An Alternative Clustering Scheme in WSN

Izadi, Davood; Abawajy, Jemal; Ghanavati, Sara

doi:10.1109/jsen.2015.2411598

Cited by 70 publications

(52 citation statements)

References 27 publications

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“…In GCHE-FL [5], the fuzzy logical system is used to select CHs and clustering. Izadi et al [14] used a type-2 fuzzy logical system to determine the probability of CH selection. Roslin [15] used the genetic algorithm directly in CH selection without considering the computing power of the node.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Balanced Routing Protocol for a Wireless Sensor Network

Lin

2018

Journal of Sensors

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The wireless sensor network is an intelligent self-organizing network which consists of many sensor nodes deployed in the monitoring area. The greatest challenge of designing a wireless sensor network is to balance the energy consumption and prolong the lifetime of the network, seeing that the nodes can be powered only by batteries in most conditions. An energybalanced routing protocol (EBRP) for wireless sensor networks is proposed in this paper. In EBRP, we divide the network into several clusters by using K-means++ algorithm and select the cluster head by using the fuzzy logical system (FLS). Since the previous researches did not demonstrate how to get the fuzzy rules for different networks, we propose a genetic algorithm (GA) to obtain the fuzzy rules. We code the rules as a chromosome, and the lifetime of the network is treated as a fit function. Then, through the selection, crossover, and mutation of each generation, the best offspring can be decoded as the best rule for each network model. Through the simulation, comparing with the existing routing protocols such as low-energy adaptive clustering hierarchy (LEACH), low-energy adaptive clustering hierarchy-centralized (LEACH-C), and stable election protocol (SEP), the EBRP prolongs the network lifetime (first node dies) by 57%, 63%, and 63%, respectively.

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Section: Related Workmentioning

confidence: 99%

An Energy-Balanced Routing Protocol for a Wireless Sensor Network

Lin

2018

Journal of Sensors

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“…A new distributed type-2 fuzzy based selfconfigurable clustering (SCCH) mechanism is proposed [15]. In the case of failure of elected CH, another backup cluster head (BCH) takes the role of new CH in order to monitor the area.…”

Section: Related Workmentioning

confidence: 99%

Biogeography-Based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application

Senniappan¹,

Subramanian

2018

AIS

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Abstract. Civil buildings are prone to various kinds of damages. The detection of damages caused in a building at an early stage is essential in order to save the invaluable human life and significant belongings. Wireless sensor networks (WSN) help to detect damages caused to a building by sensing different factors, which affect civil structures. Energy efficiency of sensor nodes and network congestion are quite common issues in wireless sensor networks that affect the network performance. In this research work, the formation of energy efficient clusters mitigates congestion by considering the buffer occupancy level and fairness index of flows to improve the network lifetime. The proposed method uses Biogeography-Based Krill Herd (BBKH) algorithm for cluster head selection. BBKH based congestion mitigation outperforms other classical evolutionary optimizations and swarm intelligence algorithms like Genetic Algorithm, Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS). Compared with PSO, the network throughput has increased by 26.18% using BBKH. The network lifetime has increased by 42.11% using the proposed BBKH, compared to PSO. The extended lifetime of the network helps damage detection in civil structures for extensive periods.

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“…When cluster head loses its energy below a threshold level, it declares backup cluster head as a new cluster head and if backup cluster head also fails, then sensor nodes select other nodes as cluster head with highest chance value. In [18] again authors have presented backup www.ijacsa.thesai.org cluster head concept with an improved algorithm using the type-2 fuzzy logic system. In [19] authors deployed sensors with different battery power and improved network lifetime by exploiting heterogeneity.…”

Section: Related Workmentioning

confidence: 99%

“…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. The heterogeneity in the network is exploited using fuzzy logic in [19].…”

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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An Alternative Clustering Scheme in WSN

Cited by 70 publications

References 27 publications

An Energy-Balanced Routing Protocol for a Wireless Sensor Network

An Energy-Balanced Routing Protocol for a Wireless Sensor Network

Biogeography-Based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application

Uniform Segregation of Densely Deployed Wireless Sensor Networks

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