effective approach for landslide monitoring using ...

International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 6, November-December 2016, pp. 378–385, Article ID: IJCIET_07_06_041 Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=6 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 © IAEME Publication

EFFECTIVE APPROACH FOR LANDSLIDE MONITORING USING WIRELESS SENSOR NETWORKS Niraj Prasad Bhatta PG Scholar, Embedded Systems Design, Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain University, Bangalore, India. Dr. Thangadurai N Associate Professor, Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain University, Bangalore, India. ABSTRACT The phenomenon that occurs in the precipitous hills and affects animate and inanimate settlements with the down slope faction of soil and rock along with unrefined materials is landslide. It is intractable type of geological phenomenon which results under the force of gravity with exgregious attack. Real time prediction and monitoring of landslides is essential for one and all living and including flora and fauna. The ascendant problems that evoke destruction of pulchritudinous hills make life fretful. Some of the preconception and exorbitant ideas are being inscribed in this paper that describes different methodologies of detection and prediction of landslide. Wireless sensor network plays most significant role here. Wireless sensor networks are littered with dense data, inexpensive long range communication to multiple locations, overall increased accuracy and robustness because of the dense deployment of sensors, and real time monitoring and prediction that makes it suitable for the use. Real time monitoring of landslides is considered to be teleological and overarching process ensued by the great loss of life and property. Key words: Wireless Sensor Network, Landslide monitoring, pre-warning Cite this Article: Niraj Prasad Bhatta and Dr. Thangadurai N, Analysis, Effective Approach for Landslide Monitoring using Wireless Sensor Networks. International Journal of Civil Engineering and Technology, 7(6), 2016, pp.378–385. http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=6

1. INTRODUCTION 1.1. Landslides Landslides can be defined as geological phenomenon that results with the egregious attack and creates the situation exacerbated. It causes coalition of land along with unrefined materials toward the bottom of hill or mountain under the force of gravity which results in the teratoid environment. Landslides are considered to be events which can cause animate discomfiture and fatality, deficit monetary inconsistency, eradicate development works along with artistic innate legacy

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Effective Approach for Landslide Monitoring using Wireless Sensor Networks

The essence of motion of land and perceptible things involved are the general categories for description of landslide. Landslides are classified into various types. These include falls ls, Topple, slides and flows. Falls plays an important role .In In this this process Rock or soil disassembles from hills and relocates to its new lounging berth. Generally correlated with undermine overhang on hill or mountain and riverbanks. riverbanks Debris of earth masses and movement of rock by a forward pivot point is called toppling. Flows are considered to be essential and plays vital role. Debris flow, Debris avalanche, earth flow,, mud flow and creep are all its types. Flows are lethargic to expeditious movements of clammy and dehydrated materials which advance by ensuing viscous fluid henceforth considering an initial sliding movement. movement A few flows may be circumscribed by fundamental and insignificant shear surfaces although commanding motion of the deranged material is carried by flowage. flowage Spreads concern the crumbling and edgeways postponement of coherent rock or soil masses due to plastic flow or liquefaction liquefaction of subjacent movement. This circumstance is distinguished by the progressive edgeways dislocation of considerable volumes of scattered materials over very gentle of flat terrain. Liquefaction leads to failure and can be defined as condensation process and triggered by rapid id ground motion. motion A slide prosper were a crack occurs at the highest level of the slope. Where two or more of these ese movements are seen together then it is said to be complex and it is considered to be very useful.

Figure 1 Landslide Scenario

1.2. Wireless sensor Networks It can be defined as collection ollection of self organized sensing sensing nodes grouped in network.Autonomous sensors is spatially dispersed. In order to observe environmental and physical conditions this includes sound, pressure, temperature etc and co-operatively co operatively pass their data through the network to a main location. Wireless sensor networks with a bridge that relates physical and virtual world. The devices present in the network communicate the information rmation collected from the field via wireless medium which chooses many nodes including gateway for the information’s to be forwarded to the other networks[15][16]. networks Generally these consist of base stations and various nodes that are used to observe physical and environment situations so that the information’s get passed to main location. Wireless sensor network works under short distance with less storage of information’s collected and limited power for its processing. Furthermore it requires minimal energy. i.e. constrains protocols and have batteries b with a fixed life span. It deals with the passive devices which provide little energy. Wireless sensor networks plays significant role in environment tracking dealing with forest detection, weather prediction, animal tracking and so on. Furthermore itt is applicable for tracking and environment monitoring surveillance applications which is considered to be an important application in military. The nodes are remotely controlled by user and can be dropped as per interest interest like enemy tracking, security detections etc. etc These are equally useful eful in health applications, transport systems and various other applications.


