Nature Inspired Algorithm-Based Improved Variants of DV-Hop Algorithm for Randomly Deployed 2D and 3D Wireless Sensor Networks

Kaur, Amanpreet; Kumar, Padam; Gupta, Govind P.

doi:10.1007/s11277-018-5704-7

Cited by 66 publications

(32 citation statements)

References 27 publications

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“…The localization accuracy is further improved by applying genetic algorithm. However, the proposed algorithm performance may be degraded, causing multiple anisotropies due to holes, nonuniform distribution of nodes, sparsity in the network, and irregular radio patterns . This paper presented two Nature Inspired Algorithm‐based improved variants for 2‐dimensional and 3‐dimensional wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

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Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Kanwar

Kumar

2020

Int J Communication

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Summary Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme.

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Section: Related Workmentioning

confidence: 99%

“…Sensor nodes that receive this information send the given information to its neighboring nodes after increasing hop count value by 1. By doing this process, all the sensor nodes in the network get the minimum value of hop count …”

Section: Traditional Dv‐hop Localization Using Multiobjective Gamentioning

confidence: 99%

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Kanwar

Kumar

2020

Int J Communication

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“…Improved DV-Hop localization algorithms with particle swarm optimization-differential evolution (PSO-DE) and cuckoo search optimization are shown in [49,50], which reduce localization error. A variant of the DV-Hop algorithm has been shown by [51] in which grey-wolf optimization (GWO) is utilized for computing the average hop-size of beacon nodes and a weighted GWO is applied for accurate localization in WSNs. An advanced DV-hop with teaching learning based optimization (TLBO) has been developed by introducing the correction factor and collinearity concept in localization [52,53,54].…”

Section: Present State Of Research and Research Gapsmentioning

confidence: 99%

Energy Efficient Range-Free Localization Algorithm for Wireless Sensor Networks

Goyat

Kumar

et al. 2019

Sensors

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In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms.

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“…The sensors' location data are conduced to calculate network consumption and attain route management etc. Therefore, localization proves to be a significant research direction in WSNs [5]- [7]. The characteristics of numerous sensor nodes, limited power, random distribution and complex communication JIAXING CHEN and WEI ZHANG are co-first authors and they have contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

CWDV-Hop: A Hybrid Localization Algorithm With Distance-Weight DV-Hop and CSO for Wireless Sensor Networks

et al. 2021

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Nature Inspired Algorithm-Based Improved Variants of DV-Hop Algorithm for Randomly Deployed 2D and 3D Wireless Sensor Networks

Cited by 66 publications

References 27 publications

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Energy Efficient Range-Free Localization Algorithm for Wireless Sensor Networks

CWDV-Hop: A Hybrid Localization Algorithm With Distance-Weight DV-Hop and CSO for Wireless Sensor Networks

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