Fuzzy Logic Based Clustering Algorithm for Wireless Sensor Networks

2020 ◽  
pp. 351-371 ◽  
Author(s):  
Hassan El Alami ◽  
Abdellah Najid
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Cluster Formation ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Modern Life ◽  
Routing Schemes ◽  
Network Operation ◽  
Simulation Results ◽  
Ch Selection

WSNs have many applications in modern life. Thus, optimization of the network operation is required to maximize its lifetime. The energy is a major issue in order to increase the lifetime of WSNs. The clustering algorithm is one of the proposed algorithms to enhance the lifetime of WSNs. The operation of the clustering algorithm is divided into cluster heads (CHs) selection and cluster formation. However, most of the previous works have focused on CHs selection, and have not considered the cluster formation process, which is the important issue in clustering algorithm based routing schemes, and it can drastically affect the lifetime of WSNs. In this paper, a Fuzzy Logic based Clustering Algorithm for WSN (CAFL) has been proposed to improve the lifetime of WSNs. This approach uses fuzzy logic for CHs selection and clusters formation processes by using residual energy and closeness to the sink as fuzzy inputs in terms of CH selection, and residual energy of CH and closeness to CHs as fuzzy inputs in terms of clusters formation. Simulation results justify its efficiency.

Fuzzy Logic Based Clustering Algorithm for Wireless Sensor Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 63-82 ◽  
Author(s):  
Hassan El Alami ◽  
Abdellah Najid
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Cluster Formation ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Formation Processes ◽  
Modern Life ◽  
Routing Schemes ◽  
Network Operation ◽  
Ch Selection

WSNs have many applications in modern life. Thus, optimization of the network operation is required to maximize its lifetime. The energy is a major issue in order to increase the lifetime of WSNs. The clustering algorithm is one of the proposed algorithms to enhance the lifetime of WSNs. The operation of the clustering algorithm is divided into cluster heads (CHs) selection and cluster formation. However, most of the previous works have focused on CHs selection, and have not considered the cluster formation process, which is the important issue in clustering algorithm based routing schemes, and it can drastically affect the lifetime of WSNs. In this paper, a Fuzzy Logic based Clustering Algorithm for WSN (CAFL) has been proposed to improve the lifetime of WSNs. This approach uses fuzzy logic for CHs selection and clusters formation processes by using residual energy and closeness to the sink as fuzzy inputs in terms of CH selection, and residual energy of CH and closeness to CHs as fuzzy inputs in terms of clusters formation. Simulation results justify its efficiency.

A Probability-Based Clustering Algorithm with CH Election for Expanding WSN Life Span

2020 ◽  
Vol 9 (1) ◽  
pp. 1-14
Author(s):  
Dimitris N. Kanellopoulos ◽  
Pratik Gite
Keyword(s):  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Signal Power ◽  
Aggregated Data ◽  
Simulation Results ◽  
Buffer Length ◽  
Energy Buffer

Clustering achieves energy efficiency and scalable performance in wireless sensor networks (WSNs). A cluster is formed by several sensors nodes, and one of them is elected as Cluster-head (CH). A CH collects information from the cluster members and sends aggregated data to the base station or another CH. This article proposes a new clustering algorithm (EMESISC) that is based on each node's probability of becoming a CH. This node's probability depends on its residual energy, buffer length, and received signal power. We compared EMESISC with LEACH algorithm. Simulation results showed that EMESISC is superior to LEACH.

