The evolution of transport protocols: An evolutionary game perspective

Today’s Internet is well adapted to the evolution of protocols at various network layers. Much of the intelligence of congestion control is delegated to the end users and they have a large amount of freedom in the choice of the protocols they use. In the absence of a centralized policy for a global deployment of a unique protocol to perform a given task, the Internet experiences a competitive evolution between various versions of protocols. The evolution manifests itself through the upgrading of existing protocols, abandonment of some protocols and appearance of new ones. We highlight in this paper the modeling capabilities of the evolutionary game paradigm for explaining past evolution and predicting the future one. In particular, using this paradigm we derive conditions under which (i) a successful protocol would dominate and wipe away other protocols, or (ii) various competing protocols could coexist. In the latter case we also predict the share of users that would use each of the protocols. We further use evolutionary games to propose guidelines for upgrading protocols in order to achieve desirable stability behavior of the system.     

An antibody network inspired evolutionary framework for distributed object computing

In order to meet the increasing scale and users requirements for the distributed object computing (DOC) systems, their infrastructures are highly desirable to be redesigned. Based on the principles of immune system and the evolution mechanisms learned from an antibody network model, a novel evolutionary framework for DOC (E-DOC) is proposed. The antibody network model as well as the evolution process including clonal proliferation, immune elimination, and immune memory is studied. Then, E-DOC framework based on the antibody network is proposed, whose simulation platform is designed and implemented. On the platform, the evolutionary features are studied by: (1) diversity and stability of antibodies and genotypes, (2) detection and elimination of antigens, (3) effect of immune memory, and (4) tendency of eliminated and stimulated antigens. The experiment results show that the proposed framework can achieve the evolution ability and the promising performance, which are critical to DOC systems. E-DOC is extendable for the future design of distributed object middleware, such as WebSphere Application Server and BEA WebLogic Application Server.     

Low-priced and efficient replica detection framework for wireless sensor networks

A replica attack is one of the most challenging problems in wireless sensor networks. Once a node is physically captured, it can be reprogrammed and replicated into a large number of replicas, and thus maliciously occupying the network. Although until now various schemes have been proposed to detect replicas, most of them suffer from high energy costs and additional device such as a global positioning system. In this paper, we introduce the efficient replica detection framework without the need of additional device. The framework consists of key establishment and replica detection protocols. The proposed replica detection protocol is executed on the proposed key establishment protocol. Furthermore, through simulation experiments, we show that the proposed replica detection protocol provides high replica detection ratio, faster replica detection, and higher energy saving than existing protocols.     

USIM-based EAP-TLS authentication protocol for wireless local area networks

Due to the rapid growth in popularity of Wireless Local Area Network (WLAN), wireless security has become one of many important research issues. For the WLAN security, the IEEE 802.1X standard provides an authentication framework that is based on the Extensible Authentication Protocols (EAP). In the EAP framework, there are many authentication protocols that have been proposed, in which each authentication protocol has some strengths and weaknesses, respectively. Most EAP authentication protocols lack two features: identity protection and withstanding man-in-the-middle attacks. In this paper, we first propose a novel symmetric-key based certificate distribution scheme based on Universal Subscriber Identity Module (USIM) cards in a cellular network. The symmetric-key based certificate distribution scheme allows mobile subscribers to obtain temporary certificates from the corresponding cellular network. Combining the proposed certificate distribution scheme with the EAP-TLS (Transport Layer Security) protocol, we present a new EAP authentication protocol called USIM-based EAP authentication protocol. The new EAP authentication protocol combining with USIM cards is an extension of the EAP-TLS protocol and also follows the EAP framework in the IEEE 802.1X standard. Compared to other EAP authentication protocols, the proposed protocol provides mutual authentication, strong identity protection and roaming capability between the cellular network and the WLAN networks.     

