3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm

Surrogate-Assisted Hybrid Meta-Heuristic Algorithm with an Add-Point Strategy for a Wireless Sensor Network

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems.

Multi-group multi-verse optimizer for energy efficient for routing algorithm in wireless sensor network

Wireless sensor network (WSN) is a network composed of a group of wireless sensors with limited energy. With the proliferation of sensor nodes, organization and management of sensor nodes become a challenging task. In this paper, a new topology is proposed to solve the routing problem in wireless sensor networks. Firstly, the sensor nodes are layered to avoid the ring path between cluster heads. Then the nodes of each layer are clustered to facilitate the integration of information and reduce energy dissipation. Moreover, we propose efficient multiverse optimization to mitigate the impact of local optimal solution prematurely and the population diversity declines prematurely. Extensive empirical studies on the CEC 2013 benchmark demonstrate the effectiveness of our new approach. The improved algorithm is further combined with the new topology to handle the routing problem in wireless sensor networks. The energy dissipation generated in routing is significantly lower than that of Multi-Verse Optimizer, Particle Swarm Optimization, and Parallel Particle Swarm Optimization in a wireless sensor network consisting of 5000 nodes.

MiA-CODER: A Multi-Intelligent Agent-Enabled Reinforcement Learning for Accurate Coverage Hole Detection and Recovery in Unequal Cluster-Tree-Based QoSensing WSN

Coverage is an important factor for the effective transmission of data in the wireless sensor networks. Normally, the formation of coverage holes in the network deprives its performance and reduces the lifetime of the network. In this paper, a multi-intelligent agent enabled reinforcement learning-based coverage hole detection and recovery (MiA-CODER) is proposed in order to overcome the existing challenges related to coverage of the network. Initially, the formation of coverage holes is prevented by optimizing the energy consumption in the network. This is performed by constructing the unequal Sierpinski cluster-tree topology (USCT) and the cluster head is selected by implementing multi-objective black widow optimization (MoBWo) to facilitate the effective transmission of data. Further, the energy consumption of the nodes is minimized by performing dynamic sleep scheduling in which Tsallis entropy enabled Bayesian probability (TE2BP) is implemented to switch the nodes between active and sleep mode. Then, the coverage hole detection and repair are carried out in which the detection of coverage holes if any, both inside the cluster and between the clusters, is completed by using the virtual sector-based hole detection (ViSHD) protocol. Once the detection is over, the BS starts the hole repair process by using a multi-agent SARSA algorithm which selects the optimal mobile node and replaces it to cover the hole. By doing so, the coverage of the network is enhanced and better QoSensing is achieved. The proposed approach is simulated in NS 3.26 and evaluated in terms of coverage rate, number of dead nodes, average energy consumption and throughput.

An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks

Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.

Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data

This paper introduces a new hybrid approach (DBH) for solving gene selection problem that incorporates the strengths of two existing metaheuristics: binary dragonfly algorithm (BDF) and binary black hole algorithm (BBHA). This hybridization aims to identify a limited and stable set of discriminative genes without sacrificing classification accuracy, whereas most current methods have encountered challenges in extracting disease-related information from a vast amount of redundant genes. The proposed approach first applies the minimum redundancy maximum relevancy (MRMR) filter method to reduce the dimensionality of feature space and then utilizes the suggested hybrid DBH algorithm to determine a smaller set of significant genes. The proposed approach was evaluated on eight benchmark gene expression datasets, and then, was compared against the latest state-of-art techniques to demonstrate algorithm efficiency. The comparative study shows that the proposed approach achieves a significant improvement as compared with existing methods in terms of classification accuracy and the number of selected genes. Moreover, the performance of the suggested method was examined on real RNA-Seq coronavirus-related gene expression data of asthmatic patients for selecting the most significant genes in order to improve the discriminative accuracy of angiotensin-converting enzyme 2 (ACE2). ACE2, as a coronavirus receptor, is a biomarker that helps to classify infected patients from uninfected in order to identify subgroups at risk for COVID-19. The result denotes that the suggested MRMR-DBH approach represents a very promising framework for finding a new combination of most discriminative genes with high classification accuracy.