Aggregation Time Control Algorithm for Time
constrained Data Delivery in Wireless Sensor
Networks
Jae Young Choi, Jong Wook Lee, Kamrok Lee, Sunghyun Choi, Wook Hyun Kwon
Hong Seong Park
School of Electrical Engineering and
Computer Science
Seoul National University
Seoul, Korea
Email: [email protected]
Department of Electrical and
Computer Engineering
Kangwon National University
Chuncheon, KangwonDo, Korea
Email: [email protected]
Abstract— This paper proposes and evaluates a new aggregation algorithm in wireless sensor networks with time constraint.
In the proposed algorithm, each aggregation node controls its
aggregation time in order to aggregate and transfer data from
child nodes within a given deadline of the sink node. The objective
of the algorithm is to deliver the time constrained data within
time bound while reducing the energy consumption resulted
from the number of transmissions. Based on the aggregation
tree, the proposed algorithm considers the number of the child
nodes to determine the aggregation time. The performance of the
proposed algorithm is evaluated using both the miss ratio and
the aggregation gain.
I. I NTRODUCTION
Wireless sensor networks (WSNs) are emerging technologies, which will be applicable to many areas such as military,
environment, health, home and other commercial areas. One
of the important features of the WSNs is that the WSNs
are extremely energy-limited because of the requirement of
unattended operation in remote or even potentially hostile
locations. Since various sensor nodes often detect common
phenomena, there is likely to be some redundancy in data.
Therefore in-network processing of the received data can help
conserve the limited energy resources. The effort to reduce
the number of packet transmissions and to remove the redundancy with in-network processing is called data aggregation.
Because the dominant energy consumer within a sensor node is
the radio transceiver [8], the data aggregation techniques have
been investigated recently as efficient approaches to achieve
significant energy savings in the WSNs [5], [6]. However, the
data aggregation has a tradeoff relationship with the delay
because it generally takes time to wait for the data from
child nodes. The time duration needed to gather the data from
the child nodes is called as aggregation time. In addition, a
tree structure rooted at the sink is generally constructed to
transfer the measured sensor data to the sink, and it is called
aggregation tree [3].
Since the WSNs mainly deal with the data of the real
world, it is often necessary to transfer the data within the
time constraint of the sink. For example, we can think about
the home application, e.g. when a user enters a room, the user
should be recognized within a very short time. The faster such
a task is accomplished, the better the system is considered. A
sensitive pressure reading might have to periodically arrive
at a monitor station on time [11]. Fire fighters monitor the
temperature of the firestorm to recognize the firing point or to
detect the explosion. The WSNs can be used to measure the
drastic change of the water level to detect and alarm the flood.
Including the above examples, there can be many applications
which require the time constrained data delivery.
To meet the time constraint of the data, it is needed to
consider the time consumption to aggregate the data because
generally it takes more time to collect and process the data than
to transmit the data. There have been much research efforts on
the aggregation. However, there is little existing aggregation
researches which focus on the time constrained data delivery.
[10] and [12] cover the transmission timing synchronization
issue. But, they do not take the aggregation time and the
time constraint into account. [2] considers the processing of
the real time query in the specific region. However, it works
only when all the sensor nodes are within the transmission
range of the gateway. Therefore it is hard to be used in the
multi hop sensor networks. Moreover, location information is
required. There are several researches related to the real-time
data delivery issues in the WSNs. RAP [7] and SPEED [4]
cover the real time data delivery topic. However, they do not
take into account the aggregation scheme which is one of the
most important energy saving techniques in WSNs.
The aggregation time of the sensor nodes can be determined
according to the aggregation policy and the application requirements. To meet the time constraint, it is needed to adjust
the aggregation time properly because the aggregation time
can be affected by the deadline, i.e., the time constraint of the
data delivery given from the sink. We propose an aggregation
time control (ATC) algorithm, which adjusts the aggregation
time of the node to achieve a better performance. This paper
uses an aggregation gain and a miss ratio as performance
measures. The aggregation gain is a measure of the benefits of
applying aggregation to the system in terms of communication
traffic reduction [9]. The aggregation gain is proportional to
the energy efficiency. High aggregation gain means that the
large number of the data packet transmissions is reduced. It
goes to the low energy consumption of the network because
the radio transmission is the dominant energy consumption
of sensor nodes. The miss ratio is the percentage of packets
that do not meet the deadlines [7]. To support the soft realtime data delivery for the time constrained data, the proposed
algorithm tries to reduce the miss ratio.
