Spatiotemporal enabled Content-based Image Retrieval

Mariana Belgiu, Martin Sudmanns, Tiede Dirk, Andrea Baraldi, Stefan Lang
2016 International Conference on GIScience Short Paper Proceedings  
Wireless Sensor Networks (WSNs) are widely used for monitoring and observation of dynamic phenomena. A sensor in WSNs covers only a limited region, depending on its sensing and communicating ranges, as well as the environment configuration. For efficient deployment of sensors in a WSN the coverage estimation is a critical issue. Probabilistic methods are among the most accurate models proposed for sensor coverage estimation. However, most of these methods are based on raster representation of
more » ... e environment for coverage estimation which limits their quality. In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on 3D vector representation of the environment. Introduction Nowadays, WSNs have found various applications in industry, security, agriculture, military and disaster management. Efficient monitoring and management of dynamic phenomena in the real world necessitates its efficient and accurate coverage. The efficiency of the coverage of a sensor network depends on optimal position of each sensor node within the network. An individual sensor covers only a limited area, which depends on its sensing capacity, range of communication as well as the environment's complexity. The total area covered by a WSN is obtained from the union of the regions covered by individual sensors. Therefore, efficient deployment of sensors in a WSN is a critical issue that affects the coverage as well as communication between sensors. Several optimization methods (i.e., global or local, deterministic or stochastic, etc.) have been proposed to detect and eliminate coverage holes and hence increase the coverage of sensor networks (Argany et al. 2015). One of the key issues of all deployment optimization algorithms is accurate estimation of the coverage of an individual sensor. Sensing model of individual sensors ─which could be binary or probabilistic, omnidirectional or directional─ has significant impact on the precise coverage estimation of a sensor network using diverse optimization algorithms. Most of the sensor coverage estimation methods use a raster representation of the environment (Akbarzadeh et al. 2013) for optimization purposes that limits their precision and efficiency. This is because raster representations are constrained by their spatial resolution, and their regular shapes result in redundant data for unoccupied areas. Few vector-based optimization algorithms are proposed in the literature, which are mostly based on 2D vector-based representation of the environment and do not adequately consider the presence of manmade and natural obstacles in the sensing areas (Wang and Cao 2011). To overcome these limitations, in this paper we propose a probabilistic sensor coverage estimation method based on precise 3D vector-base representation of the environment and we present some results of an ongoing research project that aims at better optimization of a sensor network in a 3D complex urban area.
doi:10.21433/b311729295dw fatcat:fulw4pw3kfh5nmfzcsy3pkisvm