Abstract
Locating node technology, as the most fundamental component of wireless sensor networks (WSNs) and internet of things (IoT), is a pivotal problem. Distance vector-hop technique (DV-Hop) is frequently used for location node estimation in WSN, but it has a poor estimation precision. In this paper, a multi-objective DV-Hop localization algorithm based on NSGA-II is designed, called NSGA-II-DV-Hop. In NSGA-II-DV-Hop, a new multi-objective model is constructed, and an enhanced constraint strategy is adopted based on all beacon nodes to enhance the DV-Hop positioning estimation precision, and test four new complex network topologies. Simulation results demonstrate that the precision performance of NSGA-II-DV-Hop significantly outperforms than other algorithms, such as CS-DV-Hop, OCS-LC-DV-Hop, and MODE-DV-Hop algorithms.
Highlights
As the hottest research topics currently, internet of things (IoT) contains many technologies such as cyber physical systems [1,2], embedded system technology, network information technology, and so on
We propose a multi-objective Distance vector-hop technique (DV-Hop) localization algorithm based on non-dominated sorting genetic algorithm II (NSGA-II) [48]
We propose a multi-objective DV-Hop localization algorithm based on NSGA-II, which achieves the purpose of improving the positioning accuracy by adopting multi-objective which achieves the purpose of improving the positioning accuracy by adopting multi-objective improvement on the original objective
Summary
As the hottest research topics currently, internet of things (IoT) contains many technologies such as cyber physical systems [1,2], embedded system technology, network information technology, and so on. It has begun to receive researchers’ attention that the use of interactions and connectivity information between sensor nodes for positioning Using this information, researchers have proposed a series of localization algorithms. Compared to other range-free positioning algorithms, it is easier to bring into operation, but the low positioning accuracy has become a problem to be solved For this reason, scholars propose various improved algorithms based on DV-Hop localization algorithm, including the deterministic algorithms [18,19,20] and bio-inspired optimization algorithms. Compared with the mathematics optimization methods, these biological inspired algorithms show some unique advantages They don’t depend on the requirement of any gradient information in the variable space; in addition, they are insensitive to the initial value and insusceptible to local entrapment.
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