Energy-efficient and multi-stage clustering algorithm in wireless sensor networks using cellular learning automata

Ahmadinia, Mohammad; Meybodi, Mohammad Reza; Esnaashari, Mehdi; Alinejad‐Rokny, Hamid

doi:10.4103/0377-2063.126958

Cited by 30 publications

(17 citation statements)

References 18 publications

(24 reference statements)

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“…Clustering of sequences is the process of bringing similar motifs together as a cluster. Different clustering approaches have been previously used to group biological and nonbiological data [ 17 , 18 , 19 , 20 ]. Clustering of sequences gives the user the ability to observe each group separately, identify similar patterns among samples in the same group, identify differences between different clusters, and find the most important motifs in each group.…”

Section: Methodsmentioning

confidence: 99%

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Rajaei

Jahanian

Beheshti

et al. 2021

Genes

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Bioinformatics and computational biology have significantly contributed to the generation of vast and important knowledge that can lead to great improvements and advancements in biology and its related fields. Over the past three decades, a wide range of tools and methods have been developed and proposed to enhance performance, diagnosis, and throughput while maintaining feasibility and convenience for users. Here, we propose a new user-friendly comprehensive tool called VIRMOTIF to analyze DNA sequences. VIRMOTIF brings different tools together as one package so that users can perform their analysis as a whole and in one place. VIRMOTIF is able to complete different tasks, including computing the number or probability of motifs appearing in DNA sequences, visualizing data using the matplotlib and heatmap libraries, and clustering data using four different methods, namely K-means, PCA, Mean Shift, and ClusterMap. VIRMOTIF is the only tool with the ability to analyze genomic motifs based on their frequency and representation (D-ratio) in a virus genome.

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Section: Methodsmentioning

confidence: 99%

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Rajaei

Jahanian

Beheshti

et al. 2021

Genes

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“…The increase in the distance between the transmitter and receiver results in the attenuation of transmitted power [15]. The threshold of free space can be represented by equation (1).…”

Section: Energy Consumption In Wsns Modelmentioning

confidence: 99%

“…Advances in wireless communications and the use of electronic devices have led to the development of low cost and minimum energy, multi-functional Wireless Sensor Networks (WSNs) that can operate in a complex environment and offer a wide variety of applications [1].…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network

Kaur

Aulakh

2018

ICST Transactions on Scalable Information Systems

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Clustering is known to conserve energy and enhance the network lifetime of Wireless Sensor Network (WSN). Although, the topic of energy efficiency has been well researched in conventional WSN, but it has not been extensively studied. In the research, Reinforcement Learning (RL) based energy-aware clustering algorithm is proposed by which the neighboring nodes in the cluster selects an appropriate Cluster Head (CH) by observing the environmental conditions like as energy consumption and coverage that is computed as distance from the CH to the Base Station (BS). An optimal cluster is selected by each neighboring node, which minimized the energy consumption and network lifetime. The problem of selecting an optimal CH is resolved using the RL approach. Using the RL approach, the CH having the highest reward point is selected for data communication. The results show that energy saving of 7.41%, 3.27%, 4.03%, and 2.79 % is achieved for 100, 200, 300, and 400 deployed nodes, respectively.

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“…In the clustering method, various parameters such as balancing the cluster size, clusters energy and ... taken into consideration and compared with other clustering methods, create clusters with more balanced and increases network lifetime [1].…”

Section: Previous Workmentioning

confidence: 99%

A New Method for Reducing Energy Consumption in Wireless Sensor Networks using Fuzzy Clustering and Fault Tolerance

Ashrafian¹,

Harounabadi²,

Sadeghzadeh³

2013

IJCATR

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Nowadays, wireless sensor networks, clustering protocol based on the neighboring nodes into separate clusters and fault tolerance for each cluster exists for sensors to send information to the base station, to gain the best performance in terms of increased longevity and maintain tolerance than with other routing methods. However, most clustering protocols proposed so far, only geographical proximity (neighboring) cluster formation is considered as a parameter. In this study, a new clustering protocol and fault tolerance based on the fuzzy algorithms are able to clustering nodes in sensor networks based on fuzzy logic and fault tolerance. This protocol uses clustering sensor nodes and fault tolerance exist in the network to reduce energy consumption, so that faulty sensors from neighboring nodes are used to cover the errors, work based on the most criteria overlay neighbor sensors with defective sensors, distance neighbor sensors from fault sensor and distance neighbor sensors from central station is done. Superior performance of the protocol can be seen in terms of increasing the network lifetime and maintain the best network tolerance in comparison with previous protocols such as LEACH in the simulation results.

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Energy-efficient and multi-stage clustering algorithm in wireless sensor networks using cellular learning automata

Cited by 30 publications

References 18 publications

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network

A New Method for Reducing Energy Consumption in Wireless Sensor Networks using Fuzzy Clustering and Fault Tolerance

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