Link Aware Routing Protocol for Landslide Monitoring Using Efficient Data Gathering and Handling System

Sumathi, M.; Anitha, G.

doi:10.1007/s11277-020-07169-6

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Paired adaptive regressors for cumulative sum (PARCS) were utilized for periodic data. Sumathi and Anitha [143] designed a lossless landslide-monitoring (LLM) system. During the data collection and processing phases, two algorithms were used.…”

Section: Data Loss and Size Issuesmentioning

confidence: 99%

Recent Phenomenal and Investigational Subsurface Landslide Monitoring Techniques: A Mixed Review

Ebrahim,

Gomaa,

Zayed

et al. 2024

Remote Sensing

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Landslides are a common and challenging geohazard that may be caused by earthquakes, rainfall, or manmade activity. Various monitoring strategies are used in order to safeguard populations at risk from landslides. This task frequently depends on the utilization of remote sensing methods, which include the observation of Earth from space, laser scanning, and ground-based interferometry. In recent years, there have been notable advancements in technologies utilized for monitoring landslides. The literature lacks a comprehensive study of subsurface monitoring systems using a mixed review approach that combines systematic and scientometric methods. In this study, scientometric and systematic analysis was used to perform a mixed review. An in-depth analysis of existing research on landslide-monitoring techniques was conducted. Surface-monitoring methods for large-scale landslides are given first. Next, local-scale landslide subsurface monitoring methods (movement, forces and stresses, water, temperature, and warning signs) were examined. Next, data-gathering techniques are shown. Finally, the physical modeling and prototype field systems are highlighted. Consequently, key findings about landslide monitoring are reviewed. While the monitoring technique selection is mainly controlled by the initial conditions of the case study, the superior monitoring technique is determined by the measurement accuracy, spatiotemporal resolution, measuring range, cost, durability, and applicability for field deployment. Finally, research suggestions are proposed, where developing a superior distributed subsurface monitoring system for wide-area monitoring is still challenging. Interpolating the complex nonlinear relationship between subsurface monitoring readings is a clear gap to overcome. Warning sign systems are still under development.

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Section: Data Loss and Size Issuesmentioning

confidence: 99%

Recent Phenomenal and Investigational Subsurface Landslide Monitoring Techniques: A Mixed Review

Ebrahim,

Gomaa,

Zayed

et al. 2024

Remote Sensing

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“…Social class multiobjective particle swarm optimization (SC-MOPSO) is a type of multiobjective particle swarm optimization algorithm that was developed to support variable-length solution spaces [ 17 ]. However, SC-MOPSO was developed based on blind mobility operators that do not consider the specific nature of the data-gathering application [ 18 ]. Additionally, it has not been extended to be used for multiobjective data gathering with trust-awareness applications using mobile sinks [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

Saad

Jaafar

Chellappan

2023

Sensors

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Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III.

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WSN to detect real time terrain slides using Wi-GIM instrument

Sumathi¹,

Anitha²

2023

Materials Today: Proceedings

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Link Aware Routing Protocol for Landslide Monitoring Using Efficient Data Gathering and Handling System

Cited by 3 publications

References 34 publications

Recent Phenomenal and Investigational Subsurface Landslide Monitoring Techniques: A Mixed Review

Recent Phenomenal and Investigational Subsurface Landslide Monitoring Techniques: A Mixed Review

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

WSN to detect real time terrain slides using Wi-GIM instrument

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