An Adaptive Data Gathering Strategy for Target Tracking
in Cluster-based Wireless Sensor Networks
Juan Feng, Zhonglei Wang, Jörg Henkel
Karlsruhe Institute of Technology, Chair for Embedded Sytems, Karlsruhe, Germany
{juan.feng, zhonglei.wang, henkel}@kit.edu
Abstract—In a typical cluster-based sensor network, the data is
usually gathered and fused on cluster heads. In target tracking
applications, a target is often detected by sensor nodes in multiple clusters, leading to redundant data transmissions through
multiple paths from the cluster heads to the data sink. To reduce
such redundant data transmissions and thus to save energy, this
paper proposes an adaptive data gathering strategy, called ADGS.
Our novel idea is to adaptively select one node with the most
residual energy and the least communication cost from the active
nodes around the target. This node is responsible for gathering
and aggregating the data from the other active nodes and is
therefore called Aggregation Node (AN). The aggregated data is
then transmitted only from the AN to the sink. Our experiments
demonstrate that the proposed approach achieves a significant
reduction in power consumption for data transmission and
prolongs the network lifetime by 857.6% and 85.8% compared
to two state-of-the-art data gathering approaches.
I. I NTRODUCTION
Target tracking is one of the most important applications of
Wireless Sensor Networks (WSN). In a target tracking WSN, the
sensor nodes collaboratively monitor the roaming path of moving
targets in the area of deployment. Since the sensor nodes are
always deployed in an unattended environment, it is very difficult
to replace their battery after the deployment. As a result, energy
efficiency is the most critical design issue for such WSNs.
Clustering is an efficient approach for improving the scalability
of a densely deployed WSN [1], [2]. LEACH [1], for example,
is one of the most widely used clustering protocols for WSNs.
In each cluster of sensor nodes, there is a Cluster Head (CH)
that manages the other nodes (Cluster Members (CM)). Short
distance communications are used within the cluster to increase
the channel reusability and the network throughput [3], [4].
The CH keeps active until replaced by a new selected CH.
The CH selection is periodically performed and the cluster is
correspondingly reformed.
In target tracking applications, due to the random movement of
the target and largely overlapped sensing areas between clusters,
the target is often detected by sensor nodes in multiple clusters.
If these nodes report their data to the respective CHs, each
involved CH needs to aggregate the data and transmit them to
the data sink, leading to multiple data transmission flows toward
the sink with large data redundancy. To address this problem, we
propose an Adaptive Data Gathering Strategy (ADGS) for target
tracking in WSNs. ADGS dynamically selects an Aggregation
Node (AN) for data aggregation and transmission, aimed at
reducing the redundant data transmissions and balancing the
energy consumption of each sensor node so as to prolong the
network lifetime. The contributions of this paper are twofold:
• Energy efficient data gathering: We use a single AN to
collect the data instead of multiple CHs involved in the target
tracking. In this way, the sensed data can be aggregated into
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only one data flow toward the sink, and the traffic load of
the network as well as the transmission energy is reduced.
• Better load balancing: By considering the residual energy
and the communication cost in the AN selection algorithm,
the node with the most residual energy and the least communication cost will process the data so that the energy
consumption on each sensor node is well balanced.
The rest of this paper continues in Section II with an overview
of related work. In Section III, the problem is stated based
on a description of the state-of-the-art approaches. After that,
Section IV describes the details of the proposed ADGS approach. Then, Section V provides a theoretical analysis of energy
consumption before showing experiment results in Section VI.
Finally, the paper is concluded briefly in Section VII.
II. R ELATED W ORK
The data gathering techniques for WSNs vary with different
network architectures. Here, we only discuss about the approaches for cluster-based WSNs. For a comprehensive overview
of data gathering approaches with respect to different network
architectures, survey papers like [5] can be referred to.
In a typical cluster-based WSN like LEACH [1], each cluster
head is responsible for aggregating the data sensed by its cluster
members. Then, the aggregated data is transmitted directly from
the cluster head to the data sink. In [6] an adaptive scheme is
proposed to control the degree of data aggregation with respect
to the reliability requirement. However, the way of data gathering
is the same as in [1].
In [7], the authors propose to aggregate the data hop by hop
through a multi-hop path. Using this scheme the route must be
established in advance. It is applicable for applications where the
source nodes are known at design time but not for target tracking
applications.
