INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 5, No 1, 2014 © Copyright 2010 All rights reserved Integrated Publishing services
Research article
ISSN 0976 – 4380
Landslide monitoring by using sensor and wireless technique: a review 1
Govind Singh Bhardwaj, 2Mayank Metha, 3Md. Yeasin Ahmed, 4Mohammod Aktarul Islam Chowdhury 1 Principle Investigator, DST- R&D Project, Department of Mining Engineering, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan-313001, India 2 Mayank Metha, JRF, DST- R&D Project, Department of Mining Engineering, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan-313001, India 3 Lecturer, Department of Civil Engineering, Leading University, Sylhet-3100, Bangladesh 4 Dean and Professor, Applied Sciences and Technology, Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh
[email protected]
ABSTRACT The sensor is a major device in electronics for measuring physical data from the environment. Immense applications in the field of an early warning system in space sciences, atmospheric sciences & aeronautical engineering have been explored by various workers. It has been realised that landslide is a frequently occurring natural hazard in the hilly terrains of India; consequent upon every year there is a great loss of life and property. Sensor can be used for the early prediction system of landslide and it could help in preventing the millions of the losses due to natural hazard. In the direction of the landslide prediction, sensor can play a great role, where sensor connected with wireless protocol can make it very useful for remote areas landslide mapping, detection, analysis and prediction etc. A wireless sensor network consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, including temperature, sound, pressure, etc. is found be worthwhile. Keywords: Landslide, Sensor, Zigbee, GSM/GPRS, Gateway, WSN, Mobile IP etc. 1. Introduction History of development of sensor nodes dates back to the Cold War. A system of acoustic sensors on the ocean bottom, for sound surveillance was deployed by USA to detect and track Soviet submarines which is now used by the National Oceanographic and Atmospheric Administration (NOAA) for monitoring events in the ocean, e.g., seismic and animal activity . During the same time, United States developed the network of air defence radars to defend its territory, which now is also used for drug interdiction. Research on sensor networks started around 1980 with the Distributed Sensor Networks (DSN) program at DARPA (Defence Advanced Research Projects Agency) where, Arpanet (predecessor of the Internet) approach for communication was extended to sensor networks. The network was assumed to have many spatially distributed low cost sensing nodes that collaborate with each other but operate autonomously, with information being routed to whichever node can best use the information. Technology components for a DSN were identified in a Distributed Sensor Nets Workshop in 1978. These included sensors (acoustic), communication, processing techniques and algorithms (including self-location algorithms for sensors) and distributed software.
Submitted on April 2014 published on August 2014
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Landslide monitoring by using sensor and wireless technique: a review Govind Singh Bhardwaj et al.,
Distributed acoustic tracking was chosen as the target problem for demonstration. The Accent (a network operating system that allows flexible, transparent access to distributed resources needed for DSN) was developed at CMU (Carnegie Mellon University). Afterwards/ later on it evolved into the Mach operating system, which found considerable commercial acceptance. Further, in 1980s, a multiple-hypothesis tracking algorithm based on DSN was developed by Advanced Decision Systems (ADS), Mountain View, CA, which dealt with difficult situations involving high target density, missing detections, and false alarms. MIT Lincoln Laboratory developed the real-time test bed for acoustic tracking of low-flying aircraft for demonstrations and Communication by Ethernet and microwave radio. In 1990 the communication made easy by the use of wireless protocol that enable long distance data transmission capability. A recent issue of IEEE Spectrum classified WSNs as one of the top 10 emerging technologies. Eventually, it is felt by most of the research community that it will pervade into daily life like the cell phone technology. WSNs may either connect to the rest of the world through the cellular network or through the wired internet. 2. Procedure of landslide detection by sensor networking The procedural part is dealt with the sensor nodes equipped with strain gauges are embedded in the rocks. The node designated as group and finally into clusters & each cluster has a cluster head (CH) which leads to be responsible for aggregating the data. Ordinary nodes send the data to the CH. The CHs send only the local decision to the Base Station (BS) via multi-hop shown in figure 1. BS is a special kind of a node interfaced with a GSM/GPRS modem [8]. In Threshold Based Detection (TBD) method, each CH calculates the average of the sample data. To make the decision as to whether a landslide has occurred or not, the BS compares the overall average with a predetermined threshold (Dth). If the average is below (Dth) BS decides in favour of landslide will not occur and vice-versa. The basic schematic of the landslide prediction and early warning system may be depicted and developed, as shown in figure 1.
Figure 1: Architecture for Landslide prediction of four lane road side hill cut faces. (Modified after, Mehta P. et al., 2007).