Energy Efficient Cluster-based Routing using Hybrid Approach of Fuzzy and Ida-Star for Wireless Sensor Networks

2021 ◽  
Author(s):  
Mohaideen Pitchai K
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Formation ◽  
Hybrid Approach ◽  
Cluster Head ◽  
Residual Energy ◽  
The Other ◽  
Cluster Head Selection

Abstract Appropriate cluster head selection can significantly reduce energy consumption and enhance the lifetime of the WSN. The choice of cluster heads, which is a pivotal step in the cluster-based algorithm, can seriously influence the performance of the clustering algorithm. Under normal circumstances, whether a node can be a cluster head or not depends not only on its energy level but also on the other factors such as energy consumption, channel lost, neighbor density, etc. In this sense, the selection of the cluster head can be regarded as a multiple criteria decision-making issue. This paper presents an Energy efficient Cluster Head selection using Fuzzy Logic (ECHFL) protocol, which combines the approaches of the fuzzy and IDA-star algorithm. This protocol selects the appropriate cluster head by using fuzzy inference rules. It uses three parametric descriptors such as residual energy, expected residual energy, and node centrality for the cluster formation and cluster head selection processes. These parameters contribute mainly for avoiding over-dissipation of energy in the network by selecting the suitable cluster head for the network. This protocol shows how fuzzy logic can be used in the cluster formation process to distribute the tasks and energy consumption over all the nodes. As a summary, the proposed protocol gives good performance results in comparison with the other protocols.

SPECTRUM AWARE CLUSTERING ALGORITHM BASED ON FUZZY LOGIC FOR COGNITIVE RADIO SENSOR NETWORK

2020 ◽  
Vol 5 (1) ◽  
pp. 433
Author(s):  
Noorhayati Mohamed Noor ◽  
Norashidah Md Din ◽  
Shapina Abdullah ◽  
Nor Azimah Khalid ◽  
Zolidah Kasiran
Keyword(s):  
Fuzzy Logic ◽  
Cognitive Radio ◽  
Clustering Algorithm ◽  
Cluster Formation ◽  
Data Gathering ◽  
Residual Energy ◽  
Cognitive Radio Sensor Network ◽  
Distribution Parameters ◽  
The Stability ◽  
Channel Availability

In this paper, a clustering solution for periodic data gathering over WSNs using cognitive radio technology is proposed. The cluster heads (CHs) are selected according to the channel availability, residual energy, communication cost and node distribution parameters. Fuzzy logic and weight based techniques combines the four parameters for the CH selection. The cluster formation is based on the relative channel availability between the cluster member (CM) and CH to ensure stable cluster connectivity from link failure. To evaluate the proposed clustering algorithm, the performance of sensor networks is compared with CogLEACH, LEACH and CHEF routing protocols. The simulation results show that the proposed clustering algorithm effectively has a significant improvement with respect to the network stability without reducing the network instability and network lifetime. In addition, the proposed clustering solution also has a low and almost consistent CH energy consumption during the stability period indicating an efficient cluster formation.

scholarly journals FLECH: Fuzzy Logic Based Energy Efficient Clustering Hierarchy for Nonuniform Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Baranidharan Balakrishnan ◽  
Santhi Balachandran
Keyword(s):  
Fuzzy Logic ◽  
Data Collection ◽  
Data Transmission ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Redundant Data ◽  
Energy Efficient Clustering

Lifetime of Wireless Sensor Network (WSN) is an important issue which affects its implementation in various real time applications. The major factor behind the energy consumption in WSN is its data collection mechanism. The direct data transmission from each sensor node to the Base Station (BS) consumes more energy than other alternatives. Also it is unnecessary, due to redundant data transmission because of geographically closer nodes. Clustering is the best data collection architectural model for WSN since it takes care of in-network processing which handles redundant data within the network. The techniques used for the network having uniform node distribution are not suitable for the networks which have nonuniformly distributed nodes. This paper contributes a novel clustering algorithm: Fuzzy Logic Based Energy Efficient Clustering Hierarchy (FLECH) for nonuniform WSN. The clusters in FLECH are created using proper parameters which increases the lifetime of the WSN. Fuzzy logic in FLECH is wisely used to combine important parameters like residual energy, node centrality, and distance to BS for electing best suitable nodes as CH and increases the network lifetime. FLECH performance is verified in different scenarios and the results are compared with LEACH, CHEF, ECPF, EAUCF, and MOFCA. The simulation results clearly indicate the lifetime increase by FLECH over other algorithms and its energy conservation per round of data collection in the network.