In [8], [9] the authors propose to build a hierarchy for all
clusters by flooding in a typical route discovery process [8] or by
using a greedy heuristic [9]. The sensed data are first aggregated
on cluster heads at the lowest level. Then the aggregated data
are sent to a higher-level cluster head for further aggregation.
In all these approaches the network hierarchy is fixed at design
time and therefore is not efficient for target tracking, where the
targets usually move randomly.
In [10] a dynamic clustering scheme is proposed for target
tracking applications. The idea is to dynamically construct a
cluster around the target. The sensor nodes in the dynamic
cluster perform the tracking task. When the target moves out
of the range of the cluster, the cluster is deconstructed and a
new cluster is built. However, such a dynamic cluster must be
constructed before the target moves into the range. An algorithm
for predicting the direction of the target movement is needed
such as those proposed in [11], [12]. Such a prediction algorithm
has high computational complexity but still gives no guarantee
000468
sink
sink
CH2
Na
CH1
Nd
Nc
CH1
CH3
CH5
CH4
Fig. 1.
CH4
Nc
Na
Nb
(a)
Cluster Head (CH)
CH2
Nd
CH4
Nb
sink
CH2
CH1
CH3
CH3
CH5
(b)
Sleeping Cluster Members (CM)
Active CMs
CH5
(c)
Target
···
Data Transmission
Multihop Transmission
State-of-the-art data gathering approaches for cluster-based WSNs. (a) DL-cluster. (b) MH-cluster: Scenario 1. (c) MH-cluster: Scenario 2.
of accuracy. In contrast, our ADGS is based on a statically
clustered WSN. All the sensor nodes are managed by respective
cluster heads. When a target moves into the field, the cluster
heads will wake up the nodes around the target. We just simply
select the aggregation node from the active nodes to perform data
aggregation and no prediction is needed.
III. S TATE - OF - THE -A RT AND P ROBLEM S TATEMENT
Typical data gathering approaches for cluster-based WSNs can
be categorized into: 1) CH directly sends data to the sink (DLcluster) [1], [6], and 2) CH sends data to the sink through other
CHs in a multi-hop way (MH-cluster) [8], [9]. In DL-cluster
each CH aggregates the sensed data from its CMs and directly
transmits the data to the sink, while in a MH-cluster a hierarchical
model is built for data aggregation among clusters. According to
the cluster hierarchy, the data aggregated on CHs will be further
aggregated on a CH at a higher level. Therefore, the data can be
aggregated hop by hop through multiple intermediate CHs to the
sink. The two approaches are illustrated in Fig. 1 by means of a
simple scenario:
• DL-cluster: As Fig. 1(a) shows, CH1 , CH2 and CH3 receive
the sensed data from their CMs and send them to the sink
by means of end-to-end transmission. We can see that there
are four flows of sensed data from CMs. CH3 aggregates
two data flows into one. As a result, there are three data
flows from the CHs to the sink. In this approach, the
inefficient data aggregation results in many redundant data
transmissions.
• MH-cluster: As Fig. 1(b) shows, CH1 and CH3 send the
aggregated data to CH2 , which further aggregates the two
data flows as well as the data from its own CMs into
one data flow to the sink. Therefore, MH-cluster allows for
reduction in redundant data transmissions, compared to DLcluster. However, as the hierarchy of the clusters is fixed
at design time, CH3 always transmits its data to CH2 while
CH5 transmits the data to CH4 . When the target moves to the
position in Fig. 1(c), there will be two data flows from CH2
and CH4 , respectively, to the sink. Therefore, this statically
fixed hierarchical model is not efficient for target tracking,
where the targets usually move randomly.
IV. ADGS: T HE P ROPOSED A DAPTIVE DATA G ATHERING
S TRATEGY
In a cluster-based target tracking WSN, each Cluster Member
(CM) of a cluster has the detecting and tracking states. In the
detecting state, it is in the sleep mode most of the time and wakes
up for a fraction of time to process and to react to the control
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messages from its CH. In the tracking state, a CM keeps active
to track the target until the target is out of its sensing range.
The common ground of the state-of-the-art approaches discussed in Section III is that the network hierarchy is fixed at
design time and the CMs in a cluster always send the sensed
data to respective CHs. Then, each involved CH performs data
aggregation and transmits the aggregated data either directly
(DL-cluster) or hop by hop (ML-cluster) to the sink. These
approaches do not work efficiently for target tracking due to the
changing position of the randomly moving target. To overcome
this problem, we propose an adaptive data gathering approach,
called ADGS. Our novel idea is to utilize an Aggregation Node
(AN) for data gathering. The AN is selected from the nodes in the
tracking state that are all close to the target. As the target moves,
the AN will be adaptively re-selected. Therefore, the position of
the AN is dynamically changing and is always close to the target.
Hence, the proposed approach consists of two phases: 1)
aggregation node (re-)selection and 2) data aggregation and transmission. They are described in detail in the following subsections.
A. Selection of Aggregation Node
When a target is detected, the sensor nodes around it will
be woken up by their CHs and get into the tracking state. All
sensor
nodes in the tracking state form a tracking node set
D = N1D , N2D , · · · , NmD , where m is the number of nodes in the
tracking state. Since all nodes in D are in a vicinity area, they
can directly communicate with each other. Therefore, AN can be
elected by the internal negotiation of the nodes in D without the
participation of any CHs.
To achieve load balance the node that has the most residual
energy and consumes the least energy for communication with its
CH as well as the other nodes in D is selected as AN. The pseudo
code of the AN selection is shown in Algorithm 1(a). For each
i and communication cost E i are
node NiD , the residual energy Eres
c
converted to a waiting time ti (line 3). More residual energy and
less communication cost lead to a shorter ti . Therefore, the node
i and least E i waits for the shortest time in D.
with the most Eres
c
This node first finishes waiting and is elected as the AN (line 8).
It then broadcasts a message “finish election” to the other nodes
that are still waiting (line 9). These nodes stop waiting and give
up the AN election as soon as they receive the message, and
send their sensed data to the AN (line 5 and 6). ti is calculated
by the following equation:
ti = tmax (1 −
i − Ei
Eres
c
) + tran
i
Eini
(1)
000469
Algorithm 1 The ADGS Approach
(a) Selection of Aggregation Node
for each sensor node NiD ∈ D do
i , Ei )
ti ← CalculateWaitTime(Eres
c
Wait(ti )
if “finish election” is received during the wait then
stop waiting, give up the election and send data to AN
else //wait until ti expires
NiD is elected as AN
Broadcast(“finish election”)
end if
end for
Sink
aggregated
t dd
data
t
CH of AN
CHs of the nodes in D
control messages
Tracking node set D
“gather
g
_data”
The other
AN
sensor nodes
in D
data
(b) Data Aggregation and Transmission
for each sensor node NiD ∈ D do
if NiD is AN then
for each period T do
d ← ObtainDistanceToTarget()
if d < dth then
Broadcast(“gather data”)
else //target is out of the defined range
Broadcast(“reselect AN”)
end if
end for
else //the node is not AN
if “gather data” is received from AN then
Wait(trandom )
TransmitToAN(data)
end if
end if
end for
Fig. 2.
B. Data Aggregation and Transmission
The pseudo code of data aggregation and transmission is shown
in Algorithm 1(b). After the AN is selected, it will periodically
broadcast the flag message “gather data” to the other nodes in
D to indicate that it is the AN and to ask for sensed data (line
19). When the other nodes receive this message, they will wait
for a random time before reporting their sensed data to the AN,
in order to avoid data collision (line 26). Although this random
waiting time reduces the possibility of collision, it cannot totally
eliminate it due to the transmission delay of the data packets. An
underlying carrier-sense MAC protocol is still needed to mitigate
collision at the MAC level [10].
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Communications among sensor nodes
sink
CH2
Na
AN
CH4
Nc
CH1
Nb
Fig. 3.
where tmax a design parameter, used to control the waiting time
i is the initial energy of
in a reasonable range (0 < ti < tmax ). Eini
D
node Ni . Two nodes might have the same residual energy and
communication cost and therefore have the same ti . To avoid
this, a random time tran is added. tran is an order of magnitude
smaller than tmax , so that it will not have a large impact on the
value of ti .
The AN is dynamically selected according to the changing
position of the target. It is assumed that the AN can obtain
the distance to the target (line 17 in Algorithm 1), using some
technique, for example, distance measurement by means of the
strength of the detecting signal. If the distance between the AN
and the target is larger than a defined threshold dth , the AN will
broadcast a message “reselect AN” to the other nodes to start
the next round of AN election (line 21).
In the whole process, all tracking nodes in D are kept under
the control of respective CHs. When they finish the tracking task,
their CHs will get them back to the detecting state.
agg
gregated data
a
1:
2:
3:
4:
5:
6:
7:
8:
9:
10:
11:
12:
13:
14:
15:
16:
17:
18:
19:
20:
21:
22:
23:
24:
25:
26:
27:
28:
29:
30:
CH3
CH5
ADGS approach (cf. Fig. 1 for the meaning of the symbols)
The data sensed by the nodes in a vicinity area are supposed
to have strong correlations and therefore can be effectively
aggregated by the AN. We assign the data transmission between
the AN and its CH the same priority as the data transmission
between two CHs. Therefore, the AN is able to immediately
transmit the aggregated data to its CH without waiting for a
time slot allocated by the CH.
Fig. 2 presents an overview of all communications in ADGS.
ADGS utilizes low-cost, short-distance data transmissions among
the nodes in D for AN selection and data aggregation and reduces
the need for energy-consuming, long-distance transmissions.
Fig. 3 illustrates ADGS using the same example as in Fig. 1.
Nd is selected as the AN of D = {Na , Nb , Nc , Nd }. Then, Na , Nb
and Nc send their sensed data to Nd instead of their own CHs.
Nd aggregates the received data as well as its own data into one
packet and sends it to its cluster head CH2 . Then, CH2 forwards
the aggregated data to the sink. In this example, our approach has
only one long-distance transmission (from CH2 to the sink), much
less than the aforementioned approaches illustrated in Fig. 1.
Therefore, in terms of this example, ADGS can significantly
reduce the network traffic load, diminish data collision and save
energy. The generality of the energy saving will be proved by
means of theoretical analysis in the next section.
C. Discussion of Tracking Performance
A good tracking system should not lose the target (high
tracking reliability) and can transmit the data timely to the sink
(short data transmission delay). The tracking reliability depends
on whether the nodes around the target can be awoken in time by
the CHs. This issue is out of the scope of data gathering and is
000470
not addressed in our approach. There is a large body of existing
work focused on this issue, such as [13]. In this paper, we just
assume that the right sensor nodes can be awoken in time and
ADGS has the same tracking reliability as the state-of-the-art
approaches.
ADGS will introduce a delay due to AN selection. The maximum delay is tmax (cf. Equation (1)). The value of tmax depends
on the required resolution of identifying the difference of the
energy between two nodes by means of waiting times. Suppose
NiD and N D
j are two nodes with the largest and the second largest
values of Eres − Ec . In order to successfully select NiD as the AN,
D
ND
j must receive the control message “finish election” from Ni
before its waiting time t j expires. I.e., the following condition
must be satisfied:
i − E i ) − (E j − E j )
(Eres
c
res
c
> tdelay
(2)
Eini
j
i =E . t
where it is assumed that Eini = Eini
ini delay is the transmission delay of the control message. For example, on a channel
with 250 kbps bandwidth, sending an 8-byte control message
will have a delay of around 0.25 ms. To resolve the difference
of 1% × Eini between the two nodes, tmax should be set to at least
25 ms.
On the other hand, in ADGS, since the AN is the only node
that needs to send data to the CH, it does not need to wait for
a time slot for data transmission (cf. Section IV-B). Whereas, in
the state-of-the-art approaches like DL-cluster and MH-cluster,
for a node in a cluster with x CMs, the delay of waiting for
its time slot can be (x − 1) ∗ tslot in the worst case, where tslot
is the duration of a time slot. In practice, the delay of AN
selection in ADGS and the delay of waiting for time slots in DLcluster and MH-cluster are comparable. Nevertheless, in ADGS,
AN selection is needed only once after several rounds of data
gathering. Therefore, overall, ADGS can achieve shorter data
transmission delay than DL-cluster and MH-cluster. This has
been verified by our experiments.
t j − ti = tmax
V. T HEORETICAL A NALYSIS OF E NERGY C ONSUMPTION
We evaluate the energy consumption of ADGS by comparing
to DL-cluster and MH-cluster in an analytical way. It is assumed
that the sensed data reported by the sensing nodes in D can be
aggregated into one packet. We adopt a very widely used energy
model [1], [14], [15], as described in Equation (3):
ET x (k, d) = (ET x−elec + εamp ∗ d α ) ∗ k
ERx (k) = ERx−elec ∗ k
(3)
where ET x (k, d) and ERx (k) are the respective energy consumptions of a sensor node for transmitting and receiving k bits data
over a distance of d. ET x−elec and ERx−elec represent the energy
consumptions of the transmitter and receiver electronics, respectively, and are distance-independent terms. εamp [Joule/(bit ·mα )]
is a constant that represents the energy needed to transmit one
bit to achieve an acceptable signal to noise ratio over a distance
d, and α is the path loss exponent (2 ≤ α ≤ 5), which depends
on the channel quality. We can assume ET x−elec = ERx−elec =
Eelec [1], [14], [15].
A. Comparing ADGS to DL-cluster
Let m be the number of nodes in D = N1D , N2D , · · · , NmD . These
nodes belong to n CHs (CH = {CH1 ,CH2 , · · · ,CHn } and n ≤
m). The energy consumption for data transmission in our ADGS
approach is calculated by:
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m−1
EADGS = ∑ ET x (k, di,AN ) + ET x (k, dAN,CH ) + mERx (k)
(4)
i=1
+ECH,sink +EAN (k0 , dbc ) + (m − 1)ERx (k0 )
where di,AN and dAN,CH are the distance between the sensing node
NiD (i ∈ {1, 2, · · · , m}) and the AN and the distance between the
AN and its CH, respectively. dbc is the broadcast distance of
the AN. k0 is the control message size, which is usually much
smaller than the size of generic data packets. With Equations (3),
Equation (4) can be transformed to:
EADGS = 2mkEelec + k0 mEelec + k0 εamp (dbc )α
m−1
(5)
+ kεamp [ ∑ (di,AN )α + (dAN,CH )α ] + ECH,sink
i=1
ECH,sink denotes the energy consumption for data transmission
from the CH of the AN to the sink. The data can travel through
different numbers of hops resulting in the different values of
ECH,sink . The worst case is that the data is transmitted directly
from the CH to the sink, i.e.:
ECH,sink ≤ ET x(CH,sink) = kEelec + εamp k(dCH,sink )α
(6)
Then, we obtain:
0
EADGS ≤ EADGS
= k(1 + 2m)Eelec + k0 mEelec + k0 εamp (dbc )α
m−1
+ kεamp [ ∑ (di,AN )α + (dAN,CH )α + (dCH,sink )α ]
i=1
(7)
Similarly, we can obtain the energy consumption for data
transmission in the DL-cluster approach:
m
n
EDL−cluster = ∑ ET x (k, di,CH ) + mERx (k)+ ∑ ET x (k, d j,sink ) (8)
i=1
j=1
where di,CH is the distance between the sensing node NiD (i ∈
{1, 2, · · · , m}) and its CH and d j,sink is the distance between CH j
( j ∈ {1, 2, · · · , n}) and the sink. We ignore the power consumption
of the sink, because it is assumed to be connected to an external
power supply and is not resource constrained. With Equation (3),
Equation (8) is transformed to:
m
n
EDL−cluster = k(2m + n)Eelec + kεamp [ ∑ (di,CH )α + ∑ (d j,sink )α ]
i=1
j=1
(9)
If n > 1, we obtain:
0
EDL−cluster − EADGS
= k(n − 1)Eelec − k0 mEelec − k0 εamp (dbc )α
m−1
m−1
n−1
i=1
i=1
j=1
+ kεamp [ ∑ (di,CH )α − ∑ (di,AN )α + ∑ (d j,sink )α ]
(10)
Since the distances from a sensing node to its CH and to its
AN are approximate, i.e., di,CH ≈ di,AN . Hence, we obtain:
0
EDL−cluster − EADGS
≈ Eelec [k(n − 1) − k0 m]
n−1
(11)
+εamp [k ∑ (d j,sink )α − k0 (dbc )α ]
j=1
Theoretically, the position of the target can be determined
by three nodes. The typical value of m is in the range of
[3, 6] [16]. The value of n is in the range of [2, 4] according to our
experiments. The data packet size k is usually much larger than
the control message size k0 . In many previous work such as [17],
the data packet size is set to 512 or 256 bytes, while the control
message size is set to 16 or 8 bytes. Therefore, it is obvious that
[k(n − 1) − k0 m] > 0. Since n > 1 and d j,sink >> dbc , it is also
000471
α
0
α
easy to obtain [k ∑n−1
j=1 (d j,sink ) − k (dbc ) ] >> 0. Hence, when
n > 1, we can prove:
0
EADGS ≥ EADGS
>> EDL−cluster
(12)
However, when n = 1, we obtain:
0
EDL−cluster − EADGS
≈ −k0 mEelec − εamp k0 (dbc )α < 0
(13)
This means that if the target is detected by sensor nodes in
only a single cluster, ADGS consumes more energy due to AN
selection. However, in most cases, a target will be detected by
nodes in more than one cluster. This has been verified by our
experiments. Our experiment results in Section VI show that the
possibility for the target to be detected by nodes in more than
one cluster is over 93%. Hence, overall, ADGS can achieve large
energy saving compared to DL-cluster.
B. Comparing ADGS to MH-cluster
In the same way, the total energy consumption for data
transmission in the MH-cluster approach is given by:
Parameters
Field size
Number of nodes
Number of clusters
Sensing range
Communication range
Data reporting frequency
Data packet size
Control message size
dth
Listening power consumption
Eelec
εamp
Path loss exponent α
tmax
tslot
Velocity of the target
(14)
n
+ ∑ ∑ ET x (k, d j ) + ∑ (hi − 1)ERx (k)
i=1 j=1
i=1
where hi is the number of communication hops from CHi to
the sink. d j is the distance of the j-th hop. Eda represents the
energy saving achieved by the data aggregation on the relay CHs.
Ideally, the data gathered by all n CHs can be aggregated on one
of the n CHs, from which there is only one data flow to the sink.
An example of such an ideal case is shown in Fig. 1(b). The
energy consumption of MH-cluster in the ideal case is denoted
0
as EMH−cluster
. Then, when n > 1, we have:
m
0
EMH−cluster ≥ EMH−cluster
= ∑ ET x (k, di,CH )
i=1
(15)
n−1
+mERx (k) + ∑ ET x (k, d j,aggCH ) + EaggCH,sink
where EaggCH,sink represents the energy needed for data transmission from the CH, which receives and aggregates the data from
all other CHs, to the sink. With Equation (3), Equation (15) can
be transformed to:
m
0
EMH−cluster
= k(2m + n)Eelec + kεamp ∑ (di,CH )α
i=1
+ kεamp ∑ (d j,aggCH
)α
(16)
+ kεamp (daggCH,sink )α
j=1
0
We compare EMH−cluster
and EADGS by calculating Equation (16) - Equation (5):
0
EMH−cluster
− EADGS = EaggCH,sink − ECH,sink
+k(n − 1)Eelec − k0 mEelec − k0 εamp (dbc )α
n−1
m−1
m−1
j=1
i=1
i=1
(17)
+kεamp [ ∑ (d j,aggCH )α + ∑ (di,CH )α − ∑ (di,AN )α ]
We can assume that in both ADGS and MH-cluster the
aggregated data is transmitted to the sink along a similar route,
so EaggCH,sink ≈ ECH,sink . Given di,CH ≈ di,AN , the above equation
can be simplified as:
0
EMH−cluster
− EADGS ≈ Eelec [k(n − 1) − k0 m]
n−1
+εamp [k ∑ (d j,aggCH )α − k0 (dbc )α ]
j=1
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11000
10500
10000
9500
DL-cluster
Fig. 4.
MH-cluster
ADGS
Comparison of the number of transmitted data packets
Similarly, with the typical values of m, n, k and k0 and given
0
dbc < d j,aggCH , we obtain EMH−cluster
− EADGS > 0. Then, we can
prove:
0
EMH−cluster ≥ EMH−cluster
> EADGS
(19)
When n = 1, we also obtain:
0
EMH−cluster
− EADGS ≈ −k0 mEelec − εamp k0 (dbc )α < 0
j=1
n−1
scenario 1
scenario 2
11500
Data
a packets
i=1
hi
450 m×450 m
600
30
12 m
24 m
Every 0.3 s
128 bytes
8 bytes
12 m
12.5 mw
50 nJ/b
100 pJ/(b·m2 )
2
10 ms
2 ms
8 m/s
12000
m
EMH−cluster = ∑ ET x (k, di,CH ) + mERx (k) − Eda
n
TABLE I
S IMULATION PARAMETERS
Values
(18)
(20)
VI. E VALUATION R ESULTS
In the experiments, we demonstrate the energy efficiency of
ADGS by comparing to the state-of-the-art approaches, DLcluster and MH-cluster, by means of simulations carried out on
OMNet++ 4.1 [18]. The performance metrics include the network
load (the number of packets transmitted), the energy consumption
for data transmission and the network lifetime.