In the ATC, the aggregation time is controlled to meet
the given deadline of the sink while obtaining the energy
efficiency. The ATC is designed to meet the deadline and to
reduce the energy consumption without clock synchronization
and location information. The clock synchronization and the
localization increase communication overhead. The proposed
algorithm can be adopted to the multi hop sensor networks.
The ATC assigns more aggregation time to the nodes with
more children. It gives more possibilities to aggregate the data.
The simulation results shows that the ATC has the very small
miss ratio with the slightly decreased aggregation gain.
The remainder of this paper is organized as follows. Section
2 explains the architecture of the ATC algorithm. Section 3
provides the simulation results, demonstrating the benefits of
the ATC algorithm. Finally, the paper concludes with Section
4.
II. AGGREGATION T IME C ONTROL
The structure of the aggregation tree in WSNs can be
formed arbitrarily because of the obstacles, applications, or
wireless channel environments. It is difficult to determine the
aggregation time before the network starts. Therefore, it is
necessary to determine the aggregation time continuously in
the runtime. In addition, the aggregation time is determined to
meet the time constraint of the sink. The proposed aggregation
time control (ATC) algorithm is the adaptive solution to the
network status and the time constraint. In addition, this paper
assumes that all the non-leaf sensor nodes can play a role of
aggregating the data, and they are referred to as aggregation
nodes. It is assumed that all nodes can sense the phenomena
and generate the data, and hence they are referred to as
source nodes. In addition, the ATC handles one of the most
general circumstances that all sensor nodes generate the time
constrained data periodically.
A. Basic Operation
When the sink is interested in gathering information from
the sensor field, it broadcasts a TREE REQUEST (TREQ)
message to make an aggregation tree. All sensor nodes reply to
the TREQ with TREE RESPONSE (TREP) messages carrying
required information after the TREQ broadcasting is finished.
The nodes which do not receive the TREP think of themselves
as leaf nodes. The TREQ messages, besides communicating
the application-level data, serve an additional purpose in the
field: setting up routes for the subsequent TREP messages from
the sensor nodes.
In order to apply the aggregation time control (ATC) algorithm, the TREQ and the TREP messages are extended with the
additional data. The deadline T to update the data is included
in every TREQ message. Similarly, the number N of the sensor
nodes in the network is included by the sink in each TREQ.
It is assumed that the sink node knows the number of the
sensor nodes in the network in advance. As these messages
move towards the leaf nodes, all nodes record the deadline
and the number of the nodes. The initial aggregation time of
each sensor node is calculated based on these parameters. In
addition, TREQ contains the depth field and the nodes which
receives TREQ increment the value of this field to count the
maximum depth of the aggregation tree.
The TREP conveys the maximum depth H and the number
Nt of the descendants. The maximum depth field represents
the height of the tree, i.e., the hop count from the sink to
the deepest depth of the aggregation tree. The number of
descendants field contains the number of nodes of which the
depth is deeper than the the corresponding node. In other
words, Nt is same as the number of nodes of which ancestors
or parents are the corresponding node. This information is
stored at every intermediate node. All nodes generate TREP
and the nodes can know the H and Nt by choosing the biggest
value of the depth and the number of descendants field of the
TREP. The TREP messages are generated with a pre-defined
period to adapt to the changes of the network status.
When a source node senses the data, it forwards the data
to its parent node with a sequence number. All aggregation
nodes wait for the other data packets from the child nodes
to make an aggregation. The aggregation timer is set as the
aggregation time. The aggregation time is calculated considering the number of the child nodes. The ATC assigns more
time to the nodes with more children. Hence the nodes with
more children have more possibility to receive the data from
children.
The sensor nodes which are one hop neighbor of the sink are
set to agent nodes. The agent nodes hear the TREQ message
from the sink. The data aggregation time Ti of node i is
incremented using Eq. 1.
