Details

Autor(en) / Beteiligte

• Ahmed, Hossam O.
• Elkhatib, Mohamed M.
• Adly, Ihab
• Ragai, Hani Fikry

Titel

Design and Implementation of Fuzzy Event-detection Algorithm for Border Monitoring on FPGA

Ist Teil von

• International Journal of Fuzzy Systems, 2016-12, Vol.18 (6), p.1054-1064

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2016

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The use of wireless sensor networks to protect sensitive facilities or international borders has recently attracted more and more attention. It has become a high-priority issue in many countries. In addition to the physical fences built for stopping illegal intruders from crossing the border, smart fencing has been proposed to extend intrusion-detection capabilities. Event detection is a central component in numerous wireless sensor network applications. In spite of this, the area of event description has not received enough attention. The majority of current event description approaches rely on using precise values to specify event thresholds. However, these crisp values cannot adequately handle the imprecise sensor readings. Therefore, in this paper, a hybrid event-detection algorithm based on two layers of 12-bit-resolution fuzzy logic system is used, which significantly improves the accuracy of event detection. Each sensor node has sensors to improve the precision of the detection system, as well as reducing the false alarm rate especially in a noisy environment. The proposed system was modeled using Matlab and designed using VHDL. The proposed system’s VHDL code has been compared with the Matlab model, which provided very close results to this software-based controller model, and also with two other VHDL-based designs, results of which are also close and comparable to the proposed system. The proposed design is then implemented using a FPGA kit, and the prototype is connected to a graphical user interface. Finally, a testing experiment of this proposed system shows an event-detection ratio capability of 98.4 % and the false alarm ratio of 1.6 %.