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Figure 2 Wireless Sensor Networks

2. CLASSIFICATION OF WIRELESS WI SENSOR NETWORKS KS These are classified on the basis of environment so that those can be set up underwater, on land, underground, etc and classified as: 1. 2. 3. 4. 5.

Underground sensor Networks Terrestrial sensor Networks Multimedia sensor Networks Underwater sensor Networks Mobile sensor Networks

Underground Sensor Networks: Considering arrangement, alimentation and cost considerations, cautious planning these hese types of sensor networks is more expensive. These consist of hidden types of sensor nodes that are used to observe ground situations situatio located above the ground. In order to pass the information from hidden type of sensor nodes to the base station. As it deals with the limited battery power the set up arrangement of these types of sensor networks are difficult to recharge.

Figure 3 Underground WSNs


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Terrestrial Sensor Networks: In this type of sensor networks, the set up of the nodes is done either in unstructured or structured manner. In the unstructured manner the nodes are scattered in a random order under the fixed area while in the structured nodes and the nodes are adjusted in 2D, 3D, grid and optimal manners. There is limitation in power usage. Multimedia Sensor Networks: Tracking and monitoring of events is done by multimedia sensor nodes and are in various patterns of multimedia applications. For the purpose of data compression, correlation and data retrieval the nodes are interdependent with each other and consists of low-cost sensor nodes equipped with microphones and cameras. Higher level energy utilization and requirements for the bandwidths, different techniques like data processing and compressing are the challenges with the multimedia sensor networks. Furthermore, for the purpose of contents to be dispatched properly and efficiently multimedia contents require high bandwidth. Underwater Sensor Networks: These types of networks plays important role in underwater. These consist of a set of nodes that lies under water. Self governing underwater vehicles are used to collect information’s. Propagation delay, bandwidth and sensor failures are its challenging features. Mobile Sensor Networks: These consist of collection of autonomous sensor nodes which interact with physical environment and are versatile. The most important feature is that it has the ability to compute communication. Improved coverage, better energy efficiency, supernal channel capacity are the important features of mobile sensor networks.

3. CAUSES OF LANDSLIDES Geology, morphology and human activity are the three major causes of Landslides. Loss of life, forestland, human settlements, agriculture and damage of communication routes are the results of landslides. Geological causes, Morphological causes and Human causes are the outcome for the landslides. Geological Causes: Characteristics of the material itself are defined by the term Geology. Fragile or sensitive materials, weathered materials, sheared, jointed or fissured materials , adversely oriented discontinuity that includes bedding, schiotosity, fault, unconformity, contact and forth ,contrast in permeability or stiffness of materials results under Geological causes. The earth or rock might be weak or fractured. Different layers are described by different strengths and stiffness. Morphological Causes: In General Morphology describes the Arrangement of land. For example, slopes that lose their vegetation to fire or drought are more vulnerable to landslides. Vegetation holds soil in place, and without the root systems of trees , bushes, and other plants , the land is more likely to slide away. Erosion or weakening of earth due to water is considered to be the classical morphological cause of landslide. Human Causes: Human activities increase the risk of landslides. Construction and Agriculture are considered to be the major human activities that increase the risk of landslides.

4. MONITORING AND PREDICTION Landslide is considered to be a teleological and overarching process that affects animate and inanimate settlements including forestland, agriculture, and damage of communication routes. It is an unceasing and inexorable process which is not in our control. Such kinds of disasters cannot be controlled according to our will but can be monitored. In an attempt to detect such calamitous disaster in precipitous hills the following techniques blaze an ample path to protect flora and fauna living around pristine hills [1]. Distributed decision Algorithm is considered to be one of the most challenging algorithm as good as centralized detection. This algorithm is based on distributed decision on the occurrence and non occurrence of landslide and leveled, wear, and fault tolerant energy efficient routing protocol. Centralized detection lend credence with respect to probability of missed detection, probability of false alarm with lesser energy at nodes and finally receiving operating characteristics (ROC) curve are presented to compare the relative


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performance at different presented to compare the relative performance at different nodes and centralized detection simulation was done on mica2 based on Matlab simulation [2]. Furthermore statistical modeling of the landslide strain data along with distributed algorithm has been analyzed. Gaussian process plays vital role since the strain data are modeled using variable mean of Gaussian process. Compared to centralized detection distributed decision algorithm plays significant role [3]. Wireless Geophone Network analyze the ground vibrations that may arise before, during and after the landslide and helps to collect and Analyze the relevant signals which is based on Signal processing Algorithm. It can be observed that the pilot deployment has been performed with one axis geophone but in this process 3D geophone is being introduced which is more effective in localizing the slip location and detecting the movement of the soil layers. Geophone is self excited component and during the design of the system it helps to reduce the power constraint. [Fig.3] represents the use of Geophone inside the deep earth’s pole [4].

