Enhancing the sustainability of Wireless Sensor Networks (WSN) using different Artificial Intelligence (AI) approaches is one of the promising research topics nowadays, especially for battery-based applications such as border surveillance monitoring. In this paper, we proposed a Sustainable Systolic Wireless Sensor Network for Surveillance System (SS-WSN-SS) processing unit, which consists of two Fuzzy Logic System (FLS) processing cores. The first core is responsible for the event decision-making process based on the received data from a Lidar Sensor and an ultrasonic sensor. The second core is responsible for the detection and correction of any thermo-cognitive dysfunction that could reduce the reliability of the WSN system and could be assumed as a primitive electronics somatosensory cortex unit.The proposed SS-WSN-SS architecture could significantly decrease the power consumption at the processing unit level, sensory data acquisition level, and the wireless interfacing level as well. The proposed SS-WSN-SS processing unit architecture has been designed using VHDL, and the targeted FPGA chip was the Intel Cyclone V 5CGXFC9D6F27C7. Also, the proposed SS-WSN-SS processing unit obtained a Power efficiency of 17.16 GOPS\W at a maximum operating frequency of 263.5 MHz, while draining only 50.87 mW as a core dynamic thermal power dissipation loss and dissipates about 27.33 mW as an I/O thermal power dissipation loss.


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    Titel :

    17.16 Gops\W Sustainable FLS-Based Wireless Sensor Network For Surveillance System Using FPGA


    Beteiligte:


    Erscheinungsdatum :

    2021-04-20


    Format / Umfang :

    3426857 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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