International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
Survey on various Wireless Sensor Network Techniques for Monitoring
Activities of Wild Animals
Mr Rachit R Singh1
Assistant Professor, Department of Information Tehnonology,Tulsiramji Gaikwad-Patil College of
Engineering & Technology, rachit.it@tgpcet.com
1
ABSTRACT
In present days, wildlife preservation and monitoring is given more importance for preservation of various
endangered species. Wildlife monitoring system has progressed vastly in various domains with the developments
in Global Positioning System (GPS) and wireless sensor network (WSN) system. In this paper, a study on wild
animals monitoring techniques is made and papers related to animal tracking and monitoring is studied. Also the
paper states various approaches that are used for wild animal monitoring activities.
Keywords: Wireless sensor network, wild animal, LEACH protocol, DEACH protocol, HOTSPOT Algorithm
----------------------------------------------------------------------------------------------------------------------------1. INTRODUCTION1
Wireless sensor network (WSN) is a considered as a collection of sensors devices, called sensor nodes,
which are linked by means of wireless communication channels to achieve distributed sensing tasks. A sensor node
in the wireless sensor network is a small device which is capable of collecting data from the surrounding regions .
The network consists of low-cost sensor nodes with limited battery power. With limited computational capabilities,
the sensor nodes have to use the available source of energy. A network of wireless sensor nodes can be used to
collect information from a variety of applications including military, environmental, medical, habitat monitoring,
and scientific applications. The wireless sensor network is deployed with hundreds or thousands of wireless sensor
nodes.
In this paper, habitat monitoring application is carried out, where the sensor node is deployed for
monitoring tigers is presented. Tiger, the lord of jungles is the national animal of India. India has nearly half the total
population of the tigers in the world. Indian government launched the first project named Project Tiger in 1973 to
protect the royal animal. Under this project 23 reserves have been established throughout the country. The number
of reserves has been increased and the assessment taken between 2006 and 2010 reported information on the 39
Tiger reserves. For the assessment 4,76,000 forest persons were involved. Monitoring exercise of Tigers carried out
between December 2009 and December 2010 using 800 hi-tech cameras. Analysis of habitat status of tiger forests
carried out using satellite data . By June 2011 it has grown to 42 reserves and the reserves governed by Project Tiger
are administered by National Tiger Conservation . With the advancements in WSN the real-time collected data from
the camera-traps can be wirelessly transmitted to a remote station through wireless protocols. This will
tremendously reduce the human requirements and delay in the monitoring of wild animals. The wireless sensor
nodes are capable of self-organizing a network and the nodes will communicate with each other by the use of
wireless interfaces. The sensing node is able to measure and record the position, posture and activity of the animals.
When a sensor node detects some data, it reports the event to the sink with wireless communication capabilities and
network topology. The sink is a control station with a computer and the collected data can be visualized. The
implementation of the hardware sensor network is beyond the scope of this paper. For the data transmission several
multihop transmission are required and the nodes in sensor network will act as a router. Efficient routing protocols
are needed to send the packets efficiently from the source node to the control or monitoring station. Every packet
transmitted in the WSN contains useful information, which can be utilized through packet-based computation and to
enhance congestion control.
The WSN packet computation has small packet forwarding rate and the forwarding computation capability
is limited [1]. Many of times the modeling of sensor nodes are done with limited amount of energy, as a result of
which the sensor nodes lacks recharging issues. Because of the essential characteristics of WSN, the nature of
routing is a challenging one. The WSN design will carry out data communication in order to prolong the node
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
lifetime in the network. This is also used to prevent connectivity degradation by the use of employing aggressive
energy management approaches.
2. RELATED WORK2
Here we have studied the following techniques are broadly used.
2.1 Leach Protocol1
To meet the unique requirements of wireless micro sensor networks, we developed LEACH, an
application-specific protocol architecture , [1]. The application that typical micro sensor networks support is the
monitoring of a remote environment. Since at individual nodes data are often correlated in a network of micro sensor
, the end user does not require all the redundant data; on the other hand the end user needs a high-level function of
the data that describes the events occurring in the environment. Because the correlation is strongest between data
signals from nodes located close to each other, we chose to use a clustering infrastructure as the basis for LEACH.
