Toward realistic WiFi simulation with smartphone “Physics”

Abstract

Various packet-based simulation tools (e.g., NS-3) have been employed for design, validation, and evaluation of new protocols for WiFi networks since they offer cost efficiency, scalability, and reproducibility. These benefits come, however, at the expense of lack of realism compared to live testbed experiments. This is attributed in a major part to the difficulty of capturing detailed characteristics of channel dynamics, bit-level protocol specification (PHY layer), and application/user behaviors in a high-fidelity manner. The performance gap predicted by simulation and live testbed becomes even more pronounced when one considers a wide diversity of device characteristics and the way each device is used by end users. For example, smartphones generally show worse WiFi performance than other WiFi devices (e.g., laptops and tablets) because smartphones suffer from additional signal loss due to hand-grips and the low antenna gains of their embedded antennas. The goal of this study is to significantly close the gap by incorporating survey-and measurement-based smartphone WiFi characteristics and realistic hand-grip models into traditional WiFi network simulators (NS-3 in this study). The enhanced WiFi simulation tool's performance prediction capability is validated through an comparative study between testbed experiments and simulations.

Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

“…The human body is composed of 70% water which can absorb a part of 2.4 GHz radio signals leading to large signal decay [53]. The RSS loss occurs due to shadowing and attenuation [54]. A standard deviation increase of 2.4 dBm in reported in [42] due to human presence.…”

Using Empirically Validated Simulations to Control 802.11 Access Point Density

“…Some deal with sensor networks (e.g., [8], [9]), others with vehicular networks (e.g., [10], [11]), and yet others with 802.11 networks (e.g., [12]- [15]). To the best of our knowledge, only Yoo et al [16] deal specifically with smart phones, studying how to best set the parameters in NS-3, all the other papers use laptops or different types of sensors.…”

“…The interpretation of these values can be made along two axis: the degradation of the MS's performance, and the degradation of the environment. Degrading the MS's performance is legitimate, as we make the model evolve from representing a classical 802.11 client to a lightweight smartphone, with for example a 0 dBi antenna gain, as chosen by Yoo et al [16]. Similarly, choosing values the correspond to a more hostile environment is not shocking, as there can be additional obstacles in the way of the transmission, for example internal walls or furniture affecting the Internal Wall Loss parameter.…”