International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 4, April 2015
ISSN 2319 - 4847
OBSERVING MOVMENTS OF TARGET USING
EDGE DETECTING ALGORITHM IN WSN
1.
Vinayak S. kachare,2. Prof. Shital Mali.
1.
Student in Department of Electronic and telecommunication
Engineering , RAIT Navi-Mumbai, India
2.
Assistant Professor in Department of Electronic and telecommunication
Engineering , RAIT Navi-mumbai, India
ABSTRACT
Wireless sensor networks (WSNs) have a lot of attention in both the public and the research communities because they are
expected to bring the interaction between humans, environments, and machines to a new level. It were originally developed for
military purposes in battlefield surveillance, however, the development of such networks has encouraged their use In
healthcare, environmental industries, and for monitoring or observing targets of interest . In an observing movements of target
application. Sensor nodes are active when the object of interest is found, while some nodes detect the vehicle and send a
vigilance message to the nodes on the objects expected moving path, so as to wake them up. Thus, the nodes in the objects
moving path can be active in advance and remain vigilant in front of it as it moves. To be energy efficient and to accurately
track the vehicle, only the nodes close to the path can participate in tracking and providing continuous coverage. This paper is
focused on a new tracking framework called Cell Track, in which Cell is of any shape, it may be rectangle, hexagonal, square,
polygon refers to surrounding nodes of a target in its spatial region. Brink detection algorithm is introduced in thisscheme
which generates next cell as per the target’s next movement. This helps to operate sensor nodes in timely fashion. In this
scheme, which nodes should be active during tracking is determined using optimal sensors selection method. Cell Track
scheme is more energy efficient and achieves better tracking accuracy as compared with existing schemes.
Keywords— Cell tracking, Brink detection ,selection of sensor, tracking objects, Wireless sensor network.
1.INTRODUCTION
The research topic of wireless sensor network (WSN) has attracted much attention over the past few years. The WSNs have wide
applications in environmental, medical, food-safety and habitat monitoring, assessing the health of machines, aerospace vehicles and
civil engineering structures, energy management, inventory control, home and building automation, homeland security and military
initiatives. In this work, we investigate the target tracking problem in a huge WSN where a large number of sensors are available for
taking measurements. Obviously, the tracking accuracy of a target improves with the increasing number of measurements.
Therefore, in terms of the tracking performance, it is desirable to use as many measurements as possible. However, the nodes in the
WSNs have limitations in energy consumption, computation power and sensing ranges, which means that it is inappropriate for all
available sensors to take measurements. As a result, selecting only a proper group of sensors from the available set to perform
tracking is of research value.
Sensor nodes are placed in area of interested environment and they communicate through wireless medium. Collection of
information based on their capacity and collaborate with each other in the form of cluster. Then one of the cluster members called
sink collects information. Sensors may be of different types, Acoustic, seismic , electromagnetic etc. This paper focus on observing
(track) object in WSN.
Ttarget tracking can be categorized into tree based scheme [4], cluster based scheme [10] and prediction based scheme. Cell Track
scheme provides better tracking accuracy and reduces energy consumption as compared to existing schemes. In this scheme, cells
i.e. any shape rectangle, polygons, hexagonal, square are generated initially using planarization algorithm and inter-node edges of
polygon are identified logically which are called brinks. Incase of node failures, polygons are further reconstructed. This paper
introduces brink (edge) detection algorithm [1] which generates next polygon and makes sensors nodes of next polygon active when
target crosses the brink between two polygons i.e. link. Another algorithm, called optimal selection algorithm [1] is introduced
which minimizes the number of active sensors during tracking process.
Figure.1 Observing movements of target through cell tracking [1]
Volume 4, Issue 4, April 2015
Page 23
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 4, April 2015
ISSN 2319 - 4847
Figure 1 in this the closed shapes that is called as cell(face) are of different types that is rectangle, polygons, hexagonal, square
constructed between nodes, it means by using this nodes observe the movements of target.
We can categories the nodes in different types, that means couple nodes, cell nodes(polygon nodes). Couple nodes means the nodes
connected by brink(edge).
Initially v21 and v20 of cell p1 detect the target. So, nodes are connected to each other by brink (edge),those are couple nodes and
are shown in yellow color and rest nodes are shown by green color as they are inactive when target is in P9. In this way, target is
tracked by sequence of cell p9 -p8- p7-p4.