Design of a Test-Bed System for Multimedia Uplink UCC Traffic Management in Heterogeneous 3G/4G Wireless Networks

Focusing on enriched multimedia user created contents (UCC), also known as user-generated contents, we provide a novel uplink traffic management scheme which consists on the both of network selection and traffic redistribution. A recent trend in the use of the multimedia Internet exhibits rapid growth of UCC with the potential to create a huge amount of uplink traffic for wireless operators. Our objective is finding the optimal heterogeneous network operation framework under the precise uplink UCC traffic analysis. We suggest the market-based evaluation of the relative value of resources for each network, and develop a whole new uplink UCC operation framework in heterogeneous 3G/4G networks. To prove the applicability of the proposed operation framework and analysis, we build a large-scale uplink test-bed system. The test-bed contains all necessary functions of network facilities and protocols for development of emerging multimedia UCC traffic. We can easily test the various service scenarios in heterogeneous network environment by the developed test-bed system.     

Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration

Recently an interesting evolutionary mechanism, sensibility, inherited from a concept model of Free Search (FS) was introduced and used for solving network problems. Unfortunately, the original FS is not easy to implement because it requires key knowledge that is not clearly defined in the existing literature to determine the neighborhood space that profoundly affects the performance of the original FS. This paper thus designs a new implementation for the concept model of FS, and proposes a new algorithm, called Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration (ADEQFS) to address this issue. In ADEQFS, we focus on designing a new mutation strategy by employing adaptive differential evolution techniques as well as concepts and principles from real-coded quantum-inspired evolutionary algorithm. In addition, we use the crossover operation from the traditional Differential Evolution scheme to alleviate the premature convergence for the proposed algorithm. Furthermore, we employ the greedy mechanism to preserve the best solutions found at each generation. The convergence analysis of the proposed algorithm is also presented in this paper. We give the proof of convergence by using the Markov chain model. Thirty-four optimization test functions with different mathematical characteristics are employed as benchmark set to test the performance of ADEQFS. The numerical results highlight the improved convergence rate and computation reliability.     

知识进化算法*

根据卡尔·波普尔的知识进化理论,建立了知识进化算法的基本框架,详细地阐述了该算法的原理和具体实施方案。知识进化算法主要由一个知识空间和多个群体空间组成,群体空间根据知识的指导通过选择操作不断地提出新的假说,并通过猜测操作和反驳操作与知识空间协同进化来不断地提高真理度（即不断地接近问题的最优解）。为了验证方法的有效性,选取了来自其他文献中的五个经典测试函数作为算法的测试对象,通过对其他文献中的仿真实例进行计算和结果比较,证明了算法的可行性和有效性。     

An augmented multi-objective particle swarm optimizer for building cluster operation decisions

It is envisioned that other than the grid-building communication, the smart buildings could potentially treat connected neighborhood buildings as a local buffer thus forming a local area energy network through the smart grid. As the hardware technology is in place, what is needed is an intelligent algorithm that coordinates a cluster of buildings to obtain Pareto decisions on short time scale operations. Research has proposed a memetic algorithm (MA) based framework for building cluster operation decisions and it demonstrated the framework is capable of deriving the Pareto solutions on an 8-h operation horizon and reducing overall energy costs. While successful, the memetic algorithm is computational expensive which limits its application to building operation decisions on an hourly time scale. To address this challenge, we propose a particle swarm framework, termed augmented multi-objective particle swarm optimization (AMOPSO). The performance of the proposed AMOPSO in terms of solution quality and convergence speed is improved via the fusion of multiple search methods. Extensive experiments are conducted to compare the proposed AMOPSO with nine multi-objective PSO algorithms (MOPSOs) and multi-objective evolutionary algorithms (MOEAs) collected from the literature. Results demonstrate that AMOPSO outperforms the nine state-of-the-art MOPSOs and MOEAs in terms of epsilon, spread, and hypervolume indicator. A building cluster case is then studied to show that the AMOPSO based decision framework is able to make hourly based operation decisions which could significantly improve energy efficiency and achieve more energy cost savings for the smart buildings.     

