Integrating Geophone Network to Real-Time Wireless Sensor Network System for Landslide Detection

Kunnath, Abishek Thekkeyil; Ramesh, M.

doi:10.1109/sensordevices.2010.38

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…Figure 17 shows the phases of three constituents of the proposed scheme. Figures 18,19,20,21,22, and 23 are giving the comparative performance of proposed scheme, EAR4MCDA with Threshold Based Load Balancing Protocol for Energy Efficient Routing in WSN (TLPER) [91] and EADUC. Detailed results and a comprehensive discussion thereon are given in the article at [90].…”

Section: Discussion and Analysismentioning

confidence: 99%

“…Some other important real-world applications/projects of WSN [10] are Wave Monitoring [11], Ocean Water Monitoring [12], ZebraNet [13], Cattle Herding [14], and neuRFon [15]. This emerging technology also has its applications in Habitat Monitoring [16], Traffic Surveillance [17], Ocean Water and Bed Monitoring [18], Wildlife Monitoring [19], Cold Chain Monitoring [20], Landslide Detection [21], Vital Sign Monitoring [22], Tracking Vehicles [23], and Living and Residential Monitoring [24]. A typical WSN application at the border is shown in Figure 1.…”

Section: Real-worldmentioning

confidence: 99%

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Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network

Jabbar

Habib

Minhas

et al. 2018

Wireless Communications and Mobile Computing

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Among constituents of communication architecture, routing is the most energy squeezing process. In this survey article, we are targeting an innovative aspect of analysis on routing in wireless sensor network (WSN) that has never been seen in the available literature before. This article can be a guiding light for new researchers to comprehend the WSN technology, energy aware routing, and the factors that affect the energy aware routing in WSN. This insight comprehension then makes the ways easy for them in designing such types of algorithms as well as evaluating the authenticity and extending the existing algorithms of this category, since algebraic and graphical modelling of these factors is also demonstrated. Various available techniques used by existing routing algorithms to handle these factors in making themselves energy aware are also given. Further, they are analyzed along with the suggested improvements for the researchers. At the end, we presented our previously published research work as an example and case study of discussed factors. A rich list of references is also cited for interested readers to explore the related given points.

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Section: Discussion and Analysismentioning

confidence: 99%

Section: Real-worldmentioning

confidence: 99%

Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network

Jabbar

Habib

Minhas

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…Therefore it is possible to integrate these techniques with IoT. In [200] geophones are integrated into a WSN, in order to monitor seismic precursors of landslide activation. By implementing novel data processing routines, the amount of data that needs to be transmitted through the network was reduced, thereby making it more applicable for long-term monitoring, while standard seismic recording protocols usually collected large amounts of data and thereby usually prohibit the installation of a dense network of receivers over a long time.…”

Section: B Seismic Methodsmentioning

confidence: 99%

Review of Landslide Monitoring Techniques With IoT Integration Opportunities

Hemalatha

Uhlemann

Reghunadh

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the advancements of technology in the era of big data and artificial intelligence, IoT (Internet of Things) has a major role for the purpose of monitoring natural disasters like landslides. Landslides are a catastrophic disaster worldwide that alter from terrain to terrain. In the pursuit of saving communities that are endangered by landslides, many monitoring techniques are practiced. This paper is a survey of the major landslide monitoring techniques adapted in different parts of the world to monitor unstable slopes. It provides a glance into the challenges and opportunities of integrating IoT into the major landslide monitoring techniques, which are explained briefly with emphasis on real-world case studies. Each technique is presented regarding the kind of monitoring parameters, the type of landslides that it can monitor, landslide investigating phases, advantages, disadvantages, and the possibility of IoT to integrate with each techniques. This paper also aims to provide an overview of landslide monitoring in general to non-specialist in the field. The major monitoring techniques are classified based on the type (fall, topple, slide, spread, flow, slope deformation), velocity (slow, moderate, rapid), monitoring parameters (meteorological, geological, hydro-geological, physical, geophysical), monitoring phases (spatial, temporal) and early-warning systems (spatial, temporal) of landslides. This classification will serve as a guideline (but not a replacement for expert advice) for selecting appropriate landslide monitoring technique and the classifications are expressed through visual representations.

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“…In mines geo-phones are now interconnected and used to gather micro seismic data which is further analyzed to provide safety predictions [10], [11].…”

Section: Background and Related Workmentioning

confidence: 99%

A feasibility study of SOA-enabled networked rock bolts

Eliasson

Pereira

Mäkitaavola

et al. 2014

Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)

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Abstract-The use of rock bolts in the mining industry is a widely used approach for increasing mine stability. However, when compared to the automation industry, where the use of sensors and real-time monitoring of processes have evolved rapidly, the use rock bolts have not changed a lot during the last 100 years. What is missing are technologies for keeping installed rock bolts under real-time and online monitoring. One problem is that rock bolts can become damaged by seismic activities or movements within the rock, and thus lose their load bearing capacity. If that happens, the outer shell of a tunnel's walls or ceiling can collapse, with disaster as a result. Therefore, there is a clear need for online and real-time monitoring solutions for strain and thereby stress, as well as seismic activity.In this paper, the current state of art in research around intelligent rock bolts is presented. An intelligent rock bolt is the combination of a traditional rock bolt with an Internet of Things device, i.e. a rock bolt with embedded sensors, actuators, processing capabilities and wireless communication. In the proposed architecture, every rock bolt has its own IPv6 address and can establish a wireless mesh network in an ad-hoc manner. By measuring strain and seismic activity and exposing the sensors in the form of services, large gains in terms of safety and efficiently can be achieved. A number of mining related activities such as stress on the rock bolt can be detected, falling rocks and the presence of mobile machinery can be observed. Since the network is based on standard communication protocols such as IPv6, it is vital to add security mechanisms to prevent eavesdropping and tampering of data traffic.By utilizing the real-time monitoring capabilities of a network of Internet-connected intelligent rock bolt, it is possible to drastically improve monitoring of mining activities and thereby providing workers with a safer working environment.

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Integrating Geophone Network to Real-Time Wireless Sensor Network System for Landslide Detection

Cited by 16 publications

References 7 publications

Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network

Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network

Review of Landslide Monitoring Techniques With IoT Integration Opportunities

A feasibility study of SOA-enabled networked rock bolts

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