The Application of WiFi-based Wireless Sensor Network (WSN) in Hill Slope Condition Monitoring
DOI:
https://doi.org/10.11113/jt.v73.4250Keywords:
WiFi, 802.11g, wireless sensor network, WSN802G modules, hill slope monitoring, humidity measurement, atmospheric pressure measurement, soil moisture content, low cost vibration transducerAbstract
In this paper, a wireless sensor network for landslide monitoring (WSNLM) system is described. WSNLM utilized a wireless protocol which is 802.11g. The hardware structure of the WSNLM is discussed where the important parts had been discussed in details. In order to assess the susceptibility of a hill slope to landslide, several parameters had been considered for the network. The important factors that affect landslide is the ground status, which is soil moisture, vibration in the land and also soil temperature. Other factors that can relate to landslide is the environment of the surrounding such as air temperature, humidity and atmospheric pressure. The outputs from the ADXL335 accelerometer were used for slope angle measurement. The output ofa vibration transducer was also used to monitor the hill slope. To account for the susceptibility of the hill slope to the land slide, safety factor value is calculated in real time. The outcomes show that the average moisture content in the soil is around 3 % on a sunny day and the safety factor for a sunny day is around 75. The moisture content in the soil on a rainy day increases tremendously to more than 20 %. At the same time, the safety factor drops to around 70. The system in this paper has the potential to be used as a useful tool for the detection of lanslides.
References
S. G. Evans and J. V. DeGraff. 2002. Catastrophic Landslides: Effects, Occurrence, and Mechanisms. Geological Society of America. 15.
M. P. W. Department. 2009. National Slope Master Plan 2009–2023. Kuala Lumpur.
P. D. Savvaidis. 2003. Existing Landslide Monitoring Systems and Techniques. From Stars to Earth and Culture. In honor of the memory of Professor Alexandros Tsioumis. The Aristotle University of Thessaloniki, Greece. 242–258.
H. K. Patil and S. A. Szygenda. 2012. Security for Wireless Sensor Networks Using Identity-based Cryptography. CRC Press.
N. Vyas and R. Shah. 2014. Intelligent and Efficient Cluster Based Secure Routing Scheme for Wireless Sensor Network using Genetic Algorithm. International Journal of Digital Application & Contemporary Research. 2: 1–7.
I. Mahgoub and M. Ilyas. 2006. Sensor Network Protocols. CRC Press.
G. R. Mendez, M. A. M. Yunus, and S. C. Mukhopadhyay. 2011. A WiFi Based Smart Wireless Sensor Network For An Agricultural Environment. In 2011 Fifth International Conference on Sensing Technology (ICST), Palmerston North, New Zealand. 405–410.
G. R. Mendez, M. A. M. Yunus, and S. C. Mukhopadhyay. 2012. A WiFi Based Smart Wireless Sensor Network For Monitoring An Agricultural Environment. In 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Graz, Austria, 2012, 2640–2645.
N. Wang, N. Zhang, and M. Wang. 2006. Wireless Sensors in Agriculture and Food Industry—Recent Development and Future Perspective. Computers and Electronics in Agriculture. 50: 1–14.
M. V. Ramesh. 2009. Real-Time Wireless Sensor Network for Landslide Detection. In Third International Conference on Sensor Technologies and Applications, 2009. SENSORCOMM '09, Athens/Glyfada, Greece, 405–409.
H. Z. Kotta, K. Rantelobo, S. Tena, and G. Klau. 2011. Wireless Sensor Network for Landslide Monitoring in Nusa Tenggara Timur. TELKOMNIKA Indonesian Journal of Electrical Engineering. 9: 9–18.
D. T. T. Chang, L. L. Guo, K. C. Yang, and Y. S. Tsai. 2011. Study of Wireless Sensor Network (WSN) using for Slope Stability Monitoring. In 2011 International Conference on Electric Technology and Civil Engineering (ICETCE), Lushan, China. 6877–6880.
D. Tarchi, N. Casagli, R. Fanti, D. D. Leva, G. Luzi, A. Pasuto, et al. 2003. Landslide Monitoring by Using Ground-based SAR Interferometry: An Example of Application to the Tessina Landslide In Italy. Engineering Geology. 68: 15–30.
C. Arnhardt, K. Asch, R. Azzam, R. Bill, T. M. Fernandez-Steeger, S. D. Homfeld, et al. 2007. Sensor based Landslide Early Warning System-SLEWS. Development of a Geoservice Infrastructure as Basis for Early Warning Systems for Landslides by Integration of Real-time Sensors. Geotechnologien Science Report. 10: 75–88.
J. A. Gili, J. Corominas, and J. Rius. 2000. Using Global Positioning System Techniques in Landslide Monitoring. Engineering Geology. 55: 167–192.
F. Ahmad, A. S. Yahaya, M. M. Ali, and S. H. A. Hairy. 2012. Qualitative Risk Assessment Schemes Using Selected Parameters for Hillslope Developments: A Case Study of Penang Island. Landslides. 9: 63–74, 2012/03/01.
R. Nagarajan, A. Mukherjee, A. Roy, and M. V. Khire. 1998. Technical Note Temporal Remote Sensing Data and GIS Application In Landslide hazard zonation of part of Western ghat, India. International Journal of Remote Sensing. 19: 573–585, 1998/01/01.
F. C. Dai, C. F. Lee, J. Li, and Z. W. Xu. 2001. Assessment of Landslide Susceptibility on the Natural Terrain of Lantau Island, Hong Kong. Environmental Geology. 40: 381–391.
R. F. M. Inc. 2010, 15 April. WSN802G series 802.11g wireless sensor network modules integration guide. Available: http://wireless.murata.com/datasheet?/RFM/data/wsn802g_manual.pdf.
R. L. Ray and J. M. Jacobs. 2007. Relationships Among Remotely Sensed Soil Moisture, Precipitation and Landslide Events. Natural Hazards. 43: 211–222.
R. M. Iverson, M. E. Reid, and R. G. LaHusen. 1997. Debris-flow Mobilization From Landslides 1. Annual Review of Earth and Planetary Sciences. 25: 85–138.
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