Assessing reliability of big data stream for smart city

Supadchaya Puangpontip, Rattikorn Hewett

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Proliferation of IoT (Internet of Things) and sensor technology has expedited the realization of Smart City. To enable necessary functions, sensors distributed across the city generate a huge volume of stream data that are crucial for controlling Smart City devices. However, due to conditions such as wears and tears, battery drain, or malicious attacks, not all data are reliable even when they are accurately measured. These data could lead to invalid and devastating consequences (e.g., failed utility or transportation services). The assessment of data reliability is necessary and challenging especially for Smart City, as it has to keep up with velocity of big data stream to provide up-to-date results. Most research on data reliability has focused on data fusion and anomaly detection that lack a quantified measure of how much the data over a period of time are adequately reliable for decision-makings. This paper alleviates these issues and presents an online approach to assessing Big stream data reliability in a timely manner. By employing a well-studied evidence-based theory, our approach provides a computational framework that assesses data reliability in terms of belief likelihoods. The framework is lightweight and easy to scale, deeming fit for streaming data. We evaluate the approach using a real application of light sensing data of 1,323,298 instances. The preliminary results are consistent with logical rationales, confirming validity of the approach.

Original languageEnglish
Title of host publicationICBDR 2019 - Proceedings of the 2019 3rd International Conference on Big Data Research
PublisherAssociation for Computing Machinery
Pages18-23
Number of pages6
ISBN (Electronic)9781450372015
DOIs
StatePublished - Nov 20 2019
Event3rd International Conference on Big Data Research, ICBDR 2019 - Cergy-Pontoise, France
Duration: Nov 20 2019Nov 21 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Big Data Research, ICBDR 2019
CountryFrance
CityCergy-Pontoise
Period11/20/1911/21/19

Keywords

  • Data reliability
  • IoT
  • Smart city
  • Theory of evidence

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