94,550 research outputs found
Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks
In this paper, we study the problem of processing continuous range queries in
a hierarchical wireless sensor network. Contrasted with the traditional
approach of building networks in a "flat" structure using sensor devices of the
same capability, the hierarchical approach deploys devices of higher capability
in a higher tier, i.e., a tier closer to the server. While query processing in
flat sensor networks has been widely studied, the study on query processing in
hierarchical sensor networks has been inadequate. In wireless sensor networks,
the main costs that should be considered are the energy for sending data and
the storage for storing queries. There is a trade-off between these two costs.
Based on this, we first propose a progressive processing method that
effectively processes a large number of continuous range queries in
hierarchical sensor networks. The proposed method uses the query merging
technique proposed by Xiang et al. as the basis and additionally considers the
trade-off between the two costs. More specifically, it works toward reducing
the storage cost at lower-tier nodes by merging more queries, and toward
reducing the energy cost at higher-tier nodes by merging fewer queries (thereby
reducing "false alarms"). We then present how to build a hierarchical sensor
network that is optimal with respect to the weighted sum of the two costs. It
allows for a cost-based systematic control of the trade-off based on the
relative importance between the storage and energy in a given network
environment and application. Experimental results show that the proposed method
achieves a near-optimal control between the storage and energy and reduces the
cost by 0.989~84.995 times compared with the cost achieved using the flat
(i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure
Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
The characterization of the global maximum of energy efficiency (EE) problems
in wireless networks is a challenging problem due to the non-convex nature of
investigated problems in interference channels. The aim of this work is to
develop a new and general framework to achieve globally optimal solutions.
First, the hidden monotonic structure of the most common EE maximization
problems is exploited jointly with fractional programming theory to obtain
globally optimal solutions with exponential complexity in the number of network
links. To overcome this issue, we also propose a framework to compute
suboptimal power control strategies characterized by affordable complexity.
This is achieved by merging fractional programming and sequential optimization.
The proposed monotonic framework is used to shed light on the ultimate
performance of wireless networks in terms of EE and also to benchmark the
performance of the lower-complexity framework based on sequential programming.
Numerical evidence is provided to show that the sequential fractional
programming framework achieves global optimality in several practical
communication scenarios.Comment: Accepted for publication in the IEEE Transactions on Signal
Processin
GM-Net: Learning Features with More Efficiency
Deep Convolutional Neural Networks (CNNs) are capable of learning
unprecedentedly effective features from images. Some researchers have struggled
to enhance the parameters' efficiency using grouped convolution. However, the
relation between the optimal number of convolutional groups and the recognition
performance remains an open problem. In this paper, we propose a series of
Basic Units (BUs) and a two-level merging strategy to construct deep CNNs,
referred to as a joint Grouped Merging Net (GM-Net), which can produce joint
grouped and reused deep features while maintaining the feature discriminability
for classification tasks. Our GM-Net architectures with the proposed BU_A
(dense connection) and BU_B (straight mapping) lead to significant reduction in
the number of network parameters and obtain performance improvement in image
classification tasks. Extensive experiments are conducted to validate the
superior performance of the GM-Net than the state-of-the-arts on the benchmark
datasets, e.g., MNIST, CIFAR-10, CIFAR-100 and SVHN.Comment: 6 Pages, 5 figure
Detecting hierarchical and overlapping network communities using locally optimal modularity changes
Agglomerative clustering is a well established strategy for identifying
communities in networks. Communities are successively merged into larger
communities, coarsening a network of actors into a more manageable network of
communities. The order in which merges should occur is not in general clear,
necessitating heuristics for selecting pairs of communities to merge. We
describe a hierarchical clustering algorithm based on a local optimality
property. For each edge in the network, we associate the modularity change for
merging the communities it links. For each community vertex, we call the
preferred edge that edge for which the modularity change is maximal. When an
edge is preferred by both vertices that it links, it appears to be the optimal
choice from the local viewpoint. We use the locally optimal edges to define the
algorithm: simultaneously merge all pairs of communities that are connected by
locally optimal edges that would increase the modularity, redetermining the
locally optimal edges after each step and continuing so long as the modularity
can be further increased. We apply the algorithm to model and empirical
networks, demonstrating that it can efficiently produce high-quality community
solutions. We relate the performance and implementation details to the
structure of the resulting community hierarchies. We additionally consider a
complementary local clustering algorithm, describing how to identify
overlapping communities based on the local optimality condition.Comment: 10 pages; 4 tables, 3 figure
Ordering Metro Lines by Block Crossings
A problem that arises in drawings of transportation networks is to minimize
the number of crossings between different transportation lines. While this can
be done efficiently under specific constraints, not all solutions are visually
equivalent. We suggest merging crossings into block crossings, that is,
crossings of two neighboring groups of consecutive lines. Unfortunately,
minimizing the total number of block crossings is NP-hard even for very simple
graphs. We give approximation algorithms for special classes of graphs and an
asymptotically worst-case optimal algorithm for block crossings on general
graphs. That is, we bound the number of block crossings that our algorithm
needs and construct worst-case instances on which the number of block crossings
that is necessary in any solution is asymptotically the same as our bound
Performance evaluation of an optical transparent access tier based on PON and spectral codes
The increasing amount of bandwidth requirements and quality of service needs for the next-generation access networks has boosted extensive research in the fiber-optics communication field. In this light, passive optical networks (PONs) combined with optical code division multiple access (OCDMA), provide a potentially cost-effective solution to meet such bandwidth demands. This work proposes an optical transparent architecture which enables all-optical communication between the network nodes. The encoded data streams are multiplexed at a merging point which results in multiple user interference (MUI), thus significantly reducing the network throughput. The networking nodes are able to monitor and record user activity in the PON, and further register the (past) state of activity at the merging point. In this work, we study the coherence of state between the networking nodes and the merging point, for different packet size distributions, in order to predict an optimal transmission instant of each node's data packets. We note that the states are coherent depending on the packet size distribution
Does the Shape of a Territory Influence the Locations of Human Activities? a Numerical Geography Approach
This paper aims at showing how far the shape of a studied area influences the results of optimal location-allocation models. Simulations are performed on rectangular toy-networks with an equal number of vertices but with different length/width ratios. The case of merging two such networks into a common market is also considered. We limit our experience to the Simple Plant Location Problem (SPLP) which captures the fundamental trade-off of economic geography between accessibility and economies-of-scales. Results are analysed in terms of locations, allocations and costs. The results help at understanding how far an area (country/region) has larger development problems than others just because of its shape and/or of the way this area is linked within a common market (elongation of the country and length of the common border). Several real world examples are discussed when interpreting of the results.
Multistep greedy algorithm identifies community structure in real-world and computer-generated networks
We have recently introduced a multistep extension of the greedy algorithm for
modularity optimization. The extension is based on the idea that merging l
pairs of communities (l>1) at each iteration prevents premature condensation
into few large communities. Here, an empirical formula is presented for the
choice of the step width l that generates partitions with (close to) optimal
modularity for 17 real-world and 1100 computer-generated networks. Furthermore,
an in-depth analysis of the communities of two real-world networks (the
metabolic network of the bacterium E. coli and the graph of coappearing words
in the titles of papers coauthored by Martin Karplus) provides evidence that
the partition obtained by the multistep greedy algorithm is superior to the one
generated by the original greedy algorithm not only with respect to modularity
but also according to objective criteria. In other words, the multistep
extension of the greedy algorithm reduces the danger of getting trapped in
local optima of modularity and generates more reasonable partitions.Comment: 17 pages, 2 figure
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