及比较目前4种分布式对等网络模型的各项性能,提出了一种结合SIP协议与全分布式对等网络技术的井下分布式对等通信技术方案,详细阐述了该方案的信令流程、主被叫呼叫处理、分布式交互机制与数据报文设计。测试结果表明,该方案是一种较好的井下终端脱网通信方案。     

基于遗传算法和强化学习的贝叶斯网络结构学习算法

遗传算法是基于自然界中生物遗传规律的适应性原则对问题解空间进行搜寻和最优化的方法。贝叶斯网络是对不确定性知识进行建模、推理的主要方法,Bayesian网中的学习问题(参数学习与结构学习)是个NP-hard问题。强化学习是利用新顺序数据来更新学习结果的在线学习方法。介绍了利用强化学习指导遗传算法,实现对贝叶斯网结构进行有效学习。     

基于可信对等的分布式入侵检测通信框架设计

为了提高分布式入侵检测的实时性和安全性,提出了一种可信对等的分布式入侵检测通信框架的模型.该模型借鉴了P2P和代理技术,不同网络节点中的入侵检测代理是对等的,它们之间通过共享检测信息进行整体协防.该模型还借鉴了安全通信技术,在网络中建立了一个认证服务器,不在同一网络节点的任何两个网络进程的通信必须通过该认证服务器,提高了入侵检测自身的安全性.设计实现了一个原型系统,原型系统的实验结果表明了该模型的正确性和可行性.     

基于对等网的个性化推荐系统

个性化推荐系统的研究与应用已经成为一项热点,但是在信任性、实时性、可扩展性以及对移动用户提供推荐等方面还存在很多问题和挑战。为了提高系统性能,为推荐系统的使用提供更广阔的发展前景,将推荐系统建立在双层对等网络的平台上。对等点之间通过发送消息进行通讯和处理查询,并采用动态路由表机制减少重复信息。最后,通过一个实验模拟了该系统。     

结构化P2P覆盖网络的Cayley图构造方法研究

结构化P2P覆盖网络通常都基于某个静态的图结构,而这些静态图又常常是Cayley图或其超图,这些静态图的直径、度等特性可以直接影响到覆盖网络拓扑的路由表大小、路由长度等特性,因此静态图的选择显得非常重要.Cayley图是使用代数群论建立的一类图,它的最大好处是其对称性和点传递性,利用Cayley图的这类性质,可以分析结构化P2P覆盖网络拓扑结构的本质.就几种典型的结构化P2P覆盖网络的静态拓扑,分析了其Cayley图构造方法的本质.     

Leveraging saving-based algorithms by master-slave genetic algorithms

Saving-based algorithms are commonly used as inner mechanisms of efficient heuristic construction procedures. We present a general mechanism for enhancing the effectiveness of such heuristics based on a two-level genetic algorithm. The higher-level algorithm searches in the space of possible merge lists which are then used by the lower-level saving-based algorithm to build the solution. We describe the general framework and we illustrate its application to three hard combinatorial problems. Experimental results on three hard combinatorial optimization problems show that the approach is very effective and it enables considerable enhancement of the performance of saving-based algorithms.     

Using genetic algorithms to solve quality-related bin packing problem

The Bin Packing Problem is an industrial problem which involves grouping items into appropriate bin to minimize the cost and number of used bins. It provides a solution for assigning parts to optimize some predefined measures of productivity. In this study, Ion Plating (IP) industry requires similar approach on allocating production jobs into batches for producing better quality products and enabling to meet customer deadlines. The aim of this paper is to (i) develop a Bin Packing Genetic Algorithms (BPGA) with different weighting combinations, taking into account the quality of product and service; (ii) improve the production efficiency by reducing the production unit cost in IP. Genetic Algorithm was chosen because it is one of the best heuristics algorithms on solving optimization problems. In the case studies, industrial data of a precious metal finishing company was used to simulate the proposed BPGA model, and the computational results were compared with these industrial data. The results from three different weighting combinations demonstrated that fewer resources would be required by applying the proposed model in solving BP problem in the Ion Plating Cell.     

QoS enhancements for global replication management in peer to peer networks

Replica Management is a key issue to reduce the bandwidth consumption, to improve data availability and to maintain data consistency in large distributed systems. Global Replica Management (GRM) means to maintain the data consistency across the entire network. It is preferable particularly for multi-group distributed systems. On the other hand, GRM is not favorable for many applications because a very large number of message passes is needed for replica management processes. In this paper, in order to reduce the number of message passes needed to achieve the efficient GRM strategy, an interconnection structure called the Distributed Spanning Tree (DST) has been employed. The application of DST converts the peer network into logical layered structures and thereby provides a hierarchical mechanism for replication management. It is proved that this hierarchical approach improves the data availability and consistency across the entire network. In addition to these, it is also proved that the proposed approach reduces the data latency and the required number of message passes for any specific application in the network.     

Evolving dynamic Bayesian networks with Multi-objective genetic algorithms

A dynamic Bayesian network (DBN) is a probabilistic network that models interdependent entities that change over time. Given example sequences of multivariate data, we use a genetic algorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evaluation strategy with a genetic algorithm. The multi-objective criteria are a network's probabilistic score and structural complexity score. Our use of Pareto ranking is ideal for this application, because it naturally balances the effect of the likelihood and structural simplicity terms used in the BIC network evaluation heuristic. We use a basic structural scoring formula, which tries to keep the number of links in the network approximately equivalent to the number of variables. We also use a simple representation that favors sparsely connected networks similar in structure to those modeling biological phenomenon. Our experiments show promising results when evolving networks ranging from 10 to 30 variables, using a maximal connectivity of between 3 and 4 parents per node. The results from the multi-objective GA were superior to those obtained with a single objective GA. Brian J. Ross is a professor of computer science at Brock University, where he has worked since 1992. He obtained his BCSc at the University of Manitoba, Canada, in 1984, his M.Sc. at the University of British Columbia, Canada, in 1988, and his Ph.D. at the University of Edinburgh, Scotland, in 1992. His research interests include evolutionary computation, language induction, concurrency, and logic programming. He is also interested in computer applications in the fine arts. Eduardo Zuviria received a BS degree in Computer Science from Brock University in 2004 and a MS degree in Computer Science from Queen's University in 2006 where he held jobs as teacher and research assistant. Currently, he is attending a Ph.D. program at the University of Montreal. He holds a diploma in electronics from a technical college and has worked for eight years in the computer industry as a software developer and systems administrator. He has received several scholarships including the Ontario Graduate Scholarship, Queen's Graduate Scholarship and a NSERC- USRA scholarship.