772,954 research outputs found
Convergence Analysis of Mixed Timescale Cross-Layer Stochastic Optimization
This paper considers a cross-layer optimization problem driven by
multi-timescale stochastic exogenous processes in wireless communication
networks. Due to the hierarchical information structure in a wireless network,
a mixed timescale stochastic iterative algorithm is proposed to track the
time-varying optimal solution of the cross-layer optimization problem, where
the variables are partitioned into short-term controls updated in a faster
timescale, and long-term controls updated in a slower timescale. We focus on
establishing a convergence analysis framework for such multi-timescale
algorithms, which is difficult due to the timescale separation of the algorithm
and the time-varying nature of the exogenous processes. To cope with this
challenge, we model the algorithm dynamics using stochastic differential
equations (SDEs) and show that the study of the algorithm convergence is
equivalent to the study of the stochastic stability of a virtual stochastic
dynamic system (VSDS). Leveraging the techniques of Lyapunov stability, we
derive a sufficient condition for the algorithm stability and a tracking error
bound in terms of the parameters of the multi-timescale exogenous processes.
Based on these results, an adaptive compensation algorithm is proposed to
enhance the tracking performance. Finally, we illustrate the framework by an
application example in wireless heterogeneous network
D2D Data Offloading in Vehicular Environments with Optimal Delivery Time Selection
Within the framework of a Device-to-Device (D2D) data offloading system for
cellular networks, we propose a Content Delivery Management System (CDMS) in
which the instant for transmitting a content to a requesting node, through a
D2D communication, is selected to minimize the energy consumption required for
transmission. The proposed system is particularly fit to highly dynamic
scenarios, such as vehicular networks, where the network topology changes at a
rate which is comparable with the order of magnitude of the delay tolerance. We
present an analytical framework able to predict the system performance, in
terms of energy consumption, using tools from the theory of point processes,
validating it through simulations, and provide a thorough performance
evaluation of the proposed CDMS, in terms of energy consumption and spectrum
use. Our performance analysis compares the energy consumption and spectrum use
obtained with the proposed scheme with the performance of two benchmark
systems. The first one is a plain classic cellular scheme, the second is a D2D
data offloading scheme (that we proposed in previous works) in which the D2D
transmissions are performed as soon as there is a device with the required
content within the maximum D2D transmission range..
Network delay control through adaptive queue management
Timeliness in delivering packets for delay-sensitive applications is an important QoS (Quality of Service) measure in many systems, notably those that need to provide real-time performance. In such systems, if delay-sensitive traffic is delivered to the destination beyond the deadline, then the packets will be rendered useless and dropped after received at the destination. Bandwidth that is already scarce and shared between network nodes is wasted in relaying these expired packets. This thesis proposes that a deterministic per-hop delay can be achieved by using a dynamic queue threshold concept to bound delay of each node. A deterministic per-hop delay is a key component in guaranteeing a deterministic end-to-end delay. The research aims to develop a generic approach that can constrain network delay of delay-sensitive traffic in a dynamic network. Two adaptive queue management schemes, namely, DTH (Dynamic THreshold) and ADTH (Adaptive DTH) are proposed to realize the claim. Both DTH and ADTH use the dynamic threshold concept to constrain queuing delay so that bounded average queuing delay can be achieved for the former and bounded maximum nodal delay can be achieved for the latter. DTH is an analytical approach, which uses queuing theory with superposition of N MMBP-2 (Markov Modulated Bernoulli Process) arrival processes to obtain a mapping relationship between average queuing delay and an appropriate queuing threshold, for queue management. While ADTH is an measurement-based algorithmic approach that can respond to the time-varying link quality and network dynamics in wireless ad hoc networks to constrain network delay. It manages a queue based on system performance measurements and feedback of error measured against a target delay requirement. Numerical analysis and Matlab simulation have been carried out for DTH for the purposes of validation and performance analysis. While ADTH has been evaluated in NS-2 simulation and implemented in a multi-hop wireless ad hoc network testbed for performance analysis. Results show that DTH and ADTH can constrain network delay based on the specified delay requirements, with higher packet loss as a trade-off
Comparison of Clustering Methods for Time Course Genomic Data: Applications to Aging Effects
Time course microarray data provide insight about dynamic biological
processes. While several clustering methods have been proposed for the analysis
of these data structures, comparison and selection of appropriate clustering
methods are seldom discussed. We compared probabilistic based clustering
methods and distance based clustering methods for time course microarray
data. Among probabilistic methods, we considered: smoothing spline clustering
also known as model based functional data analysis (MFDA), functional
clustering models for sparsely sampled data (FCM) and model-based clustering
(MCLUST). Among distance based methods, we considered: weighted gene
co-expression network analysis (WGCNA), clustering with dynamic time warping
distance (DTW) and clustering with autocorrelation based distance (ACF). We
studied these algorithms in both simulated settings and case study data. Our
investigations showed that FCM performed very well when gene curves were short
and sparse. DTW and WGCNA performed well when gene curves were medium or long
( observations). SSC performed very well when there were clusters of gene
curves similar to one another. Overall, ACF performed poorly in these
applications. In terms of computation time, FCM, SSC and DTW were considerably
slower than MCLUST and WGCNA. WGCNA outperformed MCLUST by generating more
accurate and biological meaningful clustering results. WGCNA and MCLUST are the
best methods among the 6 methods compared, when performance and computation
time are both taken into account. WGCNA outperforms MCLUST, but MCLUST provides
model based inference and uncertainty measure of clustering results
Member-care leadership in regional innovation networks: caring for single members – a hidden process?
