Energy Efficient Dynamic Routing Mechanism (EEDRM) with Obstacles in WSN

Selvaraj, Sharmila; Saranya, V.

doi:10.1007/s11277-020-07174-9

Cited by 7 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach works efficiently and intelligently in improving the data gathering process. 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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…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. As described in [31], Selvaraj and Vasanthamani propose a dynamic routing approach that eliminates obstacles in the WSNs by avoiding obstacles.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Trajectory for Mobile Element in WSNs with Obstacle Avoidance

Gowthami,

Jangam,

et al. 2024

Contemp. Math.

View full text Add to dashboard Cite

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.

show abstract