TECHNICAL FIELD

Embodiments of the present disclosure relates to Wireless Network Sensor Technology. More particularly, the embodiments relate to Design, Development, and Deployment of a Wireless Sensor Network for monitoring and detection of environmental disasters preferably Landslide.

BACKGROUND

Environmental disasters are largely unpredictable and occur within very short spans of time. Prior warning of environmental disasters is more challenging than other applications due to this very unpredictability of the occurrence. Therefore technology has to be developed to capture relevant signals with a minimum monitoring delay. Wireless sensors are one of the cutting edge technologies that can quickly respond to rapid changes of data and send the sensed data to a data analysis center from the remote hostile regions.

A landslide is a short lived and suddenly occurring phenomena and its causative factors can be a steep slope angle, an unstable earlier landslide area, toe cutting, and saturated soil, among others. The triggering factor can be either rainfall or earthquakes. India faces landslides every year with a large threat to human life causing annual loss of US $400 million

The evolution of wireless sensor networks has fostered development in real-time monitoring of critical and emergency applications. A Drought Forecast and Alert System (DFAS) has been proposed and developed as explained in the reference paper [2]. It uses mobile communication to alert the users, whereas the technology disclosed in the present disclosure uses real-time data collection, transmission using wireless sensor nodes, Wi-Fi, a satellite network and the Internet. The real streaming of data through broadband provides connectivity to a wider audience including scientists to laymen who can provide a better pool of experts to analyze the landslide data and provide information to the government. A wireless sensor network prototype for environmental monitoring of greenhouses is described in reference paper [3]. An experimental soil monitoring network using a wireless sensor network is presented in reference [4]. Research has shown that other than geotechnical sensor deployment and monitoring, other techniques such as remote sensing, automated terrestrial surveys, and GPS technology, etc. also can be used by themselves or in combination with other technologies to provide information about land deformations. Reference [5] describes a state-of-the-art system that combines multiple types of sensor to provide measurements to perform deformation monitoring. Reference [6] discusses the topic of slip surface localization in wireless sensor networks, which can be used for landslide prediction. A durable wireless sensor node has been developed [7], which can be employed in expandable wireless sensor networks for remote monitoring of soil conditions in areas conducive to slope stability failures.

In light of the foregoing discussion, there is a need for a method and system to solve the above mentioned problems.

References

[1] Thampi, P. K., Mathai, John., Sankar, G., Sidharthan, S. Landslides: Causes, Control and Mitigation. (Based on the investigations carried out by the Centre for Earth Science Studies, Trivandrum)

[2] Kung, H., Hua, J.,Chen, C. 2006. Drought Forecast Model and Framework Using Wireless Sensor Networks. Journal of Information Science and Engineering. 22, 751-769.

[3] Liu, H., Meng, Z., Cui, S. 2007. A Wireless Sensor Network Prototype for Environmental Monitoring in Green-houses. IEEE Xplore.

[4] Musaloiu-E, R., Terzis, A., Szlavecz, K., Szalay, A.,Cogan, J., Gray, J. 2006. Life Under your Feet: A Wireless Soil Ecology Sensor Network.

[5] Hill, C, Sippel, K. 2002. Modern Deformation Monitoring: A Multi Sensor Approach. FIG XXII International Conference, Washington DC.

[6] Terzis, A., Anandarajah, A., Moore, K., Wang, I-Jeng. 2006. Slip Surface Localization in Wireless Sensor Networks for Landslide Prediction, In Proceedings of IPSN'06.

[7] Garich, E. A. 2007. Wireless, Automated Monitoring For Potential Landslide Hazards. Master Thesis,Texas A & M University.

[8] LAN, Hengxing., ZHOU, C, Lee , C. F.., WANG, S., Faquan, WU. Rainfall-induced landslide stability analysis in response to transient pore pressure - A case study of natural terrain landslide in Hong Kong.

SUMMARY

The shortcomings of the prior art are overcome and additional advantages are provided through the provision of a method and a system as described in the description.

Additional features and advantages are realized through various techniques provided in the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered as part of the claimed disclosure.

In one embodiment, real- time deployment of a heterogeneous network for landslide detection is described in the present disclosure. The present disclosure which uses wireless sensor network for issuing early warning of landslide incorporates both theoretical and practical knowledge from diverse research areas and domains such as landslides, and geomechanics, wireless sensors, Wi-Fi, and satellite networks, power saving solutions, and electronic interface and design, among others.

In one embodiment, the system used for detecting landslides in landslide prone area uses novel data aggregation methods for power optimization in the field deployment.

In one embodiment, the present disclosure provides Fault tolerant transmission techniques are provided to transmit the data in adverse environment conditions. Real-time landslide detection system (RTLDS) requires geophysical sensor data collection, aggregation, and transmission, and efficient delivery of data in near real-time. This requires seamless connectivity with minimum delay in the network.

In one embodiment, the landslide detection system handles immense amounts of data from the wireless sensor network, maintaining its accuracy and integrity, and the timeliness of the sensed data for estimating the chance of landslides, with minimum consumption of energy. Further, new methodology for solving the concerns of energy optimization and changing environmental conditions has been developed. Also, Fault tolerant clustering, energy optimizing topological and data aggregating algorithms have been adopted in the present technology to build a reliable system.

In one embodiment, the present disclosure provides a method to tradeoff bandwidth and computation in the landslide scenario.

In one embodiment, the present disclosure provides power saving techniques by performing low frequency transmission when the power is low.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The novel features and characteristic of the disclosure are set forth in the appended claims. The embodiments of the disclosure itself, however, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. One or more embodiments are now described, by way of example only, with reference to the accompanying drawings wherein like reference numerals represent like elements and in which:

Figure 1 illustrates Multi Sensor Deep Earth Probe having heterogeneous structure with different types of geophysical sensors at different positions, in accordance with an exemplary embodiment.

Figure 2 shows exemplary block diagram of Signal Interfacing Circuit, in accordance with one embodiment of the present disclosure.

Figure 3 illustrates Wireless Sensor Network Architecture for Landslide Detection, in accordance with an exemplary embodiment.

Figures 4(a) shows field deployment of WINSOC Node with Large Antenna and Figure 4(b) shows field Deployment of WINSOC Node with Miniature Antenna.

Figure 5 shows the different modules implemented in a wireless sensor node, in accordance with an exemplary embodiment.

Figure 6 shows exemplary block diagram of the different modules implemented in the Data Management/cluster manager, in accordance with one embodiment of present disclosure.

Figure 7 shows exemplary block diagram of the modules implemented in the Data Management Center, in accordance with one embodiment of the present disclosure.

Figure 8 shows deployment of nested Piezometer, in accordance with an exemplary embodiment.

Figure 9 shows deployment of nested Strain Gauge, in accordance with an exemplary embodiment.

Figure 10 shows deployment of nested Dielectric Moisture Sensor, in accordance with an exemplary embodiment.

Figures 11a and 11b show the rain gauge sensor, and its energy consumption per day and the battery lifetime for various sampling frequencies.

Figures 12a and 12b show moisture sensor and daily Energy Consumption for Dual Sensor and Battery Lifetimes for various sampling frequencies.

Figures 13a and 13b show Piezometer and daily Energy Consumption for Tri-Sensor and Battery Lifetimes for various sampling frequencies.

