DFTDT: distributed functional tangent decision tree for aqua status prediction in wireless sensor networks

Chandanapalli, Suresh Babu; Reddy, E. Sreenivasa; Lakshmi, D. Rajya

doi:10.1007/s13042-017-0653-0

Cited by 21 publications

(23 citation statements)

References 30 publications

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“…Based on Equations (4) and (5), the location of the unknown node can be obtained by the least square method. The calculation equation can be expressed as Equation (6).…”

Section: Dv-hop Algorithmmentioning

confidence: 99%

“…And wireless sensor networks (WSNs) [3,4], as an important branch of cyber physical systems, have become an innovation and area of research under the spotlight worldwide. Moreover, WSNs technology is so popular that it has been applied in various fields, including the military and national defense, industry [5], disaster relief, medical treatment, environmental monitoring [6], and so on [7]. However, for most WSNs applications, sensor node location information plays a key role; generally, the information obtained from WSNs would be meaningless, if sensor node locations were unknown in applications such as smart grid, object tracking, and location-based routing.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things

et al. 2019

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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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“…Based on Equations (4) and (5), the location of the unknown node can be obtained by the least square method. The calculation equation can be expressed as Equation (6).…”

Section: Dv-hop Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things

et al. 2019

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“…Cross layer handover strategies can be projected to offer services that are seamless for mobile terminals within the heterogeneous networks that are wireless [33][34][35]. By intending to lower the delay period during handovers, the link layer ought to activate the handover protocols of the 3 layers in a timely manner.…”

Section: Related Workmentioning

confidence: 99%

A hybrid intelligent model for network selection in the industrial Internet of Things

Goudarzi

Anisi

Abdullah

et al. 2019

Applied Soft Computing

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This works proposes a hybrid intelligent model for network selection in Industrial Internet of Things.  The proposed model merges the biogeography-based optimization (BBO) with the markov decision process (MDP).  The MDP is utilized to establish the radio access technology (RAT) selection's probability that behaves as the input to the BBO process  The BBO determines the best radio access technology (RAT) using the described multi-point algorithm in the heterogeneous networks.

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“…The WSN has been considered as one of the most promising technologies to deal with tough issues. It has been successfully employed in disparate areas for monitoring and tracking purposes, such as military, public industry, agriculture, environment, and health care [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Distance Vector-Hop Algorithm using New Weighted Location Method for Wireless Sensor Networks

Han¹,

Mohamed²,

Liu³

et al. 2020

IJACSA

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Location is an indispensable segment for WirelessSensor Network (WSN), since when events happened, we need to know location. The distance vector-hop (DV-Hop) technique is a popular range-free localization algorithm due to its cost efficiency and non-intricate process. Nevertheless, it suffers from poor accuracy, and it is highly influenced by network topology; Especially, more hop counts lead to more errors. In the final phase, least squares are employed to address nonlinear equation, which will gain greater location errors. Aimed at addressing problems mentioned above, an enhanced DV-Hop algorithm based on weighted factor, along with new weighted least squares location technique, is proposed in this paper, and it is called WND-DV-Hop. First, the one hop count of unknown node was corrected by employed received signal strength indication (RSSI) technology. Next, in order to reduce average hop distance error, a weighted coefficient based on beacon node hop count was constructed. A new weighted least squares method was embedded to solve nonlinear equation problem. Finally, considerable experiments were carried out to estimate the performance of WND-DV-Hop, compared the outcomes with state-of-the-art DV-Hop, IDV-Hop, Checkout-DV-Hop, and New-DV-Hop depicted in literature. The empirical findings demonstrated that WND-DV-Hop significantly outperformed other localization algorithms.

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DFTDT: distributed functional tangent decision tree for aqua status prediction in wireless sensor networks

Cited by 21 publications

References 30 publications

A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things

A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things

A hybrid intelligent model for network selection in the industrial Internet of Things

An Enhanced Distance Vector-Hop Algorithm using New Weighted Location Method for Wireless Sensor Networks

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