Although MSMEs are expected to benefit the most from participating in collaborative innovation, they often struggle to gain these benefits. This study contributes knowledge about how to reduce the barriers. Three regional innovation networks were studied primarily through semi-structured interviews. They were formal networks, and the tourism sector was the main industry. Data analysis followed the grounded theory. A hidden but essential practice of network orchestration is constructed, i.e. ‘member-care leadership.’ Involving the subpractices of empathizing, engaging, and supervising single members’ to increase value from participating in the network. In particular, member-care leadership enables MSMEs to prioritize and carry out network activities and innovation at and between network gatherings. The care subpractices are interdependent, dynamic, and relational. The practice triggered learning and innovation within the enterprises and increased the enterprises ‘of-gathering activity’, knowledge sharing, and performance at the network level. This suggests that innovation network literature should take a humanistic and relational approach to orchestration. The study also provides an understanding of how network-driven innovation involves multileveled and dynamic processes, with orchestration and activity at the enterprise and network levels and between these levels. A policy implication is that member-care leadership should be acknowledged and allocated human and economic resources.publishedVersio
Change Point Detection on a Separable Model for Dynamic Networks
This paper studies the change point detection problem in time series of
networks, with the Separable Temporal Exponential-family Random Graph Model
(STERGM). We consider a sequence of networks generated from a piecewise
constant distribution that is altered at unknown change points in time.
Detection of the change points can identify the discrepancies in the underlying
data generating processes and facilitate downstream dynamic network analysis
tasks. Moreover, the STERGM that focuses on network statistics is a flexible
model to fit dynamic networks with both dyadic and temporal dependence. We
propose a new estimator derived from the Alternating Direction Method of
Multipliers (ADMM) and the Group Fused Lasso to simultaneously detect multiple
time points, where the parameters of STERGM have changed. We also provide
Bayesian information criterion for model selection to assist the detection. Our
experiments show good performance of the proposed method on both simulated and
real data. Lastly, we develop an R package CPDstergm to implement our method
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Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics
Abstract
Background
Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model.
Methods
We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence.
Results
In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present.
Conclusions
The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.http://deepblue.lib.umich.edu/bitstream/2027.42/134740/1/12874_2016_Article_274.pd
Application Of Data Mining For Reverse Osmosis Process In Seawater Desalination
Reverse osmosis (RO) membrane process has been considered a promising technology for water treatment and desalination. However, it is difficult to predict the performance of pilot- or full-scale RO systems because numerous factors are involved in RO performance, including variations in feed water (quantity, quality, temperature, etc), membrane fouling, and time-dependent changes (deteriorations). Accordingly, this study intended to develop a practical approach for the analysis of operation data in pilot-scale reverse osmosis (RO) processes. Novel techniques such as artificial neural network (ANN) and genetic programming (GP) technique were applied to correlate key operating parameters and RO permeability statistically. The ANN and GP models were trained using a set of experimental data from a RO pilot plant with a capacity of 1,000 m3/day and then used to predict its performance. The comparison of the ANN and GP model calculations with the experiment results revealed that the models were useful for analyzing and classifying the performance of pilot-scale RO systems. The models were also applied for an in-depth analysis of RO system performance under dynamic conditions
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