Figures 14a-14c show the strain gauge, the tiltmeter, and the geophone respectively, wherein daily Energy Consumption for Quad-Sensor and Battery Lifetimes for various sampling frequencies are shown in figure 14d.

Figures 15a and 15b show daily Energy Consumption and battery lifetimes (Days) comparison chart for all Sensor Configurations.

Figure 16 shows Energy Consumption and Battery Life for Threshold Level Sampling .(TLS).

Figure 17 shows Energy Consumption and Battery Life for Cooperative Threshold Level Sampling (C-TLS).

Figure 18 shows Energy Consumption and Battery Life for Differential Forecast Sampling (DFS).

Figure 19 shows comparison graph of daily energy consumption for TLS, C-TLS and DFS.
The figures depict embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.

DETAILED DESCRIPTION

The foregoing has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. Additional features and advantages of the disclosure will be described hereinafter which form the subject of the claims of the disclosure. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the disclosure as set forth in the appended claims. The novel features which are believed to be characteristic of the disclosure, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.

The first and foremost requirement of the technology disclosed in the present disclosure is to identify the required sensors necessary to monitor and detect landslide phenomena. This requires thorough knowledge and understanding about the physical phenomena that provides the insights into geophysical sensor selection. Rainfall induced landslides commonly occur due to heavy high intensity rainfall or prolonged medium intensity rainfall in landslide prone areas. Under heavy rainfall conditions, rain infiltration on the slope causes instability, a reduction in the factor of safety, transient pore pressure responses, changes in water table height, a reduction in shear strength which holds the soil or rock, an increase in soil weight and a reduction in the angle of repose. When the rainfall intensity is larger than the slope saturated hydraulic conductivity, runoff will occur [8].

The major physical phenomena to be monitored for early warning landslides are the changes in moisture content, pore pressure, rainfall, movement, and vibrations inside the earth. After careful study, the geophysical sensors needed for monitoring these phenomena are selected and used. The geophysical sensors include Dielectric moisture sensors, Pore pressure piezometers, Strain gauges, Tiltmeters, Geophones, Rain gauges, Temperature sensors.

In one embodiment, the dielectric moisture sensor selected for deployment is the capacitance type soil moisture sensor which measures the dielectric constant or permittivity of the soil in which it is buried.

In one embodiment, as rainfall increases rain water accumulates at the pores of the soil, exerting a negative pressure, which causes the loosening of soil strength. This accounts for the necessity of measuring groundwater pore pressure. Thus, the pore pressure piezometers are used for measuring groundwater pore pressure. The groundwater pore pressure is measured using either the vibrating wire piezometer or the strain gauge type piezometer.

In one embodiment, the strain gauge is used to measure the movement of soil layers by attaching itself to a Deep Earth Probe (DEP). Deflections in the Deep Earth Probe (DEP) of 0.5 mm per meter need to be detected. So strain gauges of different resistance such as 100, 350, and 1000 have been used for deployment.

In one embodiment, the Tiltmeters are used for measuring the soil layer movements such as very slow creep movements or sudden movements. High accuracy tiltmeters are required for this scenario.

In one embodiment, the geophone is used for the analysis of vibrations caused during a landslide. The characteristics of landslides demand the measurement of frequencies up to 250 Hz. The resolution should be within 0.1 Hz and these measurements need to be collected real-time.
In one embodiment, the effect of rainfall infiltration on a slope can result in changing soil suction and positive pore pressure, or the depth of the main water table, as well as raising the soil unit weight and reducing the anti-shear strength of rock and soil that may trigger a landslide. Maximum rainfall of 5000 mm per year needs to be measured using the tipping bucket. For example, a tipping bucket type of wireless rain gauge in which the tipping event is counted as .001 inch of rainfall is being used for the deployment.

In one embodiment, the physical properties of soil and water change with temperature. Hence, temperature sensors are used to measure the temperature of soil and water. Resolution of 1/1 Oth degree Celsius measured every 15 minutes is sufficient. Temperature measurements are collected using the rain gauge.

In one embodiment, all the above mentioned geophysical sensors are attached to wireless sensor nodes which are capable of real-time monitoring with bare minimum maintenance.

Wireless Probe

The construction of multi sensor Deep Earth Probe is explained in detail herein below. The plurality of sensors for landslide monitoring buried underground to measure the pertinent geological and hydrological properties. A Deep Earth Probe (DEP) was devised to deploy these many sensors as a stack in different locations. The ideal depth for the DEP to be deployed would be the same as the depth of the bedrock in that location.

In one embodiment, The DEP design uses a heterogeneous structure with different types of geophysical sensors at different positions. The geological and hydrological properties at the location of each of the DEPs determine the total number of each of the geophysical sensors needed and its corresponding position on the DEP. These geophysical sensors are deployed or attached inside or outside of the DEP according to each of their specific deployment strategies. All these geological sensors of the DEP are connected to the wireless sensor node via a data acquisition board as shown in Figure 1. The Wireless Probe (WP) consists of a multi-sensor deep earth probe, a wireless sensor node, interfacing circuits, a power circuit, a solar charging unit, and an (optional) external antenna. The interfacing circuit for each type of the geophysical sensors, the power circuits, and the solar charging units are custom designed, tested and verified and integrated with the wireless probe. The placement of the box containing interfacing circuit was designed to be closer to the geophysical sensors, whereas the placement of the battery box was designed to be near to the solar panel. This method minimizes the signal and energy loss, due to the minimum distance to be travelled by each of the connecting wires.

The design of each DEP and the spatial distribution of geophysical sensors on the DEP are determined by different factors including but are not limiting to the number of soil layers, layer structure, soil properties and its variability, hydraulic conductivity of soil layers, the presence of impermeable layers (interbedded permeable and impermeable layers generate a perched water table that will cause slope instability), water table height, bed rock location, depth of the bore hole for deploying the DEP, and the specific deployment method required for each geophysical sensor.

Certain assumptions are made to design and deployment of DEP. These assumptions are: the length of the DEP from surface of the earth be 1; the maximum number of soil layers up to a depth of 1 be n; the thickness of ith soil layer be t,, where i varies from i = 1; 2;------n;

the soil layer number at water table depth be i = wt; the thickness of im* impermeable layer be tim;

the maximum number of impermeable layers in the bore hole used for the DEP deployment be m;

the depth of impermeable layer from the surface of earth can be djm (including the soil layer thickness of the impermeable layer), where im varies from im = 1; 2; _

m; and the thickness of imth impermeable layer be tjm, where im varies from im = 1; 2;
_m.

The formula for determining the location of each sensor placement is developed and the number of sensors needed for deployment as follows:

Dielectric Moisture Sensor: Deployment of at least one dielectric moisture sensor (to measure soil moisture) in each of the soil layers is the theoretical design. So the maximum number of dielectric sensors is given by,

NoMs(ideal) = n (1)

However, soil is made up of impermeable and permeable layers. It is the impermeable layers of soil that can allow water to gather, creating a perched water table, which loosens the soil particles and leads to slope instability. Therefore, it is only necessary to insert sensors into the impermeable layers of soil (and not the permeable layers). In addition to the sensors in each impermeable layer, one more sensor is required, in the soil above the actual water table, to measure the water table's variation in hydrological properties. So, the optimum number of sensors required is given by,

NoMs(min) = m + 1 (2)

The top or bottom portion of a soil layer may include properties of its neighbouring layer. Therefore, in order for the sensors to capture the most accurate signals, they are placed in the middle portion of the soil layer..

