Robust and Efficient Self-Adaptive Position Tracking in Wireless Embedded Systems

© 2015 IEEE.Apart from static deployments, sensor nodes in Wireless Sensor Networks (WSNs) are unaware of their location information. In order to estimate their actual or relative positions with respect to other nodes, they are required to self-localize themselves by collecting information from their environment. However, due to the high dynamism and the noise introduced by the WSN environment, self-localization procedures are not straightforward and they may require quite sophisticated algorithmic techniques to satisfy precision requirements of the WSN applications. Among the self-localization procedures in the literature, the ones based upon the technique of trilateration are easy to implement and efficient in terms of resource requirements. On the other hand, their performance is fragile against environmental dynamics. Besides, even though multilateration based procedures are reported to be more robust, their practicability in WSNs seems questionable due to their high resource requirements. In this paper, our objective is to develop a practical self-localization procedure for WSNs that puts away the fragility against noisy ranging measurements in an efficient manner. To that end, we take a different approach to self-localization procedure and treat it as a search process during which sensor nodes find their relative positions without knowing the actual correct values. We present a novel trilateration-based self-localization procedure by exploiting a robust and efficient search technique, named Adaptive Value Tracking (AVT), that finds and tracks a dynamic searched value in a given search space through successive feedbacks. We evaluate this procedure on a real test bed setup and show that our approach to self-localization is efficient, robust to environmental dynamics and adaptive in the sense of reacting to position changes

Hydrology@Home: A distributed volunteer computing framework for hydrological research and applications

© IWA Publishing 2020. Web-based distributed volunteer computing enables scientists to constitute platforms that can be used for computational tasks by using potentially millions of computers connected to the internet. It is a widely used approach for many scientific projects, including the analysis of radio signals for signs of extraterrestrial intelligence and determining the mechanisms of protein folding. User adoption and clients' dependence on the desktop software present challenges in volunteer computing projects. This study presents a web-based volunteer computing framework for hydrological applications that requires only a web browser to participate in distributed computing projects. The framework provides distribution and scaling capabilities for projects with user bases of thousands of volunteers. As a case study, we tested and evaluated the proposed framework with a large-scale hydrological flood forecasting model

Robust and Efficient Self-Adaptive Position Tracking in Wireless Embedded Systems

© 2015 IEEE.Apart from static deployments, sensor nodes in Wireless Sensor Networks (WSNs) are unaware of their location information. In order to estimate their actual or relative positions with respect to other nodes, they are required to self-localize themselves by collecting information from their environment. However, due to the high dynamism and the noise introduced by the WSN environment, self-localization procedures are not straightforward and they may require quite sophisticated algorithmic techniques to satisfy precision requirements of the WSN applications. Among the self-localization procedures in the literature, the ones based upon the technique of trilateration are easy to implement and efficient in terms of resource requirements. On the other hand, their performance is fragile against environmental dynamics. Besides, even though multilateration based procedures are reported to be more robust, their practicability in WSNs seems questionable due to their high resource requirements. In this paper, our objective is to develop a practical self-localization procedure for WSNs that puts away the fragility against noisy ranging measurements in an efficient manner. To that end, we take a different approach to self-localization procedure and treat it as a search process during which sensor nodes find their relative positions without knowing the actual correct values. We present a novel trilateration-based self-localization procedure by exploiting a robust and efficient search technique, named Adaptive Value Tracking (AVT), that finds and tracks a dynamic searched value in a given search space through successive feedbacks. We evaluate this procedure on a real test bed setup and show that our approach to self-localization is efficient, robust to environmental dynamics and adaptive in the sense of reacting to position changes

Robust and Efficient Self-Adaptive Position Tracking in Wireless Embedded Systems

© 2015 IEEE.Apart from static deployments, sensor nodes in Wireless Sensor Networks (WSNs) are unaware of their location information. In order to estimate their actual or relative positions with respect to other nodes, they are required to self-localize themselves by collecting information from their environment. However, due to the high dynamism and the noise introduced by the WSN environment, self-localization procedures are not straightforward and they may require quite sophisticated algorithmic techniques to satisfy precision requirements of the WSN applications. Among the self-localization procedures in the literature, the ones based upon the technique of trilateration are easy to implement and efficient in terms of resource requirements. On the other hand, their performance is fragile against environmental dynamics. Besides, even though multilateration based procedures are reported to be more robust, their practicability in WSNs seems questionable due to their high resource requirements. In this paper, our objective is to develop a practical self-localization procedure for WSNs that puts away the fragility against noisy ranging measurements in an efficient manner. To that end, we take a different approach to self-localization procedure and treat it as a search process during which sensor nodes find their relative positions without knowing the actual correct values. We present a novel trilateration-based self-localization procedure by exploiting a robust and efficient search technique, named Adaptive Value Tracking (AVT), that finds and tracks a dynamic searched value in a given search space through successive feedbacks. We evaluate this procedure on a real test bed setup and show that our approach to self-localization is efficient, robust to environmental dynamics and adaptive in the sense of reacting to position changes