A graph-based big data model for wireless multimedia sensor networks

Cihan Küçükkeçeci, Adnan Yazıcı

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Wireless multimedia sensor networks are of interest to researchers from different disciplines and many studies have been proposed in a wide variety of application domains, such as military surveillance systems, environmental monitoring, fault monitoring and distributed smart cameras in the last decade. In a wireless sensor network, a large number of sensors can be deployed to monitor target areas and autonomously collect sensor data. This produces a large amount of raw data that needs to be stored, processed, and analyzed. In this paper, we propose a graph-based big data model for simulating multimedia wireless sensor networks. The big sensor data is stored in a graph database for the purpose of advanced analytics like statistics, data mining, and prediction. A prototype implementation of the proposed model has been developed and a number of experiments have been done for measuring the accuracy and efficiency of our solution. In addition, we present a case study using the military surveillance domain with a number of complex experimental queries by using our prototype. The experimental results show that our proposed multimedia wireless sensor network model is efficient and applicable in large-scale real life applications.

Original languageEnglish
Title of host publicationAdvances in Big Data - Proceedings of the 2nd INNS Conference on Big Data, 2016
EditorsAsim Roy, Marley Vellasco, Yannis Manolopoulos, Lazaros Iliadis, Plamen Angelov
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319478975
Publication statusPublished - Jan 1 2017
Event2nd International Neural Network Society Conference on Big Data, INNS 2016 - Thessaloniki, Greece
Duration: Oct 23 2016Oct 25 2016

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


Conference2nd International Neural Network Society Conference on Big Data, INNS 2016

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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