Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication

Lee, Jin‐Shyan; Cheng, Wei

doi:10.1109/jsen.2012.2204737

Cited by 374 publications

(206 citation statements)

References 12 publications

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“…These approaches introduced efficient routing, since WSNs need simple and fast methods to make decisions, fuzzy logic appears as an appropriate method due to its ability to calculate results fast and precisely in a user-friendly way. In order to improve the efficiency and accuracy of the route creation process and to speed it up, evaluation of node conditions through fuzzy logic is required 15 . The execution of a fuzzy logic system requires less computational power than conventional mathematical computational methods.…”

Section: Fuzzy Unequal Clustering Algorithmsmentioning

confidence: 99%

A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks

Guirguis¹,

Abdou²,

Elnaggar³

2017

IJCIS

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Clustering is carried out to explore and solve power dissipation problem in wireless sensor network (WSN). Hierarchical network architecture, based on clustering, can reduce energy consumption, balance traffic load, improve scalability, and prolong network lifetime. However, clustering faces two main challenges: hotspot problem and searching for effective techniques to perform clustering. This paper introduces a fuzzy unequal clustering technique for heterogeneous dense WSNs to determine both final cluster heads and their radii. Proposed fuzzy system blends three effective parameters together which are: the distance to the base station, the density of the cluster, and the deviation of the node's residual energy from the average network energy. Our objectives are achieving gain for network lifetime, energy distribution, and energy consumption. To evaluate the proposed algorithm, WSN clustering based routing algorithms are analyzed, simulated, and compared with obtained results. These protocols are LEACH, SEP, HEED, EEUC, and MOFCA.

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Section: Fuzzy Unequal Clustering Algorithmsmentioning

confidence: 99%

A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks

Guirguis¹,

Abdou²,

Elnaggar³

2017

IJCIS

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“…In [16] authors investigated and highlighted the limitations of LEACH [1], LEACH-C [10] and CHEF [13] techniques and further presented fuzzy logic based clustering technique LEACH-ERE. The output chance value is evaluated using two fuzzy input variables: expected residual energy (ERE) and residual energy of sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

“…It is seen that the approaches used type-1 fuzzy system perform better as compared the approaches used the type-2 fuzzy system. The neural network applications in WSN are addressed in [16]. The cluster head failure issue has been addressed in [17], [18] and introduced the backup cluster heads (BCHs) concept based upon fuzzy logic output value.…”

Section: Introductionmentioning

confidence: 99%

Uniform Segregation of Densely Deployed Wireless Sensor Networks

Singh¹,

Soni²

2017

ijacsa

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Abstract-In wireless sensor networks, the selection of cluster heads relies upon the various selection parameters, such as energy, distance, node concentration and rate of retransmission. There is always uncertainty in the suitability of sensor node for the cluster head role due to these various selection parameters. Fuzzy logic is capable of overcoming uncertainties even with incomplete available information. This quality of fuzzy logic can reduce uncertainty in cluster head selection up to large extent. Therefore, in this paper, a fuzzy logic based clustering approach is proposed to enhance the network operational lifetime. The cluster formation is done on the basis of the spatial correlation value between sensors to organize clusters uniformly in the network. The results are compared with well-known approaches CHEF and LEACH.

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“…However, when number of computing nodes is increased from 7 to 10, average time-consumption of DGFMF-WSND will increase for all test datasets. This is mainly because that with the increasing of number of computing nodes, time of data transmission and global function generation will continue to increase so that total time-consumption of DGFMF-WSND will increase according to Equation (10). The decrease of time-consumption and the improvement of prediction accuracy of DGFMF-WSND will be helpful to find domain knowledge from massive and distributed wireless sensor network data.…”

Section: Be the I-th Model-based Maximum Fitness Value Then By Defimentioning

confidence: 99%

Distributed Global Function Model Finding for Wireless Sensor Network Data

Deng

Yang

et al. 2016

Applied Sciences

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Abstract:Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.

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Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication

Cited by 374 publications

References 12 publications

A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks

A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks

Uniform Segregation of Densely Deployed Wireless Sensor Networks

Distributed Global Function Model Finding for Wireless Sensor Network Data

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