The sensor positioned in the bottom layer of the borehole, i = n is given by,

The sensor positioned in any other soil layer starting from, i = nDl to i = 1 is determined using the formula,

The sensor positioned at any impermeable soil layer is determined using the formula,

The sensor positioned at the soil layer above the water table height is determined using the formula,

The position of the optimum number of dielectric moisture sensors can be determined using the equations 5 and 6.

Pore Pressure Piezometer Transducer: Deploying at least one pore pressure piezometer to measure pore pressure in each of the soil layers is the theoretical design for pore pressure piezometer deployment.

The theoretical number of pore pressure piezometer sensors is given by,

NP (ideal) = n (7)

For the purpose of pore pressure measurement piezometers are strategically placed. Similar to dielectric moisture sensor placement, piezometers are placed in each impermeable soil layer and at the water table. Differing from dielectric moisture sensors, there is additional piezometers placed in the layers above and below the impermeable layer. Also for the piezometer placed at the water table there is additional piezometers placed in the layers above and below it. These additional piezometers are required to capture varying water levels. Therefore, the optimum number of pore pressure piezometer sensors is given by,

NP (min) = m + 2*m + 3 (8)

The sensor positioned in the bottom layer of the borehole i = n, the sensor positioned in any other soil layer starting from, i = nD 1 to i = 1, and also the sensor positioned at any impermeable soil layer is determined by adapting the formula used for dielectric moisture sensor such as Equations 3, 4, and 5 respectively. The pore pressure piezometer sensor positioned at the any other soil layer above the impermeable layer is determined using the formula,

The pore pressure piezometer sensor positioned at the any other soil layer below the impermeable layer is determined using the formula,

The pore pressure piezometer sensor positioned at the any other soil layer above the water table height is determined by adapting the Equation 6, and the pore pressure piezometer sensor positioned at any other soil layer below the water table height is determined using the formula,

Strain Gauge: Strain gauges are attached on the outside of the DEP. Strain gauges measure the movements induced by sliding soil layers. Water table depth is not relevant to strain gauge sensor placement. To efficiently capture the movements, multiple strain gauges are placed in the middle section of the DEP, and also in impermeable layers that may contribute to slope instability. These movements are recorded as the deflection in angles. We adapted the equations 3 and 4 to determine the position of a strain gauge sensor in each layer. The soil layer movement at the landslide prone areas can be in any direction. Therefore to capture the movement in multiple directions, the strain gauges in are placed in either x, y direction, or three strain gauges separated by 120 degrees. Hence the theoretical number of strain gauge sensors is given by,

NSG(ideal) = n *3 + 3 (12)

In the field, each vulnerable layer has two sensors placed in the expected direction of movement. Also, three sensors are deployed at the middle of the DEP. Therefore, the optimum number of strain gauge sensors is given by,

NSG(min) = 2 *m + 3 (13)

Tiltmeter: The purpose of the tiltmeter is to capture the change in angle experienced by DEP during slope instability. The tiltmeters are placed in each of the soil layers to capture the tilt accurately. We adapt the same equations used for dielectric moisture sensor such as Equations 1, 3 and 4 to determine the theoretical number of tiltmeters, and the position of the tiltmeter at each layer.

Intense rainfall and impermeable layers contribute to slope instability. Therefore tiltmeters are deployed in the impermeable layers, and also one tiltmeter is placed, inside, at the center of the DEP. Therefore, the optimum number of tiltmeters is given by,

NT(min) = m+l (14)

Geophone: Geophone is used to measure the vibrations experienced during slope instability. Geophones are placed on the surface of the earth. Three geophones are deployed at each region (toe, middle and crown of the hill) of the landslide prone area. The three geophones at each region will perform triangulation techniques providing detection of vibrations. Therefore, the theoretical number of geophones is given by,

NG(ideal) = r *3 (15)

Where r is the number of distinctive regions in the landslide deployment field. The vibrations due to slope instability can be captured using at least one geophone in each of the region. For example, the current deployment consists of three regions. Therefore, the optimum number of geophones is given by,

NG(min) = 3 (16)

Rain Gauge: Rain gauges measure the rainfall intensity. They can be deployed separate from the DEP in different regions. The theoretical number of rain gauges is given by,

NRG(ideal) = r (17)

The deployment field should have at least one rain gauge. Therefore, the optimum number of rain gauges is given by,

NRG(min)=l (18)

In one embodiment, there are specific deployment methods for each sensor. Dielectric moisture sensors and geophones are deployed outside the deep earth probe (DEP) in the respective soil layers. Whereas the strain gauges are attached to the outer surface of the DEP, and the tiltmeters are placed inside the DEP. Multiple pore pressure sensors are deployed in the same bore hole at different depths. Each pore pressure sensor is attached to a separate DEP. These pore pressure DEP have a lesser cross sectional area than the DEPs used for other sensors. Rain gauges are deployed on the surface of the earth at a specific height.

Interfacing circuit

In one embodiment, currently, commercially available wireless sensor nodes do not contain implanted geological sensors; therefore it is necessary to use data acquisition boards to connect the geological sensors externally to the wireless sensor nodes. However, the sensors cannot be connected directly to the data acquisition boards as the data acquisition boards used could only handle an input voltage in the range of 0 to 2.5 VDC, whereas some of the geophysical sensors can produce a maximum output voltage of 9 V. Thus, a special purpose interfacing circuits to modify the sensor outputs into a form compatible with the data acquisition boards is designed and is shown in figure 2. The output of the interfacing board was then fed into the data acquisition board inputs. Later, the signals were software adjusted to obtain the original sensor outputs, and hence the sensor data.

According to the requirements, the special purpose interfacing circuit amplifies the analog signals from the geophysical sensors using an instrumentation amplifier. The interfacing circuit perform signal conditioning using the required filters. Signals from some of the geophysical sensors were level shifted for further signal conditioning. The details are shown in Table 1.

Henceforth, the description provides how information from plurality of sensors is collected and transmitted to data center for further analysis.

One of the important requirements for any landslide detection system is the efficient delivery of data in near real-time. This requires seamless connectivity with minimum delay in the network. The architecture developed for satisfying the above requirements is shown in the figure 3.

The complete architecture is developed by integrating different wireless networks such as, Probe Network, Field LAWN, and Adaptive WAWN.

Probe network

The Probe network is developed to capture the prevailing geological and hydrological parameters in a landslide prone area. This probe network has multiple wireless sensor nodes for example 20 wireless sensor nodes spread on two different hardware platforms.

The first hardware platform is an indigenous wireless sensor network that follows a two-layer hierarchy, with a lower level (wireless probes) and a higher level (cluster head), to reduce the energy consumption in the total network. The wireless probes (lower level nodes) sample and collect the heterogeneous data from the DEP and the data packets are transmitted to the higher level. The higher level aggregates the data and forwards it to the probe gateway (sink node) kept at the deployment site.