This may allows all data from nodes within the cluster to be processed locally, reducing the amount of data set that
needs to be transmitted to the end user. In particular, the data aggregation techniques can be used to combine several
correlated data signals into a smaller set of information that maintains the effective data (i.e., the information
content) of the original signals [1]. Therefore, much less actual data needs to be transmitted from the cluster to the
base station (BS). For the development of LEACH, we made some assumptions about the sensor nodes and the
underlying network structure or model . For sensor nodes, we may assume that all nodes are able to transmit with
enough power to reach the Base Station if it is needed, that the nodes can make use of power control to vary the
amount of transmitted power, and that each node have computational power that may support different MAC
protocols and perform signal processing functions. These assumptions are reasonable due to technological advances
in radio hardware and low-power of computing. For the network, we may use a model where nodes always have
data to send to the end user and nodes located close to each other have correlated data. Although the LEACH is
optimized for the situation, it will continue to work even though if it were not true. In Section V, we discuss ways in
which LEACH may be improved when these assumptions do not hold.
In LEACH, different nodes organize themselves into some pattern of local clusters, with one of the node
acting as a cluster head. All non-cluster head nodes in the network transmit their data to the cluster head in the
network, while the cluster head receives data from all the members of the cluster and then performs the function of
signal processing on the data (e.g., data aggregation), and transmits their data towards the remote Base Station.
Thus, being one of the cluster head node is much more energy intensive rather than being a noon cluster head node.
If the cluster heads are chosen in priori and has been kept fixed throughout the systems lifetime, then these nodes
will quickly use up their limited available energy. Once the cluster head drains energy source, it is now no longer in
operational status, and all the nodes that belong to the cluster lose communication ability. Thus, LEACH
incorporates randomization of the rotation of the high-energy cluster head position among the sensors to avoid
draining away of the battery of any one of the sensor present in the network structure. In this way, the energy load
of being a cluster head is evenly distributed among the nodes.
2.2 Deeac Protocol2
Distributive Energy Efficient Adaptive Clustering (DEEAC) protocol provides a hybrid approach for data collection
that adapts to the changes in the data delivery rate . In a WSN high data generation rate may occur in some regions.
This region is considered as the hot regions. The hotness value at a wireless sensor node indicates the data rate at
which the data is generated at that wireless sensor node relative to the network. Figure 1 shows the structure of the
proposed WSN in which the sensor nodes are deployed to monitor the tigers. Since the animals live together, and the
hotspot phenomenon that causes an inconsistent network traffic because of the large volume of data packets
generated from the small are. DEEAC optimize the energy consumption of the network by ensuring the nodes
belonging to the hot regions having a high probability of becoming a cluster head node. In this way the energy
consumption is balanced over WSN.
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
2.3 Motion Tracking Technique3
Motion tracking has been an important and popular research topic in computer vision during the last decade [2].
Various motion tracking algorithms have been studied in different application fields [2]. The applications of motion
tracking include animation creation, virtual reality, navigation, performance measurement, motion analysis,
instrument tracking, view control, video coding and video object extraction [2]. In animation creation field, any
cartoon and animation films include many moving figures, humans, animals and plants which are the essential
elements of the animation films. However, the figure motion design is by no means an easy job. In traditional
cartoon creations, the motion of a figure must be drawn on paper, frame by frame, with each frame drawn slightly
different, costing a lot of time and patience of the painter. In recent years, some professional animation software
tools have been developed, such as Maya, Softimage, and so on. With these tools, the figure modeling and motion
design are getting more easily. However, they are usually expensive for ordinary people and difficult to grasp.
The components of the modeling system are shown in Fig. 1. The media player is used to select video media to play.
The Media Controller is used to send command to Media Player, such as play, pause, forward, backward, etc. It is
used to traverse through frames in the video to locate the desired frame on which joint nodes are to be put and
positioned. The function of Joint Node Editor is to place the joint nodes onto the located moving object and adjust
the positions to reflect stance of object motion. The job of Trace Recorder is to record the object motion into a series
frame and store them into a database for other applications to access.
2.4 Mobile Animal Tracking Systems using Light Sensor 4
Consider sensors. Arduino sensing board in a compact form similar to collars was practically discussed. In addition,
especially, to prolong a battery life time, an analog light sensor is chosen as an alternative over a complex motion
sensor logic using a threshold-based indication to activate GPS location transmission[3].
