# Analysis of Human Body Shadowing Effect on Wireless Sensor Networks Operating in the 2.4 GHz Band
## 4 Discussion  
The results presented above were obtained from both computer simulations and measurements of the body shadowing effect in wireless sensor networks for the very popular 2.4 GHz band. The computer simulations performed with the full wave FDTD method showed that the presence of a human body can increase path loss significantly. When a human body is close to the wireless sensor, path loss may increase (from 10 dB up to 20 dB) and could exceed the fading margin of the link. Computer simulations of path loss in wireless sensor networks using FDTD-based software are limited, however, to a relatively small range of distances. To model wireless sensors located 7 m from each other with a human body in between, 9.8 GB of memory on a computer equipped with GPU cards was required. The time required with this setup for the simulation of a single position of the receiving antenna was up to 85 min. This shows the limitation of FDTD-based software for simulating the body shadowing effect on wireless sensor networks that operate inside buildings. For such networks, it is necessary to model much larger areas and also to include the walls and other obstacles. For this, FDTD-based programs would require a computer with far more than 12 GB of memory, while the time needed for the simulations would be impractically high, especially if the wireless sensor network design demanded numerous simulations. The body shadowing effect can be successfully modeled using ray-based programs that apply the UTD method for simulating the interactions of electromagnetic waves with human bodies. In the present study, Remcom XGtd\({}^{\text{TM}}\) software was used with a human body model proposed for the purposes of the analysis. This required simplification of the human body shape, which was constructed using prisms with varying numbers of sidewalls. The smallest differences between the simulations of path loss made using FDTD and UTD methods were obtained using a model with six side wall prisms. When the receivers were located close to the human body, where the body shadowing effect has the strongest influence on path loss, the results obtained with the six-wall model were on average 1.9 dB below the reference results obtained with FDTD. With this model, across the whole considered range of receiving antenna distances, the difference between the results obtained using UTD and FDTD was approximately \(-2\) dB. This difference is sufficiently small to allow the model to be used to estimate the fade margin that should be assumed for successful transmission in wireless sensor networks operating in the presence of human bodies. The proposed model of a human body was used to analyze the influence of body shadowing on a wireless sensor network operating in an indoor environment. Due to multiple reflections from the walls, the path loss changed dynamically when the receiving antenna was moved. When there was no human body between the antennas, the median path loss calculated for different positions of the receiving antenna across a distance of 1 m was 45 dB. In the case of one person shadowing the transmitter, the median path loss increased by 9.5 dB. With two people, the median path loss increased by 10 dB. With three people, the median path loss increased by 11 dB. This shows that the presence of human bodies in the considered geometry of the experiment caused additional path loss of approximately 10 dB. For wireless sensor networks that operate with low power transmitters, this value should be considered in the link budget analysis for building interiors. The path loss simulations in the second scenario were performed for the case where there are two persons in the proximity of the transmitting antenna, but there is no shadowing of the direct path between the transmitting and receiving antenna. It was shown that even in this case, the median path loss increased to 48 dB, the minimum value was 43 dB, and the maximum value was 56 dB.