CHEATS: A cluster-head election algorithm for WSN using a Takagi-Sugeno fuzzy system

Pires, Adonias; Silva, Claudio; Cerqueira, Eduardo; Monteiro, Dionne Cavalcante; Viegas, Raimundo

doi:10.1109/latincom.2011.6107427

Cited by 19 publications

(10 citation statements)

References 12 publications

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“…Due to huge intra-cluster communication performance is deteriorated. A CHEATS approach is proposed [12], here remaining energy in the nodes and distance between nodes and the BS are used in electing CH. In [13] authors have proposed a protocol LEACH-ERE (LEACH Expected Residual Energy) uses remaining energy and expected remaining energy as fuzzy-parameters to select CH.…”

Section: Literaturementioning

confidence: 99%

Network Lifetime Enhancement in WSN using Fuzzy Based Clustering Algorithm

D. L.*,

Prasanna

2020

IJITEE

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These-days Wireless Sensor Networks (WSNs) has become integral part of many applications include tracking, monitoring and so on. Nodes are limited in battery, memory and processing capacity. Tracking and monitoring applications continue to work for longer hours; energy is the major constraint for network to transmit sensed data. State of the art specifies that by using clustering method energy-efficiency, scalability, and efficient-data-communication is achieved. Sensors deployed in the network be partitioned to clusters then one of the nodes is designated to become a Cluster Head (CH) that accumulate sensed information and sends to Sink/Base Station (BS). Normally CH is elected by considering nodes remaining energy and topological attributes related to the node in network. In this projected clustering method a centrality-metric “Cluster-Optimal-Degree-Centrality (CODC)”, is defined and also considered other parameters residual energy, distance between CHs, plus number of nodes belonging to a cluster guarantees better cluster configuration and CH selection. Fuzzy-Inference-System takes Expected-Residual-Energy (ERE) and CODC as inputs. Experiments are carried using ns-2; the proposed clustering method improves QoS, and efficiently prolongs network lifetime.

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Section: Literaturementioning

confidence: 99%

Network Lifetime Enhancement in WSN using Fuzzy Based Clustering Algorithm

D. L.*,

Prasanna

2020

IJITEE

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“…In this technique, authors have not given any method to find out data retransmission rate. In [11] authors have been presented a cluster head election algorithm for WSN using Takagi-Sugeno fuzzy system (CHEATS). In CHEATS algorithm Takagi-Sugeno fuzzy inference system is used to compute the probability to become cluster heads.…”

Section: Related Workmentioning

confidence: 99%

“…The network lifetime can extend by using other fuzzy inference systems rather than Mamdani model is shown in [11]. Some authors [12]- [15] take the advantage of fuzzy type-2 system in order to further reduce uncertainty in cluster head election.…”

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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“…Cluster heads used single hop to sink node. Cluster-head election algorithm using a Takagi-Sugeno (CHEATS) [1] was used zero order TSK fuzzy model, the fuzzy rule base consists of 9 rules (2 linguistic variables). Cluster heads delivered data to sink node by single hop.…”

Section: Introductionmentioning

confidence: 99%

Adaptive TAKAGI-SUGENO fuzzy model using weighted fuzzy expected value in wireless sensor network

Afifi

Hefny

2014

2014 14th International Conference on Hybrid Intelligent Systems

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limited energy resources of sensor nodes are the main constraint in wireless sensor networks, many researches applied multi input single output fuzzy models for cluster heads election. These models are less interpretable and built from expert's knowledge. The adaptive TS fuzzy model sensor node (ATSFMSN) protocol aims to adapt MIMO TAKAGI-SUGENO model for cluster heads and relay nodes election. Adaptive TAKAGI-SUGENO model is done by fuzzy cluster algorithms based on fuzzy expected value. In addition to, fuzzy rule base is reduced by similarity measure and fuzzy cluster algorithm. Similarity measure requires two parameters to estimate overlap degree. The simulation results show the ATSFMSN protocol is more energy efficient routing protocol against LEACH, CHEF, and FCM routing protocol.

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CHEATS: A cluster-head election algorithm for WSN using a Takagi-Sugeno fuzzy system

Cited by 19 publications

References 12 publications

Network Lifetime Enhancement in WSN using Fuzzy Based Clustering Algorithm

Network Lifetime Enhancement in WSN using Fuzzy Based Clustering Algorithm

Uniform Segregation of Densely Deployed Wireless Sensor Networks

Adaptive TAKAGI-SUGENO fuzzy model using weighted fuzzy expected value in wireless sensor network

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