Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function

Lipare, Amruta; Edla, Damodar Reddy; Kuppili, Venkatanareshbabu

doi:10.1016/j.asoc.2019.105706

Cited by 87 publications

(37 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heuristic methods [26][27][28][29][30] maintain load-balancing between all nodes. Here, every node utilises either the workload or the remained energy of the neighbour nodes to make routing decisions.…”

Section: Hole-bypassing Approachesmentioning

confidence: 99%

Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks

Nguyen

et al. 2021

Wireless Netw

View full text Add to dashboard Cite

A quest for geographic routing schemes of wireless sensor networks when sensor nodes are deployed in areas with obstacles has resulted in numerous ingenious proposals and techniques. However, there is a lack of solutions for complicated cases wherein the source or the sink nodes are located close to a specific hole, especially in cavern-like regions of large complex-shaped holes. In this paper, we propose a geographic routing scheme to deal with the existence of complicated-shape holes in an effective manner. Our proposed routing scheme achieves routes around holes with the (1+$$\epsilon$$ ϵ )-stretch. Experimental results show that our routing scheme yields the highest load balancing and the most extended network lifetime compared to other well-known routing algorithms as well.

show abstract

Section: Hole-bypassing Approachesmentioning

confidence: 99%

Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks

Nguyen

et al. 2021

Wireless Netw

View full text Add to dashboard Cite

show abstract

“…There are three stages in GWO-dependent routing: (1) initializing wolves, (2) calculation of fitness value and (3) updating speed as well as location of wolves. 21 The hunting process of wolves is clearly demonstrated in Figure 3.…”

Section: Gwo-based Routingmentioning

confidence: 99%

Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

Elhoseny

Regin

Hammoudeh

et al. 2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence–based approaches. This article presents a novel swarm intelligence–based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm–based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization–grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization–grey wolf optimization technique under all the test cases.

show abstract

“…There are various optimization techniques proposed by the researchers to address the issues in energy consumption and network lifetime [16]. 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].…”

Section: Literature Reviewmentioning

confidence: 99%

“…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%

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

Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function

Cited by 87 publications

References 15 publications

Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks

Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks

Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

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

Contact Info

Product

Resources

About