A Graph-Based Big Data Model for Wireless Multimedia Sensor Networks

  • Cihan KüçükkeçeciEmail author
  • Adnan YazıcıEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 529)


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.


Sensor Network Sensor Node Wireless Sensor Network Sink Node Level Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported in part by a research Grant from TÜBİTAK with Grant No. 114R082. We thank to each of the researchers of CEng Multimedia Database Lab. for their very valuable contributions. The first author would also like to thank AYESAŞ for providing financial support.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.AYESAŞAnkaraTurkey
  2. 2.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey

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