Fang Wang

Genetic Algorithms With Immigrants and Memory Schemes for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks

In recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.

Structure of peer-to-peer social networks

This paper presents a statistical analysis of the structure of peer-to-peer (P2P) social networks that captures social associations of distributed peers in resource sharing. Peer social networks appear to be mainly composed of pure resource providers that guarantee high resource availability and reliability of P2P systems. The major peers that both provide and request resources are only a small fraction. The connectivity between peers, including undirected, directed (out and in) and weighted connections, is scale-free and the social networks of all peers and major peers are small world networks. The analysis also confirms that peer social networks show in general disassortative correlations, except that active providers are connected between each other and by active requesters. The study presented in this paper gives a better understanding of peer relationships in resource sharing, which may help a better design of future P2P networks and open the path to the study of transport processes on top of real P2P topologies.

A computer vision model for visual-object-based attention and eye movements

This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.

Core specification and experiments in DIET: a decentralised ecosystem-inspired mobile agent system

Mobile Agent systems have attracted considerable attention as means of exploring and manipulating distributed information sources. However, many existing multi-agent platforms present limitations in terms of adaptability and scalability, indicating difficulties when trying to replicate these results on a large scale. We describe the core of a novel mobile agent toolkit known as DIET, (Decentralised Information Ecosystem Technologies), which addresses some of these limitations and provides a foundation for an open, robust, adaptive and scalable agent ecosystem. We introduce DIET core features and describe how they support basic mobile agent capabilities such as migration and real-time interaction. We then illustrate how an ecosystem-inspired design approach differs from conventional design approaches. Finally, we experiment with a simple information retrieval scenario, demonstrating the emergence of agent communities through the evolution of environmental preferences. In this way we hope to clarify how applications built on this foundation could be used to tackle problems in adaptable and open real-world scenarios.

Self-organising communities formed by middle agents

This paper attempts to construct a flexible and effective organisation involving users and middle agents in distributed information systems. In particular, middle agents are employed to organise users according to users expressed characteristics (e.g. behaviour). Due to the distributed and dynamically changing properties of users, middle agents are used to search suitable information resources in response to user requests. During search, middle agents also monitor and recognise user behaviour, and group users that have matching behaviour together. So eventually users with similar preferences or interests can be clustered into the same community. Self-organising communities reduce the difficulties of human design work on organisation and user similarity calculation, and avoid the constraints due to the design. The user preferences, at the same time, are reflected accurately. Simulated experiments have shown that self-organising communities can organise users properly, and have continuously improved search speed and search efficiency as the communities are correctly formed. In addition, the formation of communities was scalable as number of users increased.

SELF-ORGANIZING PEER-TO-PEER SOCIAL NETWORKS

Peer‐to‐peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self‐organizing peer‐to‐peer social networks (SoPPSoNs) to self‐organize distributed peers in a decentralized way, in which neuron‐like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self‐organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.

Self-Organizing and Adaptive Peer-to-Peer Network

In this paper, an algorithm that forms a dynamic and self-organizing network is demonstrated. The hypothesis of this work is that in order to achieve a resilient and adaptive peer-to-peer (P2P) network, each network node must proactively maintain a minimum number of edges. Specifically, low-level communication protocols are not sufficient by themselves to achieve high-service availability, especially in the case of ad hoc or dynamic networks with a high degree of node addition and deletion. The concept has been evaluated within a P2P agent application in which each agent has a goal to maintain a preferred number of connections to a number of service providing agents. Using this algorithm, the agents update a weight value associated with each connection, based on the perceived utility of the connection to the corresponding agent. This utility function can be a combination of several node or edge parameters, such as degree k of the target node, or frequency of the message response from the node. This weight is updated using a set of Hebbian-style learning rules, such that the network as a whole exhibits adaptive self-organizing behavior. The principal result is the finding that by limiting the connection neighborhood within the overlay topology, the resulting P2P network can be made highly resilient to targeted attacks on high-degree nodes, while maintaining search efficiency

A Natural Hand Gesture System for Intelligent Human-Computer Interaction and Medical Assistance

This paper presents a novel hand gesture system for intelligent human-computer interaction (HCI) and its applications in medical assistance, e.g. intelligent wheelchair control. The hand gesture vocabulary in the system consists of five key hand postures and three compound states, and its design strategy covers the minimal hand motions, distraction detection and user-friendly design. The experiment results show that the designed lexicon is intuitive, ergonomic, and easy to be remembered and performed. The system is tested in both of the indoor and outdoor environments and shows the robustness to lighting change and users errors. The proposed intelligent HCI system can run in real-time and offers a natural and efficient interface for people with disability in their limbs to communicate with robots.

Adaptivity in cell based optimization for information ecosystems

A cell based optimization (CBO) algorithm is proposed which takes inspiration from the collective behaviour of cellular slime molds (Dictyostellium discoideum). Experiments with CBO are conducted to study the ability of simple cell-like agents to collectively manage resources across a distributed network. Cells, or agents, only have local information can signal, move, divide, and die. Heterogeneous populations of the cells are evolved using Cartesian genetic programming (CGP). Several experiments were carried out to examine the adaptation of cells to changing user demand patterns. CBO performance was compared using various methods to change demand. The experiments showed that populations consistently evolve to produce effective solutions. The populations produce better solutions when user demand patterns fluctuated over time instead of environments with static demand. This is a surprising result that shows that populations need to be challenged during the evolutionary process to produce good results.

Evolving preferences among emergent groups of agents

Software agents can prove useful in representing the interests of human users of agent systems. When users have diverse interests, the question arises as to how agents representing their interests can be grouped so as to facilitate interaction between users with compatible interests. This paper describes experiments in the DIET (Decentralised Information Ecosystem Technologies) agent platform that use evolutionary computation to evolve preferences of agents in choosing environments so as to interact with other agents representing users with similar interests. These experiments suggest a useful way for agents to acquire preferences for formation of groups for information interaction between users, and may also indicate means for supporting load balancing in distributed systems.