# Challenges in Wireless System Integration as Enablers for Indoor Context Aware Environments
## 2 Simulation Technique and Scenario under Analysis  
Overall user experience within Context Aware environments is given by user interaction, with other users as well as with multiple autonomous systems, such as transportation, building automation or location and tracking, to name a few. In terms of communication systems, low latency levels are required (particularly in the case of real time services) and adequate transmission bit rates (which must adapt to variable user requirements). The advent of Context Aware environments is mainly foreseen by the adoption of IoT (enabling connectivity among any potential device and application) and the consolidation of 5G networks, in particular in the case of Device to Device (D2D) connectivity [5; 6; 7; 8; 9]. These scenarios are of particular interest in the case of high user/device density, particularly in confined environments, given the large number of potential users (e.g., office spaces, shopping centers, etc.) and devices which require communication capabilities. These results are determined not only by user location and density, but also by dynamic requirements in QoS levels, such as transmission rate, bit error rate or latency, among others. The analysis of confined scenarios is a complex task, given inherent complexity in terms of object density and material parameters, as well as in user location variability, particularly in the case of wearable devices. In terms of intra-device and device to concentrator links (which is the case to consider when devices require intermediate hubs, such as wireless Local Area Network (LAN) access points or coordinators in 802.15 networks), radio link analysis is compulsory in order to estimate device operation as a function of available transmitter power, receiver sensitivity and overall interference levels. Due to the large number of devices and the interaction of radio waves with the surrounding environment, precise characterization is a computationally demanding task. Multiple simulation techniques can be employed, such as empirical based ones, in which analytical expressions are obtained based on regression methods and curve fitting. Multiple models have been proposed, as a function of surrounding environment and the operational range employed (e.g., frequency of operation, transceiver heights, building heights, etc. ), such as Okumura-Hata, Xia-Bertoni, COST-231 or one slope, dual slope models, to name a few [15; 16; 17; 18; 19; 20]. These models exhibit low computational complexity but are strongly site specific, requiring intensive measurement campaigns in order to adapt the model to the specific scenarios under analysis. Moreover, effects such as diffuse scattering or multi-obstacle diffraction must be considered separately. On the other hand, deterministic-based methods directly solve Maxwell equations in every region of space. Several approaches can be followed in this case: from full wave electromagnetic simulation techniques, such as Finite Difference Time Domain, Finite Integration Time Domain or Method of Moments, to model simplifications based on geometric optics. These methods are more precise and are, in principle, non-site specific. However, their computational cost is much higher than in the case of analytical based approaches. An adequate balance between computational cost and accuracy, for the case of large scenarios (as compared with operational wavelength) in which wireless channel must be analyzed can be obtained by employing ray tracing and ray launching techniques. In this work, an ad-hoc implemented simulation tool based on 3D Ray Launching will be employed in order to analyze the behavior of wireless sensor network operation as a function of node density, transceiver specifications and scenario characteristics. The simulation method has been extensively tested in a number of different applications related with context aware environments, such as Intelligent Transportation systems, Smart Health, and sports, to name a few [21; 22; 23; 24; 25].