The second hardware platform, used, is the newly developed WINSOC wireless sensor nodes. One purpose of this WINSOC network is to extensively test and validate the WINSOC nodes, shown in Figures 4(a) and 4(b), with respect to performance reliability and energy trade-offs between the two hardware platforms in a landslide scenario. WINSOC nodes are endowed with a WINSOC distributed consensus algorithm. Another purpose of this network is to test and validate the performance and scalability of the WINSOC distributed consensus algorithm in a landslide scenario. The probe network is scalable as it provides the capability to incorporate any new field networks to the current network.

A Field LAWN (local area wireless network) is designed to transmit the data received at the probe gateway to the VSAT earth station at the Field Management Center (FMC). This is performed by using the Wi-Fi network between the probe gateway and the FMC which are separated by approximately 500 meters, and if needed routers can be used to extend the range of transmission. A Wi-Fi network, which has many infrastructure management capabilities in challenging Non Line of Sight (NLOS) terrains, is used between the gateway and the FMC to establish the connection. The data received from the probe gateway is duplicated and stored in a database server.

Adaptive WAWN (Wide Area Wireless Network) is used to provide wide area connectivity, and it consists of satellite network, GSM/GPRS network, and broadband network. The data received through Field LAWN is transmitted to the destination using Adaptive WAWN (Wide Area Wireless Network). The deployment field is 300km away from our Data Management Center (DMC) at the university; hence the data is transmitted using the VSAT (Very Small Aperture Terminal) satellite earth station to the Data Management Center (DMC). The DMC consists of the database server and an analysis station, which performs data analysis and landslide modelling and simulation on the field data to determine the landslide probability. The real-time data and the results of the data analysis are real-time streamed on the Internet. Alert services such as E-Mail, SMS and MMS are implemented to alert about the probability of landslides, status of the network and for monitoring the system components. Under extreme conditions, the WAWN adapts, for example, if the VSAT network is not available, the broadband or GPRS connectivity at the FMC is used for uploading the real-time data directly to a web page with minimum delay and thus provides fault tolerance.

The heterogeneous wireless networks such as the wireless sensor network, Wi-Fi, satellite network, and broadband network are used for the landslide detection system. The data collection, processing and transmission techniques in each of these networks are different and each of them demands different requirements to achieve seamless communication with minimum delay. The software architecture developed is capable of achieving all the above requirements. Software interfaces and modules for different processes, required for these heterogeneous wireless networks, have been designed, implemented and tested in the deployment field.

The software modules implemented for the wireless sensor network consist of three root modules such as, data acquisition module, data processing module, data communication module. Each of these modules is explained in detail herein below. The figure 5 shows the different modules implemented in a wireless sensor node.

Data Acquisition module is developed to provide the capability of collecting data from both digital and analog geophysical sensors. It is implemented for data collection from the DEPs. The digital data arriving from the rain gauge is collected using the digital drivers implemented in this module. The analog drivers are utilized for data acquisition from the sensor circuits and excitation circuits.
Data Processing Module: Large, distributed, monitoring applications require scheduling the events, and managing each node's buffer to avoid loss of events and data. The data processing module is the core component, for processing all the incoming and outgoing data from the sensors and transceivers respectively, in our wireless sensor network. The two main functionalities implemented, in this module, are scheduling the events and managing the buffers in a distributed environment. The scheduler module implements four basic functionalities such as,

Sensor sampling: This module is designed to provide efficient communication between the geophysical sensors and the wireless sensor node attached to it, through the custom developed interfacing circuits. It has the capability to sample and collect geophysical sensor data in the user defined inter-sampling rate. The data values received are in the form of a bit count, and are then converted to milliVolt (mV) units. This data is then sent to the buffer management routine.

Health monitoring: This module is designed and implemented to monitor the network health and the health of a wireless sensor node. The node health functionality provides the status of power in the node, the health of the battery and other required aspects. The network health functionality is used to identify the dead nodes in the network, by periodically updating the neighbourhood address. These neighbourhood addresses will be used for efficient routing of the data to the probe gateway.
Power saving: This module is designed to provide power saving mechanisms to the wireless sensor node. It is implemented by incorporating the functionality of wireless sensor node state transitions such as 'sleep', 'monitor', 'active', and 'off state and also the corresponding geophysical sensor state transitions such as 'sleep', 'initiate', 'monitor', and 'off state.

Time synchronization: The module provides time synchronization between the motes and it is implemented using a linear regression method between the motes. Time synchronization is currently implemented using the following two phases: [Synchronization of wireless sensor nodes to a unique (global) clock value and Synchronization of the global clock to the UTC time using GPS receiver.

The two main functionalities implemented in the data communication module are the routing modules and the configuration assistant. The routing module is used for configuring the routing method by implementing routing algorithms and time synchronization methods. The configuration assistant is used to configure the wireless sensor node to act as a configuration assistant when the need arises.

The wireless probe gateway consists of one or more sink nodes or base stations. The functionality of the sink node is to listen to the packet transmissions, in the wireless sensor network, and log and store them if they are addressed to it. This sensor data is accessed through the Wi-Fi network. During a faulty Wi-Fi network condition, the data can be retrieved through either the GSM network or through the Ethernet interface of the sink node. The wireless probe gateway is also implemented with a client utility that provides the functionality to broadcast any configuration information into the network. The software routines implemented in the wireless probe gateway are divided into four categories such as, sink node module, packet processing routine, packet parser and data logger, WSN configuration routine.

Sink node module is designed and implemented to collect and filter the incoming data packets based on their destination id, group id, or broadcast id, and processes them for transmission to the Field Management Center (FMC).

Packet processing routine module is designed and implemented to act as a central point of contact for all sink nodes, data loggers and configuration routines. All data packets from the wireless sensor network and the data packets to the wireless sensor network are received and transmitted respectively, through these processing routines.

Packet parsers and data loggers module is designed and implemented to prevent congestion by prioritizing and queuing the inflow and outflow of the packets. Each of these packets is passed on to the data logger once the CRC (cyclic redundancy check) is verified. It then, logs all the data, from the processing routines, based on the address id of each of the packets. It also stores the date, time, node id, sequence number and the sensor data values in an archive of secondary storage.

WSN configuration routines are designed and used to send the configuration packets to the network. The configuration packet includes the different sampling rates, destination id, routing id, etc.

Lightweight management framework

Real-time, continuous landslide monitoring and detection using wireless sensor network requires distributed communication between the wireless sensor nodes, seamless connectivity, and data transmission with minimum delay. The wireless sensor network uses a two-layer topology for data collection from the geophysical sensors deployed at the field. The data received at the gateway of the wireless sensor network has to be transmitted to data centre for intensive data analysis, landslide modelling, and for alert dissemination. This is achieved through the heterogeneous wireless network architecture. This network is integrated with a novel lightweight management framework (LMF), a service oriented middleware architectures for the remote management of wide spread heterogeneous wireless sensor network deployments in location transparent manner using different wireless networks.

The network must reliably deliver data continuously from a set of deep earth probe sensors in a remote hilly rainforest area to a data management, analysis, and visualization center at the data management centre hundreds of miles away. The Lightweight Management Framework (LMF) framework provides the ability to incorporate different heterogeneous networks such as 802.15.4, 802.1 lb/g, VSAT, GPRS, GSM, Internet and also proprietary wireless sensor network and hardware architectures. It also handles various network failures, data corruption, packet loss, and congestion problems. The data is analyzed to determine the factor of safety of the landslide prone area using landslide simulation software, stream data in real-time to the internet, and give automatic warnings. The architecture has been implemented in a real-time wireless sensor network deployed in the Western Ghats of Kerala, India to detect landslides.

