Da-You Liu

Smart home research

This paper is a survey for smart home research, from definition to current research status. First we give a definition to smart home, and then describe the smart home elements, typical research projects, smart home networks research status, smart home appliances and challenges at last.

Hierarchical community detection with applications to real-world network analysis

Community structure is ubiquitous in real-world networks and community detection is of fundamental importance in many applications. Although considerable efforts have been made to address the task, the objective of seeking a good trade-off between effectiveness and efficiency, especially in the case of large-scale networks, remains challenging. This paper explores the nature of community structure from a probabilistic perspective and introduces a novel community detection algorithm named as PMC, which stands for probabilistically mining communities, to meet the challenging objective. In PMC, community detection is modeled as a constrained quadratic optimization problem that can be efficiently solved by a random walk based heuristic. The performance of PMC has been rigorously validated through comparisons with six representative methods against both synthetic and real-world networks with different scales. Moreover, two applications of analyzing real-world networks by means of PMC have been demonstrated.

Towards efficient resource on-demand in Grid Computing

The essence of Grid Computing is to provide efficient Resource on Demand (RoD). This paper addresses this challenge from the perspective of network, the living platform of Grid, by providing effective Quality of Service (QoS) mechanisms (both IntServ and DiffServ) inside the Grid networking environment. Specifically, the efficiency of this QoS mechanism is maximized by policy-based management taking care of the flexible control of QoS parameters/components and active networks technology looking after the fast delivery of various QoS configurations. The first experiment exemplified the current implementation status.

Network engineering towards efficient resource on-demand in grid computing

The essence of grid computing is to provide efficient resource on demand (RoD). This paper addresses this challenge from the perspective of network, the living platform of grid, by providing effective quality of service (QoS) mechanisms (both IntServ and DiffServ) inside the grid networking environment. Particularly, the efficiency of this QoS mechanism is maximized by policy-based management taking care of flexible control of QoS parameters/components and active networks technology looking after the fast delivery of various QoS configurations. An early experiment exemplified the current implementation status.

Characterizing and Extracting Multiplex Patterns in Complex Networks

Complex network theory provides a means for modeling and analyzing complex systems that consist of multiple and interdependent components. Among the studies on complex networks, structural analysis is of fundamental importance as it presents a natural route to understanding the dynamics, as well as to synthesizing or optimizing the functions, of networks. A wide spectrum of structural patterns of networks has been reported in the past decade, such as communities, multipartites, bipartite, hubs, authorities, outliers, and bow ties, among others. In this paper, we are interested in tackling the challenging task of characterizing and extracting multiplex patterns (multiple patterns as mentioned previously coexisting in the same networks in a complicated manner), which so far has not been explicitly and adequately addressed in the literature. Our work shows that such multiplex patterns can be well characterized as well as effectively extracted by means of a granular stochastic blockmodel, together with a set of related algorithms proposed here based on some machine learning and statistical inference ideas. These models and algorithms enable us to further explore complex networks from a novel perspective.

A policy-based network management system for IP VPN

Even though IP VPN has practically proven itself to be a cost-effective solution, the lack of centralized capabilities of current IP VPN deployment makes the management of growing VPN networks an extremely tedious procedure. This paper proposes to use policy-based network management method to address this challenge. Firstly, a policy-based IP VPN management architecture is presented, mainly explaining the operational components concerning the IPsec. Then a detailed discussion with respect to policy information model is given. Finally, a case study for interdomain IP VPN configuration exemplifies the design and implementation of this management system based on the test-bed developed in the Networks & Services Group of University College London (UCL).

Rule-Driven Mobile Intelligent Agents for Real-Time Configuration of IP Networks

Even though intelligent agent has proven itself to be a promising branch of artificial intelligence (AI), its mobility capacity has yet been paid enough attention to match the pervasive trend of networks. This paper proposes to inject intelligence into mobile agent of current literature by introducing rule-driven mobile agent so as to maintain both intelligence and mobility of current agent. Particularly, this methodology is fully exemplified in the context of real-time IP network configuration through intelligent mobile agent based network management architecture, policy specification language and policy information model. A case study for inter-domain IP VPN configuration demonstrates the design and implementation of this management system based on the test-bed developed in the context of European Union IST Project CONTEXT.

A Distributed Q-Learning Algorithm for Multi-Agent Team Coordination

Q-learning is an effective model-free reinforcement learning algorithm. However, Q-learning is centralized and competent only for single agent learning but not multi-agent learning because in later case the size of state-action space is huge and will grow exponentially with the number of agents increasing. In the paper we present a distributed Q-learning algorithm to solving this problem. In our algorithm, the tasks of learning optimal action policy are distributed to each agent in team but not a central agent. In order to reduce the size of action-state space of multi-agent team we introduce a state-action space sharing strategy of agent team, through which one agent in team can use the states already explored by other agents before and need not take time to explore these states again. Additionally, our algorithm has the ability to allocate sub-goals dynamically among agents according to environment changing, which can make agent team coordinate more efficiently. Experiments show the efficiency of our algorithm when it is applied to the benchmark problem of predator-prey pursuit game, also called pursuit game, in which a team of predators coordinate to capture a prey.

Semi-supervised weighted distance metric learning for kNN classification

K-Nearest Neighbor (kNN) classification is one of the most popular machine learning techniques, but it often fails to work well due to less known information or inappropriate choice of distance metric or the presence of a lot of unrelated features. To handle those issues, we introduce a semi-supervised weighted distance metric learning method for kNN classification. This method uses a graph-based semi-supervised Label Propagation algorithm to gain more classification information with tiny initial classification information, then resorts to improved weighted Relevant Component Analysis to learn a Mahalanobis distance metric, and finally uses learned Mahalanobis distance metric to replace the original Euclidean distance of kNN classifier. Experiments on UCI datasets show the effectiveness of our method.

Communication performance optimization for mobile agent system

Communication performance is one of the most important factors affecting the efficiency of a mobile agent system. The paper studies the communication of a mobile agent system from the viewpoint of performance. We analyze four primary factors that affect the communication performance and propose a communication performance optimization model. The model has three primary functions. First, the model provides a formalism method to describe the communication task of a mobile agent. Second, the model provides a means to make quantitative analysis of the communication performance of a mobile agent system. Third, the model can plan out an optimal communication scheme for mobile agents to minimize the cost of the whole communication. The model could thus be a building block for the optimization of the communication behavior of mobile agents.