The partial solutions consideration based self-adaptive evolutionary algorithm: A learning structure of neuro-fuzzy networks

Evolutionary computation has become a popular research field due to its global searching ability. Therefore, it raises a challenge to develop an efficient and robustness evolutionary algorithm to not only reduce the evolution process but also increase the chances to meet the global solution. To this end, this study aims to provide a novel evolutionary algorithm named the partial solutions consideration based self-adaptive evolutionary algorithm (PSC-SEA) to address this issue; the proposed algorithm is applied to adjust the parameters of the neuro-fuzzy networks. More specifically, different from the existing evolution algorithms, the partial solutions consideration (PSC) tends to consider both the specializations and complementary relationships of the partial solutions from the complete solution to prevent converging to suboptimal solution. Moreover, a self-adaptive evolutionary algorithm (SEA) is proposed to dynamically adjust the searching space according to the performance. By doing so, the proposed evolutionary algorithm can consider the influence of partial solutions and provide a suitable searching space to increase the chances to meet the global solution. As shown in the results, the proposed evolutionary algorithm obtains better performance and smoother learning curves than other existing evolutionary algorithms. In other words, the proposed evolutionary algorithm can efficient tune the parameters of the neuro-fuzzy networks to meet the global solution. Base on the results, a framework is proposed to build a benchmark for developing the evolutionary algorithms that can not only consider the influence of partial solutions but also provide a suitable searching space.     

基于C-MMAS算法的组合服务动态选择研究

将大规模的具有多种组合路径的QoS最优组合服务选择转换成带约束的最优路径选择问题,并提出了一种基于文化的最大一最小蚁群优化算法(C-MMAS)来完成最优路径选择。C-MMAS计算模型由基于MMAS的群体空间、基于优秀解的信仰空间及其之间的通信协议组成。群体空间在完成基于MMAS的演化后进行基于“变异”的进化操作,并将每次演化和进化后的优秀解作为知识贡献给信仰空间,信抑空间按照一定的优化规则更新空间里的知识,当信仰空间里的知识经过若干代的积累沉淀后再对群体的演化进行指导。此计算模型在知识和群体层面使用双重进化机制支持问题的求解和知识的提取,充分利用了种群的进化机制和知识的指导作用,在很大程度上提高了种群的多样性及收敛速度,达到了防止早熟、降低计算代价的目的。理论分析和实验结果说明了该算法的可行性和有效性。     

A framework for exploring organizational structure in dynamic social networks

Recent research has provided promising results relating to discovering communities within a social network. We find that further representing the organizational structure of a social network is an interesting issue that helps gain better understandings of the social network. In this paper, we define a data structure, named Community Tree, to depict the organizational structure and provide a framework for exploring the organizational structure in a social network. In this framework, an algorithm, which combines a modified PageRank and Random Walk on graph, is developed to derive the community tree from the social network. In the real world, a social network is constantly evolving. In order to explore the organizational structure in a dynamic social network, we develop a tree learning algorithm, which employs tree edit distance as the scoring function, to derive an evolving community tree that enables a smooth transition between two community trees. We also propose an approach to threading communities in community trees to obtain an evolution graph of the organizational structure, by which we can reach new insights from the dynamic social network. The experiments conducted on synthetic and real dataset demonstrate the feasibility and applicability of the framework. Based on the theoretical outcomes, we further apply the proposed framework to explore the evolution of organizational structure with the 2001 Enron dataset, and obtain several interesting findings that match the context of Enron.     

Prototype of a secure wireless patient monitoring system for the medical community

Many medical applications set new demands on sensor network designs. They often involve highly variable data rates, multiple receivers and security. Most existing sensor network designs do not adequately support these requirements, focusing instead on aggregating small amounts of data from nodes without security. In this paper, we present a software design for medical sensor networks. This framework provides a set of protocols and services specifically tailored for this application domain. It includes a secure communications model, an interface for periodic collection of sensor data, a dynamic sensor discovery protocol and protocols that monitor and save up to 70% of the energy of a node. The framework is built in TinyOS and a JAVA based user interface is provided to debug the framework and display the measured data. An extensive evaluation of the framework of a 6-node sensor test-bed is presented, measuring scalability and robustness as the number of sensors and the per node data rate are varied. The results show that the proposed framework is a scalable, robust, reliable and secure solution for medical applications.     

