# Challenges in Wireless System Integration as Enablers for Indoor Context Aware Environments
## 3 Wireless Sensor Network Radioelectric Analysis
As stated in Section 2, the approach followed is to employ the 3D Ray Launching technique, which is based on the approximation of the impinging wavefront given by a discrete set of rays, following an approximation given by geometric optics and Uniform Theory of Diffraction. In order to analyze wireless channel performance in complex indoor scenario with variable node density, a variable number of nodes have been embedded in the scenario previously depicted in Figure 1. Node density has varied from 1 transceiver/room to 1 transceiver/2 m\({}^{2}\) following both uniform and random distributions. The resulting network configurations are schematically depicted in Figure 2. In the case of uniform distributions, symmetrically deployed sensors are activated or deactivated as a function of operating conditions. Modelling of ad-hoc network configurations has been taken into account by employing random distributions of the nodes, by obtaining a random numbering sequence of the active transceivers present in the initial high density uniform scenario, which has 108 transceivers in an overall surface of 216 m\({}^{2}\). Different layout configurations have been depicted in Figure 2. The proposed density variations allow considering multiple operation scenarios, such as conventional user connectivity (e.g., access with mobile terminals or laptops to WLAN networks, which have initially a low transceiver density, to communication enabled environments within IoT, in which a high node density, owing to embedded transceivers in elements such as furniture can be considered). Transceiver node locations have been considered following three specific configurations: uniform distribution (in which nodes are all equidistant one to another, corresponding to the case of static nodes, representing for example infrastructure nodes), random distribution (in which a sub-set of the maximum density uniform distribution is estimated following a random sequence generator) and a set of wearable nodes, corresponding to extensions given by Wireless Personal Area Networks that can be operating within the scenario. The lowest node density corresponds to the initial scenario (e.g., previous to IoT enabling technologies) in which one node per each room is placed, corresponding to a non-uniform node distribution. The equivalent node density in terms of surface unit would be: Office Rooms (nodes #86, #92, #107) 1 transceiver/18 m\({}^{2}\), upper right hallway (node #90) 1 transceiver/9 m\({}^{2}\), middle hallway (node #74) 1 transceiver/22 m\({}^{2}\), lower left hallway (node #1) 1 transceiver/36 m\({}^{2}\) and lab facilities (node #42) 1 transceiver/95 m\({}^{2}\). Simulation results have been obtained by application of the 3D Ray Launching code, in which full consideration of the indoor furnishings (shape, volume and dispersive material parameters) is employed. Results have been obtained for the complete scenario volume, for frequencies of 2.4 GHz and 5.8 GHz. Bi-dimensional received power distribution levels have been depicted in order to provide a comprehensive description on the evolution of received power levels, which has direct impact on link balance as well as potential interference levels within the scenario. Results have been obtained for uniform node distributions, with the following parameters: