• Energy detection based spectrum sensing over two-wave and diffuse power fading channels

      Chatziantoniou, Eleftherios; Allen, Ben; Velisavljević, Vladan; Karadimas, Petros; Coon, Justin; University of Bedfordshire; Queens University Belfast; University of Oxford (IEEE, 2016-04-21)
      One of the most important factors that affects the performance of energy detection (ED) is the fading channel between the wireless nodes. This paper investigates the performance of ED-based spectrum sensing, for cognitive radio (CR), over two-wave with diffuse power (TWDP) fading channels. The TWDP fading model characterizes a variety of fading channels, including well-known canonical fading distributions, such as Rayleigh and Rician, as well as worse-than-Rayleigh fading conditions modeled by the two-ray fading model. Novel analytic expressions for the average probability of detection over TWDP fading that account for single-user and cooperative spectrum sensing and square law selection diversity reception are derived. These expressions are used to analyze the behavior of ED-based spectrum sensing over moderate, severe, and extreme fading conditions and to investigate the use of cooperation and diversity as a means of mitigating the fading effects. The obtained results indicate that TWDP fading conditions can significantly degrade sensing performance; however, it is shown that detection performance can be improved when cooperation and diversity are employed. The presented outcomes enable identifying the limits of ED-based spectrum sensing and quantifying the tradeoffs between detection performance and energy efficiency for CR systems deployed within confined environments, such as in-vehicular wireless networks.
    • Wireless magnetic sensor network for road traffic monitoring and vehicle classification

      Velisavljević, Vladan; Cano, Eduardo; Dyo, Vladimir; Allen, Ben; University of Bedfordshire; European Commission, Joint Research Centre; University of Oxford (De Gruyter Open, 2016-11-23)
      Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification.