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Object-Tracking in Wireless Sensor Networks with A novel Energy Efficient Algorithm

International Journal of Scientific & Engineering Research, Volume 6, Issue 5, May-2015
ISSN 2229-5518
IJSER © 2015
Object-Tracking in Wireless Sensor Networks
with A novel Energy Efficient Algorithm
Majed Elbishti, Khaled Elleithy, Laiali Almazaydeh
AbstractIn this paper we propose and simulate an energy efficient protocol for object-tracking over Wireless Sensor Networks. The
proposed algorithm aims to use virtual clustering among the observation region to initiate duty-cycle across the border nodes and the
nodes in the inside region. The main idea is to keep the outer nodes active and the interior nodes sleep for energy saving until an object is
detected by the outer nodes. Castalia 3.2 wireless sensor network simulator is used to simulate the proposed protocol. Results indicate
that an improvement of 42% is achieved in terms of energy consumption. Furthermore, it is demonstrated that S-MAC protocol is a better
choice in high load traffic applications in terms of energy consumption.
Index Terms Object-tracking, WSN, MAC, Energy consumption, Castalia, Virtual clustering, TMAC.
n 1999, The BusinessWeek expected that the Wireless Sensor
networks (WSNs) to be one of most important technologies
[1]. The applications of wireless sensor networks are unlim-
ited; it includes all fields where monitoring of a physical quan-
tity is difficult to be performed by humans due to location,
time or environmental conflicts. Surveillance systems and con-
tinues monitoring, forest fire detection, and chemical plant
observation system are few examples of such applications [2-
This paper aims to propose and simulate an application
specific energy-efficient protocol for object tracking wireless
sensor network systems. In this type of applications it is very
important to tradeoff between power consumption and other
performance metrics because of the life-time requirements of
these systems. The simulation is used for extensive testing
because the nature of the WSN makes it impractical to use
testbeds for hundreds or thousands of nodes [5-7].
In object-tracking sensor networks, the nodes remain
awake to sense any target, although, this is the main goal, but
it is unpractical to keep all the nodes awake from the energy
consumption point of view. Virtual clustering and Regional
clustering are not the best solutions when considering the un-
predicted object entering the field. Trigged wakeup techniques
allowing the nodes to wake up as required, made it possible to
put nodes on sleep for longer time till tracking event occur.
The need to propose object-tracking specific protocols to bene-
fit from these new techniques has been established and pro-
posed [8-15].
Energy consumption is very important constraint because
the node is used once and the life-time of the node must be
long enough to support the application. The radio unit of the
sensor consumes most of the energy compared to the energy
consumed by central processing unit or the sensing device
[16]. The idle listening mode in wireless nodes consumes most
of the energy likewise transmission or receiving modes. Thus,
efficient MAC protocol designs are essential to reduce energy
consumption. Scheduled-sleep active/sleep MAC Protocols
were introduced in literature to reduce the wasted power in
idle listening. S-MAC and T-MAC are the most commonly
used to achieve this purpose [17, 18].
One of the most important applications in WSN is tracking of
mobile objects [19]. Tracking of enemy, animals, humans and
cars in highways are few examples of object tracking. The en-
ergy saving techniques may vary according the application
requirements. For example, the latency constraint in observing
animals is much less compared to observing enemy targets in
battlefield. Thus, there are application specific techniques and
protocols which can be applied to save energy depending on
the nature of the application [20].
In object-tracking, sensor nodes are deployed in different
regions. The target may not navigate through all of them.
Therefore, there is a need to reduce the energy consumption in
those inactive regions where tracking the system is unneces-
sarily active at all times. Some researchers have proposed
Wake up and Sleep mechanics such as clustering and filtering.
Clustering leads sometimes to black hole issue while filtering
involves very complicated calculations not well suitable to
sensor nodes from energy point of view [21].
This paper proposes an improvement on the target tracking
algorithm proposed by Wang Duoqiang et al. [21]. The original
algorithm of Wang Duoqiang et al. proposes an approach in
which all the boundary nodes are active at all times, and if an
intruder is detected, the inside nodes cluster is activated by
using a triggered-wake up technique. This allows the inter
nodes to sleep when no object is available to track to save en-
ergy. The authors assumed that localization protocol and
wake up technique are both available. Although the authors
tried to reduce the energy consumption of the wireless sensor
network by switching on only the nodes around the field, the
technique is not saving any energy for the boundary nodes.
Also there is no contingency plan if any of the boundary nodes
is dying. The proposed changes in this paper make the algo-
rithm more reliable and make the life-time of the boundary
nodes longer.
International Journal of Scientific & Engineering Research Volume 6, Issue 4, April-2015
ISSN 2229-5518
IJSER © 2015
In [21], the authors save energy based on the idea of a
sleep schedule for the nodes. All the nodes inside the monitor-
ing region will remain sleep until they receive a signal from
the boundary/head nodes which will be active all the time for
object detection. When an object enter the region through
boundary nodes, then the major node, which is the nearest
node to the object will send a message to the nodes in its area
of detection to wake up for some period of time then they go
to sleep mode again after the object has left their sensing area.
We propose an automated sleep and wake-up schedule
for the boundary nodes itself to make their life-time longer,
which can be achieved using T-MAC protocol. Also we pro-
pose an out-of-energy algorithm to be automatically triggered
whenever a boundary node battery is less-than or equal-to 5%
of its full energy level. The algorithm main function is to re-
place a dying boundary node with the nearest inter node.
Proposed Algorithm
This paper focuses on an automated sleep schedule for
boundary nodes. T-MAC protocol is used so that the bounda-
ry nodes will automatically start a scheduled sleep based on
their location as boundary node, while the interior nodes will
remain sleep till they receive a wakeup call from one of the
outer nodes in case of object detection as shown in Figure 1.
