Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols

Jubair, Ahmed Mahdi; Hassan, Rosilah; Aman, Azana Hafizah Mohd; Sallehudin, Hasimi; Al-Mekhlafi, Zeyad Ghaleb; Mohammed, Badiea Abdulkarem; Alsaffar, Mohammad

doi:10.3390/app112311448

Cited by 25 publications

(16 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimizing clustering processes in WSNs involves optimizing CH selection, data aggregation, cluster formation, and data communication. Besides, clustering protocols must be designed not only to optimize energy consumption, but also to ensure the QoS and to make sure a balance is achieved among numerous conflicting issues, such as service lifetime, coverage, and throughput ( Jubair et al, 2021 ).…”

Section: Background Informationmentioning

confidence: 99%

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy

Khedr

Salim

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) are becoming increasingly important, providing pervasive real-time applications that have been used to enhance smart environments in various fields such as smart cities, manufacturing, and the Internet of Things (IoT). This survey reviews and analyzes the research trends related to the utilized Artificial Intelligence (AI) methods for WSN and the potential enhancement of WSNs using these methods. We highlight the routing challenge in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods in addressing the routing challenge to meet specific objectives of WSN, during the span of 2010 to 2020. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to routing challenge in WSN. In addition, a general evaluation is provided along with a comparison of utilized AI methods in WSNs, which guides the reader in identifying the most appropriate AI methods that can be utilized for solving the routing challenge. Finally, we conclude the paper by stating the open research issues and new directions for future research.

show abstract

Section: Background Informationmentioning

confidence: 99%

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy

Khedr

Salim

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…In the field of WSN, the energy consumption of nodes should be restricted. Therefore, [20][21][22] presented an energy-efficient deployment method for sensor nodes by clustering the nodes and applying the optimization technique for optimal reduction of the nodes' battery. The optimization for nodes led to the optimization routing protocol.…”

Section: The Importance Of Optimizationmentioning

confidence: 99%

Quaternary-Child Crossover for Genetic Algorithm in Real-Time Scheduling Optimization

2023

IJIES

View full text Add to dashboard Cite

The optimization method with the genetic algorithm (GA) is well-known in different science fields. The crossover is the primary important operand of the genetic algorithm, so many researchers worked with this operand to get the optimal solutions. This article presents a new crossover operand for the genetic algorithm by supposing a new organization for genes inside the chromosome. The new gene supposed that contains two alleles, and each allele refers to an attitude; thus, each gene has two attitudes. One attitude of the gene supposes to be preferred over another. Therefore, two crossovers were presented, the first crossover applying to intense the preferred one on another as enhancing method of genes to a single individual's chromosome before mating. After the first crossover, the mating process will come by the second crossover, which mating the parents or individuals. The second crossovers' output is four children; eliminate the two lowest fitness, and select the others as new offspring, and they will be part of the next generation. This process is repeated for each population until it reaches the last generation. The new crossover is called the quaternary-child crossover. The proposed is supposed to be applicable for real-time scheduling and is not limited to that. Empirical experiments have tested the proposed work as an optimization method with different mathematical and scheduling equations that had been used in optimization by researchers, and the performance was satisfactory.

show abstract

“…Jubair et al [9] worked on optimization techniques for clustering in WSN to connect to the internet through a base station as shown in Figure 4. The authors made a useful survey and they studied all research and have resulted that hybrid optimization techniques are optimal solutions for all issues in WSNs.…”

Section: Clustering Techniquementioning

confidence: 99%

“…Several surveys for WSN have been published. The authors presented a review of the available optimization techniques in WSN, they focused on existing clustering protocols in WSNs to solve all existing problems and make WSN as optimal as possible in general [9]. They analyzed the clustering's characteristics and parameters and looked at the goals, advantages, and significant characteristics of several clustering optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Ahmad¹,

Al-Nayar²,

Mahmood³

2022

MMEP

View full text Add to dashboard Cite

In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.

show abstract

Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols

Cited by 25 publications

References 100 publications

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Quaternary-Child Crossover for Genetic Algorithm in Real-Time Scheduling Optimization

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Contact Info

Product

Resources

About