4,071 research outputs found
Mean-Field-Type Games in Engineering
A mean-field-type game is a game in which the instantaneous payoffs and/or
the state dynamics functions involve not only the state and the action profile
but also the joint distributions of state-action pairs. This article presents
some engineering applications of mean-field-type games including road traffic
networks, multi-level building evacuation, millimeter wave wireless
communications, distributed power networks, virus spread over networks, virtual
machine resource management in cloud networks, synchronization of oscillators,
energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
Streaming Graph Challenge: Stochastic Block Partition
An important objective for analyzing real-world graphs is to achieve scalable
performance on large, streaming graphs. A challenging and relevant example is
the graph partition problem. As a combinatorial problem, graph partition is
NP-hard, but existing relaxation methods provide reasonable approximate
solutions that can be scaled for large graphs. Competitive benchmarks and
challenges have proven to be an effective means to advance state-of-the-art
performance and foster community collaboration. This paper describes a graph
partition challenge with a baseline partition algorithm of sub-quadratic
complexity. The algorithm employs rigorous Bayesian inferential methods based
on a statistical model that captures characteristics of the real-world graphs.
This strong foundation enables the algorithm to address limitations of
well-known graph partition approaches such as modularity maximization. This
paper describes various aspects of the challenge including: (1) the data sets
and streaming graph generator, (2) the baseline partition algorithm with
pseudocode, (3) an argument for the correctness of parallelizing the Bayesian
inference, (4) different parallel computation strategies such as node-based
parallelism and matrix-based parallelism, (5) evaluation metrics for partition
correctness and computational requirements, (6) preliminary timing of a
Python-based demonstration code and the open source C++ code, and (7)
considerations for partitioning the graph in streaming fashion. Data sets and
source code for the algorithm as well as metrics, with detailed documentation
are available at GraphChallenge.org.Comment: To be published in 2017 IEEE High Performance Extreme Computing
Conference (HPEC
Structure and topology of transcriptional regulatory networks and their applications in bio-inspired networking
Biological networks carry out vital functions necessary for sustenance despite environmental adversities. Transcriptional Regulatory Network (TRN) is one such biological network that is formed due to the interaction between proteins, called Transcription Factors (TFs), and segments of DNA, called genes. TRNs are known to exhibit functional robustness in the face of perturbation or mutation: a property that is proven to be a result of its underlying network topology. In this thesis, we first propose a three-tier topological characterization of TRN to analyze the interplay between the significant graph-theoretic properties of TRNs such as scale-free out-degree distribution, low graph density, small world property and the abundance of subgraphs called motifs. Specifically, we pinpoint the role of a certain three-node motif, called Feed Forward Loop (FFL) motif in topological robustness as well as information spread in TRNs.
With the understanding of the TRN topology, we explore its potential use in design of fault-tolerant communication topologies. To this end, we first propose an edge rewiring mechanism that remedies the vulnerability of TRNs to the failure of well-connected nodes, called hubs, while preserving its other significant graph-theoretic properties. We apply the rewired TRN topologies in the design of wireless sensor networks that are less vulnerable to targeted node failure. Similarly, we apply the TRN topology to address the issues of robustness and energy-efficiency in the following networking paradigms: robust yet energy-efficient delay tolerant network for post disaster scenarios, energy-efficient data-collection framework for smart city applications and a data transfer framework deployed over a fog computing platform for collaborative sensing --Abstract, page iii
A Trust-Based Relay Selection Approach to the Multi-Hop Network Formation Problem in Cognitive Radio Networks
One of the major challenges for today’s wireless communications is to meet the growing demand for supporting an increasing diversity of wireless applications with limited spectrum resource. In cooperative communications and networking, users share resources and collaborate in a distributed approach, similar to entities of active social groups in self organizational communities. Users’ information may be shared by the user and also by the cooperative users, in distributed transmission. Cooperative communications and networking is a fairly new communication paradigm that promises significant capacity and multiplexing gain increase in wireless networks. This research will provide a cooperative relay selection framework that exploits the similarity of cognitive radio networks to social networks. It offers a multi-hop, reputation-based power control game for routing. In this dissertation, a social network model provides a humanistic approach to predicting relay selection and network analysis in cognitive radio networks
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