scholarly journals Enhanced Clustering Algorithm based on Fuzzy Logic (E-CAFL) for WSN

2019 ◽  
Vol 20 (1) ◽  
pp. 41-54 ◽  
Author(s):  
Pawan Singh Mehra ◽  
Mohammad Najmud Doja ◽  
Bashir Alam
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Cluster Formation ◽  
Cluster Head ◽  
Residual Energy ◽  
Inference System ◽  
Premature Failure ◽  
System A ◽  
Balanced Clustering

Since longer lifetime of the network is utmost requirement of WSN, cluster formation can serve this purpose efficiently. In clustering, a node takes charge of the cluster to coordinate and receive information from the member nodes and transfer it to sink. With imbalance of energy dissipation by the sensor node, it may lead to premature failure of the network. Therefore, a robust balanced clustering algorithm can solve this issue in which a worthy candidate will play the cluster head role. In this paper, an enhanced clustering algorithm based on fuzzy logic E-CAFL is propound which is an improvement over CAFL protocol. E-CAFL takes account of the residual energy, node density in its locality and distance from sink and feed into fuzzy inference system. A rank of each node is computed for candidature of cluster coordinator. Experiments are performed for the designed protocol to validate its performance in adverse scenarios along with LEACH and CAFL protocol. The results illustrate better performance in stability period and protracted lifetime.

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.

scholarly journals Lifetime and Energy Efficiency Improvement Techniques for Hierarchical Networks

2019 ◽  
Vol 9 (1S6) ◽  
pp. 62-72
Keyword(s):  
Cost Effective ◽  
Residual Energy ◽  
Efficiency Improvement ◽  
Hierarchical Networks ◽  
Energy Efficiency Improvement ◽  
Battery Capacity ◽  
Time Scheduling ◽  
Network Operation ◽  
Simulation Results ◽  
Hole Detection

The detection points are the detection points in the space of network. The properties of detection points include cost effective materials and longer battery capacity. WSN can span variety of applications like sensing of data related to environment entities, detection of enemy vehicles. Lifetime ratio defines the efficiency of the WSN network operation. There are multiple techniques which can help in improvement of Network Lifetime (NL) spanning from transmission nature, data connections, formation of System and time scheduling. This paper provides the analysis of how energy consumption happens and its effect on lifetime ratio. LEACH and CHEF algorithms responsible for hierarchical kind of routing are discussed in detail with simulation results. The parameters used for comparison includes delay, hops, consumption of energy. Non-Hole detection points, Hole detection points, Non-Hole to Hole Ratio, residual energy, routing overhead and throughput.

Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks

2020 ◽  
pp. 33-46
Author(s):  
A. Sariga ◽  
◽  
◽  
J. Uthayakumar
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Hot Spot ◽  
Wireless Sensor ◽  
Unequal Clustering ◽  
Clustering Protocols ◽  
Hot Spot Problem

Wireless sensor network (WSN) is an integral part of IoT and Maximizing the network lifetime is a challenging task. Clustering is the most popular energy efficient technique which leads to increased lifetime stability and reduced energy consumption. Though clustering offers several advantages, it eventually raises the burden of CHs located in proximity to the Base Station (BS) in multi-hop data transmission which makes the CHs near BS die earlier than other CHs. This issue is termed as hot spot problem and unequal clustering protocols were introduced to handle it. Presently, some of the clustering protocols are developed using Type-2 Fuzzy Logic (T2FL) but none of them addresses hot spot problem. This paper presents a Type-2 Fuzzy Logic based Unequal Clustering Algorithm (T2FLUCA) for the elimination of hot spot problem and also for lifetime maximization of WSN. The proposed algorithm uses residual energy, distance to BS and node degree as input to T2FL to determine the probability of becoming CHs (PCH) and cluster size. For experimentation, T2FLUCA is tested on three different scenarios and the obtained results are compared with LEACH, TEEN, DEEC and EAUCF in terms of network lifetime, throughput and average energy consumption. The experimental results ensure that T2FLUCA outperforms state of art methods in a significant way.

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