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Landslide monitoring by using sensor and wireless technique: a review Govind Singh Bhardwaj et al.,
The OTDR (Optical Time Domain Reflectometry) method tried at several places, as one of the successful optical fiber sensing system in landslide detection. The sensor is a mechanical device in which part of an optical fiber bends in response to landslide displacement. Several sensors are installed along the optical fiber measurement line, and the OTDR detector detects the transmission loss of the light caused by bending of the optical fiber, at the locations of several sensors simultaneously. The landslide displacement is calculated from the change of transmission loss. As a result of measurement, tensile displacement was detected; this is similar to the tensile displacement of an adjacent extensometer. 3. Algorithm of sensor detection & types Detection is performed through a three-state algorithm i.e. first-state may be explained as sensors collectively detect small movements consistent with the formation of a slip surface separating the sliding part of hill from the static one. Once the sensors agree on the presence of such a surface, they conduct a distributed voting algorithm to separate the subset of sensors that moved from the static ones. In the second phase, moved sensors self-localize through a trilateration mechanism and their displacements are calculated. Trilateration is a method of surveying in which the lengths of the sides of a triangular area are measured, usually by electronic means (sensors), and, from this information, angles are computed. By constructing a series of triangles adjacent to one another, one can calculate other distances and angles that would not otherwise be measurable. The development of electronic distance-measuring devices has made trilateration a common and preferred system. Except that only lines are measured, while all angles are computed, the field procedures for trilateration are like those for triangulation. Finally, the directions of the displacements as well as the locations of the moved nodes are used to estimate the position of the slip surface. This information along with collected soil measurements (e.g. soil pore pressure) are subsequently passed to a Finite Element Model that predicts whether and when a landslide will occur. The sensor column is given in figure 2.
Figure 2: Schematic diagram of Sensor Column (After Terzis A. et al., 2006)
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Landslide monitoring by using sensor and wireless technique: a review Govind Singh Bhardwaj et al.,
3.1 Distributed Detection using Wireless Sensor Networks According to Jagyasi (2008) and his team, various data fusion (aggregation) algorithms useful for binary event detection using wireless sensor networks have been proposed. The wireless sensor networks topologies considered are single-hop (star topology) and multi hop i.e. particularly tree topology. The schemes proposed by them are capable to aggregate onebit and multi-bit information from various sensor nodes. The schemes ranges from that requiring complete knowledge of the a-priori probabilities to the schemes which do not require any such a-prior knowledge. Further, the proposed aggregation schemes are based on different criterions like, Bayesian or likelihood ratio, Minimum Mean Square Error (MMSE) and adaptive Least Mean Square (LMS) criterion. This versatility of the proposed methods makes them usable for many practical scenarios. 3.2 Multi hop cellular sensor networks (MCSN) The ubiquitous use of cell phones motivates the idea of using cell phones and other hand-held devices as carriers of sensors which when networked can cater to a large number of urban applications e.g. environmental monitoring, urban planning, natural resource management, tourism, civic hazard information sharing, crime patrolling etc. Multi hop Cellular Sensor Networks (MCSN), which combines the advantages of mobile Cellular Sensor Networks (CSN) and the Multi hop Cellular Network (MCN) infrastructure has been advocated by Deepthi Chander et al. (2009). In addition to an increase in coverage and energy efficiency compared to a static WSN. MCSNs can have the involvement of a human user (for e.g. fire detection application) to enhance the application. In Distributed Velocity Dependent (DVD), the Waiting Time based aggregation and routing protocol using MCSN for a moving event localization application have been proposed. The particulars of the wireless sensor networking category useful in landslide detection are given in table 1. Table 1: Wireless sensor networking category (After Deepti Chander et al.2009) SL. No
Category 1 WSNs
Category 2 WSNs
1.
Invariably mesh-based systems with multi hop radio connectivity.
Point-to-point or multipoint-topoint (star based) systems generally with single-hop radio connectivity.
2.
3.
Highly distributed high-nodecount applications. Compatible: Military theatre systems environmental monitoring, national security systems.
Confined short-range distribution. Compatible: home, a factory, a building, or the human body.