SecureSMS: A secure SMS protocol for VAS and other applications

Nowadays, the SMS is a very popular communication channel for numerous value added services (VAS), business and commercial applications. Hence, the security of SMS is the most important aspect in such applications. Recently, the researchers have proposed approaches to provide end-to-end security for SMS during its transmission over the network. Thus, in this direction, many SMS-based frameworks and protocols like Marko's SMS framework, Songyang's SMS framework, Alfredo's SMS framework, SSMS protocol, and, Marko and Konstantin's protocol have been proposed but these frameworks/protocols do not justify themselves in terms of security analysis, communication and computation overheads, prevention from various threats and attacks, and the bandwidth utilization of these protocols. The two protocols SMSSec and PK-SIM have also been proposed to provide end-to-end security and seem to be little better in terms of security analysis as compared to the protocols/framework mentioned above. In this paper, we propose a new secure and optimal protocol called SecureSMS, which generates less communication and computation overheads. We also discuss the possible threats and attacks in the paper and provide the justified prevention against them. The proposed protocol is also better than the above two protocols in terms of the bandwidth utilization. On an average the SecureSMS protocol reduces 71% and 59% of the total bandwidth used in the authentication process as compared to the SMSSec and PK-SIM protocols respectively. Apart from this, the paper also proposes a scheme to store and implement the cryptographic algorithms onto the SIM card. The proposed scheme provides end-to-end SMS security with authentication (by the SecureSMS protocol), confidentiality (by encryption AES/Blowfish; preferred AES-CTR), integrity (SHA1/MD5; preferred SHA1) and non-repudiation (ECDSA/DSA; preferred ECDSA).     

Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

Classification of intrusion attacks and normal network traffic is a challenging and critical problem in pattern recognition and network security. In this paper, we present a novel intrusion detection approach to extract both accurate and interpretable fuzzy IF-THEN rules from network traffic data for classification. The proposed fuzzy rule-based system is evolved from an agent-based evolutionary framework and multi-objective optimization. In addition, the proposed system can also act as a genetic feature selection wrapper to search for an optimal feature subset for dimensionality reduction. To evaluate the classification and feature selection performance of our approach, it is compared with some well-known classifiers as well as feature selection filters and wrappers. The extensive experimental results on the KDD-Cup99 intrusion detection benchmark data set demonstrate that the proposed approach produces interpretable fuzzy systems, and outperforms other classifiers and wrappers by providing the highest detection accuracy for intrusion attacks and low false alarm rate for normal network traffic with minimized number of features.     

基于进化谱分方法的动态社团检测

为了有效地分析动态网络中的社团结构功能和特性,在进化时间平滑框架下基于进化聚类方法对模块密度函数和否定平均关联函数进行了优化,论证了理论可行性;在此基础上提出了检测动态网络社团结构的进化谱分算法,并对两类算法进行了详细的谱分分析。分别在计算机合成的动态网络以及真实网络中检验了所提算法的准确性和有效性,并将其与其他算法进行对比。实验结果表明,所提算法对动态网络中的社团检测仍有很高的准确性和有效性。     

