Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks

Zhao, Wei; Su, Shoubao; Shao, Fei

doi:10.1007/s11277-017-5017-2

Cited by 42 publications

(32 citation statements)

References 14 publications

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“…Chuan proposed the concept of weights and pointed out that the weight should decrease as the minimum hop count increases. Then, many scholars use different optimization algorithms to solve the weighted DVHop algorithm, such as oriented cuckoo search algorithm based on the Lévy‐Cauchy distribution (OCSLC‐DVHop), weighted centroid localization algorithm (WCLA), and so on.…”

Section: Related Workmentioning

confidence: 99%

A Gaussian error correction multi‐objective positioning model with NSGA‐II

Wang

Huang

Cui

et al. 2019

Concurrency and Computation

165

140

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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...

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Section: Related Workmentioning

confidence: 99%

A Gaussian error correction multi‐objective positioning model with NSGA‐II

Wang

Huang

Cui

et al. 2019

Concurrency and Computation

165

140

View full text Add to dashboard Cite

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“…Singh et al applied an improved algorithm for localization and then used particle swarm optimization to refine the results [36]. Zhao et al proposed an improved localization algorithm based on hybrid chaotic strategy [10]. Harikrishnan et al used the differential evolution algorithm to minimize localization error in wireless sensor network [12], while Cui et al improved the values of hop-count by the values of common single-hop nodes between adjacent nodes and converted the discrete hop-count values into more accurate continuous values [37].…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Han

et al. 2020

Sensors

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The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

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“…DV-hop [12] consists of two message flooding phases to determine average hop length between two anchors, and an unknown node estimates its position by trilateration when it receives at least three anchor messages. There is much room to improve localization accuracy of DV-hop, and many enhancements have been proposed, such as checkout DV-hop [13], selective 3-anchor DV-hop [13], DV-hop algorithm based on locally weighted linear regression [14], etc., but they also need flood messages through WSNs. Both MDS and DV-hop are based on the shortest path between sensor nodes, but this path is often zigzag especially in WSNs with coverage holes.…”

Section: Related Workmentioning

confidence: 99%

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

Cui

Liang

Zhou

et al. 2018

Wireless Communications and Mobile Computing

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Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

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Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks

Cited by 42 publications

References 14 publications

A Gaussian error correction multi‐objective positioning model with NSGA‐II

A Gaussian error correction multi‐objective positioning model with NSGA‐II

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

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