Open AccessConnectivity analysis of finite wireless multihop networks incorporating boundary effects in shadowing environments
Author(s) -
Nagar Jaiprakash,
Chaturvedi Sanjay Kumar,
Soh Sieteng
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2020.0043
Subject(s) - node (physics) , computer science , range (aeronautics) , boundary (topology) , topology (electrical circuits) , standard deviation , computer network , wireless , transmission (telecommunications) , wireless network , probability distribution , mathematics , statistics , telecommunications , physics , engineering , mathematical analysis , quantum mechanics , combinatorics , aerospace engineering
A binary transmission range model, widely utilised for the connectivity analysis of wireless multihop networks (WMNs), ignores the stochastic nature of wireless channels leading to erroneous results and conclusions in estimating the connectivity metrics. This work examines the influence of boundary effects along with the stochastic nature of wireless channels on the connectivity metrics of WMNs. Specifically, this work proposes analytical closed‐form solutions for the minimum node degree distribution and node isolation probability of a WMN deployed in a circular region by considering boundary effects in shadowing environments. Furthermore, it investigates the influence of node's transmission range, node's count, and the standard deviation of shadowing effects on minimum node degree distribution, node isolation probability, and κ ‐ connectivity. The authors simulation results on WMNs show that with the increase in the standard deviation of shadowing effects, node isolation probability increases, and minimum node degree distribution as well as κ ‐ connectivity decreases. Furthermore, the node isolation probability decreases, and minimum node degree distribution as well as κ ‐ connectivity increases with the increase in node's count and node's transmission range. The results produced by their analytical approach have only up to 0.0033 root mean square error as compared to simulated results, showing the accuracy of their approach.