Bacteria Foraging Algorithm based clustering in Wireless Sensor Networks

Pitchaimanickam, B.; Radhakrishnan, S.

doi:10.1109/icoac.2013.6921949

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Pitchaimanickam and Radhakrishnan have have applied BFO technique on clusterhead selection process using simulation-based approach where the study outcome shows enhanced protocol operation. Usage of BFO was also reported in the work of Zhao et al [30] in the area of robotics using nanorobots. Therefore, this background information eventually proved that BFO was investigated for multiple case studies of optimization problems.…”

Section: Introductionmentioning

confidence: 62%

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Hemavathi¹,

Nandakumar²

2018

IJECE

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Optimization techniques based on Swarm-intelligence has been reported to have significant benefits towards addressing communication issues in Wireless Sensor Network (WSN). We reviewed the most dominant swarm intelligence technique called as Bacteria Foraging Optimization (BFO) to find that there are very less significant model towards addressing the problems in WSN. Therefore, the proposed paper introduced a novel BFO algorithm which maintains a very good balance between the computational and communication demands of a sensor node unlike the conventional BFO algorithms. The significant contribution of the proposed study is to minimize the iterative steps and inclusion of minimization of both receiving / transmittance power in entire data aggregation process. The study outcome when compared with standard energy-efficient algorithm was found to offer superior network lifetime in terms of higher residual energy as well as data transmission performance.

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Section: Introductionmentioning

confidence: 62%

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Hemavathi¹,

Nandakumar²

2018

IJECE

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“…A development over LEACH, the low energy intelligent clustering protocol (LEICP), 26 uses BFOA to locate CHs. In comparison to LEACH‐C, BFA‐LEACH‐C, 27 a CH selection technique based on BFOA, produced better outcomes. However, BFA‐LEACH‐C uses BFOA, which lengthens the implementation process and adds complexity to the execution.…”

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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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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“…Bacteria Foraging Algorithm (BFA) is used for various applications in optimization such as the linear array of antennas (Datta et al, 2008), damping controller of the power system (Abd-Elazim et al, 2012), power flow controller (Tripathy et al, 2007), planning of complex radio frequency identification problem (Chen et al, 2010), and coefficients of proportional plus integral controllers for the active filter (Mishra, 2006). (Pitchaimanickam et al, 2013) have presented the bacteria foraging algorithm for selecting the optimal clustering in Wireless Sensor Networks. This method is mainly used to extend the network lifetime and minimize energy consumption.…”

Section: Problem Definition and Scopementioning

confidence: 99%

Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Bose

2022

International Journal of Cognitive Informatics and Natural Intelligence

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Network lifetime and energy constraint are the main issues for the applications of Wireless Sensor Network. Sensor nodes spend more energy in the communication process and affect the network lifetime. Clustering is the technique for choosing the optimal cluster head from the clusters. LEACH-C is the clustering protocol in WSN. BFA is applied in the LEACH-C protocol to form the optimal clusters. This optimization obtains more number of steps in the tumbling process and reaches the global optimum solution very slowly. This method directly affects the network lifetime. The above limitations are overcome by introducing the hybrid approach of Bacteria foraging algorithm by integrating the PSO and DE is applied in LEACH-C algorithm for finding the optimal cluster head. The best foraging solution utilized in the chemotactic behavior of the bacterium by using PSO and DE algorithm. The proposed methodology increases by 66% and 77% of the alive nodes when compared to FA and BFPSO.

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Bacteria Foraging Algorithm based clustering in Wireless Sensor Networks

Cited by 7 publications

References 20 publications

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

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

Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

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