Browse > Article
http://dx.doi.org/10.7472/jksii.2019.20.5.95

An Implementation of an Intelligent Access Point System Based on a Feed Forward Neural Network for Internet of Things  

Lee, Youngchan (School of Information Technology Engineering, Daegu Catholic University)
Lee, SoYeon (School of Information Technology Engineering, Daegu Catholic University)
Kim, Dae-Young (School of Information Technology Engineering, Daegu Catholic University)
Publication Information
Journal of Internet Computing and Services / v.20, no.5, 2019 , pp. 95-104 More about this Journal
Abstract
Various kinds of devices are used for the Internet of Things (IoT) service, and IoT devices mainly use communication technology that uses the frequency of the unlicensed band. There are several types of communication technology in the unlicensed band, but WiFi is most commonly used. Devices used for IoT services vary in computing resources from devices with limited capabilities to smartphones and provide services over wireless networks such as WiFi. Most IoT devices can't perform complex operations for network control, thus they choose a WiFi access point (AP) based on signal strength. This causes a decrease in IoT service efficiency. In this paper, an intelligent AP system that can efficiently control the WiFi connection of the IoT devices is implemented. Based on the network information measured by the IoT device, the access point learns using a feed forward neural network algorithm, and predicts a network connection state to control the WiFi connection. By controlling the WiFi connection at the AP, the service efficiency of the IoT device can be improved.
Keywords
Internet of Things; WiFi; Feed Forward Neural Network; Access Point; Network Control;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, "Internet of Things (IoT): A vision, architectural elements, and future directions," Elsevier Future Generation Computer Systems, Vol. 29, No. 7, pp. 1645-1660, 2013. https://doi.org/10.1016/j.future.2013.01.010   DOI
2 V. Gunes, S. Peter, T. Givargis, and F. Vahid, "A survey on concepts, applications, and challenges in cyber-physical systems," KSII Transactions on Internet Information Systems, Vol. 8, No. 12, pp. 4242-4267, 2014. https://doi.org/10.3837/tiis.2014.12.001   DOI
3 M.W. Hong and Y.H. Kang, "IoT based Data centric sensory data aggregation environment," Journal of Platform Technology, Vol. 7, No. 1, pp. 20-25, 2019. http://jpt.ictps.org/contents/7-1
4 G. Kim and S. Lee, "Access Point Selection Algorithm for Densely Deployed IEEE 802.11 WLANs", The Journal of Korean Institute of Communications and Information Sciences, Vol. 41, No. 6, pp. 707-713, 2016. https://doi.org/10.7840/kics.2016.41.6.707   DOI
5 Y. Shim and I. Lee, "A Study on Impact on the Throughput of Station According to Separation Distance between WLAN APs", The Journal of Korean Institute of Information Technology, Vol. 10, No. 3, pp. 117-121, 2012. http://www.ki-it.or.kr/sobis/kiit.jsp
6 H. Choi, H. Kim, and S. Choi, "Impact of Interference between APs on WLAN Throughput", Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp. 392-393, 2013.
7 S. Kim, Y. Kim, and C. Lee, "The Way of IoT Management Hub Connection for Convenient IoT Service", Journal of the Korea Institute of Information and Communication Engineering, Vol. 19, No. 11, pp. 2656-2664, 2015. https://doi.org/10.6109/jkiice.2015.19.11.2656   DOI
8 U. Heo, Y. Peng, and K. You, "Energy Efficient Access Point Selection Method for IEEE802.11 Wireless LANs", Journal of the Korea Contents Association, Vol. 11, No. 12, pp. 578-585, 2011. https://doi.org/10.5392/JKCA.2011.11.12.578   DOI
9 N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, Vol. 16, No. 1, pp. 321-357, 2002. https://jair.org/index.php/jair/article/view/10302   DOI
10 I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, pp. 163-265, The MIT Press, 2016.
11 P. Harrington, Machine Learning in Action, pp. 83-100, Manning publication, 2012.
12 Raspberry Pi 3, https://www.raspberrypi.org/ (Accessed 2018)
13 LattePanda, https://www.lattepanda.com/(Accessed 2018)
14 TensorFlow, https://www.tensorflow.org/(Accessed 2018)
15 D. Kim, Damian's Wi-Fi On, pp. 53-62, Midasbooks, 2011.
16 Chart.js, https://www.chartjs.org/ (Accessed 2019)