Figure 4 Use of geophone

Another concept deals with the Non geodetic monitoring which plays an important role regarding Earth’s natural environment. In this process wireless sensor network is used which deals with real time monitoring over a long distance and inhospitable terrains [6]. Digital sensor connector with IRIS mode and Digital sensor connection with EKO system are being interfaced to wireless module in order to sample the heterogeneous data. Sensors are a type of electronic device for measuring physical data from the environment. Wireless sensor networks s consists of spatially autonomous sensors to monitor Physical as well as environment conditions including temperature, sound, pressure, etc strain gauge sensor can be used for the hill regions. OTDR is flexible in use in every situation; it bent and indicates the displacement. [5], [7], [8]. Autonomous landslide monitoring system is based on wireless sensor Networks and based on self contained, autonomous software programs. ”software agents” are embedded into wireless sensor nodes. In these process software agents helps in continuously analyzing sensor data, such as ground accelerometer and orientation of the sensor nodes along with the slope. [Fig.4] represents cluster division using multihopping protocol and is controlled by control room followed by base stations [9], [10].


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Figure 5 Cluster Division

Figure 6 Cluster Division Process

5. DISCUSSION Various concepts mentioned above recapitulate with prediction of landslide through different techniques and prevents flora and fauna in idyllic hills. Even at less transmission costs distributed algorithm (DD) gives good performance at the nodes. In furtherance to validate the accuracy of planned algorithm, roc has been presented. Distribution algorithm plays vital role and provides more excellent conclusion in contrast with centralized detection Algorithm. Furthermore, to authenticate productiveness of the planned DD Algorithms the prediction correctness of landslide is not good. The Accuracy of landslide prediction is not good enough by the use of the above method as it deals with many factors such as rain storm, snowfall, and artificial influence and so on. It gives good result considering the chances of missed detection and false alarm. Furthermore this algorithm deals with the massed information’s in real time monitoring and prediction of events. Wireless geophones are self exciting component and are based on signal processing algorithm. It helps in lowering the power constraint and lowering the load by collected information’s. Wireless sensor network is also an emerging, reliable and inexpensive technology and is capable of presenting the real time monitoring over a long distance and inhospitable terrains. Using sensor networks on some experiments, it shows bit rates that remains some fixed value. This range coincides with communication standards of wireless sensor networks. The cost is low for online monitoring and can be implemented by excellent characteristics of wireless sensor network that includes low power consumption, http://www.iaeme.com/IJCIET/index.asp

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high working hours, low cost, self adaptive communications and convenient installation at danger zones or broad zone. Compared with tradition monitoring system, new system obtains better performance and practically. Various features like indoor security, intelligent transportation, industrial control is also included in the range of the system application. Sensor connected with wireless protocol can make it very useful for remote areas landslide mapping, detection, analysis and prediction etc. Self contained autonomous software programs are embedded into the wireless sensor nodes. Software agents are continuously collecting and analyzing sensor data, such as recorded ground acceleration and the orientations of the sensors nodes along the slope. If movements are observed, the collected data sets are automatically transmitted to a connected server system for further diagnosis. The costs for cable installation and maintenance are avoided because of the utilization of wireless sensor nodes. Due to the flexibility and adaptability of the software agents embedded into the wireless sensor nodes, a resource efficient reduction of measured data is achieved. Wireless Sensor network technology has provided the capability of developing large scale systems for real time monitoring. This system uses heterogeneous network composed of wireless sensor nodes, Wi-Fi, and satellite terminals for efficient delivery of real time data to the data Management center. It is useful for low cost wireless sensor network for landslide detection.

6. CONCLUSION AND FUTURE SCOPE It has been made known that there are variety of methods that are applicable for landslide detection using various Algorithms that makes life fretful such as Distributed algorithm, use of geophone network , geodetic monitoring, etc which are considered to be essential. Thus in future various natural disasters along with landslide can be monitored using wireless sensor networks. Our aim is to design an effective hardware and software system to predict the landslide in real time and monitor all the time. And also a plan is there to design the hardware system by integrating with GNSS receivers to update the location information.