The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation

The analysis of worst-case behavior in wireless sensor networks is an extremely difficult task, due to the complex interactions that characterize the dynamics of these systems. In this paper, we present a new methodology for analyzing the performance of routing protocols used in such networks. The approach exploits a stochastic optimization technique, specifically an evolutionary algorithm, to generate a large, yet tractable, set of critical network topologies; such topologies are then used to infer general considerations on the behaviors under analysis. As a case study, we focused on the energy consumption of two well-known ad hoc routing protocols for sensor networks: the multi-hop link quality indicator and the collection tree protocol. The evolutionary algorithm started from a set of randomly generated topologies and iteratively enhanced them, maximizing a measure of “how interesting” such topologies are with respect to the analysis. In the second step, starting from the gathered evidence, we were able to define concrete, protocol-independent topological metrics which correlate well with protocols’ poor performances. Finally, we discovered a causal relation between the presence of cycles in a disconnected network, and abnormal network traffic. Such creative processes were made possible by the availability of a set of meaningful topology examples. Both the proposed methodology and the specific results presented here – that is, the new topological metrics and the causal explanation – can be fruitfully reused in different contexts, even beyond wireless sensor networks.     

mReno: a practical multipath congestion control for communication networks

In current networks, end-user devices are usually equipped with several network interfaces. The design of a multipath protocol that can cooperate with current single-path transport protocols is an interesting research field. Most previous works on multipath network utility maximization (NUM) lead to rate-based control protocols. Moreover, these studies do not model a case in which paths from a source may have different characteristics. Thus, these approaches are difficult to deploy to the Internet. In this paper, we introduce a multipath NUM model for a network with both multipath and single-path users. The proposed algorithm converges to a global solution to the multipath NUM. Based on the mathematical framework, we develop a multipath TCP called mReno. Analysis and simulations indicate that mReno is completely compatible with TCP Reno and achieves load-balance, fairness, and performance improvement targets.     

Systematically incorporating domain-specific knowledge into evolutionary speciated checkers players

The evolutionary approach for gaming is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages. It is, therefore, sometimes inefficient to evolve simple heuristics that may be created easily by humans because it is based purely on a bottom-up style of construction. Incorporating domain knowledge into evolutionary computation can improve the performance of evolved strategies and accelerate the speed of evolution by reducing the search space. In this paper, we propose the systematic insertion of opening knowledge and an endgame database into the framework of evolutionary checkers. Also, the common knowledge that the combination of diverse strategies is better than a single best one is included in the middle stage and is implemented using crowding algorithm and a strategy combination scheme. Experimental results show that the proposed method is promising for generating better strategies.     

基于长短期记忆网络的社区演化预测

现实生活中的网络通常是动态的,网络结构随着时间的推移而改变,检测社区演化可以深入了解网络的基本行为。针对动态社区演化预测问题,提出一种结合演化树和长短期记忆网络的社区演化预测方法,从动态网络中提取社区的多元特征,并使用长短期记忆网络对特征进行学习分类,最终预测社区下一时间段的变化情况。在两个真实数据集上进行了实验,实验结果证明该方法可以有效地预测社区演化行为,与其他方法相比具有较好的准确性。     

Cooperative coevolution of artificial neural network ensembles for pattern classification

This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a sufficient number of neurons in the hidden layer would suffice to solve any problem, in practice many real-world problems are too hard to construct the appropriate network that solve them. In such problems, neural network ensembles are a successful alternative. Nevertheless, the design of neural network ensembles is a complex task. In this paper, we propose a general framework for designing neural network ensembles by means of cooperative coevolution. The proposed model has two main objectives: first, the improvement of the combination of the trained individual networks; second, the cooperative evolution of such networks, encouraging collaboration among them, instead of a separate training of each network. In order to favor the cooperation of the networks, each network is evaluated throughout the evolutionary process using a multiobjective method. For each network, different objectives are defined, considering not only its performance in the given problem, but also its cooperation with the rest of the networks. In addition, a population of ensembles is evolved, improving the combination of networks and obtaining subsets of networks to form ensembles that perform better than the combination of all the evolved networks. The proposed model is applied to ten real-world classification problems of a very different nature from the UCI machine learning repository and proben1 benchmark set. In all of them the performance of the model is better than the performance of standard ensembles in terms of generalization error. Moreover, the size of the obtained ensembles is also smaller.