Distance vector-hop (DVHop), as a range-independent positioning algorithm, is a significant positioning method in wireless sensor networks (WSNs). It is composed of three parts, including connectivity detection, distance estimation, and position estimation. However, this simple positioning method results in a larger positioning error. Therefore, to enhance the positioning precision, this paper investigates the characteristic of error distribution between the estimated and real distance in the DVHop algorithm and reveals that the error is subjecting to the Gaussian distribution, N∼(0,1/3CR). Furthermore, to improve positioning accuracy, we propose a Gaussian error correction multi-objective positioning model with non-dominated sorting (NSGA-II), which named GGAII-DVHop. Finally, this model is tested on three complex network topologies, and the results demonstrate that it is significantly superior to other four algorithms in both positioning precision and robustness. KEYWORDSGaussian error correction, GGAII-DVHop, multi-objective DVHop model, wireless sensor networks INTRODUCTIONRecently, the smaller and smarter sensors are deployed in WSNs 1-3 with the micro electromechanical systems technologies advanced. These seemingly tiny nodes are made up of devices such as memory, transceivers, power supplies, etc, which constitute inexpensive, independent, and low-powered WSNs. In addition, WSNs are widely applied to industry, 4 medical treatment, home care, machine learning, and so on. However, their communication radius (CR) is limited due to their low power consumption, which determines the nodes that communicate with each other by transmitting packets, that is multi-hop routing. The Internet of Things (IoT) 5-7 is composed of many such small and relatively independent WSNs.In these applications, the location of the sensor nodes is an unavoidable topic because many of the data will be meaningless without location information. For this reason, various localization algorithms were designed to sharpen the positioning accuracy of nodes, including the range-dependent and range-independent localization algorithms. Among them, the received signal strength indicator (RSSI), 8 time of arrival (TOA), 9 and Ad hoc positioning system (APS) 10 are typically range-dependent positioning algorithms; the distance vector hop (DVHop), 11 convex position estimation (CPE), 12 and multi-dimensional scaling (MDS) 13 are representatives of range-independent localization algorithms, such as Figure 1. Because of the limitation of the computer hardware and overall cost, the range-independent localization algorithms 14,15 are more popular with researchers.The DVHop is widely used due to its simple operation and low hardware requirements. It uses the multi-hop routing between sensor nodes, and the average distance of per hop between each anchor node to locate. The positioning process of DVHop can be divided into three parts, including connectivity detection, distance estimation, and position estimation. However, this simple positioning principle us...

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.

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A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.