Ti = Tiprev +
X
T
i
Ntj )
ag (Nt −
NI Nt
(1)
j∈CHi
where Tiprev represents the previous aggregation time of node
i. Initial value is −Tthresh , the threshold time value to stop
aggregation time control. If Ti − Tiprev is smaller than the
Tthresh , the current aggregation time is not changed. Nti is
defined as the number of nodes that use node i to transfer
the data to the sink. Ntag represents the number of the nodes
that use the agent node to transfer the data to the sink.
P CHj i
represents the child node group of node i. (Nti −
Nt )
j∈CHi
represents the number of child nodes of node i. NTag (Nti −
t
P
Ntj ) means the basic increment of the nodes. NI plays
j∈CHi
a role of refined controlling of the aggregation time as the ATC
algorithm goes on. NI is used to diminish the aggregation time
increment. NI is increased whenever the nodes are notified the
deadline miss.
Before increasing Ti , the ATC algorithm waits until aggregation time waiting counter, Acnt , reaches aggregation time
window size, Awnd . Acnt is used to count how many times
the nodes wait before increasing the aggregation time. The
Acnt value is increased by one whenever the aggregation timer
expires and the aggregated data are transmitted.
The equation 1 shows that the aggregation time Ti is
increased continuously as time goes. This results in running
out the deadline T . When the sink node finds that the time
delay to transfer the data from source nodes to the sink, Tup ,
is greater than T , then the sink sends the TIME OVERFLOW
(TOVF) messages to reduce the aggregation time. The TOVF
encloses Ntag and the aggregation time pruning rate (R) field.
The aggregation time pruning rate field contains the ratio of
aggregation time overflow to the deadline T . The value of
this field is set to R = 1 − (Tup − T )/T . The sensor nodes
continuously check the difference of the current and previous
aggregation time until the difference is smaller than the predefined threshold value. If so, as time goes, the aggregation
time of the each node converges. In the viewpoint of the sink,
the time constrained data will arrive within the deadline.
B. Aggregation Time Control Architecture
The ATC is composed of three function blocks as shown in
Figure 1. The three components are TOVF processing routine,
aggregation time decision routine, and aggregation function
routine.
changed to AGG T IM E W AIT to wait for some time. The
aggregation timer is scheduled to Ti . The TOVF processing
routine is initiated when the TOVF message is received. The
aggregation time is reduced as indicated by the received TOVF
message using Eq. 2. The aggregation timer is rescheduled
to Ti . Ast is set to AGG T IM E DEC, which means that
the deadline is missed and the aggregation time should be
decreased.
Ti = R × Tiprev , 0 < R < 1
where R is the aggregation time pruning rate. The aggregation
time is decreased as much as the deadline exceeded portion. If
the Ast is AGG T IM E W AIT , the aggregation time waiting
counter Acnt is increased by one. Then, Acnt is checked
whether Acnt reaches Awnd or not. Awnd is the predefined
value. If Acnt reaches Awnd , Ast is set to AGG T IM E IN C
and the aggregation time is ready to be increased next time. If
Acnt is smaller than Awnd , Ast is set to AGG T IM E W AIT
and wait more time to increase the aggregation time. Then the
aggregation timer is rescheduled. When the aggregation timer
expires, the buffered data are aggregated into one packet and
transferred to the parent. This process is repeated until the
time difference between the Ti and the Tiprev is stabilized,
i.e. smaller than Tthresh . Finally, the aggregation time of each
aggregation node is determined to meet the time constraint.
The state transition of the aggregation nodes can be shown as
the state transition diagram shown in Fig. 2.
Ti
ce
On eased
or
r
Ff
inc TOV es
m
No wnd ti
A
Aggregation Time Control
TOVF processing
routine
Aggregation time
decision routine
Aggregation
function routine
(2)
Wait
Increase
Stable
TOVF
received
No TOVF for
Awnd times
Decrease
Stable
End
TOVF received
Fig. 1.
ATC architecture
When a sensor node is started, it initializes Ti , Tiprev ,
and Ntag to 0, −Tthresh and N , respectively. If the data are
received from the child nodes, it first checks that there exist
the buffered data which represent whether aggregation is on
going or not. In such a case, the received data are buffered
and wait for the expiration of the aggregation timer. After
expiration, the aggregated result is sent to the parent. This is
the aggregation function routine. If there is no buffered data,
it goes to the aggregation time decision routine. The aggregation nodes check that the difference of current and previous
aggregation time is greater than the threshold value Tthresh .
