GA-UCR: Genetic Algorithm Based Unequal Clustering and Routing Protocol for Wireless Sensor Networks

Gunjan,; Sharma, Ajay K.; Verma, Karan

doi:10.1007/s11277-022-09966-7

Cited by 25 publications

(14 citation statements)

References 32 publications

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“…In various fields, such as health monitoring, intelligent transportation system, military surveillance, unmanned aerial vehicular networks, proposed clustering protocol can provide better results; and its capabilities can be further extended for developing real-time packet routing, robust and fault-tolerant systems. 25,[32][33][34]…”

Section: Discussionmentioning

confidence: 99%

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A novel approach based on bio‐inspired efficient clustering algorithm for large‐scale heterogeneous wireless sensor networks

Lohar

Agrawal

Gupta

et al. 2023

Int J Communication

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In large-scale heterogeneous wireless sensor networks (WSNs), clustering is particularly significant for lowering sensor nodes (SNs) energy consumption and creating algorithm more energy efficient. The selection of cluster heads (CHs) is a crucial task in the clustering method. In this paper, optimised Kmeans clustering algorithm and optimised K-means based modified intelligent CH selection based on BFOA for large-scale network (lar-OK-MICHB) is hybridised for CH selection process. Here, we utilised the extended capabilities of OK-MICHB algorithm for large-scale network. Furthermore, in many applications where energy is a primary constraint, such as military surveillance and natural disaster prediction, the stability region is also a significant factor, with a longer network lifespan being a primary requirement. In the proposed approach, only the CH selection is made after every round in place of cluster and CH change as done in conventional hierarchical algorithm. The simulation results reveal that, while keeping the distributive structure of WSNs, suggested lar-OKMIDEEC can locate real greater leftover energy nodes for selection of CH without utilising randomise or estimated procedures. Furthermore, as compared with the multi-level MIDEEC protocol, this offers a larger stability region with 68.96% increment, more consistent selection of CH in every round, and greater packets (i.e., in numbers) received at the base station (BS) with a longer network lifetime with 327% increment.

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Section: Discussionmentioning

confidence: 99%

“…After that, authors have introduced the optimal path finding and data routing procedure from CHs to BS using ACO technique. In GAUCR, 25 a genetic algorithm‐based unequal clustering procedure is employed for clustering and routing the data to the base station, which also works in improving the network lifetime of WSNs.…”

Section: Related Workmentioning

confidence: 99%

A novel approach based on bio‐inspired efficient clustering algorithm for large‐scale heterogeneous wireless sensor networks

Lohar

Agrawal

Gupta

et al. 2023

Int J Communication

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“…The sink is also capable of directing commands or beacons to the sensor nodes. The data transfer can take place through long‐distance transmission in a single hop 18–22 . Long‐distance transmission is not suitable for communication from the sensor nodes to the sink due to energy inefficiency.…”

Section: Related Workmentioning

confidence: 99%

“…The data transfer can take place through long-distance transmission in a single hop. [18][19][20][21][22] Long-distance transmission is not suitable for communication from the sensor nodes to the sink due to energy inefficiency. The transmission distance needs to be reduced for longer network lifetimes and to conserve energy.…”

Section: Related Workmentioning

confidence: 99%

Energy‐efficient adaptive clustering (EEAC) with rendezvous nodes and mobile sink

Gupta,

Verma,

Gupta

et al. 2023

Int J Communication

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SummaryWireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively.

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“…Authors used Genetic Algorithm and developed GA-UCR, 47,48 a "genetic algorithm based unequal clustering and routing protocol for wireless sensor networks." For CH election, the genetic algorithm (GA) has been utilized with three fitness functions-remaining energy of CH nodes, the distance between CH and BS/sink, and inter-cluster separation.…”

Section: Related Workmentioning

confidence: 99%

FUCBR: Fuzzy‐based unequal clustering and bat‐based routing protocol for WSNs assisted IoT

Agrawal

Pandey

2023

Concurrency and Computation

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SummaryThe internet of things (IoT) has recently become extremely important in our lives. With the advancement in IoT, wireless sensor networks (WSNs) have gained much importance. In WSNs, the routing of clusters has gained more interest. However, there is still a problem with the hotspot. One way to address this issue is uneven clustering. In these, nodes closer to the base station have the smallest cluster size. It decreases the relay pressure of the nodes which are close to the base station. In this paper, the unequal clustering routing based on fuzzy logic and bat algorithm is proposed, that is, FUCBR. Relative intra‐cluster costs and relative inter‐cluster costs are used in a very innovative manner. Fuzzy logic is used to create unequal clusters while the bat algorithm is used for multi‐hop routing. Unequal clustering minimizes the energy utilization for the member of the clusters. A novel probability technique is implemented in the cluster‐forming process to allow cluster members to determine which cluster they wish to join. The bat algorithm's objective function is carefully chosen to optimize the cluster heads' energy usage. After simulation, it was discovered that the proposed algorithm utilizes less energy and extends the network lifespan. The simulation results show that FUCBR's performance is 30% more improved than fuzzy‐based unequal clustering algorithm, genetic algorithm based unequal clustering and routing protocol, and grey wolf optimizer‐C algorithms. It is suitable for networks that require a long life.

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GA-UCR: Genetic Algorithm Based Unequal Clustering and Routing Protocol for Wireless Sensor Networks

Cited by 25 publications

References 32 publications

A novel approach based on bio‐inspired efficient clustering algorithm for large‐scale heterogeneous wireless sensor networks

A novel approach based on bio‐inspired efficient clustering algorithm for large‐scale heterogeneous wireless sensor networks

Energy‐efficient adaptive clustering (EEAC) with rendezvous nodes and mobile sink

FUCBR: Fuzzy‐based unequal clustering and bat‐based routing protocol for WSNs assisted IoT

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