The red circles indicate the active nodes, while the green cir-
cles are the sleep nodes.
Fig 1 Virtual- Clustering Automated sleep
schedule for boundary nodes
We also propose an algorithm that acts as a contingency
plan for any boundary node that is dying. Hence we are pro-
tecting the whole system from being destroyed when losing
boundary nodes. For this purpose an algorithm was devel-
oped to transfer the responsibility of the dying node to the
adjacent node in its sensing area while it passes on the infor-
mation to the nodes in that area. The algorithm works as fol-
If (E ≥ 0.05*Ef AND i is a boundary node)
Send WM to Ni nodes
localize Ni , and calculate di
loop (K=1 to k= n , where n is Number of Ni nodes)
If (Jn a boundary node & di= Min(dk, dk=1,
{ send, New ID = ID , Tw, die };
Else { ignore , k=k+1}
where, Battery level can be measured
~ Ef is max level
~ Ni is the nodes in sensing area of node (i)
~ di 1 ,2 ,3…K is the distance between node i and it’s sens-
ing radius neighbors i(n)
The minimum number of nodes in the boundary coverage
is given by [21]:
Nmin= L/R
Where, L is the length of the boundary of the monitoring
region and R is the sensing radius. In this paper when using
the T-MAC model at any instant of time, the minimum num-
ber of active nodes in the boundary will be approximated:
Nmin= L/2R
This means that the number of active boundary nodes at any
instant of time is 50% less. According to [21], the proportion of
boundary nodes to all nodes is:
P = Nmin/ N= (4L/R)/(pL²) = 4/LpR
According to our proposed improvement this will be:
P=2/(LpR), where p the node density
Based on this mathematical model, we expect to save more
energy in the boundary nodes and make their life-time longer
with 40% more.
Castalia WSNs simulator has been selected to demonstrate
the performance of the proposed algorithms. 136 nodes were
deployed on field of 10x10 meters according to the configura-
tion given in Table 1.
Table 1 NODE Deployment Configurations table
Deployment type
Code used
0 to 99
SN.deployment =
100 to
Manually located
around the field
SN.deployment =
SN.node[ID].xCoor = X
SN.node[ID].yCoor = Y
The configuration in Table 1 creates a deployment that is
shown in Figure 2. The outer nodes are manually allocated by
using Cartesian coordinates to simulate the assumption that
automatic allocation algorithm is implemented in the nodes.
By grouping the nodes according to their ID to two groups;
Outer and Interior nodes; a virtual clustering is created within
these two groups. The main goal of this simulation is to com-
pare the power consumption when no energy saving protocol
is used and the case when an approximate configuration to the
proposed protocol is used.
In the first scenario we simulate the case when no energy
saving protocol is used. This means that all the nodes in the
field are active all the time. The assumptions used in this case
International Journal of Scientific & Engineering Research Volume 6, Issue 4, April-2015
ISSN 2229-5518
IJSER © 2015
Fig 2 Node Deployment Setup
- All nodes are active.
- No energy saving MAC is used.
- CC2420 Radio parameters will be used.
- Throughput Test application is used.
In the second scenario we simulate the case using our
proposed protocol. The assumptions used in this case are:
- The interior nodes are sleep until an object is detected
after sometime then they start to be active.
- Throughput Test is used.
- The outer nodes are active at the beginning of the
- TMAC protocol is enabled for the outer nodes to sim-
ulate the duty cycle sleep schedule within the outer
- CC2420 Radio parameters are used.
Simulation results
Figure 3 shows the improvement in energy consumption
using OTP (Object-Tracking Proposed Protocol) compared
with no energy saving protocol.
Figure 4 shows the effect of using different MAC pro-
tocols on energy consumption, with high traffic application
throughput test. Figure 5 shows the effect on energy con-
sumption of using different power transition levels for each
MAC protocol used in conjunction with the proposed pro-
Figure 6 shows that TMAC has better performance over
SMAC for 1000 maximum MAC layer packet size, while keep-
ing the payload at 2000. Also, it demonstrates that changing
the payload and the packet rate has significant effect on the
type of MAC used in terms of energy saving.
Figure 7 shows the effect of using different payload on
energy consumption for each MAC. Figure 8 shows that there
almost no effect on energy consumption while changing the
data packet rate in the application we used, because all the
nodes are sending to the sink node in this application. Alt-
hough there is low packet rate for each node, the overall chan-
nel will be busy.
Fig 3 Power Consumption VS Time with and without
using the proposed
Fig 4 Energy Consumption VS Time with use of different MAC Protocols
Fig 5 Energy consumption VS TX Power for of different MAC protocols,
2500 Max packet size
International Journal of Scientific & Engineering Research Volume 6, Issue 4, April-2015
ISSN 2229-5518
IJSER © 2015
Fig 6: Energy consumption VS TX Power for of different
MAC protocols, 1000 Max packet size
Fig 7 Energy consumption VS payload for each
MAC used with OTP.
Fig 8 Energy Consumption vs Data Packet Rate per
second, with 1000 payload, 2000 Max MAC Packet size
This paper proposes an application specific protocol to be
used for object-tracking systems. The protocol is based on vir-
tual clustering by dividing the nodes into two groups; interior
and boundary nodes. The results demonstrate an improve-
ment of performance in terms of energy consumption. The
simulation establishes the following results:
- The proposed protocol improves energy consumption,
- SMAC has better performance over TMAC,
- Changing the payload size affects only TMAC’s perfor-
mance and it is better to use TMAC with larger payload,
- Changing the data rate has no effect on the energy per-
formance in case of using an object tracking application.
These results emphasize that an application oriented en-
ergy efficient protocol is better solution for the energy prob-
lem in object-tracking systems.
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