The middleware is built using a Service Oriented Architecture (SOA) that provides the distributed, virtual environment for the heterogeneous wireless networks used for this system development. SOA communicates through service brokers such as an Enterprise Service Bus (ESB). ESB provides a number of higher level services that facilitate service reuse and event driven service initiation. These higher level services are used to implement the required functionalities needed for the different wireless networks such as packet loss handling, bulk data transfer, packet format handling, and remote administration. This architecture provides the feeling of a virtual network rather than the complicacy of handling the different wireless networks. Since the functionalities are designed under ESB, these services are scalable and follow well-defined interfaces, so that any client (independent of platform) can talk to these services. Also the different subsystems, in the middleware, are designed independently so that failure of one system will not affect the other. These systems use a distributed computing architecture that allows the systems to be controlled remotely. Figure 6 shows the block diagram of the middleware.

Figure 7 shows the block diagram of the different modules implemented in the Data Management Center. One of the important requirements of any real-time network is traffic control in the network. This is implemented using the module Bandwidth Utilizer that provides the capability to calculate the amount of traffic in the network and to control the injection of data in accordance with the current traffic. Other than this module, the Congestion Controller module is also implemented. Both the modules are customized to work independently for the connected network e.g. VSAT, Internet. The system logs all the events and it works both in synchronous and asynchronous modes. These modes provide the capability to connect to any external services such as a database, messaging servers, http support, etc.

Real Time Data (RTD) services are implemented to collect the real-time data. Depending on bandwidth availability, these services provide the capability, to dynamically adjust the frequency with which the real-time data is collected and acknowledgements send. The BDE (Bulk Data Enabled) services are implemented for packet loss handling, bulk data handling, packet error handling, detection of network termination, and for varying acknowledgement levels. The RNC (Real-time Network Configurator) services are implemented for the remote administration of wireless sensor network from the DMC. Packet tracking performs two functions. Firstly, it monitors the status of the packet transmission. Secondly, it allows access to only one user, for each RNC service, at any one time. The thread pools and load balancing on the shared resources are monitored and analyzed to prevent dead locks. This design architecture is developed for the universal handling of messages from existing nodes and also with other nodes. The whole functionality of this architecture provides a virtual, distributed environment for the different wireless networks used in the development of the landslide detection system.

The wireless sensor network system for landslide detection is deployed in a remote, hostile region that experiences extreme climatic conditions. The network has very low energy source, and it should have the capability of having the maximum lifetime. The extreme conditions in the field may cause the network to experience problems such as non-responding FMC network, system crash, and depletion of resources. These rare events are also handled using the middleware modules.
Whenever the network is down, the sensor data is cached in the gateway and once the network is reactivated or reconnected, then the cached data is relayed to FMC. If the system crashes, due to some unexpected rare events, the state of the gateway is logged and reverted back when the system reactivates. Resource allocation is controlled and maintained to prevent the depletion of resources. The different modules implemented in the heterogeneous wireless networks, using service-oriented architecture, provide a virtual, distributed network for the real-time wireless sensor network for landslides. All these modules together provide a seamless connectivity, with reliable minimum data packet loss, and low energy consumption. The complete software architecture provides a lightweight management framework for the real-time landslide detection system.

The service oriented architecture divides the system into the application architecture, service architecture and component architecture. The application architecture focuses mainly on client interfaces for the end user, such as web pages, survey systems, and user interfaces for accessing the lower level wireless sensor node services. The service architecture provides the virtual platform for invoking the service requested by the end user in the lower level wireless sensor nodes. The component architecture focuses mainly on the hardware abstraction layer of the lower level wireless sensor nodes. The component architecture consists of the implementation dependent modules. These modules are hardware and sensor specific and may vary between the cluster members. The component architecture is connected to the various services through abstracted management entities.

The wide, area monitoring of the wireless sensor network can be performed in a centralized or distributed manner. The wireless sensor network architecture, for landslide detection in this disclosure, uses a two-layer hierarchy. The wireless probes (lower levelnodes) sample and collect the heterogeneous data from the DEPs and the data packets are transmitted to the cluster head (higher level node). The cluster head aggregates the data and forwards it to the probe gateway.

Data aggregation technique implemented in each deep earth probe fin one PEP)

The aggregation technique used in the DEP that has adopted threshold based temporal data collection technique is averaging the sensor value and transmitting it when the new data overshoots the pre-determined threshold value for each alert state. In this case the data between different sensors are not aggregated together. The data aggregation technique for each sensor is dealt separately.

The aggregation technique used in the DEP that has adopted sensor triggered measurement initiation technique has aggregated related sensors to derive the correlation between their sensor data. These data will be forwarded to the higher layer sensor nodes. So the amount of data transmitted will be less and the processing time will be reduced.

The geological and hydrological properties of each of the locations, of the landslide prone area, differ with respect to the different regions they belong to. So the data received from each of the sensors cannot be aggregated together due to the variability in soil geological and hydrological properties. So the whole landslide prone area is divided as regions of unique properties. In this particular case, the deployment area is divided into three regions such as crown region, middle region, and toe region, and numerous wireless probes (wireless sensor nodes attached to the DEPs) are deployed in these regions.

Power circuits

Power constraints are one of the major problems faced for real-time deployments. Power can be efficiently utilized, using either hardware or software solutions. The whole deployment's power requirements vary depending on a spatio-temporal basis, taking into account the amount of connected components. The geophysical sensor excitation requires different levels of power such as each of the dielectric moisture sensors, pore pressure sensors, and strain gauges require 30 mW, 300 mW, and 435 mW respectively, and the geophones are self excited, so no power is needed. The interfacing circuit and power circuit needs approximately 635 mW, 900 mW respectively. The wireless sensor node and data acquisition board, gateway, Wi-Fi access point, and satellite network requires 81 mW, 6600

mW, 10000 mW, and 8000 mW respectively. The field management center and the data management center needs approximately 1620 W.

Indigenous power circuits are developed to provide constant power for the excitation of the geophysical sensors, wireless sensor nodes, and interfacing circuits. The power (circuit) board provides multiple outputs from a single power battery input, a non-regulated 6 Volts DC supplied from rechargeable lead acid batteries. The power board is designed with high efficiency regulator chips to provide multiple outputs such as +2.5V, -2.5V, +3V, +15 Volts for different requirements. These IC's are loaded to only 5 to 10 % of the rated full load current.

The lifetime of the lead acid battery used for the deployment depends on the rate of power consumption by the geophysical sensors, wireless sensor nodes, and the interfacing circuits. These batteries are automatically recharged by the solar recharging unit using the charge controller. The function of the charge controller is to provide the correct voltage for charging the battery and prevent overcharging the battery.

Each solar panel can output 3 watt of power at 8 volts under maximum sunlight conditions. For each sensor column, six panels are connected in parallel for a total of 18 watts. This would be satisfactory enough for charging drained batteries in one hour's time of sunlight. This is necessary since the deployment site experiences many months with a high percentage of cloud cover and rain. The batteries used are rechargeable sealed lead acid type with 36 Amp-hours capacity at 6 Volts. The charge controllers were also brought inside the waterproof battery to keep them isolated from the electronics circuits.

