DV-Hop based localization methods for additionally deployed nodes in wireless sensor network using genetic algorithm

Kanwar, Vivek; Kumar, Ashok

doi:10.1007/s12652-020-01907-1

Cited by 25 publications

(15 citation statements)

References 20 publications

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“…A hybrid DECHDV-Hop localization algorithm with DV-Hop and DE is designed in [31], and its effectiveness is tested in random, grid, C-shaped random and C-shaped grid network situations. [46] proposes a new framework to localize newly deployed nodes in a pre-localized network using GADV-Hop algorithm which is simulated in random topology, C-shaped topology and W-shaped topology. As can be seen from Fig.…”

Section: Related Work On Dv-hop Derivations In Isotropy and Anisotmentioning

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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Section: Related Work On Dv-hop Derivations In Isotropy and Anisotmentioning

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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“…The nodes are then localized using the least squares method. Another framework called DV-Hop based genetic algorithm is reported in [23] for situations where few nodes become dead due to depletion of battery with time. This requires the deployment of additional nodes to fill the communication gap.…”

Section: Related Workmentioning

confidence: 99%

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Bhat

Santhosh

2021

IEEE Access

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Location information of nodes in Wireless Sensor Networks (WSN) is essential to identify the origins of events and to act on them. Several localization algorithms are developed for this purpose. In this work, we have considered hop based localization algorithms, which are popularly used in WSN applications. These algorithms use a few reference nodes with location information and localize other nodes with reference to these nodes. But, in practical scenarios, some reference nodes may turn faulty and report incorrect location information to other nodes. This reduces the localization accuracy of the entire network. Therefore, it is essential to identify and filter out faulty reference nodes from the localization process. But, in Heterogeneous Wireless Sensor Networks (HWSN), since both faulty nodes and heterogeneous nodes modify hop distances, it becomes even more challenging to identify only faulty nodes among a set of heterogeneous nodes. In this work, we have reported a fault filtering method that can be used with any of the existing hop based localization algorithms for fault-tolerant localization. This method first normalizes the distance estimations using the communication radius of nodes and then uses the Jenks Natural Breaks algorithm for filtering out the nodes producing inconsistent distance estimations. The reported method is incorporated into existing localization algorithms and tested in 2D/3D, isotropic/anisotropic environments. The results show an improvement of 14%, 52%, and 51% in localization accuracy when tested with DV-Hop, Weighted DV-Hop, and HHO-AM algorithms, respectively.

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“…The results demonstrated that the hybrid model has improved by about 67%. A model based on the hybrid of DV-Hop-GA based on random topology, C-Shaped, and W-Shaped topology has been proposed for Positioning [24]. The Positioning process saves computing time and energy consumption.…”

Section: Previous Workmentioning

confidence: 99%

“…The distance between anchor node and unknown node t is calculated according to the definition of Eq. (24). The ℎ parameter is the number of hop size between anchor node and unknown node t.…”

Section: = | −́|mentioning

confidence: 99%

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Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

Kamfar¹,

Zandhessami²,

Alborzi³

2021

Preprint

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Nowadays, Wireless Sensor Networks (WSNs) are significantly applied in engineering and scientific research. WSNs consist of a group of distributed space sensors that track the environment's physical conditions and control the collected data at one central location. Examples of these sensors' applications are smart cities, transport, volcano surveillance and environmental activity, earthquake monitoring, medicine, post-disaster response, and military control. Wireless sensor networks have a lot of research issues like access to the media, implementation, time synchronization, network security and localization of the nodes. One of the most critical problems in this network research is the optimum position of the sensors to have access to maximum coverage and increase network life span to decrease maintenance costs, develop and manage the network. One of the main causes of the failure in these networks is running out of sensor battery and replacing them which impose high costs to maintenance and managing of the network. In order to solve the issues related to optimization and localization, researchers have focused on the algorithms like Swarm Intelligence (SI), because they enable us to solve complicated issues of optimization and NP-Hard issues to solve optimization. However, most of these algorithms are specialized for a purpose or a special program, and the majority of the solutions are not compatible with most of the wireless network sensors. The DV-Hop is one of the most popular node algorithms. But the main problem of the DV-Hop is the possibility of error in calculating the assessed distance between the unknown node and the nodes of anchor. Therefore, minimizing this error is the key to improve this algorithm. To reduce the problem of high localization error, two meta-heuristic algorithms have been proposed based on a combination. In this paper, a new optimization method based on a combination of Krill Herd Algorithm (KHA) and Particle Swarm Optimization (PSO) called KHAPSO is suggested to improve DV-Hop. Simulation results in MATLAB 2016 show that the KHAPSO model has a lower mean error compared to the DV-Hop, DV-Hop-KHA and DV-Hop-PSO models. Also, energy consumption in the KHAPSO model is less in comparison to the other models. The KHAPSO model with 400 unknown nodes and 30 anchor nodes was able to reduce energy consumption by about 35% and at the same time 27% reduction in Average Localization Error (ALE) compared to DV-Hop.

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DV-Hop based localization methods for additionally deployed nodes in wireless sensor network using genetic algorithm

Cited by 25 publications

References 20 publications

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

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

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

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