A <i>K</i>-means based firefly algorithm for localization in sensor networks

Liu, Yuan; Chen, Junjie

doi:10.1080/17445760.2018.1446211

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…The fruitfly optimization algorithm (FOA) [ 21 ] is another typical SI algorithm that uses the social behavior of fruit fly odorant foraging and updates individual positions by smell. In addition, FA [ 22 ] and FOA [ 23 ] have been applied to the NLO problem of WSNs. Moreover, it is worth noting that the classic intelligent algorithms include GA [ 24 ], PSO [ 25 ], DE [ 26 ], FA [ 20 ] and GWO [ 27 ], among others [ 28 , 29 , 30 , 31 ].…”

Section: Related Workmentioning

confidence: 99%

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring

Xia,

Yang,

Zhang

et al. 2023

Biomimetics

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Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals’ smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results.

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Section: Related Workmentioning

confidence: 99%

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring

Xia,

Yang,

Zhang

et al. 2023

Biomimetics

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“…e radio also uses up energy to accept a h-bit message given in (10). E elec depends on factors for instance modulation, filtering, digital coding, and combining the dispersion of signals, but the amount of energy to amplify the system, ε fs d 2 or ε mp d 4 , relies on the receiving structure per the distance travelled and the suitable error per bit. E elec is defined as the electronic energy required by the electronic circuit and d 0 � �� ε fs \ε mp .…”

Section: Energy Efficiencymentioning

confidence: 99%

“…Presently, the persistent distribution of such systems is prohibited as a result of their gelatinous intricacy and their costs of maintenance [2]. Primarily, this is owing to the fact that, in interior environments, precise localization in the occurrence of unembellished multipath fading frequently necessitates a huge amount of static sensing nodes which are referred to as anchors (also called beacon) with a recognized location [3] and the utilization of complex signal processing algorithms for disseminated proximity approximation [4]. In a broader spectrum, accumulating more anchor nodes means higher position accuracy.…”

Section: Introductionmentioning

confidence: 99%

Energy‐Efficient Clustering and Localization Technique Using Genetic Algorithm in Wireless Sensor Networks

et al. 2021

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Localization is recognized among the topmost vital features in numerous wireless sensor network (WSN) applications. This paper puts forward energy-efficient clustering and localization centered on genetic algorithm (ECGAL), in which the residual energy, distance estimation, and coverage connection are developed to form the fitness function. This function is certainly fast to run. The proposed ECGAL exhausts a lesser amount of energy and extends wireless network existence. Finally, the simulations are carried out to assess the performance of the proposed algorithm. Experimental results show that the proposed algorithm approximates the unknown node location and provides minimum localization error.

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“…The proposed blind localization method requires a number of iteration and huge computation to locate the UAV. Liu and Chen 30 developed improved K -means based on firefly algorithm to find accurate target location in a network. First, it measures the distance among the nodes and target RSS identifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Salam

Javaid

Ahmad

2021

International Journal of Distributed Sensor Networks

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Farming is the major profession in several republics for centuries. However, due to the immigration of individuals from rural to urban, there is prevention in farming. The use of modern technology in the precision agriculture field increases productivity and also improves the exports of a country. The productivity may suffer due to different environmental factors, diseases and insects attacks on the crops, especially tomatoes. The target area (i.e. the affected crops area due to environmental factors) identification and delivery of timely information about diseases in the crops to the ground station are mandatory to make the precautionary measurements. In flying sensor networks, the localization and clustering of multiple unmanned aerial vehicles for target areas identification is a challenging task due to energy constraints, communication range, frequent change in topology, link expiration and high mobility. In this article, we proposed the localization and clustering of multiple unmanned aerial vehicles for the identification of affected target areas in the tomato crop field. The localization of unmanned aerial vehicles depends on the weights of environmental factors, that is, relative humidity, soil moisture, temperature, light intensity, NPK (nitrogen (n), phosphorus (p) and potassium (k)) and power of hydrogen (pH). A honey bee optimization approach is used for the localization and formation of multiple unmanned aerial vehicles’ cluster to accurately identify the target areas. The performance of our bio-inspired approach is compared in terms of communication overhead, packet delivery ratio, mean end-to-end delay and energy consumption with the existing swarm intelligence–based schemes and validated via a simulation. The simulation result shows that the bio-inspired approach performs better among the selected approaches.

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A K-means based firefly algorithm for localization in sensor networks

Cited by 6 publications

References 29 publications

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring

Energy‐Efficient Clustering and Localization Technique Using Genetic Algorithm in Wireless Sensor Networks

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

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