Efficient use of power and an optimized lifetime has been achieved by these hardware and software solutions.

Deployment of Geophysical Sensors:

Specific deployment methods are adopted for each of the geophysical sensors connected to a DEP. Multiple pore pressure sensors are deployed, in the same bore hole, at different depths. A nested piezometer is deployed at water table, another in the layer above, and another piezometer below the water table as shown in figure 8. The piezometers are also deployed at other impermeable layers, and the layers above and below it. This nested deployment method captures variations in ground water levels and also the variations of perched water levels. These signals will provide the saturation level of pore pressure, and these values will be fed into the data analysis software for further data processing and analysis.

Each pore pressure sensor is attached to a separate DEP. These pore pressure DEPs have a lesser cross sectional area than the DEPs used for other sensors. The remaining area in the bore hole is back filled with the grout mix. The grout mix is used to achieve the same soil strength and compactness inside the borehole. This mix is prepared by using a predetermined ratio of water, bentonite, and cement.

The nested strain gauges are attached to the outer surface of the DEP, in the respective soil layers, as shown in Figure 9. As mentioned earlier, to capture the soil layer movement in multiple directions, the strain gauges are placed in either x, y direction, or three strain gauges separated by 120 degrees.

Nested tiltmeters are placed inside the DEP at respective soil layers. A tiltmeter is fixed inside the DEP. Only part of the sensor tube will move as the slope slowly deforms because of the DEP's length and due to the fact that the DEP is anchored in the solid weathered rock or bedrock below the soil. This will cause part of the tube to become bent. The tiltmeter measures this bend in the tube. Trigonometric formulas can be applied to determine the amount of movement of the slope. The sensor tube movement is very slight. Ground velocities in the range of millimetres per hour need to be detected.

Nested dielectric moisture sensors are deployed outside the deep earth probe (DEP) in the respective soil layers as shown in figure 10. Dielectric moisture sensors are inserted into the side walls of the bore hole, in the respective soil layers, and the bore hole is back filled with the grout mix. The grout mix is used to achieve the same soil strength and compactness inside the bore hole. This mix is prepared by using a predetermined ratio of water, bentonite, and cement.

Geophones were also deployed outside the deep earth probe (DEP). Currently it is deployed in the top soil layer. The geophone was installed at the toe region, on a concrete stand constructed next to the DEP. A 60 cm X 30 cm X 30 cm hole was dug and filled with concrete. The concrete was extended approximately 30cm above the surface. While the concrete was still wet, the geophone was inserted on the top of this structure. The leads from the geophone were run up into a box that contained the signal conditioning circuitry and the wireless sensor node. This box is placed inside a weather resistant enclosure.

Rain gauges are deployed on the surface of the earth at a specific height. In the current deployment rain gauge is placed at the middle position (C5) of the mountain since the middle position provides line-of-sight wireless connectivity to FMC.

Frequency of measuring each of the sensors (in one PEP)

In a system collecting real-time data continuously 24/7, a huge amount of energy could be saved by avoiding unnecessary collection and transmission of redundant data. An example of redundant data would be data collected at times of low landslide risk, such as during a dry period of no rain, where sensors values are nearly static. During this time, the sampling rate of the sensors can be significantly reduced. On the other hand, in a high risk time, e.g. during heavy rainfall, sensor values change rapidly and the data needs to be collected at a higher rate. Two approaches are used to achieve these goals.

a) Threshold Based Temporal Data Collection Methods: One approach is the continuous measurement of all sensors in specific constant intervals. These intervals vary, adapting to the current environmental condition. The frequency of measurements increases when the rainfall rate increases. A three level threshold approach is used in this approach - low, medium, and high rain fall threshold. When the threshold rate of rainfall crosses the low threshold the frequency measurement will also increase. As long as the rainfall rate continues to be in the same range of measurement, the frequency of sensor sampling and measurement will not change. This approach is threshold based temporal data collection and aggregation technique.

The rain gauge reading of the rate and duration of rainfall, determines the alert level of the network and if a transition from one alert level to another is required. The network remains in low alert level, if the deployment site receives zero mm to five mm of rain. The network will transition to medium alert, if the rainfall rate increases above a pre-determined threshold level, determined from the historic rain fall patterns (currently the threshold value is 10mm). The network will transition from medium to high alert, if the rainfall rate increases above the pre-determined threshold level, determined from the historic rain fall rate. Currently the alert level transitions to the highest alert level (of sensor sampling), if either a rate of rainfall of 30mm per 24 hours of rainfall, or if a rate of rainfall of 50mm per hour is experienced. The pre-determined rainfall rate thresholds will be modified, after analyzing the experimental test results received from the landslide laboratory set up, for various climatic conditions. Thus this approach of threshold based temporal data collection method, efficiently collects only required data.

b) Sensor Triggered Measurement Initiation Technique: During low landslide risk, the data will be collected only from rain gauges, moisture sensors, and the piezometers. When the data received from the piezometer sensor crosses the low threshold it will initiate the data collection from the strain gauges, tiltmeters, and geophones. The data collection all the sensors will continue, once the moisture sensor becomes saturated, the frequency of its measurement interval will be increased. In the same manner, once the piezometer readings saturate, the frequency of its measurement interval will be increased. This will reduce the energy consumption.

Three level warning system

The present disclosure provides a novel and innovative decision support system for landslide warning. This system will help save lives due to its capability of issuing warnings in three levels. This will provide enough time for the local community to evacuate. The three level landslide warning systems - Early, Intermediate and Imminent, were developed by considering the signals received from the field and its analysis.

In a rainfall induced landslide scenario, different sensors will react to rainfall in a different time frame and intensity, as they monitor different physical processes. This property is used in the development of the three level warning system and the details of them are:

• Early Warning (Level ONE criteria): During heavy rainfall, the moisture sensor is the first sensor to saturate relative to the other types of sensors. The data will then remain unchanged after the volumetric water content has approached approximately 100 percent. At this condition, the system will issue the early warning or the first level of warning to the web server. The web server will issue the alerts through email, sms, etc., to the research group involved in this work.

• Intermediate Warning (Level TWO criteria): As time progresses and if the rainfall rate remains the same or has increased, changes in pore water pressure values can be seen with respect to the infiltration rate. When the system identifies that the pore pressure value is increasing at a high rate, leading towards the saturation of the soil layer, the system will issue the intermediate or second level warning. At this point in time, the alerts will be issued to the local community and government officials. If the rainfall still persists, the local community will be advised to evacuate the location to save human life from future disaster through email, sms, television broadcast, newspaper etc.

• Imminent Warning (Level THREE criteria): Further, if the system receives a change in the movement sensor values along with the high pore pressure value, it will issue the imminent or third level of warning. Alerts will then be issued to the local community and government officials. The local community will be advlfced to evacuate the location to save human lives through email, sms, television broadcast, newspaper etc.

If at any time the pore pressure value reduces sharply, due to a reduction in rainfall, the landslide warning issued will be removed. Along with the three level warning system, the results of the landslide modeling software will be compared to avoid false alarms.

