Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs

Rathore, Rajkumar Singh; Sangwan, Suman; Prakash, Shiv; Adhikari, Kabita; Kharel, Rupak; Cao, Yue

doi:10.1186/s13638-020-01721-5

Cited by 41 publications

(23 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rajkumar Singh Rathore et al, [18] have introduced the hybrid whale and grey wolf optimization (WGWO)-a remote sensor system-based energy-based ensemble system. In the introduced research, two metaheuristic calculations were used, in particular, the whale and the dark wolf to extend reliability of the grouping tool.…”

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

Karunkuzhali¹,

Meenakshi²,

Lingam

2021

Preprint

View full text Add to dashboard Cite

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and Internet of Things (IoT) is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, Adaptive Fuzzy C means clustering and Seagull Optimization Algorithm (AFCM-SOA) is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of Fuzzy C Means Clustering and Seagull Optimization Algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The Fuzzy C-Means Clustering is utilized to empower the energy efficient operation and load balancing. In the Fuzzy C-Means Clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as Atom Search Optimization (ASO) and Emperor Penguin Optimization (EPO) respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

Karunkuzhali¹,

Meenakshi²,

Lingam

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Reeta Bhardwaj et al [12] and Lipare et al presented [15] a Multi-objective algorithms. Xiaoqiang Zhao et al [27] and Lipare et al [15] proposed the Grey Wolf Optimization (GWO) and Rathore et al proposed a hybrid whale and grey wolf optimization (WGWO) [26]. Anand et al developed a genetic algorithm (GA)-based clustering and PSO based routing procedure [21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…It helped to increase the optimization capability of GWO and to guarantee the optimal choice of the cluster heads (CHs). Rathore et al proposed a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs) [26]. Khoshraftar, K et al proposed a genetic algorithm to increase the clustering process of the nodes present in a wireless sensor network and moreover to find an optimal route of transmission of data through these nodes [17].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

Samiayya

Ramalingam

2021

Preprint

View full text Add to dashboard Cite

In wireless sensor network (WSN), the gateways far away from the base station (BS) uses the gateways nearer to the BS to forward the data. It causes heavy traffic to the gateways in proximity with the BS. They need to manage this heavy traffic load but it leads to additional energy consumption and reduction in network lifetime. In order to overcome these issues, loads around the gateways need to be balanced. In this paper, multi objective based spider monkey optimization (MOSMO) has been presented to balance the load and to improve the network lifetime through energy efficient routing and clustering. The objective functions such as routing fitness and clustering fitness have been considered for optimal routing and clustering. The routing fitness function is found by incorporating both the minimum distance traversed by the gateways and minimum number of the gateway hops. The clustering fitness function is the minimum fitness function of gateways. The fitness function of each gateway is computed based on both the mean load of gateways as well as the distance between gateways and BS. The performance of the proposed MOSMO based routing and clustering scheme is compared with the existing Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) based routing and clustering scheme. The QoS features such as delay, energy consumption, delivery ratio, throughput and network lifetime with various node density are analyzed. The proposed work is simulated using MATLAB. The results show that, the reduction in delay and energy consumption is about 18% and 17% respectively whereas improvement in delivery ratio, throughput and network life time is about 15%, 24% and 19% respectively when compared to the existing PSO and GWO methods.

show abstract

“…Also, in WSNs, the bit error rate should be minimum, and on the other hand, signal-to-noise ratio should be maximum, and for achieving these conditions, transmission power should be increased. The increment in the transmission power has a severe impact on several key attributes of [143] Energy consumption, delay, delivery ratio, and throughput (i) A new clustering framework is presented utilizing a hybrid optimization approach consisting of whale and grey wolf optimizer.…”

mentioning

confidence: 99%

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Rathore

Sangwan

Kaiwartya

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) have emerged as a backbone technology for the wireless communication era. The demand for WSN is rapidly increasing due to their major role in various applications with a wider deployment and omnipresent nature. The WSN is rapidly integrated into a large number of applications such as industrial, security, monitoring, tracking, and applications in home automation. The widespread use in many different areas attracts research interest in WSNs. Therefore, researchers are taking initiatives in exploring innovation day by day particularly towards the Internet of Things (IoT). But, WSN is having lots of challenging issues that need to be addressed, and the inherent characteristics of WSN severely affect the performance. Energy constraints are one of the primary issues that require urgent attention from the research community. Optimal energy optimization strategies are needed to counter the issue of energy constraints. Although one of the most appropriate schemes for handling energy constraints issues is the appropriate energy harvesting technique, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy. In this high-level systematic and taxonomical survey, we have organized the energy optimization strategies for EH-WSNs into eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. We have also prepared the summarized view of various protocols/algorithms with their remarkable details. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH-WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era.

show abstract

Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs

Cited by 41 publications

References 45 publications

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Contact Info

Product

Resources

About