Two more algorithm required are Edge detection algorithm and optimal node selection algorithm. In edge detection algorithm three
steps are square phase detection, rectangular phase detection and brink detection . In optimal node selection from all nodes of cell
only most informative two couple nodes are selected .In this way saves the energy of the other nodes because they are in sleep mode.
2.RELATED WORK
Kaplan [2][3] introduced node selection algorithms, Global Node Selection algorithm(GNS) and Autonomous Node Selection
algorithm(ANS) as the solution for the problem of target tracking. GNS algorithm [2] considers of use sensor node within the range
of active node for next information to decide which nodes should be active.ANS algorithm [3] uses a only local node information.
ANS is a modification to GNS. In this each node keeps the knowledge of it self nodes (active) nodes in previous information from
that they decide which node should be active . in this network any node can be added or removed it will not create any problem to
whole system.
Zhang and Cao [4] introduced a tree based approach for target tracking, called Dynamic Convoy Tree Based Collaboration (DCTC).
When target of interest enters into detection region, sensor nodes communicate with each other and decide a best node for
collection of information related to object (target). When objet moves away from the best node, it becomes energy consuming.
In RSSI [5] mechanism, sensor node emits some sort of signal, by using this signal movements of target are observed. Sensor nodes
exchange information continuously. When target enters into network ,there is variation in received signal strength , and variation
used as existence of target in network. But there are many reasons for variation in received signal strength ,so it may be considered
preens of target even it is not present actually.
In intensity based event localization scheme [7], event localization and detection framework is introduced. In this, two algorithms
are proposed, distributed election winner notification algorithm (DENA) and intensity based localization algorithm (ILA) [7].DENA
determines nearest sensor node to an object of interest (i.e. winner node) and informs all other sensor nodes about winner. DENA
uses dynamic broadcast protocol (DDB) which causes additional delay.
In many existing schemes, a large number of nodes are woken up form sleep mode when target approaches. Many of the nodes do
not participate into tracking but kept active for long period which results in wastage of significant amount of energy.
Guojun Wang [1] Introduced cell (Face)Track scheme helps to select optimized nodes those who are provide most informative , in
existing scheme all the nodes are active irrespective to target .In this all other nodes are in sleep mode. Also, in case of sensor faults
or loss of tracking, this scheme maintains tracking accuracy by extending polygon area coverage without reconstructing whole
network.
In cell (Face)Track scheme, initially cell Construction takes place in the plane. After planarization, each node in a cell has the all
information about the corresponding cells. Each sensor node has following states i.e. Active(they are members of active
cell),awakening(nodes active for particular period),inactive(sleep mode). When target is detected by some node it collaborate
information with other nodes and cell is constructed. The nodes forms the cell and the cell in which target is active cell i.e. current
cell Cc . When target is about to cross the brink between to polygons then edge detection algorithm is invoked. Then, critical region
is identified. Critical region means the region having highest probability of target’s existence. Message is sent to further cell
(forward cell)Cf. After receiving message, nodes in Cf change their state to awakening. When target crosses the brink, message is
sent to nodes of previous Cc except couple nodes CN to return to inactive state. Then observation of target is done to detect whether
it is steady or moving. All the nodes may not provide useful information for tracking the target. So, optimal selection algorithm is
used which chooses appropriate sensors. Sensor nodes which provide result to improve tracking accuracy and minimize energy cost
for transmission of data are chosen. Initially constructed polygons may not be preserved during tracking due to situations like battery
depletion, failure detected by node, link failure or missing from its location. In such cases, polygon area is expanded to continue
tracking. In case of variable brink lengths, when the target moves far from the sink, the energy consumption for communication and
transmission of data is more. Chen and Rowe [6] proposed a method in which energy consumption is minimized by adjusting
transmission power levels. Sensors collaborate with each other and
adjust their transmission power to minimize the energy consumption.
3.CONCLUSION
In wireless sensor network Tracking target in the area of interest is a main applications . In sensor network each tiny node has
limited power to process all things(receiving signal, transmitting signal ,processing information) . that means node is WSNs have
limited energy due to limited battery power of sensor nodes. This paper discusses a cell tracking scheme which is energy efficient,
Limited number of nodes only active during target tracking , for this optimal selection algorithm is used. By adjusting the
transmission power energy can be used efficiently.
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Volume 4, Issue 4, April 2015
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 4, April 2015
ISSN 2319 - 4847
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