2016
DOI: 10.1007/s40815-016-0181-1
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Enhanced Zone Stable Election Protocol based on Fuzzy Logic for Cluster Head Election in Wireless Sensor Networks

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Cited by 22 publications
(5 citation statements)
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“…Probabilistic heterogeneous-aware cluster head selection method proposed by Mary et al [25]; It has improved the network lifetime compared to the fuzzy logic-based ZSEP-E (Zone-based Stable Election Protocol-Enhanced) protocol for cluster head selection, taking into account the energy of the remaining node, its density, and its distance from the base station.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Probabilistic heterogeneous-aware cluster head selection method proposed by Mary et al [25]; It has improved the network lifetime compared to the fuzzy logic-based ZSEP-E (Zone-based Stable Election Protocol-Enhanced) protocol for cluster head selection, taking into account the energy of the remaining node, its density, and its distance from the base station.…”
Section: Literature Reviewmentioning
confidence: 99%
“…distance,density, remaining energy, vulnerability index, Centrality and distance between CH to select the cluster head by using two step fuzzy logic. In [ [15] fuzzy logic is used for cluster head selection with three input parameters Remaining Energy, Distance to BS and Density. In [ [16]] fuzzy logic is used for the selection of cluster head with three parameters 'Energy', 'Distance to Base Station' and 'Density' with the concept of area division.…”
Section: Related Workmentioning
confidence: 99%
“…Three linguistic variables were defined for each membership function as shown in Figure 1 The value for residual energy is divided evenly into low, medium, and high that has an interval of 0 to 0.5, 0.2 to 0.8, and 0.5 to 1, respectively. The linguistic variable for centrality is divided into 0 to 50 for near, 30 to 70 for satisfactory, and 50 to 100 for far [25]. Designed for RSSI, the linguistic variables are divided into -45 to 10 for good, -10 to 45 for acceptable, and 30 to 80 for bad [5].…”
Section: Fuzzy Logic Approachmentioning
confidence: 99%