Algorithms for comparing large pedigree graphs

The importance of pedigrees is translated by geneticists as a tool for diagnosing genetic diseases. Errors resulting during collection of data and missing information of individuals are considered obstacles in deducing pedigrees, especially larger ones. Therefore, the reconstructed pedigree graph evaluation needs to be undertaken for relevant diagnosis. This requires a comparison between the derived and the original data. The present study discusses the isomorphism of huge pedigrees with labeled and unlabeled leaves, where a pedigree has hundreds of families, which are monogamous and generational. The algorithms presented in this paper are based on a set of bipartite graphs covering the pedigree and the problem addressed is parameter tractable. The Bipartite graphs Covering the Pedigree (BCP) problem is said to possess a time complexity of $f(k).mod(X)^{O(1)}$ where $f$ is the computing function that grows exponentially. The study presents an algorithm for the BCP problem that can be categorized as a polynomial-time-tractable evaluation of the reconstructed pedigree. The paper considers pedigree graphs that consist of both labeled and unlabeled leaves that make use of parameterized and kernelization algorithms to solve the problem. The kernelization algorithm executes in $O(k^3)$ for the BCP graphs

Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm

As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote Optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks Optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption

Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm

As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote Optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks Optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption