GM-WOA: a hybrid energy efficient cluster routing technique for SDN-enabled WSNs

Tyagi, Vikas; Singh, Samayveer

doi:10.1007/s11227-023-05263-7

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…In this model, the fuzzy system is also implemented to choose the cluster head. In another research [ 18 ] , the WOA algorithm was used to alleviate energy utilization and clustering in WSN, and in this model, genetic mutation operators were utilized to increase performance. Genetic operators can greatly help in increasing the performance of meta-engineering algorithms in solving the issue of clustering in WSNs.…”

Section: Related Workmentioning

confidence: 99%

Reducing Energy Consumption and Clustering in Wireless Sensor Networks using an Improved Discrete Gorilla Troops Optimization Algorithm with Fuzzy Rules

Rashid

2023

JZS

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Among the most common and extensively operated methods in wireless sensor networks (WSN) to increase the efficiency and performance of WSN is network clustering because, through this solution, data is transmitted through the closest possible path. In the clustering method, a portion of the nodes in the network become cluster heads, then a cluster is formed by joining the nodes close to the cluster head. However, the main and important problem in this issue is preventing the creation of unbalanced clusters since the spreading of cluster heads in the network can be unequal. In this paper, we have presented an algorithm based on the discrete Gorilla Troops Optimizer (DGTOA) algorithm and K-Means with a fuzzy clustering approach. In this model, first, several cluster heads are chosen by applying the discrete DGTOA algorithm, and then the output of the DGTOA algorithm is given as initial points and coordinates in K-means. Finally, the cluster head is utilized to wield the fuzzy system. Also, different criteria and graphs were used to compare the proposed model, and the obtained results were measured with other methods, and the obtained outcomes indicate the high performance of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Reducing Energy Consumption and Clustering in Wireless Sensor Networks using an Improved Discrete Gorilla Troops Optimization Algorithm with Fuzzy Rules

Rashid

2023

JZS

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“…To check the necessity of the proposed design for the particular application, this comparative analysis has been performed with recent existing models implemented in the NS-3 platform. Hence, the existing models that were considered in this present research work is Mobile Broadband SDN (MB-SDN) [26], Cellular SDN (C-SDN) [27], Dijkstra-SDN (D-SDN) [28], fybrrlink SDN (F-SDN) [29], SDN Routing (SDN-R) [30], Sway-SDN (S-SDN) [31], Clustered Routing SDN (CR-SDN) [32], Traditional hexagonal SDN (TH-SDN) [33], dynamic clustered routing SDN (DCR-SDN) [34], and dynamic traditional hexagonal SDN (DTH-SDN) [35].…”

Section: Comparison Analysismentioning

confidence: 99%

Design of an Optimal Software-Defined Network for Reducing the Greenhouse Gas Effect

2023

IJIES

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Currently, the high-load transmission system uses high energy resources, and due to the utilization of high energy, greenhouse gases are polluted. High load transmission caused more resource usage, causing traffic over the communication channel, and fewer packets were transmitted in more time. So to avoid the issue mentioned above, a novel buffalo-based gossip load balancing routing protocol (BGLBRP). Initially, this research created the desired number of nodes in the software defined network (SDN) environment. Avoid high-load data transmission; the system monitored the transmission with the help of the proposed model. For that, the system identifies the high-load data; it then shares the data into a free hub and performs the load-balancing operation. However, the proposed model needs a lower rate of energy resources for performing the packet transmission operation. The proposed model consumes lower energy about 0.051J, so it reduces greenhouse gas. Moreover, the performance rate of the proposed model was calculated, and a comparison was made through the existing model. In comparison, the proposed model attains a higher parameter rate than the existing models. Consequently, the parameters were measured in terms of node lifetime, communication delay, packet drop, packet delivery rate, and energy consumption. The presented model has recorded a high PDR of 97.5%. Compared to the past works, 10% of PDR has been improved. Subsequently, the communication latency was reduced by 25% than the compared models.

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Energy-Optimization Route and Cluster Head Selection Using M-PSO and GA in Wireless Sensor Networks

Prakash,

Singh,

Pandey

et al. 2024

Wireless Pers Commun

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GM-WOA: a hybrid energy efficient cluster routing technique for SDN-enabled WSNs

Cited by 6 publications

References 31 publications

Reducing Energy Consumption and Clustering in Wireless Sensor Networks using an Improved Discrete Gorilla Troops Optimization Algorithm with Fuzzy Rules

Reducing Energy Consumption and Clustering in Wireless Sensor Networks using an Improved Discrete Gorilla Troops Optimization Algorithm with Fuzzy Rules

Design of an Optimal Software-Defined Network for Reducing the Greenhouse Gas Effect

Energy-Optimization Route and Cluster Head Selection Using M-PSO and GA in Wireless Sensor Networks

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