A. Simulation Setup
Table I shows the simulation parameters. Although the number
of sensor nodes and the number of clusters are fixed, the distribution of sensor nodes varies for each simulation run and each
result is averaged over five runs. The selection of the cluster head
is periodically performed. The target moves randomly in the field.
The AN is reselected when the target moves out of its sensing
range, so the threshold of the distance between the AN and the
target is set to dth = 12m, the same as the sensing range. The
listening power consumption is set to 12.5 mw, while the power
consumption in the sleep mode is ignored. The transmission and
receiving energy is calculated using Equation (3). We set typical
values to Eelec , εamp and α, derived from [1], [14], [15]. The
location of the sink can also affect the network performance,
so we simulated two scenarios: (1) Scenario 1 (s1): The sink is
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Avverage enerrgy consum
mption (J)
14
s2 DL-cluster
s2 MH-cluster
s2 ADGS
s1 DL-cluster
s1 MH-cluster
s1 ADGS
12
10
8
1CH: 7%
4CHs: 16%
2CHs: 45%
3CHs: 32%
6
4
2
Fig. 6.
0
400
600
Fig. 5.
800
Approaches
TABLE II
T RANSMISSION D ELAY
Delay (s1)
DL-cluster
MH-cluster
ADGS
25.0ms
32.2ms
25.0ms
1000 1200 1400 1600 1800 2000
Simulation time (s)
Comparison of energy consumption
located at the edge of the field and its coordinate is (0, 0); (2)
Scenario 2 (s2): The sink is located in the center of the field and
its coordinate is (225, 225). In order to examine the scalability
of ADGS and study the impact of the velocity of the target, we
also simulate different network sizes and different velocities of
the target.
B. Simulation Results
Fig. 4 shows the total number of transmitted data packets after
300s simulation time. Using DL-cluster, since all CHs directly
transmit the data to the sink, the number of transmitted data
packets is not affected by the location of the sink. Therefore,
DL-cluster has the same number of transmitted data packets for
both scenarios. Whereas, using MH-cluster, each CH transmits
data hop by hop to the sink. The larger distance between the data
source and the sink, the more hops are needed, leading to more
data packets transmitted between CHs. Therefore, the number
of data packets transmitted in scenario 1 is larger than that in
scenario 2. Due to the hop-by-hop data transmission between
CHs, MH-cluster transmits more data packets than DL-cluster.
As shown, ADGS can significantly reduce the network load in
both scenarios, compared to DL-cluster and MH-cluster, because,
using ADGS, all the sensed data can be completely aggregated
on a single AN.
Fig. 5 shows the average energy consumption of each sensor
node versus simulation time. As shown, all three approaches
consume more energy in scenario 1 because the sink is located
at the edge of the field so that many data packets are transmitted
over long distances. Compared to DL-cluster, ADGS achieves
43.7% energy saving for scenario 1 and 44.6% for scenario 2
after 2000s simulation time. Although MH-cluster provides a
mechanism for data aggregation, the most data are aggregated
after multiple hops of transmission. In contrast, in our ADGS,
all data can be aggregated on the AN before being delivered to
the sink. Therefore, ADGS achives 20.5% energy saving for both
scenarios compared to MH-cluster.
The energy saving is highly related to the number clusters
involved in the tracking. If the target is detected by sensor nodes
in n clusters, using ADGS there is always one data flow from the
AN to the sink, while using DL-cluster and MH-cluster, in the
worst case, there might be n data flows from the cluster heads
to the sink. In the simulation, we traced the value of n. Fig. 6
illustrates the distribution of the value n in the whole tracking
process. As shown, in most cases (≈ 93%) the target is detected
by sensor nodes in more than one cluster.
