Impulsive Interference Avoidance in Dense Wireless Sensor Networks

Abstract. Wireless sensor networks (WSNs) are subject to interference from other users of the radio-frequency (RF) medium. If the WSN nodes can recognize the interference pattern, they can benefit from steering their transmissions around it. This possibility has stirred some interest among researchers involved in cognitive radios, where special hardware has been postulated to circumvent non-random interference. Our goal is to explore ways of enhancing medium access control (MAC) schemes operating within the framework of traditional off-the-shelf RF modules applicable in low-cost WSN motes, such that they can detect interference patterns in the neighbourhood and creatively respond to them, mitigating their negative impact on the packet reception rate. In this paper, and based on previous work on the post-deployment characterization of a channel aimed at identifying "spiky" interference patterns, we describe (a) a way to incorporate interference models into an existing WSN emulator and (b) the subsequent evaluation of a proof-of-concept MAC technique for circumventing the interference. We found that an interference-aware MAC can improve the packet delivery rates in these environments at the cost of increased, but acceptable, latency

Impulsive Interference Avoidance in Dense Wireless Sensor Networks

ABSTRACT As with all wireless communication devices, wireless sensor networks (WSNs) are subject to interference from other users of the radio-frequency (RF) medium. Such interference is practically never random: originating in applications generally performing some practical and sensible activities, it naturally exhibits various regularities amounting to perceptible patterns, e.g., regularly-spaced short-duration impulses that correlate among multiple WSN nodes. If those nodes can recognize the interference pattern, they can benefit from steering their transmissions around it. This possibility has stirred some interest among researchers involved in cognitive radios, where special hardware has been postulated to circumvent non-random interference. Our goal is to explore ways of enhancing medium access control (MAC) schemes operating within the framework of traditional off-the-shelf RF modules applicable in low-cost WSN motes, such that they can detect interference patterns in the neighbourhood and creatively respond to them mitigating their negative impact on the packet reception rate. In this paper, we describe (a) a method for the post-deployment dynamic characterization of a channel aimed at identifying spiky interference patterns, (b) a way to incorporate interference models into an existing WSN emulator, and (c) the subsequent evaluation of a proof-of-concept MAC technique for circumventing the interference. We found that an interference-aware MAC can improve the packet delivery rates in these environments at the cost of increased latency. Notably, the latter is quite acceptable in the vast majority of WSN applications

teigen: An R Package for Model-Based Clustering and Classification via the Multivariate t Distribution

The teigen R package is introduced and utilized for model-based clustering and classification. The tEIGEN family of mixtures of multivariate t distributions is formed via an eigen-decomposition of the component covariance matrices and subsequent componentwise constraints. The teigen package implements all previously published tEIGEN family members as well as eight additional models: four multivariate and four univariate. The resulting family of 32 mixture models is implemented in both serial and parallel, with useful dedicated functions. Methodology and examples that illustrate teigen's functionality are presented

CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits

Funding Information: This research has been conducted using the UK Biobank Resource. This research has been conducted using the Danish National Biobank resource. The authors are grateful to the Raine Study participants and their families, and to the Raine Study research staff for cohort co-ordination and data collection. QIMR is grateful to the twins and their families for their generous participation in these studies. We would like to thank staff at the Queensland Institute of Medical Research: Anjali Henders, Dixie Statham, Lisa Bowdler, Ann Eldridge, and Marlene Grace for sample collection, processing and genotyping, Scott Gordon, Brian McEvoy, Belinda Cornes and Beben Benyamin for data QC and preparation, and David Smyth and Harry Beeby for IT support. HBCS Acknowledgements: We thank all study participants as well as everybody involved in the Helsinki Birth Cohort Study. Helsinki Birth Cohort Study has been supported by grants from the Academy of Finland, the Finnish Diabetes Research Society, Folkhälsan Research Foundation, Novo Nordisk Foundation, Finska Läkaresällskapet, Juho Vainio Foundation, Signe and Ane Gyllenberg Foundation, University of Helsinki, Ministry of Education, Ahokas Foundation, Emil Aaltonen Foundation. Finrisk study is grateful for the THL DNA laboratory for its skillful work to produce the DNA samples used in this study and thanks the Sanger Institute and FIMM genotyping facilities for genotyping the samples. We thank the MOLGENIS team and Genomics Coordination Center of the University Medical Center Groningen for software development and data management, in particular Marieke Bijlsma and Edith Adriaanse. This work was supported by the Leenards Foundation (to Z.K.), the Swiss National Science Foundation (31003A_169929 to Z.K., Sinergia grant CRSII33-133044 to AR), Simons Foundation (SFARI274424 to AR) and SystemsX.ch (51RTP0_151019 to Z.K.). A.R.W., H.Y. and T.M.F. are supported by the European Research Council grant: 323195:SZ-245. M.A.T., M.N.W. and An.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). For full funding information of all participating cohorts see Supplementary Note 2. Publisher Copyright: © 2017 The Author(s).There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01-0.2%), with large effects on height (> 2.4 cm), weight ( 5 kg), and body mass index (BMI) (> 3.5 kg/m(2)). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m2 for each Mb of total deletion burden (P = 2.5 x 10(-10), 6.0 x 10(-5), and 2.9 x 10(-3)). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.Peer reviewe