A fuzzy three-level clustering method for lifetime improvement of wireless sensor networks

Ayati, Moosa; Ghayyoumi, Mohammad Hossein; Keshavarz-Mohammadiyan, Atiyeh

doi:10.1007/s12243-018-0631-x

Cited by 19 publications

(7 citation statements)

References 20 publications

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“…It utilizes sink distance, residual energy, and node density attributes as deciding parameters to evaluate ranks while selecting the appropriate CH. Ayati et al [16] proposes fuzzy logic system, within which the nodes parameters are used. Parameters used in primary level are centrality, residual-energy, as well as secondary level parameters like communication and distance from BS are considered.…”

Section: Related Workmentioning

confidence: 99%

Genetics Based Compact Fuzzy System for Visual Sensor Network

Rahman¹,

Jayakumar²,

Dahiya³

et al. 2023

Computer Systems Science and Engineering

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As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks (MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani's fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).

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Section: Related Workmentioning

confidence: 99%

Genetics Based Compact Fuzzy System for Visual Sensor Network

Rahman¹,

Jayakumar²,

Dahiya³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…While inter-cluster communications have made between CHs. Among the previous methods, low-energy adaptive cluster hierarchy (LEACH) is a popular clustering protocol in which CH is chosen by a threshold value [8]. It is defined by…”

Section: Cluster Communication Is Divided Into Two Classes: Intra-clumentioning

confidence: 99%

“…While inter‐cluster communications have made between CHs. Among the previous methods, low‐energy adaptive cluster hierarchy (LEACH) is a popular clustering protocol in which CH is chosen by a threshold value [8]. It is defined by

t ()(, N) = ({, \begin{matrix} 1em4pt \\ \frac{ρ}{1 - ρ ()(, R \mod ()(, 1 / ρ))} & if N \in G \\ 0 & otherwise \end{matrix})

where

ρ

is the desired rate of probability to act as a CH, R is the current round and G is the set of nodes which are formed in a group.…”

Section: Introductionmentioning

confidence: 99%

Adaptive PSO with optimised firefly algorithms for secure cluster‐based routing in wireless sensor networks

Pavani

Rao

2019

IET wirel. sens. syst.

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One of the important considerations in a sensor network is energy consumption. Cluster-based routing protocols are often proposed to reduce energy consumption. However, clusters are vulnerable due to malicious nodes. Malicious nodes inject fake messages to cluster-header, which increases communication overhead and energy consumption. Secure cluster-based routing is one of the most required solutions to consume more energy during data transmission. In this study, the authors propose a secure cluster-based routing protocol (SCBRP) that uses adaptive particle swarm optimisation (PSO) with optimised firefly algorithms during data transmission in a wireless sensor network. The objective of this study is to minimise energy consumption over an individual node to improve the whole network lifetime. The proposed SCBRP is based on the hexagonal sensor network architecture, and it is designed by three processes to include energy-efficient clustering, secure routing, and security verification. The performance of the proposed SCBRP is evaluated using NS-3, and it is estimated by different metrics such as encryption time, decryption time, energy consumption, packet drop rate, and network lifetime. The simulation results are compared with the previous approaches and finally, the authors' proposed SCBRP is proved that it obtained better performance than previous approaches.

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“…In [34],SCHFTL is proposed based on fuzzy logic system, in which the SN parameters are used at different levels. The first level parameter is remaining energy and centrality, the second level parameter used are communication quality and distance from BS and the third level are total energy and DOS attack, with the help of such parameters we select super cluster head out of the CH.SCHFTL avoids the data overload, data loss and data retransmission, increasing the network lifespan.…”

Section: Relevant Workmentioning

confidence: 99%

FEECA: Fuzzy based Energy Efficient Clustering Approach in Wireless Sensor Network

Dwivedi

Sharma

2018

ICST Transactions on Scalable Information Systems

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With huge advancements in Microelectronics, Wireless Sensor Network (WSN) is conquering its domain with low powered tiny sensors. These sensors are commonly used in monitoring and surveillance of remote and urban areas. Since the power supply to these tiny sensors is a battery, for the maximal efficiency of WSN, there arises a need for a maximal lifetime of these tiny sensors wherever they are deployed. Clustering is a promising solution but there is uncertainty in the selection of cluster heads. Fuzzy logic, however, can contribute to the selection of optimal candidates to play the role of such cluster heads. To muddle through the issue, we have proposed a fuzzy-based energy-efficient clustering approach (FEECA) in wireless sensor networks and also designed a fuzzy inference system that defines influential parameters for selecting optimal candidates for cluster head(CH) role. To reduce the expenditure borne by communication, master nodes are selected among the chosen cluster heads so that their communication distance to the sink can be reduced. In the proposed scenario the area is divided into the diagonal form to reduce the load of the network. Each part consists of a Sensor Node (SN), cluster head, and master node. In the diagonal form, the network is divided so that the distance travelled by the data packet through the diagonal path is always smaller than the distance in which the horizontal path exists, based on the triangular inequality theorem. Simulation experiments were conducted and experimental results unveiled better performance of proposed work in terms of stability period, more packet delivery to sink and extended lifetime.

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A fuzzy three-level clustering method for lifetime improvement of wireless sensor networks

Cited by 19 publications

References 20 publications

Genetics Based Compact Fuzzy System for Visual Sensor Network

Genetics Based Compact Fuzzy System for Visual Sensor Network

Adaptive PSO with optimised firefly algorithms for secure cluster‐based routing in wireless sensor networks

FEECA: Fuzzy based Energy Efficient Clustering Approach in Wireless Sensor Network

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