Effects of Correlated Shadowing Modeling on Performance Evaluation of Wireless Sensor Networks

Abstract

Abstract-Wireless links are typically modeled in isolation as independent and parallel links. However, in reality, there is often a correlation in defects and impairments between collocated links. The spatial correlation of shadowing among proximate wireless links has been frequently observed. Although different types of correlated shadowing models have been proposed for wireless channels, these models are not present in popular network simulators. Ignoring such correlations results in the diversity of adjacent links being over-estimated. Our hypothesis is that this could lead to incorrect results when evaluating the performance of wireless systems and network protocols. In this paper, the ns-3 simulation environment has been enhanced with a prototype correlated shadowing model developed in our previously published work. We study the end-to-end packet delivery ratio and delay in wireless sensor networks using both correlated and independent propagation models. Simulation results show that correlated and independent shadowing models produce significant differences in network performance. Moreover, we compare the complexity of different modeling options. It is shown that this correlated propagation model in ns-3 can improve simulation realism without inducing any considerable degradation of scalability.

Energy-efficient forwarding strategies for wireless sensor networks in fading channels

“…The authors show that fading can have a significant influence on network performance, notably in multichannel CDMA systems. Similar conclusions are reported in [12]. In [13] it is recognized that fading affects the wireless network and they analyze the connectivity of multihop radio networks in a log-normal shadow fading environment.…”

“…Appendix II: Denoting byx the mean value of a random variable x, the subfix o for PR-control, p for P-control, and r for R-control, from (12), considering β = 0 for simplicity, and taking into account that in all cases the pair (v, P o ) is the same, which means the comparison is made over the same fading, we obtain the following mean values for the energy in decibels:…”

“…In those cases is necessary to check each one to choose the optimal. This equation together with ( 16) and ( 17) is used to obtain R and P and also the energy consumed by one bit of information to travel one meter, given by (12).…”

Distributed Dynamic Channel Allocation in 6G in-X Subnetworks for Industrial Automation

“…with P tx denoting the transmit power, c ∼ 3 • 10 8 m/s is the speed of light, d is the distance between the transmitter and receiver, d 0 is the reference distance, ε denotes the pathloss exponent, f is the center frequency and X s denotes the lognormal shadow fading component (in dB). The shadow fading is modelled in this paper using the correlated model based on Gaussian random fields [17], [18]. To simulate from the model, we generate a zero-mean Gaussian random process at equally spaced points on a rectangular grid with exponential co-variance function.…”

Enhanced Interference Management for 6G in-X Subnetworks

“…• Path-loss: We consider the 3GPP path-loss model for infactory environment with sparse clutter and low antenna heights [40]. • Shadowing: A correlated log-normal shadowing model based on a 2D Gaussian random field is considered [41]. • Small-scale fading: The small scale fading, h are modelled as Rayleigh distributed random variables.…”