GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

Shahzad, Muhammad K.; Islam, S. M. Riazul; Hossain, Mahmud; Abdullah-Al-Wadud, M.; Alamri, Atif; Hussain, Mehdi

doi:10.3390/math9010043

Cited by 21 publications

(8 citation statements)

References 42 publications

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“…Various streamlined methods have been proposed in an attempt to discover the most energy-efficient variation of CH [30,31]. As a result, there is a need for a metaheuristic approach that can satisfy critical parameters in the optimization phase [32,33].…”

Section: Related Workmentioning

confidence: 99%

An Improved Bat Algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

Sahoo

Amgoth

2021

SN COMPUT. SCI.

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Clustering is one of the imperative solutions to the problem of energy unbalancing prevailing in the network. The major issues of wireless sensor network with unequal clustering and multi-hop communication are the energy stability and network longevity. In this paper, we consider the improved Bat optimization algorithm to improve the unequal clustering techniques by forming groups before selecting Cluster Heads. In terms of saving energy, knowing the positions of sink nodes in WSNs plays a vital role. Bat algorithm (BA), optimization of particle swarm, differential evolution, and whale optimization algorithm is now becoming efficient clustering methods as per the metaheuristic approach. Evaluate the life span of the entire network; this paper proposes an improved BA. The BA's primary goal of unequal clustering is to reduce energy consumption and extend the lives of the WSNs. An objective function has been formulated to reduce energy consumption and increase the network's lifespan to achieve these goals. Compared with three existing optimization methods, the experimental results showed that the proposed improved BA achieved better efficiency in reducing total power consumption: whale optimization for topology control, and improved particle swarm optimization algorithm.

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Section: Related Workmentioning

confidence: 99%

An Improved Bat Algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

Sahoo

Amgoth

2021

SN COMPUT. SCI.

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“…In sensor networks, information gathered from various sensors is collaboratively fused to enhance the overall system performance [1]. Sensor fusion techniques have found extensive applications in fields such as autonomous mobile systems [2], remote sensing [3], wireless sensor networks [4], and target tracking [5], benefiting from the rapid development of sensor technology. Spatial registration is a critical aspect of data fusion in multi-sensor systems.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration

Zeng,

Wang,

Wei

et al. 2024

Mathematics

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Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively.

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“…However, it is almost impossible to make the best decision using manual rule generation in traditional protocols based on expertise and practice, and this is especially unsuitable for different applications. Thus, several optimization algorithms, such as the genetic algorithm [ 22 ], shuffled frog algorithm [ 23 ], particle swarm optimization [ 13 ], and artificial bee colony [ 14 ], have been adopted to tune the rule base. After the CH is selected and the CMs have joined the clusters, the clusters are formed, followed by the data transmission process, which is usually completed by a routing algorithm or protocol [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

Wang

2023

Sensors

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Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors’ residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH’s number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime.

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GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

Cited by 21 publications

References 42 publications

An Improved Bat Algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

An Improved Bat Algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

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