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Niraj Prasad bhatta and Dr. Thangadurai.N ,Detection and prediction of calamitous landslide in precipitous hills, International Conference on Advanced Communication Control & Computing , pp(274-276)2016 Prakshep Mehta, Mohamed shahim, Kalyana Tejaswi,S.N. Merchant and U.B Desai Distributed Detection for landslide Prediction using Wireless sensor Network , IEEE Conference,2007 Zhao yo ,Shuibao Zhang and Xiaomei Yang ,Distributed Detection in Landslide Prediction Based on Wireless sensor Network , Conference on Dependable computing ,pp(235-238) ,2010 Abishek Thekkeyilkunnath,and Maneeshav. Ramesh, Integrating Geophone Network to real time Wireless sensor network system for landslide Detection, International ,Conference on Sensor Device Technologies and Applications,pp(167-178), 2010 He Yueshun and Zhang Wei, The research on wireless sensor Monitoring, International Journal on Smart sensing and Intelligent systems,Vol.6,No.3, 2013 P.k Mishra,S. K. Shukla, S. Dutta, S. K. Chaulya and G. M. Prasad, Detection of Landslide using wireless sensor Networks ,IEEE Vol5,pp(1-5),2011 Govindsingh Bhardwaj,Mayank Metha, Md. Yeasin Ahmed, Mohammod Aktarul Islam Chowdhury, Landslide Monitoring by using sensor and wireless Technique, International Journel of Geomatics and Geosciences ,2014 k. Georgieva ,K. Smarsly, M. König1 and K. H. Law,An autonomous Landslide Monitoring system based on wireless sensor Networks Prakshep Mehta,et al, Distributed Detection strategies for Landslide prediction using wireless sensor Networks ,2006


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Hu,cheng, shuiBaozhang, shouzhixu, and Bo xu.”Distribution Landslide Monitoring by wireless sensor Nodes”, Advanced Materials Research,2012. Syahmi,M.z., W.A. Wan Aziz, M.A. Zulkarnaini, A Anuar, and Z. Othman.”The Movement detection on the landslide surface by using Terrestrial Laser scanning”, 2011 IEEE control and system Graduate Research colloquium,2011. Kunnath,et al. “Integrating Geophone Network to Real-Time Wireless sensor Network system for Landslide Detection”,2010 First International conference on Sensor Device Technologies and Applications,2010. Hu, Cheng, ShuiBao Zhang, ShouZhiXu, and Bo Xu. "Distributed Landsilde Mornitoring by Wireless Sensor Nodes", Advanced Materials Research, 2012. B., Kiran Y., J. D. Mallapur, Sharanappa P. H., and Somu P. P. "Angular Variation Methodology for Landslide Measurement",Fourth International Conference on Computational Intelligence Communication Systems and Networks, 2012. N Thangadurai, R Dhanasekaran, “Energy Efficient Cluster based Routing Protocol for Wireless Sensor Networks” International Journal of Computer Applications, vol. 71, Iss. 7, pp-43-48, 2013. N Thangadurai, R Dhanasekaran, A Review of Clustering Based Energy Efficient Genetic Algorithms for Wireless Sensor Networks, Europian Journal of Scientific Research, vol.101 Iss.3-4 pp-360-371, 2013. Robert Nini, New Landslides Susceptibility Mapping Method: A Case Study. International Journal of Civil Engineering and Technology (IJCIET), 6(1), 2015, pp.161–171. Chaudhari Komalben Kanjibhai , Improvement of LEACH and its Variants In Wireless Sensor Network , International Journal of Computer Engineering and Technology (IJCET), 7(3 ), 2016, pp. 99 – 107 .

AUTHORS PROFILE Niraj Prasad Bhatta, is pursuing his Master of Technology Degree in Embedded Systems Design from Jain University, Bangalore. He has obtained his Bachelor in Electronics and communication engineering from Anna University, Chennai in the year 2014. His research interests are Embedded Systems, Wireless Sensor Networks, Networking, Satellite communications and Telecommunication Engineering.

Dr. Thangadurai. N is working as an Associate Professor with the Department of Electronics and Communication Engineering, Jain University, Bangalore. He has obtained his Ph.D in Wireless Sensor Networks from Bharathiar University, Coimbatore. He has obtained his Bachelor’s Degree in Electronics and Communication Engineering from Coimbatore Institute of Technology and Master’s Degree in Applied Electronics from Mohamed Sathak Engineering College under Anna University. He has published 50 research papers in both International and National Journals and conferences. He has supervised 50 numbers of undergraduate and postgraduate Students for their project completion and guiding PhD scholars now. He is also working with sponsored research and consultancy projects. His research interests are Networking, Wireless Communication, Satellite Communications, Mobile Adhoc and Wireless Sensor Networks, Embedded Systems, Telecommunication Engineering and Navigation Systems. He is also a Life member of following professional bodies like ISCA, ISTE, IETE, IAENG and IACSIT.


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