A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Manjeet Singh ◽  
Surender Kumar Soni
Keyword(s):  
Cluster Head ◽  
Sensor Nodes ◽  
Tabular Form ◽  
Clustering Methods ◽  
Content Type ◽  
Clustering Techniques ◽  
Variables Selection ◽  
Comprehensive Survey ◽  
Decision Making Processes ◽  
Art Research

Purpose This paper aims to discuss a comprehensive survey on fuzzy-based clustering techniques. The determination of an appropriate sensor node as a cluster head straightforwardly affects a network’s lifetime. Clustering often possesses some uncertainties in determining suitable sensor nodes as a cluster head. Owing to various variables, selection of a suitable node as a cluster head is a perplexing decision. Fuzzy logic is capable of handling uncertainties and improving decision-making processes even with insufficient information. Then, state-of-the-art research in the field of clustering techniques has been reviewed. Design/methodology/approach The literature is presented in a tabular form with merits and limitations of each technique. Furthermore, the various techniques are compared graphically and classified in a tabular form and the flowcharts of important algorithms are presented with pseudocodes. Findings This paper comprehends the importance and distinction of different fuzzy-based clustering methods which are further supportive in designing more efficient clustering protocols. Originality/value This paper fulfills the need of a review paper in the field of fuzzy-based clustering techniques because no other paper has reviewed all the fuzzy-based clustering techniques. Furthermore, none of them has presented literature in a tabular form or presented flowcharts with pseudocodes of important techniques.

scholarly journals Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 537
Author(s):  
Mohammad Baniata ◽  
Haftu Tasew Reda ◽  
Naveen Chilamkurti ◽  
Alsharif Abuadbba
Keyword(s):  
Energy Efficiency ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Sensor Nodes ◽  
Clustering Methods ◽  
Mimo Antenna ◽  
Improve Energy Efficiency ◽  
Art Research

One of the major concerns in wireless sensor networks (WSNs) is most of the sensor nodes are powered through limited lifetime of energy-constrained batteries, which majorly affects the performance, quality, and lifetime of the network. Therefore, diverse clustering methods are proposed to improve energy efficiency of the WSNs. In the meantime, fifth-generation (5G) communications require that several Internet of Things (IoT) applications need to adopt the use of multiple-input multiple-output (MIMO) antenna systems to provide an improved capacity over multi-path channel environment. In this paper, we study a clustering technique for MIMO-based IoT communication systems to achieve energy efficiency. In particular, a novel MIMO-based energy-efficient unequal hybrid clustering (MIMO-HC) protocol is proposed for applications on the IoT in the 5G environment and beyond. Experimental analysis is conducted to assess the effectiveness of the suggested MIMO-HC protocol and compared with existing state-of-the-art research. The proposed MIMO-HC scheme achieves less energy consumption and better network lifetime compared to existing techniques. Specifically, the proposed MIMO-HC improves the network lifetime by approximately 3× as long as the first node and the final node dies as compared with the existing protocol. Moreover, the energy that cluster heads consume on the proposed MIMO-HC is 40% less than that expended in the existing protocol.

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jasleen Kaur ◽  
Punam Rani ◽  
Brahm Prakash Dahiya
Keyword(s):  
Artificial Bee Colony ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Swarm Optimization ◽  
Content Type ◽  
Bee Colony ◽  
Glowworm Swarm Optimization ◽  
Optimal Cluster

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

scholarly journals REGION BASED CLUSTERING FOR DATA COLLECTION IN WSN

2017 ◽  
Vol 16 (5) ◽  
pp. 6913-6919
Author(s):  
Ramandeep Kaur ◽  
Dinesh Kumar
Keyword(s):  
Energy Efficient ◽  
Large Scale ◽  
Smart Cities ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Clustering Methods ◽  
Mobile Nodes ◽  
Improved Performance

The lower cost and easier installation of the WSNs than the wired counterpart pushes industry and academia to pay more attention to this promising technology. Large scale networks of small energy-constrained sensor nodes require techniques and protocols which are scalable, robust, and energy-efficient. The most efficient approach provided by clustering the nodes is hierarchy. The one node will send the data to another node and the another node will send to its neightbouring node. In smart cities, wireless sensor networks (WSNs) act as a type of core infrastructure that collects data from the city to implement smart services. Our thesis work included the region based clustering, cluster head selection and energy efficient communication using static base station and movable mobile nodes. Since it was earlier proposed that clustering improves the network lifetime. We modified the region based clustering by dividing the network area into n regions with cluster head chosen for each region and proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has improved performance to that of the other clustering approaches. We have used the mobile nodes for each section with controlled trajectory path as a reference to compare the performance of each of the clustering methods.

scholarly journals Numerous Clustering Techniques with Application and Limitations in Wireless Sensor Network

2019 ◽  
Vol 9 (1) ◽  
pp. 991-995
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Levels ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Processing Unit ◽  
Clustering Methods ◽  
Storage Unit ◽  
Clustering Techniques ◽  
Unit Energy

Wireless Sensor Network (WSN) is a very common area to work with different technology which is very useful for real time application. There are various application works with the help of WSN .some of internet of things, machine learning, big data etc. in WSN there are four basic component of WSN that is sensing unit, transceiver unit, processing unit, and storage unit. Energy is the important critical design issue in WSN. By applying clustering approach we can save the energy and attain load balancing of a network. Sensor nodes are viewed as homogeneous since the looks into in the field of WSNs have been advanced, however, a few nodes might be of various energy levels and it is draw out the lifetime of a WSN and its unwavering quality. Clustering methods are required so that sensor networks can work long time with different area. In this paper, we discussed the various clustering techniques those are helpful to maximize the lifespan of WSN.

