A mobile sink–integrated framework for the collection of farmland wireless sensor network information based on a virtual potential field

Yang, Wude; Wu, Haiyuan; Miao, Yisheng

doi:10.1177/15501477211030122

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…An obstacle-aware path for MS is introduced using the cluster-based approach in [36] for WSNs. In [37], Yang et al present a dynamic path for MS using virtual potential fields in WSNs.…”

Section: Path Construction In Obstaclementioning

confidence: 99%

Data Acquisition through Mobile Sink for WSNs with Obstacles Using Support Vector Machine

Sulakshana

Kamatam

2022

Journal of Sensors

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Mobile sink-based data collection in wireless sensor networks has become an attractive research area to mitigate hotspot issues. It further increases the efficiency of the WSN, such as throughput, lifetime, and energy efficiency, while decreasing delay and packet losses. Mobile sink algorithms developed by many researchers in recent years have only contributed to obtain efficient path planning, and only a few researchers have focused on solving the problem of network environment with obstacles. Here, constructing an obstacle-aware path for the mobile sink to collect data in WSN is a challenging issue. In this context, we present the data acquisition through mobile sink for WSNs with obstacles using support vector machine (DAOSVM). The DAOSVM algorithm works in two phases: visiting point selection and path construction. The visiting point selection uses spanning tree approach, and the path selection uses SVM. The computational complexity of the proposed DAOSVM is estimated and compared using the existing techniques, and it is lower. The DAOSVM also outperforms traditional methods concerning multiple performance metrics under various scenarios.

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“…An obstacle-aware path for MS is introduced using the cluster-based approach in [36] for WSNs. In [37], Yang et al present a dynamic path for MS using virtual potential fields in WSNs.…”

Section: Path Construction In Obstaclementioning

confidence: 99%

Data Acquisition through Mobile Sink for WSNs with Obstacles Using Support Vector Machine

Sulakshana

Kamatam

2022

Journal of Sensors

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“…The results showed that AI-based approaches could improve WSN performance with regard to obstacle avoidance and coverage. A sink-hole problem for WSNs is addressed using an information-based clustering approach in [28]. The [29] paper uses clustering for an obstacle-aware approach to WSN data acquisition using MS, whereas the [30] paper uses trajectory optimization for WSN data collection.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Trajectory for Mobile Element in WSNs with Obstacle Avoidance

Gowthami,

Jangam,

et al. 2024

Contemp. Math.

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In wireless sensor networks (WSNs), mobile sink-driven data acquisition can mitigate hotspot issues, which further increases WSN efficiency, such as throughput, lifetime, and energy efficiency, while reducing delay and packet loss. Recently, most mobile sink algorithms have focused on efficient paths, and few consider obstacles in the network environment. Nevertheless, constructing an obstacle-aware trajectory in a WSN is challenging. In this context, this paper proposes a bug algorithm based on an obstacle-aware intelligent trajectory (CSOBUG) for a mobile sink to acquire data from sensor nodes in WSNs efficiently with the help of cat swarm optimization (CSO). The proposed CSOBUG algorithm has two phases: selecting visiting points and constructing a trajectory. A CSO-based clustering approach is used to select visiting points, and a bug algorithm is used to select a trajectory. Comparing CSOBUG with existing techniques, it is found that CSOBUG is less computationally intensive than the existing techniques. As well as outperforming traditional methods based on multiple performance metrics, the CSOBUG achieves superior results in a variety of scenarios.

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“…At the same time, a path-based equalization algorithm (PEABR) is proposed to adjust the path of the mobile Sink, and the result shows the feasibility and effectiveness of the algorithm. In order to overcome the limitations of traditional data collection methods in large-scale farmland wireless sensor networks, Yang et al [ 27 ] introduced mobile Sink and proposed a path planning strategy for mobile Sink based on virtual potential field. The results show that the method has good transmission efficiency and prolongs the lifetime of the network.…”

Section: Related Workmentioning

confidence: 99%

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

Yue

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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For the sensing layer of the Internet of Things, the mobile wireless sensor network has problems such as limited energy of the sensor nodes, unbalanced energy consumption, unreliability, and long transmission delay in the data collection process. It is proved by mathematical derivation and theory that this is a typical multiobjective optimization problem. In this paper, the optimization goal is to minimize the energy consumption and improve the reliability under time-delay constraints and propose a path optimization mechanism to optimize the mobile Sink of mobile wireless sensor networks based on the improved dragonfly optimization algorithm. The algorithm takes full advantage of the abundant storage space, sufficient energy, and strong computing power of the mobile Sink to ensure network connectivity and improve network communication efficiency. Through simulation comparison and analysis, compared with random movement method, artificial bee colony algorithm, and basic dragonfly optimization algorithm, the energy consumption of the network is reduced, the lifespan of the network is increased, and the connectivity and transmission delay of the network are improved. The proposed algorithm balances the energy consumption of the sensors nodes to meet the network service quality and improve the reliability of the network.

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A mobile sink–integrated framework for the collection of farmland wireless sensor network information based on a virtual potential field

Cited by 4 publications

References 33 publications

Data Acquisition through Mobile Sink for WSNs with Obstacles Using Support Vector Machine

Data Acquisition through Mobile Sink for WSNs with Obstacles Using Support Vector Machine

Intelligent Trajectory for Mobile Element in WSNs with Obstacle Avoidance

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

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