Fuzzy Logic Based Clustering Algorithm for Wireless Sensor Networks

Alami, Hassan El; Najid, Abdellah

doi:10.4018/ijfsa.2017100105

Cited by 32 publications

(12 citation statements)

References 22 publications

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“…The Energy-efficient fuzzy logic cluster head (EEFL-CH) routing scheme was proposed by El Alami et al [46], to address the load balancing issue in WSNs and extend network lifetime with better results. El Alami et al [47], proposed the Fuzzy Logic based Clustering Algorithm (CAFL) for WSNs to maximize their lifetime with balanced energy consumption. The topology control protocol based on learning automation (LBLATC) was suggested by Javad et al [48], to prolong WSNs lifetime in an operational environment.…”

Section: Literature Reviewmentioning

confidence: 99%

An Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks to Maximize Their Lifespan in an Operational Environment

et al. 2020

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Owing to the limited resources of sensor nodes, we propose an efficient hybrid routing scheme using a dynamic cluster-based static routing protocol (DCBSRP), leveraging the ad hoc on-demand distance vector (AODV) routing protocol and low-energy adaptive clustering hierarchy (LEACH) protocol. In the proposed scheme, the cluster head (CH) nodes are formed dynamically for a fixed interval, whereas static routing is applied in the designated clusters by utilizing the AODV routing protocol. The static routing condition of the proposed scheme limits all connected nodes of the cluster for a defined interval of time (T) to share their collected information through a specific CH node. Once the time (T) interval is completed, all ordinary nodes connected with the specific CH are released and they are free to advertise their CH candidateship within the network. Likewise, the node receiving the maximum number of route replies (RREPs) is selected as the next CH node in the vicinity of deployed sensor nodes. However, with the DCBSRP protocol, the recently selected CH node does not advertise its candidateship for five consecutive cycles and acts as an ordinary node. The simulation result shows significant improvement in the lifetime and participation of ordinary nodes in the network until the end-stage of the network. In the proposed scheme, the participation of ordinary nodes in the network is 95.9 %, which not only balances load between participating nodes but also improves the network lifetime in the presence of field-proven schemes. Moreover, the simulation results show an out-performance of rival schemes in terms of communication cost, end to end delay, throughput, packet lost ratio, and energy consumption.

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Section: Literature Reviewmentioning

confidence: 99%

An Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks to Maximize Their Lifespan in an Operational Environment

et al. 2020

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“…The clustering algorithm is one of the important processes to enhance the WSN's lifetime. 26 The k-means clustering algorithm is used in this work for cluster formation. By using this algorithm, the data sets are divided into k clusters, and the estimation of k value is expressed as 27…”

Section: Formation Of Clustermentioning

confidence: 99%

A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

Sreedharan

Pete

2020

Int J Communication

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Summary One of the famous approaches to decision making is named as multicriteria decision making (MCDM). In order to solve the MCDM issues, a better way is provided by the fuzzy logic. Expendability, cost, maintenance, availability of software, and performance characteristics are such problems considered by the decision. The precise estimation of the pertinent data is one of the vital phases in DM systems. This paper presents a fuzzy MCDM‐based cluster head (CH) selection and hybrid routing protocol to solve the most common issues. In this research article, the generalized intuitionistic fuzzy soft set (GIFSS) approach is utilized to select the optimal CH, and hybrid shark smell optimization (SSO), and a genetic algorithm (GA) is introduced for the effective routing. Initially, the wireless sensor network (WSN) system and energy models are designed, and then the nodes are grouped into several clusters. Next, based on the GIFSS, the CH nodes are selected, and finally, an effective routing is placed based on the hybrid optimizations. The implementation is performed on the NS2 platform, and the performances are evaluated by packet delivery ratio (PDR), delay, packet loss ratio (PLR), network lifetime, bit error rate (BER), energy consumption, throughput, and jitter. The existing approaches named energy centers examining using particle swarm optimization (EC‐PSO), variable dimension‐based PSO (VD‐PSO), energy‐efficient PSO‐based CH selection (PSO‐ECHS), low‐energy adaptive clustering hierarchy‐sugeno fuzzy (LEACH‐SF), SSO, and GA are compared with the proposed strategy. According to the implemented outcomes, it displays the proposed strategy and gives improved outcomes than the others.

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“…El Alami and Najid 26 presented a method named enhanced clustering hierarchy (ECH) to attain EE in WSNs by the mechanism of sleeping waking for overlapping and neighboring nodes. Thus, the data redundancy is minimized, and then network lifetime is maximized.…”

Section: Related Workmentioning

confidence: 99%

Energy efficient hotspot problem mitigation techniques using multiple mobile sink in heterogeneous wireless sensor network

Gupta

Tripathi

Singh

2020

Int J Communication

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Summary Wireless sensor networks (WSNs) are application‐oriented and mainly used in the following fields, such as battleground surveillance, border security, and disaster management. The sensor nodes should be extra reliable, and also, high consistency is needed to perform these tasks. Therefore, heterogeneous wireless sensor networks (HWSNs) are introduced to enhance the network lifetime and stability period by minimizing the node's communicative distance from mobile sinks (MSs). The maximum amount of energy is consumed while communicating the data from nodes to a single sink. In this work, multiple mobile sinks (MMSs) are used to transfer the data from nodes to sink with less energy consumption and a reduced number of hops. Also, higher connectivity is ensured as the sinks are placed close to the nodes, and data can be successfully delivered to sinks. This work reveals the importance of multiple MSs in HWSNs. The use of MMSs and cluster‐based routing approaches improves the efficiency of data collection in HWSNs. K‐medoid with adaptive sunflower optimization algorithm is used for the selection of cluster head (CH). To achieve energy efficiency (EE), detecting an optimal route for MS is a critical task. Then, the hybrid Harris Hawk and ant colony optimization (HANT) algorithm‐based sink moving strategy is used for finding an optimal traversal path. The proposed heterogeneous network is implemented in the network simulator (version 2) (NS2) platform. Simulation results show enhancement in EE in terms of network lifetime, stability period, several dead and alive nodes, and network's remaining energy.

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Fuzzy Logic Based Clustering Algorithm for Wireless Sensor Networks

Cited by 32 publications

References 22 publications

An Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks to Maximize Their Lifespan in an Operational Environment

An Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks to Maximize Their Lifespan in an Operational Environment

A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

Energy efficient hotspot problem mitigation techniques using multiple mobile sink in heterogeneous wireless sensor network

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