Behavior recognition system based fog cloud computing

  • Lee, Seok-Woo (Department of Information System, Kwangwoon University Graduate School of Information Contents) ;
  • Lee, Jong-Yong (Ingenium College of liberal arts, KwangWoon University) ;
  • Jung, Kye-Dong (Ingenium College of liberal arts, KwangWoon University)
  • Received : 2017.06.25
  • Accepted : 2017.07.28
  • Published : 2017.09.30


The current behavior recognition system don't match data formats between sensor data measured by user's sensor module or device. Therefore, it is necessary to support data processing, sharing and collaboration services between users and behavior recognition system in order to process sensor data of a large capacity, which is another formats. It is also necessary for real time interaction with users and behavior recognition system. To solve this problem, we propose fog cloud based behavior recognition system for human body sensor data processing. Fog cloud based behavior recognition system solve data standard formats in DbaaS (Database as a System) cloud by servicing fog cloud to solve heterogeneity of sensor data measured in user's sensor module or device. In addition, by placing fog cloud between users and cloud, proximity between users and servers is increased, allowing for real time interaction. Based on this, we propose behavior recognition system for user's behavior recognition and service to observers in collaborative environment. Based on the proposed system, it solves the problem of servers overload due to large sensor data and the inability of real time interaction due to non-proximity between users and servers. This shows the process of delivering behavior recognition services that are consistent and capable of real time interaction.


Behavior Recognition;Fog Cloud;Sensor;DbaaS


  1. J. Yang, "Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phones", Proc. of Int. Workshop on Interactive Multimedia for Consumer Electronics, 2009.
  2. T. Scott, J. Lester, J. E. Froehlich, et al., "iLearn on the iPhone: Real-Time Human Activity Classification on Commodity Mobile Phones", CSE Technical Report, University of Washington, 2008.
  3. Lotfi, Ahmad, et al. "Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour." Journal of Ambient Intelligence and Humanized Computing 3.3 (2012): 205-218.
  4. Lara, Oscar D, Miguel A. Labrador. "A survey on human activity recognition using wearable sensors." Communications Surveys & Tutorials, IEEE 15.3 (2013): 1192-1209.
  5. H.K. Yang and H.S. Yong, "Physical Activity Recognition using Accelerometer of Smartphone," Proceeding of Korea Computer Congress 2012, Vol. 39, No. 2(D), pp. 7-9, 2012.
  6. E.M. Tapia, S.S. Intille, and K. Larson, "Activity Recognition in the Home using Simple and Ubiquitous Sensors," Proceeding of PERVASIVE 2004, Vol. 3001, pp. 158-175, 2004.
  7. S.W. Jeong and Y.H. Park, "Integrated Management System for Vehicle Black Box Video Using Mobile Cloud", Journal of the Korea Institute of Information and Communication Engineering, v17, no.10, pp.2352-2358, 2013.
  8. Melvin B. Greer, Jr., John W. Ngo, "Personal Emergency Preparedness Plan (PEPP) Facebook App: Using Cloud Computing, Mobile Technology, and Social Networking Services to Decompress Traditional Channels of Communication during Emergencies and Disasters", in the proceedings of IEEE Ninth Inter'national Conference on Services Computing, Hawaii, USA, 24-29 June, 2012.
  9. Y.C. Peter Chang, S. Chen, T.J. Wang and Y. Lee, "Fog computing node system software architecture and potential applications for NB-IoT industry", 2016 Internaltional Computer Symposium.
  10. M. Aazam and E.N. Huh, "Fog Computing and Smart Gateway Based Communication for Cloud of Things", 2014 International Conference on Future Internet of Things and Cloud.
  11. O.D. Incel, M. Kose, and C. Ersoy, "A Review and Taxonomy of Activity Recognition on Mobile Phones," BioNanoScience, pp.1-27, 2013.
  12. B. Najafi, K. Aminian, A. Paraschiv, et al., "Ambulatory System for Human Motion Analysis Using a Kinematic Sensor: Monitoring of Daily Physical Activity in the Elderly", IEEE Trans. on Biomedical Engineering, Vol.50, No.6, 2003.
  13. M. Aazam and E.N. Huh, "E-HANC: Leveraging Fog Computing for Emergency Alert Service", The Fifth International WorkShop on Pervasive Networks for Emergency Management, 2015.
  14. Soror Sahri, Rim Moussa, Darrell D. E. Long, Salima Benbernou, DBaaS-Expert: A Recommender for the Selection of the Right Cloud Database, Foundations of Intelligent Systems, Lecture Notes in Computer Science, 8502, pp.315-324, 2014.
  15. H. Hacigumus, B. Iyer and S. Mehrotra, Providing database as a service, in Proc. of IEEE 18th ICDE, pp. 29-38, 2002.