Optimization of in-network data reduction

A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.

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Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

Endocrine Practice ◽

10.1016/s1530-891x(20)42480-2 ◽

2015 ◽

Vol 21 ◽

pp. 301

Author(s):

Armand Krikorian ◽

Lily Peng ◽

Zubair Ilyas ◽

Joumana Chaiban

Keyword(s):

Social Network ◽

Network Data ◽

Social Network Data

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The Multilevel p2 Model

Methodology ◽

10.1027/1614-2241.2.1.42 ◽

2006 ◽

Vol 2 (1) ◽

pp. 42-47 ◽

Cited By ~ 47

Author(s):

Bonne J. H. Zijlstra ◽

Marijtje A. J. van Duijn ◽

Tom A. B. Snijders

Keyword(s):

Social Network ◽

Network Data ◽

Mcmc Algorithm ◽

Independent Observation ◽

Single Observation ◽

Multiple Networks ◽

Social Network Data ◽

Multiple Observations ◽

Practical Support ◽

The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

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Collection of Ego-Centered Network Data with Computer-Assisted Interviews

Methodology ◽

10.1027/1614-2241.2.1.7 ◽

2006 ◽

Vol 2 (1) ◽

pp. 7-15 ◽

Cited By ~ 10

Author(s):

Joachim Gerich ◽

Roland Lehner

Keyword(s):

Survey Methods ◽

A Priori ◽

Dynamic Structure ◽

Computer Assisted ◽

Network Data ◽

Great Effort ◽

Self Administration ◽

Face To Face ◽

Full Network

Although ego-centered network data provide information that is limited in various ways as compared with full network data, an ego-centered design can be used without the need for a priori and researcher-defined network borders. Moreover, ego-centered network data can be obtained with traditional survey methods. However, due to the dynamic structure of the questionnaires involved, a great effort is required on the part of either respondents (with self-administration) or interviewers (with face-to-face interviews). As an alternative, we will show the advantages of using CASI (computer-assisted self-administered interview) methods for the collection of ego-centered network data as applied in a study on the role of social networks in substance use among college students.

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