If the difference is greater than Tthresh , the aggregation time
control status Ast is checked to proceed to the next step. If
the difference is smaller than Tthresh , the aggregation timer
is rescheduled as the same time and stops the algorithm. Ast
is used to represent the aggregation time control status of the
nodes. It can have three values, namely, AGG T IM E IN C,
AGG T IM E DEC, and AGG T IM E W AIT . If Ast is
AGG T IM E IN C, Ti is increased using Eq. 1. Ast is
Fig. 2.
State transition diagram of the aggregation nodes
III. P ERFORMANCE E VALUATION
The aggregation time control algorithm is implemented in
ns-2 simulator [1]. The existing implementation of the IEEE
802.11 MAC protocol is used. The sensor working field is a
square of 400m by 400m. All nodes are distributed randomly
in the area. For all simulation scenarios, all nodes are the data
generation sources. The sink resides at the lower left corner,
the (0,0) position of the area. All sensor nodes generate and
send the data packets with period of 2 seconds. All simulations
are performed during 500 seconds. All the data packets
P have
Ntj ).
the same size. Tthresh is set to the 0.7× 2NTag (Nti −
t
j∈CHi
The sensor’s energy settings are similar to those used in Direct
Diffusion (DD) [5]. Each sensor node has a radio range of
100m. Free space radio propagation model is used, and the
network is assumed to be static.
ATC is compared with two basic aggregation algorithms and
Directed Diffusion (DD), well known sensor networking algorithm, to show the performance. The first basic aggregation
1
algorithm, referred to as Basic 1 in this paper, uses an equal
distribution of the deadline among the nodes on the path.
0.8
(3)
This algorithm is simple to implement and is described
in the related work on the aggregation [6]. In the second
algorithm, which is referred to as Basic 2 in this paper, the
aggregation nodes gather the data from their child nodes until
the data are received from all child nodes. This algorithm does
not consider the time constraint but focuses on maximizing the
lifetime. It is expected that nearly maximum aggregation gain
can be obtained in the given aggregation tree.
This paper uses a miss ratio and an aggregation gain as
performance measures. The miss ratio is defined as the ratio
of the number of the delay bound missed data to the number of
all the received data. Small miss ratio means better delivery
for the time constrained data. The aggregation gain G can
be a measure of the communication traffic reduction [9]. Let
to be the number of transmissions to perform a given task.
The number of transmissions to perform the same task when
aggregation is applied is represented by ta . To calculate ta ,
the number of transmissions of all nodes are summed up.
All nodes count the number of received packets and the
sum of the values leads to to . The aggregation gain can be
expressed as the following expression 4. The aggregation gain
is proportional to the energy efficiency.
ATC
Basic 1
Basic 2
DD
0.7
0.6
Miss ratio
T
H
0.5
0.4
0.3
0.2
0.1
0
0.5
1
1.5
2
Time
Fig. 3.
Miss ratio vs. deadline
1
0.9
0.8
0.7
Aggregation gain
Ti =
0.9
0.6
0.5
0.4
0.3
ta
G=1−
to
(4)
0.2
ATC
Basic 1
Basic 2
DD
0.1
At first, the aggregation tree for the proposed algorithm is
constructed. TREQ is forwarded as broadcast and the nodes
memorize the sender of the message as parent. An arbitrary
aggregation tree in the field is established in this way. The
deadline T and the aggregation time window size Awnd are
set to 2 seconds and 3, respectively. 100 sensor nodes are
deployed to get the figure 3 and 4.
Our first objective is to check how many packets miss T
to evaluate the performance of the ATC. Figure 3 shows the
miss ratio as different deadlines. The aggregation time of each
aggregation node is controlled not to miss the deadline T . The
ATC algorithm outperforms the basic algorithms and DD at
all cases of T . Thus, it is concluded that it is better to use the
ATC algorithm to update the data within a predetermined time.
The average of the miss ratio of the ATC is 3.7% and those
of Basic 1 and Basic 2 are 28.8 % and 26.1 %, respectively.
DD has the average 90.3 % of miss ratio.
