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    Öğe
    Simulation of a smart sensor detection scheme for wireless communication based on modeling
    (Mdpi, 2020) Koyuncu, Hakan; Bagwari, Ashish; Tomar, Geetam Singh
    The principle of sensing and sending information applies to all kinds of communications in different media like air, water, etc. During data transmission through sensors, the most important issue is sensing failure problems. In communication systems, sensing failure problems generally occur and affect sensor system performance. In this study, sensing failure issues are discussed, and a smart sensor is introduced that detects the communication signals. The characteristics of these sensors are identified with signal-to-noise ratio (SNR) readings. The proposed sensor system has high performance and it is better than many systems in the literature. Graphical findings show that the suggested scheme increases device efficiency and has more performance than the energy detector (ED) and ED-ADT-2013 by 33.5%, adaptive spectrum sensing (ASS)-2012 by 41.5%, energy detecting technique for adaptive spectrum sensing (EDT-ASS)-2015 by 28.2%, energy detector, and cyclo-2010 by 57.0%, and energy-SNR adaptive threshold (ADT) detector-2016 by 52.8% at -12 dB SNR, respectively. The proposed sensor detection scheme performs well compared to other existing sensors regarding sensing time. It was found that cooperative spectrum sensing (CSS) with the proposed method outperforms HwQ-2012 and CSS-EDT-ASS-2015 by 22.70% and 16.10% in terms of probability at -12 dB SNR, respectively, and detects the signal at -19.70 dB SNR.
  • [ X ]
    Öğe
    Various simulation tools for wireless sensor networks
    (CRC Press, 2023) Koyuncu, Hakan; Bagwari, Ashish
    Networks of specialized, geographically scattered wireless sensors that track and record physical environmental conditions and transmit the collected information to a central point are called wireless sensor networks. They are formed by a large number of wireless sensor nodes. WSNs are capable of measuring environmental factors such as sound, wind, temperature, humidity, and pollution levels. It is difficult to model a WSN analytically because it is complex and infeasible, resulting in simplified analysis and low confidence. Therefore, simulation plays a crucial role in the study of WSNs. It requires a good model built on reliable premises and a suitable framework that facilitates implementation. Simulation results also depend on assumptions about the environment, physical layer, and hardware, which are often too inaccurate to accurately reflect the actual behavior of a WSN, calling into question their validity. However, due to the enormous number of nodes that need to be simulated depending on the application, advanced models have scalability and performance issues. Thus, in modeling WSNs, the trade-off between scalability and accuracy becomes a significant problem. In this chapter, we present several simulation tools for wireless sensor networks that are widely used in the industry.