Performance Evaluation of DV-HOP Localization Algorithm in Wireless Sensor Networks

Gupta, Vikas; Singh, Brahmjit

doi:10.1007/978-981-10-5699-4_40

Cited by 2 publications

(3 citation statements)

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“…Where: z is a random crayfish individual as shown in equation (9). The position Mi of crayfish i is adjusted according to the position Mz of another crayfish to expand the search range.…”

Section: ) Competition Stagementioning

confidence: 99%

“…In this chapter, the performance of the DDCO algorithm is compared with the DV-Hop algorithm [9], ABCDV-Hop algorithm [22], and WODV-Hop algorithm [23], in order to reduce chance, the average value is taken after 50 times of experimental tests, the population size of the algorithm used in the experiment is set to 40, and the number of iterations is set to 50. When 100 nodes are randomly distributed in a network space of 100 m × 100 m, the number of beacon nodes is 30 and the communication radius is 30 m, the network node distribution graph is shown in Figure 6.…”

Section: Comparison Of Simulation Environments and Algorithmsmentioning

confidence: 99%

“…The latter utilizes information such as topology, signal strength, and relative position of nodes in the network to estimate the location of the unknown node, which is less costly and consumes less energy compared to the former. The DV-Hop algorithm [9] is a highly representative one of the distance-independent algorithms, which is favored because it does not require any additional hardware, is low-cost and is easy to implement.…”

Section: Introductionmentioning

confidence: 99%

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Localization algorithm based on deep neural network and improved COA optimization

yu,

ye,

liu

et al. 2024

Preprint

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To improve the positioning accuracy of range-independent positioning algorithms in wireless sensor networks, a DV-Hop localization algorithm (DDCO) based on deep learning and improved crayfish optimization was proposed. Firstly, the dual communication radius subdivision of the minimum number of hops is introduced to reduce the error due to the number of hops; then the trained deep neural network model is used to correct the estimated distance to reduce the distance estimation error; finally, the random center of gravity inverse learning and the improved crayfish algorithm with nonlinear function are introduced to calculate the coordinates of the unknown nodes, and the global optimization capability of the intelligent algorithm is used to reduce the error generated by the DNN. The simulation results show that the positioning error of the DDCO algorithm is reduced by 54.4%, 23.4%, and 10.5%, respectively, compared with DV-Hop and other comparison algorithms under different communication radii. Under different beacon node densities, the error decreases by 46.2%, 24.2%, and 10.6%, respectively. Under different node densities, the error decreases by 49.6%, 30.3%, and 17.3%, respectively.

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“…Where: z is a random crayfish individual as shown in equation (9). The position Mi of crayfish i is adjusted according to the position Mz of another crayfish to expand the search range.…”

Section: ) Competition Stagementioning

confidence: 99%