19-07-2012, 01:31 PM
Classification of Object Tracking Techniques in Wireless Sensor Networks
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Abstract
Object tracking is one of the killer applications for wireless sensor networks (WSN) in which the network of
wireless sensors is assigned the task of tracking a particular object. The network employs the object tracking
techniques to continuously report the position of the object in terms of Cartesian coordinates to a sink node
or to a central base station. A family tree of object tracking techniques has been prepared.In this paper we
have summarized the object tracking techniques available so far in wireless sensor networks.
Keywords: Object Tracking in WSN, Classification of Object Tracking Techniques in WSN
1. Introduction
The techniques are mainly classified based on (see Figure
1)
- Network architecture used
- Algorithm or technique used
- Type of sensors used
- Number of targets to be tracked.
- Technology used for implementation.
Each of these categories has been described carefully
in section 2, 3, 4, 5 and 6 respectively.
2. Network Architecture
The following are the main types of network architectures
that can be used for object tracking in sensor networks as
shown in Figure 2.
Cluster based architecture.
Decentralized architecture.
Tree based architecture.
2.1. Cluster Based Architecture
In cluster based architecture there are several sensor
nodes and for a certain group of nodes, they are assigned
a cluster leader or cluster head. The ordinary nodes just
sense the reading and send them to the cluster thereby
shifting the burden from them to the cluster head. The
cluster head is normally a high energy and high resource
node. The introduction of cluster heads can reduce sufficient
cost of network as one can deploy low cost low energy
sensor nodes [1].
The cluster based architecture of wireless sensor networks
can also be classified as
- Static clustering
- Dynamic clustering
Tracking in WSN
Network
architecture
used
Algorithm or
technique used
Type of
sensors used
Number of
targets tracked
Technology
used
Figure 1. Basic classification of tracking techniques in
WSN.
M. FAYYAZ
Copyright © 2011 SciRes. WSN
122
Clustered based
architecture
Decentralized
architecture
Tree based
architecture
Sattic
clustering
Dynamic
clustering
Network
architecture used
Prediction
based (Proactive)
clustering
Non prediction
based clustering
Figure 2. Further classification of techniques on the basis of
network architecture used.
2.1.1. Static Clustering
In static clustering the cluster heads are assigned to the respective
sensor nodes at the time of formation of network
and they can not be changed. This means that throughout
the working of wireless sensor network the nodes remain
attach to the same cluster head as they were pre assigned
[2].
2.1.2. Dynamic Clustering
Again the dynamic clustering scheme can be of two types
- Prediction based or Proactive clustering.
- Non prediction based.
2.1.2.1. Prediction Based or Proactive Clustering
This scheme is mostly employed in a network of sleep
sensors, where most of the sensors stay in the sleep mode.
In prediction based clustering when a target moves from
the region of one cluster head to the other, the current
cluster head has to make an estimation or prediction about
where the target is moving and correspondingly wakeup
a cluster head where the target is moving [1,3].
2.1.2.2. Non Prediction Based Clustering
This is similar to the scheme described under the heading
of dynamic clustering. This scheme is used in a network
of non sleep sensors. Here the energy saving is not an
issue instead the proper selection and the life time of cluster
head is an issue. So based in some criteria based on
the application environment a cluster head selection algorithm
is run on each individual node, and the nodes collaboratively
select the cluster head.
2.2. Decentralized Architecture
In the decentralized architecture, there is no cluster head
type of central entity in the network and in this case all
the network nodes are at the same level in terms of work
responsibility. So the information regarding the target localization
travels through the network to a central base
station that is not the part of wireless sensor network. That
base station can be a computer or some other computation
entity. It runs an algorithm through which it can estimate
the current location of the target [4].
2.3. Tree Based Architecture
In this case a tree structure is maintained across the network.
The tree is rooted at the node that is closest to the
target. Thus as the target moves some nodes get added to
the tree and some get deleted. This scheme reduces the
overhead in terms of energy and information flow, as the
information flows from the root to the end or periphery
of the network through a particular route, as the information
flows is controlled so energy consumption automatically
gets controlled [5-9].
3. Algorithm or Technique
According to algorithm or technique used the tracking techniques
can be classified as (see Figure 3)
- For the network of sleepy sensors.
- Target reporting.
- Target chasing.
For the network of sleepy sensors there is a further classification
based on whether prediction heuristics are used
or sensor scheduling is used.
3.1. For the Network of Sleep Sensors
As all the sensors are in sleep mode. Thus there needs to
be a mechanism through which the sensors can be woken
up when the target approaches in the region of sleepy sensors.