The network lifetime of the three approaches is compared in
978-1-4673-2713-8/12/$31.00 ©2012 IEEE
Delay (s2)
24.4ms
31.7ms
23.9ms
Fig. 7. We assume that each node has an initial energy of 10
joules, which is much less than the energy a normal battery has,
in order to measure the network lifetime within a short simulation
time. There are different definitions of network lifetime. Some
define it as the time when first node dies [19], while others
define it as the time when a certain percentage of nodes die.
In the figure, we present a comparison of network lifetime
for different definitions. As shown, ADGS can significantly
prolong the network lifetime in all cases. For example, if the
lifetime is defined as the time when 10% of nodes die, ADGS
achieves lifetime extentions by 857.6% and 85.8% compared to
DL-cluster and MH-cluster, respectively, for scinario 1. Similar
lifetime extensions are achieved for the other cases. The main
reasons for the large lifetime extension are twofold: 1) ADGS
avoids redundant long-distance transmissions and saves energy,
as shown in Fig. 5; 2) The energy consumption is better balanced
because the node with the highest residual energy and the lowest
communication cost is selected as AN.
We further investigated the average data transmission delay,
which is defined as the time between the moment a source
transmits a packet and the moment the sink receives the packet,
averaged over all source-sink pairs in the two scenarios. The
results after 3000s simulation time are shown in Table II. We can
see that ADGS has the lowest data transmission delay because
the AN can send the data to its CH without waiting for the time
slot. The delay of waiting for the time slots in DL-cluster and
MH-cluster is larger than the delay of AN selection in ADGS.
DL-cluster has longer data aggregation delay than ADGS but
shorter transmission delay because of its end-to-end transmission.
Therefore, the overall data transmission delay of DL-cluster and
DL-cluster
4000
Simulation time (s)
200
Distribution of the value of n
MH-cluster
ADGS
Scenario 2
Scenario 1
3000
2000
1000
0
1 node
10%
20%
30%
1 node
10%
20%
30%
Percentage of dead nodes
Fig. 7.
Comparison of network lifetime
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Ave
erage energ
gy consumption (J)
2.5
that has the most residual energy and the least communication
cost as the AN to balance the energy consumption among the
sensor nodes. Simulation results showed that our ADGS approach
outperforms two state-of-the-art approaches in terms of energy
consumption and data transmission delay. Up to 857.6% and
85.8% network lifetime extensions were achieved by ADGS
compared to the two approaches, respectively.
DL-cluster
MH-cluster
ADGS
2
1.5
1
R EFERENCES
0.5
0
8
16
24
32
40
Velocity
y of the target
g ((m/s))
Avverage enerrgy consum
mption (J)
Fig. 8.
The energy consumption versus the velocity of the target
3
2.5
DL-cluster
MH-cluster
ADGS
2
1.5
1
0.5
0
500
600
700
800
900
1000
Number of nodes
Fig. 9.
The energy consumption versus the network size
ADGS are close. Now, we can conclude that ADGS can reduce
the energy consumption and extend the lifetime of the clusterbased WSNs for target tracking, without sacrificing the system
performance.
We also examined the impact of the moving speed of the target,
while fixing the sink at (0, 0). Fig. 8 shows the average energy
consumption after 300s simulation time versus the velocity of
the target. In all the three approaches, we assume that the data
reporting frequency does not change with the velocity of the
target (the sensing nodes always report their data every 0.3
seconds). As shown, ADGS is more affected by the velocity
of the target than the other two approaches, because the faster
moving target leads to more frequent AN selection. Nevertheless,
ADGS still outperforms the other two approaches even for
tracking fast moving targets at the speed of 40m/s.
Finally, we studied the scalability of ADGS by varying the
number of nodes from 500 to 1000. Fig. 9 illustrates the
change of energy consumption after 300s simulation time as the
network size increases. As shown, ADGS achieves even more
energy saving for a larger network. This is because the longdistance transmissions in DL-cluster and MH-cluster consume
more energy when the network size increases, while in ADGS
there is always only one data flow toward the sink, which has
less increase in energy consumption compared to multiple data
flows.
VII. C ONCLUSIONS
This paper proposed a novel adaptive data gathering strategy,
ADGS, for target tracking WSNs. In ADGS, an Aggregation
Node (AN) is dynamically selected to aggregate the sensed data
and to transmit the aggregated data to the sink. It utilizes the
strong correlations among the sensed data in the vicinity area to
achieve efficient data aggregation. Moreover, we select a node
978-1-4673-2713-8/12/$31.00 ©2012 IEEE
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