31594321
Hypercube Network Fault Tolerance: A Probabilistic Approach
Extensive experiments and experience have shown that the well-known hypercube networks are highly fault tolerant. What is frustrating is that it seems very difficult to properly formulate and formally prove this important fact, despite extensive research efforts in the past two decades. Most proposed fault tolerance models for hypercube networks are only able to characterize very rare extreme situations thus significantly underestimating the fault tolerance power of hypercube networks, while for more realistic fault tolerance models, the analysis becomes much more complicated. In this paper, we develop new techniques that enable us to analyze a more realistic fault tolerance model and derive lower bounds for the probability of hypercube network fault tolerance in terms of node failure probability. Our results are both theoretically significant and practically important. From the theoretical point of view, our method offers very general and powerful techniques for formally proving lower bounds on the probability of network connectivity, while from the practical point of view, our results provide formally proven and precisely given upper bounds on node failure probabilities for manufacturers to achieve a desired probability for network connectivity. Our techniques are also useful and powerful for analysis of the performance of routing algorithms, and applicable to the study of other hierarchical network structures and to other network communication problems.
1021255
A minimal and complete set of operators for the development of robust manifold mesh modelers
In this paper, we identify a minimal and complete set of fundamental operators, which is necessary and sufficient for performing all homeomorphic and topological operations on 2- manifold mesh structures. Efficient algorithms are developed for the implementation of these operators. We also developed a set of powerful, user-friendly, and effective operators at the level of user-interface. Using these operators, we have developed a prototype system for robust, interactive, and user-friendly modeling of orientable 2-manifold meshes. Users of our system can perform a large set of homeomorphic and topological changes with these user-interface level operators. Our system is topologically robust in the sense that users will never create invalid 2-manifold mesh structure with these operators. In our system, the homeomorphic and topological surgery operations can be applied alternatively on 2-manifold meshes. With our system, users can blend surfaces, construct rinds, and open holes on these rind shapes. With our system, the shapes that look like solid, non-manifold, or 2-manifold with boundary can be manipulated. The system also provides automatic texture mapping during topology changes.
31382426
Strong Fault-Tolerance: Parallel Routing in Networks with Faults
A d-regular network G is strongly fault-tolerant if for any copy Gf of G with at most d - 2 faulty nodes, every pair of nonfaulty nodes u and v in Gf admits min{df(u), df (v)} node-disjoint paths in Gf from u to v, where df(u) and df(v) are the degrees of the nodes u and v in Gf. We show that popular network structures, such as hypercube and star networks, are strongly fault-tolerant. We develop efficient algorithms that construct the maximum number of node-disjoint paths of optimal or nearly optimal length in these networks with faulty nodes. Our algorithms are optimal in terms of their running time.
16331940
Optimal Parallel Routing in Star Networks
Star networks have recently been proposed as attractive alternatives to the popular hypercube for interconnecting processors on a parallel computer. In this paper, we present an efficient algorithm that constructs an optimal parallel routing in star networks. Our result improves previous results for the problem.
2261180
Graph Ear Decompositions and Graph Embeddings
Ear decomposition of a graph has been extensively studied in relation to graph connectivity. In this paper, a connection of ear decomposition to graph embeddings is exhibited. It is shown that constructing a maximum-paired ear decomposition of a graph and constructing a maximum-genus embedding of the graph are polynomial-time equivalent. Applications of this connection are discussed.
5242879
Interactive rind modeling
In this paper, we describe a technique, with roots in topological graph theory, that we call rind modeling. It provides for the easy creation of surfaces resembling peeled and punctured rinds. We show how the method's two main steps of: 1) creation of a shell or crust like the rind of an orange, and 2) opening holes in the crust by punching or peeling can be encapsulated into a real time semi-automatic interactive algorithm. We include a number of worked examples, some by students in a first modeling course, that demonstrate the ease with which a large variety of intricate rind shapes can be created.
40634947
Experiments Study on a Dynamic Priority Scheduling for Wireless Sensor Networks
Wireless Sensors Networks (WSNs) represent a new generation of embedded systems, which deploy wireless ad hoc communication mechanisms to route a sensory data from the originator sensor node to the base station. One of the most important issues in wireless sensor networks is to support real-time QoS, which is fundamentally a challenging problem. In this paper, we introduce EDBP scheduling algorithm for sensor network along with a MAC protocol based on the dynamic priority assignment EDBP. Two different platforms based experiment projects are designed to test our proposal.
9663015
Customizing a geographical routing protocol for wireless sensor networks
Several problems are required to be fixed in order to apply geographical routing protocol greedy perimeter stateless routing (GPSR) in wireless sensor networks. First, GPSR is designed under the assumption of symmetric links (i.e., bidirectional reachability), which is not realistic for many practical sensor networks, since wireless links in sensor networks often are asymmetric. Second, in sensor networks, packet destinations are often marked with locations instead of identifiers like IP address. Therefore, packets are routed to the home node, which is the node geographically closest to the destined location. Under GPSR, when the target location is outside the exterior perimeter of the sensor network, each packet has to visit all nodes on the boundary of the sensor network topology before it identifies the home node, which results in energy inefficiency. Third, due to the dynamic nature of sensor networks, maintaining data consistency, that is, data retrieved from the home node for a location should be consistent with the data sent to the same location, becomes a challenge when home nodes change. We propose on-demand GPSR (OD-GPSR), a data driven geographical routing protocol customized for sensor networks with solutions to the above three problems. Simulation results show that OD-GPSR performs well in terms of energy efficiency and packet delivery rate at the cost of a little bit more packet delivery delay.
26682479
Web-Based Collaborative Tools for Geospatial Data Exploration
Federal government agencies have collected large and highly complex federal statistical summaries and geo-reference data. Methods and tools for identifying patterns and uncovering multivariate relationships in the complex datasets have drawn attention in multiple fields. The approach we present here extends from and integrates perspectives from multiple fields, including exploratory data analysis (EDA), geovisualization, information visualization, and data mining. Important components of our research are supported by a NSF Digital Government grant (#99883451). Other support comes from a National Cancer Institute (NCI) contract. Our DG research is now being extended to develop comprehensive multivariate analysis methods and tools, through a grant from NCI (CA95949).
12280706
A distributed multi-agent system for collaborative information management and sharing
In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents.
28375225
User-centered indexing for adaptive information access
We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Learning and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.