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.

title: Resilient multicast using overlays
abstract: We introduce Probabilistic Resilient Multicast (PRM): a multicast data recovery scheme that improves data delivery ratios while maintaining low end-to-end latencies. PRM has both a proactive and a reactive components; in this paper we describe how PRM can be used to improve the performance of application-layer multicast protocols especially when there are high packet losses and host failures. Through detailed analysis in this paper, we show that this loss recovery technique has efficient scaling properties-the overheads at each overlay node asymptotically decrease to zero with increasing group sizes. As a detailed case study, we show how PRM can be applied to the NICE application-layer multicast protocol. We present detailed simulations of the PRM-enhanced NICE protocol for 10 000 node Internet-like topologies. Simulations show that PRM achieves a high delivery ratio (>97%) with a low latency bound (600 ms) for environments with high end-to-end network losses (1%-5%) and high topology change rates (5 changes per second) while incurring very low overheads (<5%).

title: Merging Back-propagation and Hebbian Learning Rules for Robust Classifications
abstract: By imposing saturation requirements on hidden-layer neural activations, a new learning algorithm is developed to improve robustness on classification performance of a multi-layer Perceptron. Derivatives of the sigmoid functions at hidden-layers are added to the standard output error with relative significance factors, and the total error is minimized by the steepest-descent method. The additional gradient-descent terms become Hebbian, and this new algorithm merges two popular learning algorithms, i.e., error back-propagation and Hebbian learning rules. Only slight modifications are needed for the standard back-propagation algorithm, and additional computational requirements are negligible. This saturation requirement effectively reduces output sensitivity to the input, which results in improved robustness and better generalization for classifier networks. Also distributed representations at hidden-layers are successfully suppressed to accomplish efficient utilization of hidden neurons. Computer simulations demonstrates much faster learning convergence as well as improved robustness for classifications and hetero-associations of binary patterns.

title: NodeWiz: peer-to-peer resource discovery for grids
abstract: Efficient resource discovery based on dynamic attributes such as CPU utilization and available bandwidth is a crucial problem in the deployment of computing grids. Existing solutions are either centralized or unable to answer advanced resource queries (e.g., range queries) efficiently. We present the design of NodeWiz, a grid information service (CIS) that allows multi-attribute range queries to be performed efficiently in a distributed manner. This is obtained by aggregating the directory services of individual organizations in a peer-to-peer information service.

title: Adapting Content for Mobile Devices in Heterogeneous Collaboration Environments
abstract: The portability of new miniaturized devices, together with their ability to connect conveniently to networks in different places, makes mobile computing possible. Recent advances that have led to increasing bandwidths, for wireless communications, enable mobile users to utilize a vast range of sophisticated services such as real-time collaborative services. Garnet Collaborative system is a universal accessible collaborative environment supporting synchronous communication and universal accessibility from a wide range of devices. Garnet Message System Micro Edition (GMSME) is developed to integrate heterogeneous mobile devices into the Garnet collaborative system. Garnet integrates desktop in traditional LAN environments, wireless LAN based mobile devices, and pervasive 3G communication technology seamlessly. In this paper, we describe the process of adapting content in GMSME. GMSME provides application-aware transcoding, to tune the shared resources, for different collaborative models. Also, it optimizes shared resources for each heterogeneous client environments.

title: A hotspot mitigation protocol for ad hoc networks
abstract: Hotspots represent transient but highly congested regions in wireless ad hoc networks that result in increased packet loss, end-to-end delay, and out-of-order packets delivery. We present a simple, effective, and scalable hotspot mitigation protocol (HMP) where mobile nodes independently monitor local buffer occupancy, packet loss, and MAC contention and delay conditions, and take local actions in response to the emergence of hotspots, such as, suppressing new route requests and rate controlling TCP flows. We use analysis, simulation, and experimental results from a wireless testbed to demonstrate the effectiveness of HMP in mobile ad hoc networks. HMP balances resource consumption among neighboring nodes, and improves end-to-end throughput, delay, and packet loss. Our results indicate that HMP can also improve the network connectivity preventing premature network partitions. We present analysis of hotspots, and the detailed design of HMP. We evaluate the protocol’s ability to effectively mitigate hotspots in mobile ad hoc networks that are based on on-demand and proactive routing protocols.

title: Building information systems based on neural networks