Minimizing Energy Consumption in Wireless Sensor Networks by Partition

2016 ◽  
Vol 850 ◽  
pp. 23-29
Author(s):  
Wen Zhi Zhu ◽  
Feng Xu
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clustering Methods ◽  
Improve Energy Efficiency

In wireless sensor networks, clustering class routing protocol is an important protocol type. Different clustering methods, and cluster head selection method directly affects the energy consumption of the entire network communication. This paper studies the effect of different partition methods of the network energy consumption, and to study the partitioning methods under the conditions of uneven distribution of nodes. We believe that energy efficiency clustering method should adapt the distributed of sensor nodes in order to improve energy efficiency. And according to the partition method we propose a low-power adaptive clustering routing protocol based on node distribution to partition. The protocol can effectively extend the lifetime of a wireless sensor network. Simulation results show that the proposed protocol can effectively prolong the network lifetime.

Energy-Efficient Rotation Technique of Cluster-Head Method for Wireless Sensor Networks

2020 ◽  
pp. 229-242
Author(s):  
Aruna Pathak ◽  
Ram Shringar Raw ◽  
Pratibha Kamal
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Cluster Head ◽  
Early Death ◽  
Head Rotation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clustering Methods ◽  
Rotation Technique

Sensor nodes are supposed to function independently for a long timespan through a restricted source of energy in Wireless sensor networks (WSNs). For prolonging the network lifespan, sensor nodes need to be energy-efficient. To split the sensing region of WSNs into clusters is a noble methodology is to lengthen network lifespan. Clustering methods rotate the extra burden of head of cluster nodes among other nodes of the network through head rotation and re-clustering techniques. Overhead cost is greater in case of Re-clustering as compared to rotation method due to its global approach. Head rotation takes place when residual energy of cluster head falls below a fixed energy threshold. However, this fixed threshold does not consider the existing load of cluster head which become foremost cause for enhancing their early death. This chapter proposes an Energy-Efficient Rotation Technique of Cluster Head (EERTCH) for WSNs, which takes existing load of cluster head in consideration for their rotation.

scholarly journals ARCS: An Energy-Efficient Clustering Scheme for Sensor Network Monitoring Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Dongmin Choi ◽  
Sangman Moh ◽  
Ilyong Chung
Keyword(s):  
Data Transmission ◽  
Cluster Head ◽  
Transmission Efficiency ◽  
Sensor Nodes ◽  
Link Failure ◽  
Clustering Methods ◽  
Relay Nodes ◽  
Energy Hole ◽  
Clustering Protocol ◽  
Energy Efficient Clustering

To achieve efficiency in prolonging the lifetime of sensor networks many schemes have been proposed. Among these schemes, a clustering protocol is an efficient method that prolongs the lifetime of a network. However, in applying this method, some nodes consume energy unnecessarily because of an environment in which the collected data of the sensor nodes easily overlap. In this paper we propose a clustering method which reduces unnecessary data transmission among nodes by excluding the duplication of data. Our method alleviates the problem where nearby nodes collect the same data from adjacent areas by electing all nodes that form a cluster in consideration of the sensing coverage of the nodes. Also, it introduces relay nodes, also called repeaters, which help to hop the data transmission along to cluster head nodes in order to cope with energy-hole and link failure problems. This method prevents data loss caused by link failure problem and thus the data is collected reliably. According to the results of the performance analysis, our method reduces the energy consumption, increases the transmission efficiency, and prolongs network lifetime when compared to the existing clustering methods.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN

2016 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
Razulaimi Razali
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Energy Usage ◽  
Network Cluster ◽  
Fuzzy Logic Approach ◽  
The Stability

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy. 

Performance Analysis of RNC Clustering Protocol in Wireless Sensor Network

2019 ◽  
Vol 10 ◽  
Author(s):  
Piyush Rawat ◽  
Siddhartha Chauhan
Keyword(s):  
Energy Consumption ◽  
Network Performance ◽  
Energy Levels ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Threshold Function ◽  
Efficient Manner ◽  
Clustering Protocol

Background and Objective: The functionalities of wireless sensor networks (WSN) are growing in various areas, so to handle the energy consumption of network in an efficient manner is a challenging task. The sensor nodes in the WSN are equipped with limited battery power, so there is a need to utilize the sensor power in an efficient way. The clustering of nodes in the network is one of the ways to handle the limited energy of nodes to enhance the lifetime of the network for its longer working without failure. Methods: The proposed approach is based on forming a cluster of various sensor nodes and then selecting a sensor as cluster head (CH). The heterogeneous sensor nodes are used in the proposed approach in which sensors are provided with different energy levels. The selection of an efficient node as CH can help in enhancing the network lifetime. The threshold function and random function are used for selecting the cluster head among various sensors for selecting the efficient node as CH. Various performance parameters such as network lifespan, packets transferred to the base station (BS) and energy consumption are used to perform the comparison between the proposed technique and previous approaches. Results and Discussion: To validate the working of the proposed technique the simulation is performed in MATLAB simulator. The proposed approach has enhanced the lifetime of the network as compared to the existing approaches. The proposed algorithm is compared with various existing techniques to measure its performance and effectiveness. The sensor nodes are randomly deployed in a 100m*100m area. Conclusion: The simulation results showed that the proposed technique has enhanced the lifespan of the network by utilizing the node’s energy in an efficient manner and reduced the consumption of energy for better network performance.

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