4. Discussion For the networking of the sensor, wireless technology, standard configurations and software are selected on the basis of the utility and application. Some of the key technology and standard elements that are relevant to sensor networks for landslide monitoring detection are as follows:
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Landslide monitoring by using sensor and wireless technique: a review Govind Singh Bhardwaj et al.,
1. Sensors are chosen according to their functionality viz. Signal processing capability, Compression, forward error correction, encryption, Control/actuation, Clustering and in-network computation etc. 2. Wireless radio technologies meant for the wireless data transmission, transmission range, Transmission impairments, Modulation techniques, Network topologies are mainly considered for transmission medium air. 3. Standards: IEEE 802.11a/b/g together with ancillary security protocols, IEEE 802.15.1 PAN/Bluetooth, IEEE 802.15.3 ultra wideband (UWB), IEEE 802.15.4/ZigBee (IEEE 802.15.4 is the physical radio, and ZigBee is the logical network and application software), IEEE 802.16 WiMax, IEEE 1451.5 (Wireless Sensor Working Group), Mobile IP (IEEE standard) followed according to the short range or long range data transmission. 4. Software type to be used depends on the nature of applications and networking. Operating systems are just used for managing hardware and software, Network software are available to manage and monitor networks of all sizes, Direct database connectivity software, Data management software, Middleware software connects two separate applications. Landslide problem is worldwide but the geographic conditions are different therefore according to the geographic conditions variety of sensors and transmission technology should be adopted. Strain gauge sensors are used everywhere but proves to be suitable for mountainous area contain with rocks. OTDR is flexible in use in every situation, it bent and indicates the displacement, although it is costly and hence it should not be used at terrain with hard fragmented rock it is best for soil area. Tilt and soil moisture sensor may be used in soil area and Bluetooth technology are for short range data transmission , WAN are best for long distance data transmission where lease line or internet facility available, long distance and remote area data transmission can be achieved by the GSM/GPRS technology. 5. Conclusions Present study describes some of the important characteristics and application of sensor networking systems for landslide detection along four lane roads in hilly area. The study is equally applicable in monitoring of landslides prone areas. Study concludes about early warning of landmass likely to fail in due course of time. Acknowledgements Department of Science and Technology, Government of Rajasthan, Jaipur is highly acknowledged for providing financial support to carry out the study vide sanction No P.7 (3)S.T./R&D/2010-11/3280-91 Jaipur, Dated 20.03.2012. We are also thankful to Professor P. L. Maliwal, Director Research, MPUAT, Udaipur, for facilitating us in the day today work at university level. 6. References 1. Rathnaweera T. D., Palihawadana M. P., Rangana H. L. L., and Nawagamuwa U. P. (2012),Civil Engineering Research Exchange Symposium 2012 Faculty of Engineering University of Ruhuna, Web pages: “Effects of climate change on landslide frequencies in landslide prone districts in Sri Lanka; Overview”,
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Landslide monitoring by using sensor and wireless technique: a review Govind Singh Bhardwaj et al.,
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15. Web page: http://www.pwri.go.jp/team/landslide/outcome/102.pdf. First North American Landslide Conference [higuchi.070603.pdf - 1356kb], pp.1-9 16. Terzis A., Anandarajah A., Moore K., Wang I. J. (2006), “Slip Surface Localization in Wireless Sensor Networks for Landslide Prediction”, Appeared in the Proceedings of IPSN 2006. 17. Desai U. B., Jain B. N., Merchant S. N. (2007), “Wireless Sensor Networks: Technology Roadmap, Workshop on Wireless Sensor Networks at IITB on April 20, 2007. 18. Jagyasi B. G., Dey B. K., Merchant S. N., and Desai U. B.(2008), “An Efficient Multibit Aggregation Scheme for Multihop Wireless Sensor Networks”, EURASIP Journal on Wireless Communications and Networking, (EJWCN) 2008, Article ID 649581, 11 pages, 2008. doi:10.1155/2008/649581. 19. Desai U., Jagyasi B.G., Chander D., Merchant S. N. and Dey B.K.(2010), “Blind Adaptive Weighted Aggregation Scheme for Event Detection in Multihop Wireless Sensor Networks” , Springer Wireless Personalized communication (WPC), 2010. 20. Jagyasi B.G. (2008), “Distributed Detection in Wireless Sensor Networks”, Ph.D. Thesis, IIT Bombay, 2008. 21. Chiti, Michele Ciabatti, Giovanni Collodi, Davide Di Palma, Antonio Manes,: “An Embedded GPRS Gateway for Environmental Monitoring Wireless Sensor Networks Francesco”. 22. Markku Hautamäki Vaasa (2003), “Using GPRS as a wireless core network for wireless local areanetworkstechnologyand communication”. 23. Chander D., Jagyasi B.G.(2009) Desai U., Merchant S. N. Conference on Communications, ICC 2009, June 14-18 2009.
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Landslide monitoring by using sensor and wireless technique: a review Govind Singh Bhardwaj et al.,
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