Cooperative Energy Minimization in Wireless Sensor Networks

The present disclosure provides innovative method to minimize energy by each type of sensors. In addition, an intelligent processor is added to each probe. These intelligent probes cooperatively work to throttle the frequency of measurements in real time based on climatic conditions. The probes also work cooperatively to identify who among them is sensing the maximum parameter, after which all other sensors are switched off for a forecasted duration.

The cooperative energy minimization algorithms are implemented in Intelligent Wireless Probes (IWPs) each consisting of a group of 4 sensors (rainfall, moisture, pore pressure, and movement), a wireless mote and an intelligent.

All of the above sensors use solar power for their energy needs. Solar power tends to rapidly diminish during the rainfall season. Hence minimizing the energy becomes overwhelming priority for sustained operation of the network, particularly during the imminence of landslides. This indeed is the goal of this paper. Using a heterogeneous wide area network consisting of Wi-Fi and long-haul satellite extensive data is collected regarding the rainfall, moisture, pore pressure, and movement in the earth for over periods. Four different policies are experimented for relating the frequencies of measurement of the climate parameters to energy without unduly impacting the efficacy of landslide detection. Based on the experiments, the innovative algorithms are generated to cooperatively compute statistically significant parameters such as maximum, identify the sensors generating the maximum. All other sensors except the one that is generating the maximum are switched off for a forecasted duration so as to further minimize energy.

Single senor approach, dual sensor approach, tri sensor approach and quad sensor approach is performed. And also, various threshold level sampling are performed to minimize the energy consumption.

Single Sensor Approach

One tipping bucket type rain gauge was mounted on a pole above the surface of the earth per acre. The tipping bucket rain gauges were connected to the wireless mote through the data acquisition board, and the wireless motes transmit the sensor data to data management centre via a combination of wireless networks, satellite networks, and intervening gateways.

The rain gauge sensor was programmed to collect and transmit rainfall levels at various frequencies. For each frequency the energy consumed was measured, and together with the battery life times. The rain gauge sensor, its energy consumption per day and the battery lifetime for various sampling frequencies, are presented in Figures 1 la and 1 lb. The energy consumed per day is computed from the sum of the sensor sampling energy (Esmp), and the wireless transmission energy (E^t * Bsmp - energy/bit times bits/sample), both multiplied by the number of samples per day, as indicated by Eq. 19.

The battery lifetime, in the monsoon season, of this system is given by the equation
(20)

Where EChg is the daily average energy restored by solar charging and is computed as the total charge accumulated by a battery during the rainy monsoon season divided by the number of rainy days.

For a range of sampling frequencies from 1 sample/sec to 1 sample/hour, data collected ranges from 86400 samples per day to 24 samples per day, and the energy consumed ranges from 134.353 Whours per day to 38,02 milliWh per day, and battery lifetime ranges from 17 minutes to 26.29 days.

Dual Sensor Approach

A second set of sensors the Dielectric Moisture (DM) sensors are added to measures the level of wetness at a particular soil layer. As the water penetrates the soil, it passes through several soil levels. Each sensor measures only the soil level where it is located. Thus, DM sensors were placed vertically down as far as three meters in order to test the infiltration at a variety of soil levels. The dielectric moisture DM sensors were attached to the data acquisition board and the wireless mote as shown in Figure 12a.

The data generated, the power consumed, and the overall costs associated with placing two sensors and the accompanying network are investigated before deploying. The results are presented in Figure 12b. For a range of sampling frequencies from 1/sec to 1/hour, data collected ranges from 268.735 Wh to 105.340milliWh, the energy consumed ranges from 268.735Wh to 105.340milliWh, and battery lifetime ranges from 9 minutes to ~ 14 days.

Tri-Sensor Approach

The vibrating wire piezometer and the strain gauge type piezometer are both used for measuring the groundwater pore pressure. As rainfall increases, the rainwater infiltration on a slope can result in variance in soil suction, pore pressure, and water table height leading to slope instability. Thus, both versions of the piezometer are used during the deployment. So now the sensor mote had three sensors attached to it: the rain gauge, the dielectric moisture (DM) sensor, and the piezometer. The piezometers as shown in figure 13a were placed along with the dielectric moisture sensor in the soil. The pore pressure measurements were given in KPa (Kilo Pascal).

The data from the tri sensor is collected and transmitted at various frequencies. The result of tri-sensor approach is reflected in Figure 13b. For a range of sampling frequencies from 1/sec to 1/hour, the energy consumed ranges from 403.38Wh to 443milliWh, and
1battery lifetime ranges from 5 minutes to 2 4 days.

Quad Sensor Approach

To capture the soil movements caused by the slope instability, three different movement sensors can be used individually or combined: the strain gauge, the tiltmeter, and the geophone are shown in figures 14a-14c.

The purpose of the tiltmeter is to capture the change in angle of the soil layer during slope instability while the purpose of the strain gauge is to measure the strain experienced in the soil layer during slope instability. The purpose of the geophone is to measure the vibrations generated during slope instability. When comparing the three sensors, the output from the tiltmeter sensor is easily understood with minimal processing, and the signal is only minimally compromised by noise. Both the tiltmeter and the strain gauge need excitation power, but the geophone sensor is self-excited. The geophone requires a high data sampling which leads to a higher bandwidth requirement.
The data from the quad sensor is collected and transmitted at various frequencies and result is presented in Figure 14d. Now the energy consumed increases to 265213W for 1 sample per second to 2.5W for 1 sample per hour.

Energy Minimization

In order to reduce the total energy consumption in the quad sensor setup to acceptable levels, the battery life time should last at least 365 days. Therefore, it is necessary to reduce the sampling frequency of all the sensors to just a few samples per day. However this may make the system vulnerable to missing occurrence of landslides there by voiding the very purpose of the setup. Some dynamic and adaptive techniques are developed for throttling the rates of measurement and transmission of each of the sensors so as to significantly reduce the energy without impacting the effectiveness of landslide detection. Daily Energy Consumption and Battery Life Time (Days) for all sensor configurations such as single, dual, tri and quad sensor approach is shown in figures 15a and 15 b respectively.

Threshold Level Sampling (TLS)

In this technique, sensors sample and transmit only when the individual sensor values exceed their respective thresholds. The total energy consumed by each quad sensor under this policy could be obtained by using

Applying the above on real rainfall data, we obtain the graphs as shown in figure 16.

As seen from Figure 16, threshold level sampling (TLS) improves the battery lifetimes from 43 days at the lowest threshold (20 mm, 0%, 0 KPa) to 63 days at the highest threshold (500 mm, 100%, 60 KPa). In order to not diminish the efficacy of landslide prediction, the threshold is set in one of two ways: (a) If historical rainfall and landslide data for adequate number of prior years is available, the lowest observed rainfall leading to a landslide, however minor it may be, can be used to set the threshold value, (b) In the absence of historical data, the threshold values can be arrived at by using theoretical landslide models applied on soil properties.

