Wireless sensor networks and fusion of contextual information for weather outlier detection

A. Amidi, N. A.S. Hamm, N. Meratnia

Research output: Journal PublicationConference articlepeer-review

1 Citation (Scopus)


Weather stations are often expensive hence it may be difficult to obtain data with a high spatial coverage. A low cost alternative is wireless sensor network (WSN), which can be deployed as weather stations and address the aforementioned shortcoming. Due to imperfect sensors in WSNs context, provided raw data may be drawn in from of a low quality and reliability level, expectedly that is an emergence of applying outlier detection methods. Outliers may include errors or potentially useful information called events. In this research, forecast values as contextual information are utilized for weather outlier detection. In this paper, outliers are identified by comparing the patterns of WSN and forecasts. With that approach, temporal outliers are detected with respect to slopes of the WSNs and forecasts in the presence of pre-defined tolerance. The experimental results from the real data-set validate the applicability of using contextual information in the context of WSNs for outlier detection in terms of accuracy and energy efficiency.

Original languageEnglish
Pages (from-to)37-41
Number of pages5
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue number1W3
Publication statusPublished - 2013
Externally publishedYes
EventSMPR Conference 2013 - Tehran, Iran, Islamic Republic of
Duration: 5 Oct 20138 Oct 2013


  • Contextual information
  • Forecasts
  • Pattern formation
  • Similarity assessment
  • Slopes
  • Temporal outlier detection
  • Weather

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development


Dive into the research topics of 'Wireless sensor networks and fusion of contextual information for weather outlier detection'. Together they form a unique fingerprint.

Cite this