Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization

Ensuring the expected quality of materials is still a challenge, mainly in order to precisely plan improvement actions that allow for stabilization of the production process. Therefore, the purpose of this research was to develop a novel procedure to identify critical causes of material incompatibility—the causes that have the largest negative impact on material deterioration, and the natural environment. The main originality of this procedure is developing a way to coherent analyse the mutual influence of the many causes of incompatibility of any material, after which the critical causes are identified and a ranking of improvement actions to eliminate these causes is created. A novelty is also developed in the algorithm supporting this procedure, which can be realized in three different ways to solve this problem, i.e.; by considering the impact of material incompatibility on: (i) the deterioration of the material quality; (ii) the deterioration of the natural environment; and (iii) simultaneously the deterioration of the quality of the material and the natural environment. The effectiveness of this procedure was confirmed after tests on 410 alloy, from which a mechanical seal was made. However, this procedure can be useful for any material or industrial product.

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“…The process of evaporation and pheromone leaving by ants is carried out for all edges, as shown in Equation (9) [ 66 , 69 ]:

where: m—number of ants in each iteration,

.…”

Section: Procedures To Identify Critical Causes Of Materials Incompat...mentioning

confidence: 99%

A Novelty Procedure to Identify Critical Causes of Materials Incompatibility

Siwiec

Pacana

2023

Materials

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“…In 2023, Zheng et al [27] have experimented with multi-objective particle swarm optimization and Fuzzy C. Initially, the process starts with the clustering of networks and determining the optimal number of each cluster head (CH) using the algorithm fuzzy c. Then from the CH two CH were selected using multi-objective particle swarm optimization (MOPSO). After that, Mobile Sink's ideal trajectory was determined after selecting the CH using Ant Colony Optimization Algorithm (ACO).…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling-based Atom Search Bat Optimization for Energy-Constrained and Delay Sensitive Wireless Sensor Networks

Deshpande¹

2022

JNACS

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Over the past decades, the Wireless Sensor Network (WSN) has reached greater heights. The main challenge in the WSN is the handling of the traffic load in the network without the reduction in the energy efficiency of the network. In this research, a scheduling algorithm is developed in the MAC protocol for energy constraint and delay sensitive WSN. The scheduling algorithm is developed using the proposed ASBAT-MAC protocol, which is developed by integrating the Atom Search Optimization (ASO) and Bat algorithm (BA). The scheduling is carried out based on the fitness function by considering the factors, like delay, energy, and fairness. The conception of the distributed energy fair scheduling MAC protocol and the residual energy is provided by the scheduling algorithm for the nodes to maintain a residual-energy queue in the node of the neighbour so that the network overhead is avoided and energy consumption is reduced. The simulation of the proposed ASBAT-MAC protocol is done based on the performance metrics, such as number of alive nodes, Fairness, Normalized energy and Throughput for nodes 50, 100 and 150 by varying the number of rounds. The proposed ASBAT-MAC protocol obtained a maximum number of alive nodes of 94, maximum fairness of 0.3520, maximum normalized energy of 0.1339 and maximum throughput of 0.5933, respectively for 150 nodes when compared with the existing protocols.

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“…Data is gathered pertaining to the location of nodes, battery capacity, and signal intensity. The data is utilised to generate a dataset that is employed for the purpose of training our machine learning model [4].…”

Section: Data Collectionmentioning

confidence: 99%

An Improved Method Of Cluster Head Selection Using Machine Learning In Wsn

Kohli¹

2021

MSEA

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Wireless Sensor Networks (WSNs) are comprised of numerous small, low-cost, and energy-limited sensor nodes that gather information from their immediate surroundings and relay it to a sink node. The utilisation of cluster-based routing protocols has been widely employed in Wireless Sensor Networks (WSNs) with the aim of enhancing network efficacy and extending network longevity. In the context of Wireless Sensor Networks (WSNs) that are cluster-based, the network is partitioned into clusters, with each cluster being assigned a Cluster Head (CH) responsible for data aggregation and forwarding to the sink node. The process of selecting Cluster Heads (CHs) is of utmost importance for optimising the performance of a network in terms of energy efficiency, network longevity, and communication overhead.

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Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization

Cited by 8 publications

References 40 publications

A Novelty Procedure to Identify Critical Causes of Materials Incompatibility

A Novelty Procedure to Identify Critical Causes of Materials Incompatibility

Scheduling-based Atom Search Bat Optimization for Energy-Constrained and Delay Sensitive Wireless Sensor Networks

An Improved Method Of Cluster Head Selection Using Machine Learning In Wsn

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