# Challenges in Wireless System Integration as Enablers for Indoor Context Aware Environments
## 3 Wireless Sensor Network Radioelectric Analysis  
As previously stated, one of the main considerations in order to provide assessment on context aware system performance is to obtain coverage/capacity estimations. The proposed increase in transceiver density inherent to connectivity requirements to provide interactive environments gives also rise to potential increase in interference values, as well as a complex received power level profiles, which are dependent on the configuration of the elements within the scenario under analysis. In this sense and taking into consideration requirements of IoT, networks based on multiple and heterogeneous transceivers, such as Narrowband LTE (NB-LTE), 5G Device to Device (5G D2D), wireless personal area networks and wireless local area networks are employed. This leads to a large, non-uniform set of transceivers, with multiple transmission schemes, in which interference as well as energy control play key roles in overall system performance [31; 32]. Absolute radio channel levels are strongly dependent in the case of indoor scenarios (in which non line of sight communications coexist with line of sight links) of elements such as furnishings, materials or user locations, among others [32]. Different approaches can be followed to perform wireless channel characterization, many of them based on simplified empirical based propagation models, such as one slope, dual slope, COST 231 multi wall, etc. [32]. In this case, path loss estimations can be obtained in a straightforward manner in large scenarios. The main drawback following these approaches is oversimplification, in terms of homogeneous transceiver consideration (i.e., all of the transceivers within the scenario are parameterized in the same way, owing to inherent restrictions in terms of the employed models) as well as on the interaction with the surrounding environment, in terms of fast fading components given by multipath propagation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **2.4 GHz** & **5.8 GHz** \\ \hline Transmitted power & 10 dBm & 10 dBm \\ Transmission data rate & 250 Kbps & 1 Mbps \\ Antenna type & Monopole & Monopole \\ Antenna gain & 0 dB & 0 dB \\ Launched rays angle resolution & \(1^{\circ}\) & \(1^{\circ}\) \\ Maximum permitted reflections & 6 & 6 \\ Cuboids resolution & 20 cm & 20 cm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: 3D-Ray launching simulation parameters. On the other hand, full wave simulation techniques such as Finite Difference Time Domain or Method of Moments can be employed in order to explicitly solve Maxwell equations in the simulation scenario. These methods provide precise results, but require large computational resources, specially in the case of increasing the frequency of operation. In the case of IoT indoor scenarios, the use of wireless transceivers in the microwave spectrum (mainly 2.4 GHz and 5.8 GHz) is compulsory, in order to consider service provision between devices (D2D communication schemes in the case of 5G networks or Machine to Machine, M2M in the case of 3G or 4G networks), between users (considering mobile terminals as well as wearables) or between service gateways and infrastructure nodes (for example in the case of access points in Wireless Local Area Networks or aggregators of Wireless Sensor Networks). Taking into consideration the large number of potential transceivers located within indoor context aware environments, with values spanning from 1 transceiver/100 m\({}^{2}\) to (1-2) transceiver/1 m\({}^{2}\) (which can increase in the case of specific body area networks) and the fact that wireless channel characterization is the prime element in system level analysis (i.e., to obtain Key Performance Indicators in order to evaluate QoS), alternative methods must be sought.