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
Consider sensors. Arduino sensing board in a compact form similar to collars was practically discussed. In addition,
especially, to prolong a battery life time, an analog light sensor is chosen as an alternative over a complex motion
sensor logic using a threshold-based indication to activate GPS location transmission.
System Architecture
Fig. 1 shows an overall of tracking and searching systems for animals, especially cats and dogs, using a mobile
phone. In general, there are two main components: sensing and tracking devices. Fig. 2 also shows details of each
component as follows.
1)Sensing Device: this component performs as the main function to collect environmental information, and then
send it over mobile and wireless networks to a tracking device.
Motion Detection: this module is purposely used to detect the movement of the animal in order to decide whether
the current location is changed, and whether location information should be sent back to the tracking device.
GPS Location Processing: similar to the previous module; however, this is mainly used to enable a location
processing logic to transmit the positioning data to the SMS-GSM module
SMS-GSM Processing: with environmental and location data available, this module performs the actual data
transmission using SMS services back to the tracking device in a character-based format. Note that all required
information can be fitted in one SMS message (160 bytes).
RF Location Processing: this module functions as a radio frequency transmitter over short distance to the tracking
device using signal strength measurement as an indicator of how far the target is from the current location of the
mobile device.
2)Tracking Device: this component is the main interaction to the human for tracking, searching, tracing, and
monitoring. Aside from Android OS Processing Unit, there are four modules for mobile uses (Fig. 3).
SMS-GSM Processing: this module receives environmental and location information toward GSM networks using
SMS services, and then stored in a local database for further processing. In addition, this module supports a control
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
transmission logic sending back to the sensing device, such as to activate a GPS function so that an updated location
will be sent back in order to update the timely location position, to adjust the sensing interval, or to set a specific
threshold in order to make a transmission decision.
Environmental Monitoring: with environmental data stored in a local database, this module displays a human
readable information regarding the temperature and humidity information, such as temperature=25º and
humidity=19%.
Map and Tracking: this module performs three main functions: first to display the exact location given GPS
information (latitude and longitude) on the map using Google MAP API, second to find out the direction from the
current location (using GPS built-in Android OS phones) to the target, and finally to show a tracing pattern using the
location history for animal behavior analysis.
RF Location and Sound Processing: this module works together with an IOIO interface connecting to an external
RF circuit. Similar to RF Location Processing, RF Solutions Module Receiver was used [3]. Note that the main
function of this module is to warn/alert mobile users when they come closer to the target, especially without GPS
signals. The warning is in both adaptive sound alert and adaptive frequency of bar sign displayed on the mobile
device. Consider all these modules. Controller, Sensor, GPS, and RF logic are self-contained and available for
online orders with reasonable prices.
2.5 Rapid Prototyping for Wildlife and Ecological Monitoring-“Econet”5
EcoNet was inspired by one of our previous projects called YushanNet [4], which is a delay-tolerant wireless sensor
network deployed in Taiwan’s Yushan National Park. The objective of YushanNet is to provide a reliable and robust
system for tracking hikers in the national park. The aggregated information also helps the park’s administrators to
provide various services to tourists, such as maps and the locations of sightseeing spots, toilets and pavilions.
Moreover, the collected hiking traces can provide more precise information to professional rescue teams if hikers get
lost in the mountains. Since full wireless coverage is impossible in wilderness environments, network
communications in YushanNet are inevitably intermittent and thus very challenging. To overcome this difficulty,
YushanNet applies a delay/disruption tolerant network technique and makes use of opportunity, temporary and
short-range wireless communications to disseminate data over the network in a store-carry-and-forward fashion.
More precisely, in the YushanNet system, each hiker is required to carry a device, called Taroko mote, which has a
GPS receiver, a Zigbee radio, and 10 kB of memory. When hikers Fig. 1. General scenarios of YushanNet. (a)
Hikers moving in the same direction. (b) Hikers moving in opposite directions. encounter each other on hiking trails,
the motes automatically exchange their IDs and record the encounter information (i.e., the time and location) in their
respective memories. The devices continue exchanging their stored data as long as possible (i.e., depending on the
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
encounter time and the wireless bandwidth). Then, when a hiker reaches one of the base-stations installed at
frequently visited spots in the park, all the information stored in his/her device is uploaded to an Internet server from
the base station via GPRS or Wi-Fi. Fig. 1 shows two typical scenarios in YushanNet. Suppose the entrance of the
trail is on the right-hand side of the figure, and the base station is deployed on the left-hand side. Fig. 1(a) shows the
scenario where two hikers, and , are hiking in the same direction. Suppose is ahead of initially, but catches up with
in the middle of the trail at time . and ’s devices record the encounter information and exchange their stored data.
