Design of a WSN for the Sampling of Environmental Variability in Complex Terrain

Abstract

In-situ environmental parameter measurements using sensor systems connected to a wireless network have become widespread, but the problem of monitoring large and mountainous areas by means of a wireless sensor network (WSN) is not well resolved. The main reasons for this are: (1) the environmental variability distribution is unknown in the field; (2) without this knowledge, a huge number of sensors would be necessary to ensure the complete coverage of the environmental variability and (3) WSN design requirements, for example, effective connectivity (intervisibility), limiting distances and controlled redundancy, are usually solved by trial and error. Using temperature as the target environmental variable, we propose: (1) a method to determine the homogeneous environmental classes to be sampled using the digital elevation model (DEM) and geometric simulations and (2) a procedure to determine an effective WSN design in complex terrain in terms of the number of sensors, redundancy, cost and spatial distribution. The proposed methodology, based on geographic information systems and binary integer programming can be easily adapted to a wide range of applications that need exhaustive and continuous environmental monitoring with high spatial resolution. The results show that the WSN design is perfectly suited to the topography and the technical specifications of the sensors, and provides a complete coverage of the environmental variability in terms of Sun exposure. However these results still need be validated in the field and the proposed procedure must be refined.

Investigation of Error Probability for Grid and Random Uniform Topologies in a Variable Noise Environment

“…In addition to topological arrangement, other elements that affect WSN node transmission include distance between communicating nodes and their alignment, channel characteristics and signal attenuation and fading, and presence of noise and other interfering signals, such elements are approximated using path loss and shadowing models. WSN topologies are linked to path loss parameters, and better designs can be achieved when values resulting from node communication are obtained using an appropriate path loss model, which also includes routing protocols and WSN nodes life time [21][22][23][24][25][26].…”