Figure 4 shows the aggregation gain of the ATC and the
others. As the deadline T increases, the aggregation gain also
increases. It is because there is much time to wait for gathering
the data as the deadline is loosened. Generally, the ATC has
lower aggregation gain than the basic algorithms. The average
aggregation gain of the ATC is 47.1 %, and that of the basic 1
and 2 algorithms are 54.2 % and 80 %, respectively. Directed
Diffusion has average 26.4 % of aggregation gain.
0
0.5
1
1.5
2
Time
Fig. 4.
Aggregation gain vs. deadline
The simulation results demonstrate that the ATC can achieve
as much as 96 % decrease of the miss ratio compared with
DD. Opportunistic aggregation of DD only fuses 26.4 %
of the packets. The ATC incurs nearly 13 % decrease of
the aggregation gain compared with the basic 1 algorithm.
However, the miss ratio of the ATC is 87 % smaller than that
of Basic 1. The basic 2 algorithm has the largest aggregation
gain. The aggregation gain of the ATC is 47 % of the Basic 2.
However, the ATC has 86 % smaller miss ratio than Basic 2.
The ATC increases the possibility to meet the deadline T with
slightly decreased energy efficiency. Thus, the ATC is proved
to be helpful to meet the deadline with the slightly increased
energy consumption for communication.
Figure 5 shows the aggregation gains of the proposed
algorithm and others with the varying number of the nodes.
The deadline T is 2 seconds in the following simulation
results. The ATC shows better aggregation gain than Directed
Diffusion. However, the ATC has lower aggregation gain than
basic algorithms. The average aggregation gain of the ATC is
57.8 % while the average aggregation gains of Basic 1, Basic
2, and DD are 65.2 %, 81.6 %, and 20.7 %, respectively.
can conclude that the ATC can meet the time constraint with
slightly increased energy consumption. This can be supported
by more simulation results but omitted due to space limitation.
1
IV. C ONCLUSION
0.9
0.8
Aggregation gain
0.7
0.6
ATC
Basic 1
Basic 2
DD
0.5
0.4
0.3
0.2
0.1
0
50
100
150
200
Number of nodes
Fig. 5.
Aggregation gain vs. the number of nodes
Figure 6 compares the miss ratio of the ATC and the other
algorithms including DD. It is shown that nearly all packets
of DD exceed the deadline. Also, we can observe that the
ATC achieves the very small miss ratio. The basic 1 and 2
algorithms have larger miss ratio than the ATC. The average
miss ratio of the ATC is 3.6 % while the average miss ratios
of Basic 1, Basic 2 and DD are 18.1 %, 21.7 %, and 82.9 %,
respectively.
1
0.9
0.8
ATC
Basic 1
Basic 2
DD
0.7
Miss ratio
0.6
0.5
0.4
0.3
0.2
0.1
0
50
100
150
200
Number of nodes
Fig. 6.
Miss ratio vs. the number of nodes
From Figures 5 and 6, it can be derived that the aggregation
gain and the miss ratio have a tradeoff relationship. In comparison with DD, the ATC has nearly 3 times larger aggregation
gain and 23 times smaller miss ratio. The aggregation gain of
the ATC is 88.7 % of Basic 1. However, The miss ratio of the
ATC is only about one fifths of Basic 1. The ATC has about 30
% smaller aggregation gain than Basic 2, but the miss ratio of
Basic 2 is 6 times larger than that of the ATC. Therefore, we
In this paper, an aggregation time control (ATC) algorithm
to meet the time constraint while reducing energy consumption
is proposed. The ATC determines the aggregation time of each
aggregation node trying to meet the deadline, T , of the sink.
Also, the number of the child nodes is considered to determine
the aggregation time. Simulation results show that the ATC
achieves the smallest miss ratio. It means that the ATC has
the capability to meet the time constraint. On the contrary,
there is a loss of the aggregation gain in the ATC. However,
the percentage of the loss of the aggregation gain is much
smaller than that of the benefit of the miss ratio. From these
results, it can be concluded that the ATC is a good solution to
support energy efficiency while meeting the time constraint.
The ATC can enlarge the application area of the WSNs to the
time-bounded applications. Also it can be applicable to the
general multi hop wireless sensor networks.
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