Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Pattnaik, Satyajit; Sahu, Pradip Kumar

doi:10.1002/dac.4354

Cited by 32 publications

(32 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…39 The performance is compared with some existing methods of the EPO and EPO Greedy. 25 The performance metrics are end to end delay, energy consumption, latency, network lifetime, packet delivery ratio (PDR), throughput, time complexity and energy computation which are computed using several nodes. In MATLAB, the performances are taken by using the plot function of line graph and bar function of bar graph.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

Pattnaik

Sahu

2021

Int J Communication

Self Cite

View full text Add to dashboard Cite

Summary The selection of good rendezvous points (RPs) is a significant role for M2obile Sink (MS) in the wireless sensor network (WSN). For the mobile sink, the selection of RP is one of the major problems in WSN. The rendezvous points are only selected based on the local information, so the possibility of selecting an optimal sensor node as RP will be extremely low. The next problem is to find the mobile sink path which visits all the RPs. The above problem is comprehended by utilizing an optimization algorithm. In this paper, Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization based clustering approach and hybrid Moth‐flame cuttlefish optimization (MFO‐CFO) algorithm for efficient routing in WSN is proposed. Initially, the number of clusters is formed due to the Fuzzy C‐means (FCM) based Ant lion optimization (ALO) clustering approach. An Adaptive Neuro‐Fuzzy Inference System (ANFIS) based Particle swarm optimization (PSO) technique is presented for best cluster head (CH) selection. Here, residual energy (RE), node degree, and histories are considered as an input parameter to find the optimal cluster head. At last, we use hybrid MFO‐CFO techniques to find the minimum RPs which decreases energy consumption. The performance metrics of end to end (E2E) delay, energy consumption, channel load, throughput, bit error rate (BER), Network Life Time (NLT), packet delivery ratio (PDR), latency, jitter, and packet loss are computed using several nodes. The performance is compared with some existing algorithms such as Genetic Approach (GA), Fuzzy, PSO (Particle Swarm Optimization), Multiple Access Data Gathering using Mobile Sink for Path Constrained Environment (MADGAMSPCE).

show abstract

Section: Resultsmentioning

confidence: 99%

“…In WSN, the assimilation of the fuzzy clustering method and EHO‐greedy algorithm was proposed by Pattnaik et al 25 for efficient routing process. To diminish the usage of energy, their work considers the different sink nodes of a fixed sink and movable sink.…”

Section: Literature Surveymentioning

confidence: 99%

Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

Pattnaik

Sahu

2021

Int J Communication

Self Cite

View full text Add to dashboard Cite

show abstract

“…( , ) = √( − ) 2 + ( − ) 2 (17) where ( , ) and ( , ) indicate the coordinates for sensor and BS, respectively.…”

Section: B Distance Between Nodes and Bsmentioning

confidence: 99%

“…Moreover, several of the applications such as temperature and environment monitoring transfers the redundant data packets to the BS. Therefore, clustering is a good option to increase the network lifetime to prevent redundant data transmission [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

show abstract

“…In Pattnaik and Sahu, 34 energy in the network has been balanced by the two types of sink nodes. Static and mobile sinks are involved for final data collection.…”

Section: Related Workmentioning

confidence: 99%

An optimized clustering using hybrid meta‐heuristic approach for wireless sensor networks

Rajaram¹,

Kumaratharan²

2020

Int J Communication

View full text Add to dashboard Cite

SummaryPower efficiency is one of the major attributes that has to be concentrated in wireless sensor network (WSN). Efficiency in the consumption of power is achieved by clustering, routing, and balancing the load in the network. The proposed work focuses on clustering to balance the load in the network, which in turn improves the power consumption by the sensor nodes. Clustering is one of the prominent techniques in WSN where research is still going on to improve efficiency. In the proposed work, the sensor nodes are collected together for the formation of multiple groups called as clusters. Cluster heads are selected using efficient satin bower bird optimization algorithm where the weight of the node is taken as a parameter. Among all these multiple cluster heads, the highly powered cluster heads are named as chief cluster head utilizing crow search optimization. In a multihop manner, the sensor nodes sense the collected data to the cluster heads, which in turn send the aggregated data to the chief cluster heads. All the selected chief cluster heads send the collected data to the central server node. Simulation is carried out in MATLAB R2020a and the performance of the proposed heuristic‐based clustering is compared with other clustering protocols in terms of energy efficiency, throughput, and delivery ratio, and it is verified that the proposed protocol gives better results.

show abstract

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Cited by 32 publications

References 30 publications

Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

An optimized clustering using hybrid meta‐heuristic approach for wireless sensor networks

Contact Info

Product

Resources

About