Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Ying, Zhang; Wang, Jim; Han, Dong‐Guk; Wu, Huafeng; Zhou, Rundong

doi:10.3390/s17071554

Cited by 92 publications

(52 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Theorem 1 WABL value of a trapezoidal fuzzy number A(l, m l , m r , r) with degree-importance function (8) can be calculated as below ( Figure 5): In their study, [16] proved the following theorem in case of the equal weighted levels ( k = 0 ):…”

Section: Wabl Defuzzification Methods For Complex Fuzzy Numbersmentioning

confidence: 99%

A new model to determine the hierarchical structure of the wireless sensornetworks

Nasiboglu¹,

Erten²

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Wireless sensor networks are one of the rising areas of scientific research. Common purpose of these investigations is usually constructing optimal structure of the network by prolonging its lifetime. In this study, a new model has been proposed to construct a hierarchical structure of wireless sensor networks. Methods used in the model to determine clusters and appropriate cluster heads are k-means clustering and fuzzy inference system (FIS), respectively.The weighted averaging based on levels (WABL) defuzzification method is used to calculate crisp outputs of the FIS. A new theorem for calculation of WABL values has been proved in order to simplify getting the crisp values from complex fuzzy outputs of the FIS. The proposed methodology is experimented via simulation example, and experiments confirm its validity.

show abstract

Section: Wabl Defuzzification Methods For Complex Fuzzy Numbersmentioning

confidence: 99%

A new model to determine the hierarchical structure of the wireless sensornetworks

Nasiboglu¹,

Erten²

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Otherwise, developed systems might be used in replace of K-means algorithm, and then the learning task is performed by centralized and resource capable computational units. [34,35,36,37], little of them have considered the use of the data science clustering techniques in a direct way. Instead, those data clustering techniques are used for the purpose of finding the similarities or correlations in data between neighboring nodes, and partition sensor nodes into clusters accordingly.…”

Section: K-means Clustering Based Query Processing Algorithmmentioning

confidence: 99%

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

Wan

Zhao

Wang

et al. 2019

Future Generation Computer Systems

169

100

View full text Add to dashboard Cite

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multidimensional data indexing approaches. In this paper, we present an energy-and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide

show abstract

“…The routing algorithms involved in the first and second step of the DV-Hop algorithm in this paper are based on the Flooding, including the original DV-Hop algorithm. To reduce network communication overhead, reduce network energy consumption as well as to extend the network life cycle, the EEDCF routing algorithm [42] is considered and applied.…”

Section: Process Of Floodingmentioning

confidence: 99%

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Han

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

show abstract

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Cited by 92 publications

References 32 publications

A new model to determine the hierarchical structure of the wireless sensornetworks

A new model to determine the hierarchical structure of the wireless sensornetworks

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Contact Info

Product

Resources

About