15590392
Performance-guided neural network for rapidly self-organising active network management
We present a neural network for real-time learning and mapping of patterns using an external performance indicator. In a non-stationary environment where new patterns are introduced over time, the learning process utilises a novel snap-drift algorithm that performs fast, convergent, minimalist learning (snap) when the overall network performance is poor and slower, more cautious learning (drift) when the performance is good. Snap is based on a modified form of Adaptive Resonance Theory (CGIP 37(1987)54); and drift is based on Learning Vector Quantization (LVQ) (Proc. IJCNN 1(1990a)545). The two are combined within a semi-supervised learning system that shifts its learning style whenever it receives a significant change in performance feedback. The learning is capable of rapid re-learning and re-stabilisation, according to changes in external feedback or input patterns. We have incorporated this algorithm into the design of a modular neural network system, Performance-guided Adaptive Resonance Theory (P-ART) (Proc. IJCNN 2(2003)1412; Soft computing systems: Design, Management and application, IOS Press, Netherland, 2002; pp. 21-31). Simulation results show that the system discovers alternative solutions in response to significant changes in the input patterns and/or in the environment, which may require similar patterns to be treated differently over time. The simulations involve attempting to optimise the selection of network services in a non-stationary, real-time active computer network environment, in which the factors influencing the required selections are subject to change.
5485745
Performance-guided Neural Network for Self-Organising Network Management
A neural network architecture is introduced for real-time learning of input sequences 
using external performance feedback. Some aspects of Adaptive Resonance Theory (ART) 
networks [1] are applied because they are able to function in a fast real-time adaptive active network environment where user requests and new proxylets (services) are constantly being introduced over time [2,3]. The architecture learns, self-organis es and self-stabilises in response to user requests, mapping the requests according to the types of proxylets available. However, in 
order make the neural networks respond to performance feedback, we introduce a modification to the original ART1 network in the form of the ‘snap-drift’ algorithm, that uses fast convergent, minimalist learning (snap) when the overall network performance is poor, and slow learning (drift towards user request input pattern) when the performance is good. Preliminary simulations 
evaluate the two-tiered architecture using a simple operating environment consisting of simulated training and test data.
14204864
Handheld routers: intelligent bandwidth aggregation for mobile collaborative communities
Multi-homed, mobile wireless computing and communication devices can spontaneously form communities to logically combine and share the bandwidth of each other's wide-area communication links using inverse multiplexing. But membership in such a community can be highly dynamic, as devices and their associated WAN links randomly join and leave the community. We identify the issues and tradeoffs faced in designing a decentralized inverse multiplexing system in this challenging setting, and determine precisely how heterogeneous WAN links should be characterized, and when they should be added to, or deleted from, the shared pool. We then propose methods of choosing the appropriate channels on which to assign newly-arriving application flows. Using video traffic as a motivating example, we demonstrate how significant performance gains can be realized by adapting allocation of the shared WAN channels to specific application requirements. Our simulation and experimentation results show that collaborative bandwidth aggregation systems are, indeed, a practical and compelling means of achieving high-speed Internet access for groups of wireless computing devices beyond the reach of public or private access points.
16363066
Design and implementation of a multi-threaded TMN agent system
A telecommunications management network adopts the OSI manager-agent model in which an agent system plays a more important role than a manager system. Because the existing agent systems sequentially perform a CMIS operation on selected managed objects, it cannot guarantee quick response time when many managed objects are selected. We propose a multi-threaded process structure for the agent system which performs CMIS operations concurrently on managed objects. We implemented our agent system using OSIMIS-4.O TMN platform in Solaris. We measured response time to a CMIS operation to evaluate the performance of our system. Experimental results show that the performance of our system is not so good when network resources reside within the agent system, but very good when network resources reside outside the agent system.
17617945
INSIGNIA: An IP-Based Quality of Service Framework for Mobile ad Hoc Networks
We present the design, implementation, and evaluation of INSIGNIA, an IP-based quality of service framework that supports adaptive services in mobile ad hoc networks. The framework is based on an in-band signaling and soft-state resource management approach that is well suited to supporting mobility and end-to-end quality of service in highly dynamic environments where the network topology, node connectivity, and end-to-end quality of service are time varying. Architecturally INSIGNIA is designed to support fast reservation, restoration, and end-to-end adaptation based on the inherent flexibility and robustness and scalability found in IP networks. We evaluate the framework, paying particular attention to the performance of the in-band signaling system, which helps counter time-varying network dynamics in support of the delivery of adaptive services. Our results show the benefit of our framework under diverse mobility, traffic, and channel conditions.
38607714
Cooperative peer groups in NICE
A distributed scheme for trust inference in peer-to-peer networks is presented. Our work is in context of the NICE system, which is a platform for implementing cooperative applications over the Internet. We describe a technique for efficiently storing user reputation information in a completely decentralized manner, and show how this information can be used to efficiently identify noncooperative users in NICE. We present a simulation based study of our algorithms, in which we show our scheme scales to thousands of users using modest amounts of storage, processing, and bandwidth at any individual node. Lastly, we show that our scheme is robust and can form cooperative groups in systems where the vast majority of users are malicious.
24083039
High Quality Perceptual Steganographic Techniques
Recently, several steganographic algorithms for two-color binary images have been proposed [16,1,14,2]. In this paper, we propose steganographic algorithms which embeds a secret message into bitmap images and palette-based images. To embed a message, the suggested algorithm divides a bitmap image into bit-plane images from LSB-plane to MSB-plane for each pixel, and considers each bit-plane image as a binary one. The algorithm splits each bit-plane image into m × n blocks, and embeds a r-bit(\(r = \lfloor {\rm log_2}(mn + 1) \rfloor - 1\)) message into the block. And our schemes embed a message to every bit-plane from LSB to MSB to maximize the amount of embedded message and to minimize the degradation. The schemes change at most two pixels in each block. Therefore, the maximal color changes of the new algorithm are much smaller than other bit-plane embedding schemes’ such as the sequential substitution schemes [10,7,11].