14988023
Modeling the Effect of Transmit Power and Physical Carrier Sense in Multi-Hop Wireless Networks
In this paper, we extend both Bianchi's and Kumar's models and characterize the channel activities governed by IEEE 802.11 DCF in multi-hop wireless networks from the perspective of an individual sender. In particular, we incorporate the effect of PHY/MAC attributes (such as transmit power and physical carrier sense) that need not be considered in WLANs but become extraordinarily important in multi-hop wireless networks, and derive the throughput attained by each sender. With the use of the analytical model derived, we investigate the impact of transmit power and carrier sense threshold on network capacity, and identify a simple operating condition under which the network may attain throughput that is close to its optimal value.
16965968
Performance of Full Text Search in Structured and Unstructured Peer-to-Peer Systems
While structured P2P systems (such as DHTs) are often regarded as an improvement over unstructured P2P systems (such as super-peer networks) in terms of routing efficiency, it is not clear which architecture is better for full text search. This paper provides a quantitative comparison of full text keyword search in structured and unstructured P2P systems. We examine three techniques (and optimizations to those techniques) proposed in the literature: using a DHT along with inverted lists and Bloom filters; using a super-peer network; and using a random walk over an unstructured network. We use real Web documents and user queries to measure the cost for both document publishing and query processing, in terms of bandwidth and response time. Our results show that all three techniques use roughly the same bandwidth to process queries (with the super-peer technique having a slight edge). The structured network provides the best response time (30 percent better than a super-peer network), but has a high cost of document publishing, using six times as much bandwidth as the super-peer system. The random walk technique requires no publishing, but has a very long response time unless multiple random walks operate in parallel.
17411004
Parameter tuning for induction-algorithm-oriented feature elimination
Feature selection has long been an active research topic in machine learning. Beginning with an empty set of features, it selects features most necessary for learning a target concept. Feature elimination, a newer technique, starts out with a full set of features and eliminates those most unnecessary for learning the target concept. Feature elimination tends to be more effective, can capture interacting features more easily, and suffers less from feature interaction than feature selection. Because the most unnecessary features are eliminated from the beginning, they will not mislead the induction process in terms of efficiency or accuracy. Induction-algorithm-oriented feature elimination, with particular parameter configurations, can achieve higher predictive accuracy than existing popular feature selection approaches. We propose two sets of well-tuned parameters based on empirical analysis. To understand how to achieve the best performance possible from IAOFE, we conducted a comprehensive analysis of IAOFE parameter tuning.
39342835
Guaranteed Mutually Consistent Checkpointing in Distributed Computations
In this paper, we explore the isomorphism between vector time and causality to characterize consistency of a set of checkpoints in a distributed computing. A necessary and sufficient condition, to determine if a set of local checkpoints can form a consistent global checkpoint, is presented and proved using the isomorphic power of vector time and causality. To the best of our knowledge, this is the first attempt to use the isomorphism for this purpose. This condition leads to a simple and straightforward algorithm for a guaranteed mutually consistent global checkpointing. In our approach, a process can take a checkpoint whenever and wherever it wants while other related process may be asked to take an additional checkpoint for ensuring the mutual consistency. We also show how this condition and the resulting algorithm can be used to obtain a maximum and minimum global checkpoints, another important paradigm for distributed applications.
2911118
A Theory of Network Localization
In this paper, we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly
14593921
Stochastic link and group detection
Link detection and analysis has long been important in the social sciences and in the government intelligence community. A significant effort is focused on the structural and functional analysis of "known" networks. Similarly, the detection of individual links is important but is usually done with techniques that result in "known" links. More recently the internet and other sources have led to a flood of circumstantial data that provide probabilistic evidence of links. Co-occurrence in news articles and simultaneous travel to the same location are two examples.We propose a probabilistic model of link generation based on membership in groups. The model considers both observed link evidence and demographic information about the entities. The parameters of the model are learned via a maximum likelihood search. In this paper we describe the model and then show several heuristics that make the search tractable. We test our model and optimization methods on synthetic data sets with a known ground truth and a database of news articles.
7174562
Implementation of Agent-based Ontology Mapping and Integration
The achievement of some business goals requires more than individual capabilities and knowledge. Hence, it is necessary for different organisations to work together in order to achieve the goals. In this paper, an innovative agent-based architecture is developed to model ontology mapping and integration in a distributed and dynamic business environment where interactions among organisations may take place at unpredictable times, for unpredictable reasons, and between unpredictable organisations. Functionalities and implementation of involved agents are discussed. In addition, a three-layer user ontologies are introduced. Finally, demonstrations of ontology mapping and integration are presented