Improved Performance of Wireless Sensor Network Based on Fuzzy Logic for Clustering Scheme

Nguyen, Trong-The; Pan, Jeng‐Shyang; Chu, Shu-Chuan; Dao, Thi-Kien; Do, V.L.

doi:10.1007/978-3-030-04585-2_13

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…Nguyen et al [35] proposed a novel clustering scheme for WSN based on fuzzy logic to improve the lifetime of the network. In this work, the authors used parameters such as residual energy, distance from the sink, and the density of the nodes in a locality as the inputs to the fuzzy system.…”

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

“…Figure 4 depicts a diagram summarizing the different parameters discussed in this paper used in cluster formation utilizing fuzzy logic. Furthermore, in most of the fuzzy-based clustering algorithms discussed in the literature, the metrics such as first node dies (FND), half of the nodes dies (HND), and last node dies (LND) were used to evaluate the network lifetime [33,35,37]. The authors validated the performance of the proposed techniques under different network densities and for different locations of base station.…”

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

“…The projected data demonstrate the variations in the performance on the basis of the fuzzy descriptors utilized in the fuzzy-based clustering algorithms. Fuzzy logic for clustering scheme [35] densities, distances, residual 675 Fuzzy logic-based clustering algorithm for WSN [36] battery power, mobility, centrality 300 Self-management model [37] residual energy, radio energy dissipation, centrality 500…”

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

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On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters’ formation, packets’ routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes’ distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes’ mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay.

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Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

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On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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“…The original MAP can only operate up to 9824 iterations, but the implementation of multi-hop technique can reach up to 14284 iterations (with the advantage of 4459 cycles) with 30% increment from the previous implementation. This paper can also be enhanced by implementing other multi-hop routing protocol with different parameters and metric units which can produce a more accurate result [25]. Plus, the coordination of the sensor nodes could be improved by using other approach such as K-Means and the energy consumption will be conserved during data transmission either in single-hop or multi-hop.…”

Section: Discussionmentioning

confidence: 99%

Performance Analysis of Multi-hop Technique on Two-tier Network Clustering in Wireless Sensor Network

Din¹

2020

IJATCSE

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Wireless Sensor Network (WSN) is a distributed connection that consists of many wireless sensor devices. It is used to get information from surrounding activities or environment and to send the details to the user for future computation. Due to its advantages, the WSN has been widely used to help people to collect, monitor and analyse the data. However, the biggest limitation of WSN is limited energy consumption which can affect the network performance. In order to reduce the energy usage, the process of selecting a path to the destination is very important. The current implementation uses the single-hop technique to send and receive data from source to destination and vice versa. The investigation shows that, the sensor nodes lifetime can be increased if some changes are made to the data transmission method. Therefore, the goal of this research is to increase energy efficiency using multi-hop technique as the primary transmission to make the network able to operate in a long period. An enhancement based on the Multi-tier Algorithm Protocol (MAP) has been produced. The enhancement technique is implemented to avoid direct communication between sensor nodes and cluster head, which is believed to be the main reason that caused inefficient use of energy (wastage). The proposed approach will give some advantages to data transmission, especially to the nodes that are located far from the cluster head. Subsequently, the experiment has been conducted to investigate the current implementation and this information has been used to make some improvements, before the performance of both techniques will be evaluated. The MATLAB is used to simulate and generate a graph. The results show that the enhancement algorithms will save energy up to 30% and is able to prolong the WSN lifetime with an advantage of 4462 cycles (to become 14283) compared to the original MAP which recorded only 9824 cycles.

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“…A fuzzy-logic based method for clustering to improve the performance of WSN is given in [16] Fuzzy-variables considered are nodes remaining energy, local node density, and measured distance from the sink. CH selection decisions are made by calculating the rank of all nodes.…”

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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Improved Performance of Wireless Sensor Network Based on Fuzzy Logic for Clustering Scheme

Cited by 8 publications

References 3 publications

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

Performance Analysis of Multi-hop Technique on Two-tier Network Clustering in Wireless Sensor Network

Network Lifetime Enhancement in WSN using Fuzzy Based Clustering Algorithm

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