A Localization Algorithm Based On SFLA and PSO for Wireless Sensor Network

Ren, Wuling; Zhao, Cuiwen

doi:10.3923/itj.2013.502.505

Cited by 24 publications

(9 citation statements)

References 3 publications

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“…The hybrid PSO-ANN algorithm significantly improved the distance estimation accuracy more than the traditional LNSM method without additional components [206]. Due to the network cost and the restricted energy of the sensor nodes, most of the localization algorithms are not well suitable for wireless sensor networks and furthermore the positioning accuracy is relatively low, in [207] the authors presented a localization algorithm based on shuffled frog leaping algorithm (SFLA) and particle swarm optimization (PSO), the proposed location algorithm has a higher accuracy. A novel iterative localization algorithm based on improved particle swarm optimization (PSO) was proposed for monitoring environment like lakes, rivers or other water bodies [208].…”

Section: G Location Optimizationmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people's requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Section: G Location Optimizationmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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“…The DV-hop algorithm is not much complicated to implement and the problem associated with it is the improvement to localization accuracy, it can localize target nodes which have no neighbor or few neighbors anchor nodes but with less precision. For that reason, many of enhanced versions of the algorithm has been proposed in the literature [7,8,9,[10][11][12][13][14][15][16][17]. Mehrabi et.al implemented an extended version of the DV-Hop algorithm on the basis of SFLA combining GA-PSO.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Prevention Mechanism for Replay Attack in Distance Vector - HOP Localization Scheme

2019

IJITEE

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Securing the process of node localization in Wireless Sensor Network (WSN) is an important area of research. In this paper, we focus on securing Distance Vector (DV)-Hop localization scheme from Replay Attack. The DV-Hop localization algorithm is a limit free hop based reference Wireless Sensor Network architecture. It faces a couple of issues like a route discovery mechanism and threat prevention issues. This paper focuses on the establishment of a prevention mechanism for DV-Hop scheme against a Replay attack. The proposed architecture uses an Artificial Bee Colony (ABC) and Neural Network in order to prevent the attack. To prove the efficacy of the proposed scheme, the network scenario without replay attack, with replay attack and with attack after prevention mechanism is considered. The simulations are conducted for 200 iterations using MATLAB. The evaluation is done on the basis of parameters like localization error and transmission loss. Localization error of the proposed framework is similar to localization error without an attack which is about 0.57, and the maximum localized error is about 0.92. The maximum transmission loss without attack and after prevention is very close and varies by only 2 %, which is very good. The experimental results reveal that the proposed technique successfully prevents replay attack in WSN

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“…But the method has the advantages of low localization accuracy if the measured distance error is big, node wireless range is small or anchor node ratio is low. Wuling Ren and others proposed a localization algorithm based on shuffled frog leaping algorithm and particle swarm optimization(PSO)algorithm [5], which has a good localization accuracy. The method has a good localization effect, notably if the measured distance error is big enough, but its consumption and stability remain to be improved.…”

Section: Introductionmentioning

confidence: 99%

Node Localization Method for Wireless Sensor Networks Based on Hybrid Optimization of Differential Evolution and Particle Swarm Algorithm

Zhang¹,

Ji²

2014

TOAUTOCJ

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Regarding the node localization problems for wireless sensor network, a hybrid optimization method was proposed accordingly on differential evolution(DE) algorithm and particle swarm optimization(PSO) algorithm. Firstly, the position and velocity of the initial population were randomly generated by PSO, and the fitness function was constructed according to the mean square error of estimated and measured distance between the unknown nodes and their adjacent anchor node. Secondly, the mutation and selection operation of DE algorithm were executed to find out the optimum position of the population. Lastly, the current velocities and positions of all particles of the population were updated, and the crossover operation and selection operation of DE algorithm were executed to update the current global optimum position of the whole population. Population global optimum solution of iterative search algorithm is the position coordinate of the unknown node. Simulation results indicate that the proposed localization method has smaller average localization error and higher localization accuracy than that of DE algorithm and PSO algorithm in the same environment.

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A Localization Algorithm Based On SFLA and PSO for Wireless Sensor Network

Cited by 24 publications

References 3 publications

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

A Novel Prevention Mechanism for Replay Attack in Distance Vector - HOP Localization Scheme

Node Localization Method for Wireless Sensor Networks Based on Hybrid Optimization of Differential Evolution and Particle Swarm Algorithm

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