9757380
Algorithmic aspects of communication in ad-hoc networks with smart antennas
Smart antennas have gained significant importance in multi-hop wireless networks in recent years, because of their sophisticated signal processing capabilities that hold the potential for increased data rates and reliability. In this work, we consider the problem of communication in multi-hop wireless networks with smart antennas (specifically digital adaptive arrays). These smart antennas provide degrees of freedom (DOFs) that can be used to suppress co-existing communication links, thereby increasing spatial reuse in the network. Thus, the communication problem comprises of not just determining a channel access mechanism to be used by the communication links, but also involves the determination of the communication pattern (usage of DOFs) to be used by each node during channel access. To the best of our knowledge, our work is the first step towards addressing this problem.We first consider the problem of determining the communication pattern to be used by the nodes and formulate it combinatorially with the goal of optimizing network performance through interference minimization. We present efficient centralized and distributed algorithms that are within a factor of ¾ and ½ of the optimum solution respectively. We then extend the distributed algorithm to incorporate TDMA-based scheduling in a purely localized manner. The distributed algorithms are then evaluated through simulations in ns2 and insights are drawn into the potential performance benefits of smart antennas in multi-hop wireless networks.
16100691
Design multicast protocols for non-cooperative networks
Conventionally, most network protocols assume that the network entities that participate in the network activities will always behave as instructed. However, in practice, most network entities will try to maximize their own benefits instead of altruistically contribute to the network by following the prescribed protocols, which is known as selfish. Thus, new protocols should be designed for the non-cooperative network, which is composed of selfish entities. In this paper, we specifically show how to design strategyproof multicast protocols for non-cooperative networks such that these selfish entities will follow the protocols out of their own interests. By assuming that a group of receivers is willing to pay to receive the multicast service, we specifically give a general framework to decide whether it is possible, and how if possible to transform an existing multicast protocol to a strategyproof multicast protocol. We then show how the payments to those relay entities are shared fairly among all receivers so that it encourages collaboration among receivers. As a running example, we show how to design the strategyproof multicast protocol for the currently used core-based multicast structure. We also conduct extensive simulations to study the relations between payment and cost of the multicast structure.
9657581
k-anycast game in selfish networks
Conventionally, the majority of network routing protocols assume that every network terminal or link forwards data for others without any deviation. However, this may not be true when the terminals or links are owned by individual selfish users, who always tries to maximize their own benefits instead of faithfully following a prescribed protocol. In this paper, we propose a new routing protocol, called k-anycast routing, that works well even if network links (or terminals or both) are selfish. In our protocol, the source node first finds a tree that spans k receivers out of a set of possible receivers and pay the relay links to compensate their costs. We prove that every relay link follows the routing protocol: it maximizes its profit when it declares its actual cost
3184000
Revealing true subspace clusters in high dimensions
Subspace clustering is one of the best approaches for discovering meaningful clusters in high dimensional space. One cluster in high dimensional space may be transcribed into multiple distinct maximal clusters by projecting onto different subspaces. A direct consequence of clustering independently in each subspace is an overwhelmingly large set of overlapping clusters which may be significantly similar. To reveal the true underlying clusters, we propose a similarity measurement of the overlapping clusters. We adopt the model of Gaussian tailed hyper-rectangles to capture the distribution of any subspace cluster. A set of experiments on a synthetic dataset demonstrates the effectiveness of our approach. Application to real gene expression data also reveals impressive meta-clusters expected by biologists.
385307
STING: A Statistical Information Grid Approach to Spatial Data Mining
Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exist in spatial databases, is a challenging task due to the huge amounts of spatial data and to the new conceptual nature of the problems which must account for spatial distance. Clustering and region oriented queries are common problems in this domain. Several approaches have been presented in recent years, all of which require at least one scan of all individual objects (points). Consequently, the computational complexity is at least linearly proportional to the number of objects to answer each query. In this paper, we propose a hierarchical statistical information grid based approach for spatial data mining to reduce the cost further. The idea is to capture statistical information associated with spatial cells in such a manner that whole classes of queries and clustering problems can be answered without recourse to the individual objects. In theory, and confirmed by empirical studies, this approach outperforms the best previous method by at least an order of magnitude, especially when the data set is very large.
11248403
Mining Shifting-and-Scaling Co-Regulation Patterns on Gene Expression Profiles
In this paper, we propose a new model for coherent clustering of gene expression data called reg-cluster. The proposed model allows (1) the expression profiles of genes in a cluster to follow any shifting-and-scaling patterns in subspace, where the scaling can be either positive or negative, and (2) the expression value changes across any two conditions of the cluster to be significant. No previous work measures up to the task that we have set: the density-based subspace clustering algorithms require genes to have similar expression levels to each other in subspace; the pattern-based biclustering algorithms only allow pure shifting or pure scaling patterns; and the tendency-based biclustering algorithms have no coherence guarantees. We also develop a novel patternbased biclustering algorithm for identifying shifting-andscaling co-regulation patterns, satisfying both coherence constraint and regulation constraint. Our experimental results show that the reg-cluster algorithm is able to detect a significant amount of clusters missed by previous models, and these clusters are potentially of high biological significance.
2548187
Mining asynchronous periodic patterns in time series data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min rep and max dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space
62615149
Mining Asynchronous Periodic Patterns in Time Series Data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min/spl I.bar/rep and max/spl I.bar/dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space efficiency.