Subsequently, hikes faster than and reaches the next base station at time . ’s device uploads its stored data to the
Internet via the base station, and ’s whereabouts become accessible even though he is still on the way to the base
station. In Fig. 1(b), two hikers, and , are hiking in opposite directions on the same trail, and they encounter each
other at time . After the encounter, and continue hiking in their respective directions, and reaches the next base
station at time . ’s device uploads its stored data to the Internet, and thus makes ’s whereabouts available even
though is still on the trail. The results of the YushanNet project are not specific to hiker tracking and rescuing
applications. In fact, they can be generalized to a variety of applications, such as wildlife tracking, scientific
monitoring, landslide and debris flow monitoring, and disaster monitoring networks. We present the details of one of
the extended systems, called EcoNet, in the next subsection.
B. EcoNet Design
The design of the EcoNet system is based on the four observations: 1) since wildlife habitats are usually extremely
large and uninhabited by humans, it is costly and infeasible to deploy a wireless network with full coverage in such
environments for
wildlife tracking and monitoring; 2) there are common places that animals are likely to visit on a regular basis, e.g.,
creeks or lakes for drinking water; 3) different species may encounter each other opportunistically in the wilderness
area; and 4) a prototype for the large wildlife monitoring system is necessary to prevent errors in design. Clearly,
network scenarios that involve wildlife tracking and monitoring are similar to the scenarios encountered by
YushanNet. In the EcoNet system, GPS-enabled sensor nodes are carried by wildlife, Internet-enabled base-stations
are deployed at spots usually visited by the wildlife, and data communication over the network can be implemented
in a store-carry-and-forward fashion, as implemented in the YushanNet system. Fig. 2 shows a general scenario of
the EcoNet system. In the scenario, buffalos, sheep, and eagles live in the observation area. Suppose each animal
carries a client device, and a base station is deployed on a tree next to a lake. While the buffalos and sheep may
encounter each other when wandering in the grass or drinking around the lake, the eagles may encounter other
animals while flying or resting on the tree. Since data communication starts automatically when any two tracked
objects move into each other’s transmission range, the EcoNet system can record the inter/intra-species encounters
and upload the recorded data to the base station, so long as some of the animals pass by the tree. The data can then
be uploaded to the Internet directly via a long-distance wireless communication network (e.g., 3G/GPRS) or
retrieved by message ferries periodically ]. Note that, even though EcoNet shares many design concepts with the
YushanNet system, it has four distinguishing characteristics. First, EcoNet tracking devices must be customized for
the animal of interest. For instance, biologists advise that a sensor node should not weigh more than 5% of the
tracked animal’s body weight. Since the client device for each hiker inYushanNet weighs about 100 g, it can only be
used in the EcoNet system if the animal weighs more than 2 kg; hence, it can not be used for small animals, such as
squirrels and turtles. Second, EcoNet client devices must have a much longer battery life than those used in the
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
YushanNet system. The client device is only required to be alive for one week in the YushanNet system (hikers
usually take less than a week to finish a trip). In contrast, several months of battery life must be provided in the
EcoNet system, since it is extremely labor-intensive to recharge/replace batteries after the network is deployed.
However, as the battery’s capacity depends to a large extent on its shape and size, which are constrained by the
tracked animal’s body size and weight, energy management is one of the most important issues in the EcoNet
system. In the developed system, a simple energy management protocol that controls the duty cycle of the GPS
receiver. The process could be enhanced by controlling the CPU and radio duty cycles, or by applying modern
battery technologies (e.g., solar panels and fuel cell batteries). Third, the EcoNet client device must be more resilient
against extreme environmental conditions than the YushanNet client device, as the mobility of wildlife is less
predictable and much more diverse than that of hikers. For example, the EcoNet client device must be resistant to
shaking, as animals may run and jump very fast in the wilderness. Moreover, the device must be waterproof, since
some animals may live in wet areas and/or the device may be soaked in water (e.g., when the animals drink from or
cross a river). However, while proper packing of EcoNet client devices is necessary to provide the anti-shaking and
waterproofing functionalities, radio and GPS signals must be able to pass through the packing with acceptable
receiving rates; otherwise, the system will consume more energy during data retransmission and GPS repositioning. Finally, in the EcoNet system, base stations must be placed at locations that have good Internet
connectivity (e.g., via GPRS/3G, long distance WiFi, UHF/VHF, and satellites) and a reliable power supply (e.g.,
via electricity cables or solar energy). Under the YushanNet system, hikers’ movements are fairly predictable and
the base stations can be deployed on the trails. In contrast, when deploying the EcoNet system, it is needed to
determine the locations that the tracked animals visit most frequently and deploy the base stations accordingly in
order to ensure efficient data harvesting.
