M-Curves path planning model for mobile anchor node and localization of sensor nodes using Dolphin Swarm Algorithm

Kannadasan, K; Edla, Damodar Reddy; Kongara, Mahesh Chowdary; Kuppili, Venkatanareshbabu

doi:10.1007/s11276-019-02032-4

Cited by 29 publications

(22 citation statements)

References 29 publications

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“…Simulated annealing [ 34 ], dolphin swarm [ 35 ], bat algorithm [ 36 ], elephant herding optimization [ 37 ], diversified local search [ 38 ], firefly algorithm [ 55 ], bacterial foraging algorithm [ 56 ] but especially genetic algorithms [ 39 , 40 , 41 , 57 , 58 ] have been used for solving the NLP.…”

Section: Localization Node Location Problemmentioning

confidence: 99%

“…Therefore, a heuristic solution is recommended for addressing the optimization. Many different metaheuristic techniques such as simulated annealing [ 34 ], dolphin swarm algorithm [ 35 ], bat algorithm [ 36 ], elephant herding optimization [ 37 ] or diversified local search [ 38 ] have been used for approaching the node location problem, but especially Genetic Algorithms (GA) have been used in the node location problem [ 39 , 40 , 41 ] due to the excellent trade-off of the GA between diversification (i.e., the capacity to explore the space of solutions) and intensification (i.e., the finding of the optimal solution in a reduced part of the space of solutions) [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

Díez-González

Verde

Ferrero-Guillén

et al. 2020

Sensors

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Local Positioning Systems (LPS) have shown excellent performance for applications that demand high accuracy. They rely on ad-hoc node deployments which fit the environment characteristics in order to reduce the system uncertainties. The obtainment of competitive results through these systems requires the solution of the Node Location Problem (finding the optimal cartesian coordinates of the architecture sensors). This problem has been assigned as NP-Hard, therefore a heuristic solution is recommended for addressing this complex problem. Genetic Algorithms (GA) have shown an excellent trade-off between diversification and intensification in the literature. However, in Non-Line-of-Sight (NLOS) environments in which there is not continuity in the fitness function evaluation of a particular node distribution among contiguous solutions, challenges arise for the GA during the exploration of new potential regions of the space of solutions. Consequently, in this paper, we first propose a Hybrid GA with a combination of the GA operators in the evolutionary process for the Node Location Problem. Later, we introduce a Memetic Algorithm (MA) with a Local Search (LS) strategy for exploring the most different individuals of the population in search of improving the previous results. Finally, we combine the Hybrid Genetic Algorithm (HGA) and Memetic Algorithm (MA), designing an enhanced novel methodology for solving the Node Location Problem, a Hybrid Memetic Algorithm (HMA). Results show that the HMA proposed in this article outperforms all of the individual configurations presented and attains an improvement of 14.2% in accuracy for the Node Location Problem solution in the scenario of simulations with regards to the previous GA optimizations of the literature.

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Section: Localization Node Location Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

Díez-González

Verde

Ferrero-Guillén

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Additionally, Han et al [26] proposed another method called the Localisation algorithm with a MAN based on Trilateration (LMAT) and designed a path for the MAN with regard to the equilateral triangle to reduce the collinear position packets which improve the accuracy. Recently some trajectory were introduced based on alphabet patterns such as Z ‐curve [27], M ‐curve [28], H ‐curve [29], D ‐connect [30] and also Σ‐scan [31] to overcome the collinearity issue. These methods generally divide network area to several sub‐squares and create their MAN trajectory by connecting these sub‐squares.…”

Section: Related Workmentioning

confidence: 99%

Obstacle‐resistant hybrid localisation algorithm

Kargar

Mahani

2020

IET wirel. sens. syst.

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“…The DSA simulates the predatory process of dolphins for multi-dimensional numerical optimization problems with four phases, i.e., search, call, reception, and predation. Many practical engineering optimization problems can be solved by DSA due to its high efficiency, low computational burden and excellent optimization performance [ 15 , 16 ]. In [ 16 ], the authors optimize the localization process by using DSA to process the sensed data in Wireless Sensor Networks.…”

Section: Introductionmentioning

confidence: 99%

“…Many practical engineering optimization problems can be solved by DSA due to its high efficiency, low computational burden and excellent optimization performance [ 15 , 16 ]. In [ 16 ], the authors optimize the localization process by using DSA to process the sensed data in Wireless Sensor Networks. However, in the process of dolphin updating, the positions of the dolphins are updated based on a random method.…”

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

Zhang

Huang

Rong

et al. 2021

Sensors

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With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype “Forbot” four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.

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M-Curves path planning model for mobile anchor node and localization of sensor nodes using Dolphin Swarm Algorithm

Cited by 29 publications

References 29 publications

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

Obstacle‐resistant hybrid localisation algorithm

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

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