To wake up either of the methods can be used:
- Prediction heuristics.
- Sensor scheduling.
Algorithm or
technique used
In the network of
sleepy sensors
Target
reporting Target chasing
Prediction
heuristics
Sensor
scheduling
Figure 3. Classification of tracking techniques in WSN according
to algorithm or technique used.
M. FAYYAZ
Copyright © 2011 SciRes. WSN
123
3.1.1. Prediction Heuristics
In this case most of sensors stay in sleep mode. The current
node predicts the future movement and location of
the target and correspondingly wakes up the nodes in the
region where the target is moving. The critical performance
parameters for prediction based reporting are the
miss rate and energy consumption. The network or the regions
where the current wireless sensor nodes were active.
The heuristics reflect the prediction model that can be
used for the prediction based object tracking in WSN.
The prediction heuristics that can be used can either of
Heuristic INSTANE
In this heuristic it is assumed that the target’s future
speed and direction will be the same as it is currently
having.
Heuristic AVERAGE
Here it is assumed that the future direction and movement
of the target will be equal to the average of previous
direction and movement.
Heuristic EXP_AVG
Here also the average of past readings is carried out
except it is exponential weighted average, which means
that more weight is assigned to near future reading than
the far future readings [1].
3.1.2. Sensor Scheduling
Instead of incorporating prediction based scheme sensors
can be scheduled for their wakeup and sleep time.
In this scheme it is to be determined that which sensors
stay in awake over the time in order to have an appropriate
trade off between the tracking performance and
the overall sensor usage. The objective is to minimize the
estimation errors while still reducing the sensor usage
over a period of time [10].
3.2. Target Reporting
In target reporting only the information target location in
terms of Cartesian coordinates is sent to a sink node or
some central entity. Thus the data continuously travels through
the network. Thus the main task here is to device
efficient routing and target location calculation tecniques
in order to minimize the overall energy consumption
by the network and minimizing the tracking error
[11].
3.3. Target Chasing
In target node the sink node has to physically follow the
target. Thus the sink node has to continuously consult the
neighboring nodes and the information of target has to be
disseminated in the network for sink to follow the target
[10].
4. Type of Sensors
The tracking techniques are widely different depending
on the type of sensors used: (see Figure 4)
- Ordinary sensors.
- Binary sensors.
4.1. Ordinary Sensors
The ordinary sensor network consists of the type of sensor
nodes that operate on original values of signals. Thus
the distance, speed and direction of target have to be calculated
on the basis of signal strength measured by the
sensor nodes.
4.2. Binary Sensors
The binary sensors work only on two binary values.
They can just detect the presence or absence of the target
in their sensing range by signaling either by 1 or 0. Thus
the tracking mechanism in this case is more complicated
than ordinary sensor networks [4,12].
5. Number of Targets Used
The tracking technique can be either for single target
tracking or multiple target tracking. (see Figure 5)
5.1. Single Target Tracking
Tracking in single target is relatively simple. Less data is
produced that results in a low traffic in the network. Less
traffic is easier to handle and the routing mechanism is not
complex.
Type of sensors
used
Ordinary sensors Binary sensors
Figure 4. Classification of tracking techniques on the basis
of types of sensors.
Number of targets
Multiple target
tracking
Single target
tracking
Figure 5. Classification on the basis of number of targets
tracked.
M. FAYYAZ
Copyright © 2011 SciRes. WSN
124
5.2. Multiple Target Tracking
In this case the location of multiple targets has to be tracked
simultaneously. Increasing the number of targets to be
tracked increases the network traffic and thus more complex
routing schemes and energy minimization tecniques
have to be incorporated to compensate for the network
performance [13].
6. Technology Used for Implementation of Tracking in Wireless Sensor Networks
Various technologies can be used for implementation of
tracking techniques in wireless sensor networks including
Zigbee, Bluetooth etc. Mostly the technology which
uses omni directional antennas can be used for the implementation
of tracking in wireless sensor networks. This
is because the presence of target has to felt, what ever the
direction of motion of target is. The omni directional antennas
solve the purpose of tracking.
7. Conclusion and Results
● A wide range of technologies, network architectures
and types of sensors are available for tracking in wireless
sensor networks.
● There is a performance tradeoff in energy, tracking
error and other performance parameters.
● To design a wireless sensor network for object tracking
or to do research in proposing new techniques the classification
of techniques has to be kept in to mind with their
relative trade offs to achieve affective results.