3. CONCLUSION
In this paper, a study on various Wireless Sensor Network Techniques for Monitoring Activities of Wild Animals
has been done. We have studied that all the various techniques that are used extensively today have certain
drawbacks such as high power consumption and low data processing rate and efficiency. Thus we have reached to
the conclusion that there is a requirement of a new and more efficient protocol such as hotspot algorithm that may
reduce majority of drawbacks and thus may prove to be efficient tool for saving wild animals.
REFERENCES
[1] Wendi B. Heinzelman, Ana ntha P. Chandrakasan, and Hari Balakrishnan, “An Application n-Specific Protocol
Architecture for Wireless Microsensor Network ks”, IEEE Transactions on Wireless Communication, Vol. 1,
No. 4, ppp. 660-670, October 2002.
[2] Jingchang Pan, and Qiang Guoo, “The Design and Implementation of Video-based Interactive Motion Tracking
Model”, InternationalConference on Audio, Language, and Image Processing (ICALIP 2008), pp. 1565-1568,
2008.
[3] Chakchai So-In, Phaudphut Comdet, Tesana Smaarn, WeeramongkonlertNutnicha, Wijitsopon Kasidit, Kokaew
UrachartWaikham Boonsup, and Saiyod Saiyan, “Mobile Animal TrackingSystems using LightSensor for
Efficient Power and Cost Saving Motion Detection”, 8thInternational Symposium on Communication
Syystems, Networks andDigital Signal Processing (CSNDSP), pp. 1-6, 2012
[4] Jyh-How Huang, Ying-Yu Chenn, Yu-Te Huang, Po-Yen Lin, Yi-ChaoChen, Yi-Fu Lin, Shin-Ching Yenn, and
Polly Huang, “Rapid Prototyping for Wildlife and Ecological Monitoring”, IEEE Systems Journal, Vol. 4, No.
2, pp. 198-209, June 2012.
[5] Wendi Heinzelman, Anantha Chandrakasan, and Hari
Balakrishnan. Energy- Efficient Communication Protocol for Wireless Microsensor Networks. Proceedings of
the
Hawaii International Conference on System Sciences, January 4-7, 2000 © 2000 IEEE.
[6] M. J. Handy, M. Haase, and D. Timmermann, "Low
energy adaptive clustering hierarchy with deterministic
cluster-head selection," in Proc. 4th IEEE International Workshop on Mobile and Wireless Communications
Network (MWCN '02), pp. 368-372, Stockholm, Sweden, September 2002.
[7] A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott, “Impact of human mobility on the design of
opportunistic forwarding algorithms,” in Proc. IEEE Infocom, Barcelona, Spain, Apr. 2006.
[8] L.-J. Chen, Y.-C. Chen, T. Sun, P. S. , K. T. Chen, C.-H. Yu, and H.-H.Chu, “Finding self-similarities in
opportunistic people networks,” in Proc. IEEE Infocom, Anchorage, AK, May 2007.
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
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[9] Yuh-Ren Tsai, “Coverage-Preserving Routing Protocols for Randomly Distributed Wireless Sensor Networks”,
IEEE Transactions on Wireless Communications, Vol. 6, No. 4, pp. 1240-1245, April 2007
[10] Yuanzhu Peter Chen, Arthur L. Liestman, and Jiangchuan Liu, “A Hierarchical Energy-Efficient Framework
for Data Aggregation in Wireless Sensor Networks”, IEEE ransactions on Vehicular Technology, Vol. 55, No.
3, pp. 789-796, May 2006.
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