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 ‐ 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 ‐ connectivity decreases. Furthermore, the node isolation probability decreases, and minimum node degree distribution as well as ‐ 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.

In this study, the authors perform the asymptotic analysis of two‐layer D‐BLAST transmission scheme with group detection receiver under broad class of fading channels. The asymptotic analysis includes both diversity‐multiplexing trade‐off (DMT) and throughput‐reliability trade‐off (TRT) analyses. The D‐BLAST considered here is a multi‐layered space–time transmission scheme, wherein each diagonal of code‐word forms a group. On the other hand, group detection considered detects symbols by nulling out interference from other groups and performs maximum‐likelihood decoding on the chosen group. The reason for choosing this scheme is that the first inflexion of DMT of this system ceases at multiplexing gain ≪1, i.e. second inflexion began at multiplexing gain <1 under Gaussian fading scenario. The results of DMT indicate that the system behaves differently in different fading channel constraints when multiplexing gain ≪1. The results of the TRT analysis show that the system performs different in different fading channels in the zeroth operating region.

Traditional Failure Mode and Effects Analysis (FMEA) has shown its effectiveness in defining, identifying, and eliminating known and/or potential failures or problems in products, process, designs, and services to help ensure the safety and reliability of systems applied in a wide range of industries. However, its approach to prioritize failure modes through a crisp risk priority number (RPN) has been highly controversial. This paper proposes two models for prioritizing failures modes, specifically intended to overcome such limitations of traditional FMEA. The first proposed model treats the three risk factors as fuzzy linguistic variables, and employs alpha level sets to provide a fuzzy RPN. The second model employs an approach based on the degree of match and fuzzy rule-base. This second model considers the diversity and uncertainty in the opinions of FMEA team members, and converts the assessed information into a convex normalized fuzzy number. The degree of match (DM) is used thereafter to estimate the matching between the assessed information and the fuzzy number characterizing the linguistic terms. The proposed models are suitably supplemented by illustrative examples.

This paper presents a minimal cutsets-based unified framework to evaluate some commonly used reliability measures: g-terminal, 2-terminal, and k-terminal reliability. In doing so, the paper proposes a single algorithm, which generates the network node sets. From these node sets, depending on the reliability measure(s), only those node sets are kept, which contains at least one node from the specified node set. These selected node sets form link-minimal cutsets. These minimal cutsets are used as an input to multi-variable inversion-based sum-of-disjoint product approach to obtain the unreliability expression thereafter. By solving several network examples of varied complexities, the comparison with the existing approach is provided.