Cooperative Threshold Level Sampling (C-TLS)

Next, a cooperative approach is tried among the different sensors to further reduce the energy. In this approach the rainfall and movement sensor thresholds were set at various levels, but the moisture and pore pressure thresholds were activated only when either the rainfall or the movement sensors reached their respective thresholds. This cooperative sensor approach reduces the energy and increases the battery lifetimes to 150 days, with very little chance of missing any landslide occurrences. Figure 17 shows Daily Energy and Battery Lifetime for C-TLS

Differential Forecast Sampling (DFS)

In the event of availability of a historical rainfall and other climatic data a forecast model can be used to predict imminent rainfall and other parameters. The forecast value can be as simple as the repeat of the previous value or a statistical mean of prior years' measurements is used as the forecast value. Each sensor determines the extent to which its current actual measurement differs from the forecast and transmits the difference only if it exceeds a threshold. The total energy consumed by each quad sensor will now have an additional factor, Ercv, which represents the energy consumed by the sensor node to receive the forecast value, in addition to Eday, given by Eq. 19. This can be under both type of forecast could be expressed as Eq. 21,

Where Ercv denote the energy spend to receive a single bit of the forecasted value, SdfS is the daily number of forecast values received, Sday is the number of times the forecast differential exceeds the threshold

Figure 18 shows energy Consumption and Battery Life for DFS. Now referring to figure 19, which shows comparison graphs for Daily energy consumption for TLS, C-TLS, and DFS from figure 19, it is clear that DFS yields, by far, the least daily energy consumption and therefore the maximum battery life.

The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and devices within the scope of the disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims. The present disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

We claim:

1. A system for monitoring and detecting environmental disasters using Wireless Sensor
Network (WSN), said system comprises:

i. at least one Deep earth Probe (DEP) configured to deploy one or more different
types of heterogeneous sensors as a stack at a predetermined positions, said DEP comprises
at least one dielectric moisture sensor configured to measure soil moisture in each of soil layers,
at least one nested piezometer configured measure water pressure inside earth and to monitor the variation of pressure levels according to the climatic condition, plurality of strain gauges configured to determine the strain experienced during the soil layer movements in X, Y direction,
plurality of tiltmeters configured to measure angle of tilt experienced by the soil layers during the pre- initiation or initiation of landslide, plurality of geophones configured to capture the vibrations generated during pre-initiation or initiation of landslide and adopted to determine the direction of landslide movement and its velocity, andplurality of rain gauges configured to determine rainfall rate and its pattern;

ii. wireless sensor node connected to said sensors through data acquisition board forcollecting predetermined respective data from the sensors;

iii. interfacing circuits, a power circuit, a solar charging unit and an optional externalantenna is connected to the DEP;

iv. wireless probe gateway comprising one or more sink nodes or base stations to listen to the data transmissions in the wireless sensor network; and

v. Field management center and data management center for receiving the data from the wireless sensor nodes using filed Local Area Wireless Network (LAWN) and Adaptive Wide Area Wireless Network (WAWN) to process and to detect environmental disaster.

2. The system as claimed in claim 1, wherein the nested piezometers are placed below and
above the water table to monitor the variation of pressure levels according to the climatic
condition and also deployed in the impermeable layers of the soil.

3. The system as claimed in claim 1, wherein the moisture sensors are deployed in several layers above the water table, above the impermeable layers and near to the surface to capture the soil moisture conditions with respect to the rainfall rate.

4. The system as claimed in claim 1, wherein the strain gauge sensors are deployed in the impermeable layers and the layer above the impermeable layer to measure the strain variation experienced in the impermeable layer and the layer above it.

5. The system as claimed in claim 1, wherein the tiltmeter sensors are deployed in the impermeable layers and the layer above the impermeable layer to measure the angle of deformation experienced in the impermeable layer and the layers above it.

6. The system as claimed in claim 1, wherein the Geophones are deployed above the impermeable layer.

7. The system as claimed in claim 1, wherein the field management center comprises DB server for storing data received from the probe gateway, DAQ controller and field network controller.

8. The system as claimed in claim 1, wherein the data management server comprises central management gateway, DB server, web server, modeling software and alert system.

9. The system as claimed in claim 1, wherein the wireless sensor nodes includes Data Acquisition Module configured to collect data from the sensors, Data Processing Module configured to process all the incoming and outgoing data from the sensors and transceivers respectively, and Data Communication Module for providing efficient communication between the sensors and the wireless sensor nodes.

10. The system as claimed in claim 9, wherein the Data Processing Module includes health monitoring module for monitoring the network health and health of the wireless sensor node, and Power Saving Module to provide power saving mechanisms to the wireless sensor node, Time synchronization module, and sensor sampling module.

11. The system as claimed in claim 1, wherein the system is integrated with lightweight management framework which is service oriented middleware architecture for remote management of said heterogeneous wireless sensor network.

12. The system as claimed in claim 11, wherein the lightweight management framework is capable of incorporating different heterogeneous networks selected from a group comprising 802.15.4, 802.22b/g, VSAT, GPRS, GSM, and Internet.

13. The system as claimed in claim 1, wherein the system is configured to provide three level warning comprising Early warning, Intermediate warning, and Imminent warning.

14. The system as claimed in claim 1, wherein cooperative energy minimization methodology is implemented in intelligent wireless Probes each consisting of a group of 4 sensors, a wireless mote and an intelligent processor, said intelligent probes cooperatively works to throttle the frequency of measurements in real time based on climatic conditions, and identify the sensor which is sensing the maximum parameter among them to switch off other sensors for a forecasted duration.

15. The system as claimed in claim 14, wherein the energy minimization is performed using predetermined sensor approach selected from a group comprising single sensor approach, dual sensor approach, tri sensor approach and quad sensor approach.

16. The system as claimed in claim 1, wherein the system includes interfacing circuits to modify the sensor outputs into a form compatible with the data acquisition boards.

17. A method for monitoring and detecting environmental disasters using Wireless Sensor
Network (WSN), said method comprising acts of:
collecting heterogeneous data from plurality of sensors configured with DEP and aggregating the collected data before forwarding it to probe gateway;
transmitting the data received at the probe gateway through wireless network to VSAT earth station at Filed Management Center (FMC) using Field Local Area Wireless Network (LAWN);
transmitting the data received at the FMC through wireless network to data management center using adaptive Wide Area Wireless Network (WAWN); and

performing data analysis and land slide modeling on the field Data to determine environmental disasters and issuing disaster warning through plurality of communication means using analyzed data.

18. The method as claimed in claim 17, wherein real-time data and results of the data analysis are real-time streamed to alert about the probability of landslides using at least one communication network selected from a group comprising VSAT network, Broadband and GPRS.

19. The method as claimed in claim 17, wherein incoming data packets are collected and filtered by sink node module implemented in the wireless probe gateway based on their destination id, group id, or broadcast id.

20. The method as claimed in claim 17, wherein the data from plurality of sensors is measured continuously in predetermined constant intervals depending on environmental condition.

21. The method as claimed in claim 20, wherein the frequency of measurements of the data from the sensors varies based on the rainfall rate, said rainfall rate includes low, medium, and high rain fall threshold.

22. The method as claimed in claim 21, wherein the frequency measurement increases when the threshold rate of rainfall crosses the low threshold.

23. The method as claimed in claim 17, wherein during environmental disaster preferably low landslide risk, the data is collected from rain gauges, moisture sensors, and piezometers, said piezometer sensor initiate data collection from strain gauges, tiltmeters, and geophones when the data received from the piezometer sensor crosses the predetermined threshold.

24. The method as claimed in claim 23, wherein the frequency of sensor data measurement interval is increased when the sensors becomes saturated.