Design of Gradient and Node Remaining Energy
Constrained Directed Diffusion Routing for WSN
Li Zhiyu
Shi Haoshan
Northwestern Polytechnical University, Xi’an, China, 710072
E-mail: [email protected]
Abstract—In the conventional directed diffusion routing protocol
(DD), the intermediate nodes retransmit the received interest
message to all of the neighbor nodes by flooding, which can bring
about great energy consumption to the network. In this paper, we
propose an energy-efficient routing algorithm for wireless sensor
networks called Gradient and Node Remaining Energy
Constrained Directed Diffusion Routing (GRE-DD). By setting
the maximum gradient diffusion depth, GRE-DD can help to
reduce the times of retransmitting interest message at the interest
propagation stage, leading to the reduction of transmitted data.
By setting the minimum node remaining energy, GRE-DD help
to increase the each node’s probability of being selected to
perform retransmission task, prolonging the node average
working time and improving the network load balance.
Simulation results indicate that GRE-DD greatly cuts down the
average end-to-end delay, reduces energy consumption and
extends the network lifetime.
Keywords—Wireless Sensor Network; Data Aggregation; Directed
Diffusion; Diffusion Depth; Remaining Energy
I.
INTRODUCTION
Wireless sensor network (WSN) can perform real-time
sensing and collecting of the required data from the monitored
target in the sensor field. The data is transmitted to the
corresponding users after being processed by nodes
themselves and co-processed by several nodes. With the fast
development and high integration of the sensor technology,
micro-electro-mechanism system, embedded computation
technology and communication technology, the sensing
approaches of sensors become abundant, the processing
capability becomes stronger, and the bulk tends to be smaller
and smaller. Besides, the network, a new organizing form of
system, possesses higher reliability and it can be easily
extended and deployed. Meanwhile, the energy consumption
( a ) Interest propagation
and cost can be cut down immensely. Accordingly, WSN can
be widely applied to military, civil and industrial production
etc. Therefore, WSN has played an important role in the
present researches [1].
Each sensor node of WSN possesses limited observing
region and energy. Thus, when deploying the nodes, it is very
necessary to make the node observing region overlap, so that
the robustness and accuracy of the received information from
the entire network can be greatly improved. While there will
be some redundant collected information by nodes, which can
result in the decrease of the network lifetime. Data
aggregation technology plays an essential role in the WSN
research [2].
Directed Diffusion (DD), designed for the wireless sensor
network, provides a data-centric routing protocol scheme. DD
establishes paths by flooding without in-network data
aggregation function, resulting in the network energy waste.
On the basis of DD, we propose an energy-efficient routing
algorithm for wireless sensor networks, called Gradient and
Node Remaining Energy Constrained Directed Diffusion
Routing (GRE-DD). By setting the maximum gradient
diffusion depth, GRE-DD can help to reduce the times of
retransmitting interest message at the interest propagation
stage, leading to the reduction of transmitted data. By setting
the minimum node remaining energy, GRE-DD help to
increase each node’s probability of being selected to perform
retransmission task, prolonging the node average working
time and improving the network load balance.
II.
DIRECTED DIFFUSION ANALYSIS
DD is a routing protocol based on query [3] [4]. It consists
of three stages: interest propagation, initial gradient setup and
data delivery along reinforced path as shown in Fig1.
᧤b᧥Initial gradient setup
᧤c᧥Data delivery along reinforced path
Fig.1 Operations of the directed diffusion routing algorithm
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III.
A. Interest Propagation
In wireless sensor network, sink node cannot get the
specific location information of the source node. At the
interest propagation stage of DD, sink node can transfer
interest message (describing the attribute value of the target
data message) to all of the network nodes periodically by
flooding. The node which has received the data message can
cache the message temporarily and search for all of the
matching target data (target data message, which will be
called data message for short in the following part) as shown
in Fig.1 (a).
GRADIENT AND NODE REMAINING ENERGY
CONSTRAINED DIRECTED DIFFUSION ROUTING FOR WSN
(GRE-DD)
Gradient, an important term in DD, determines the data
transmission direction and speed. The purpose of putting
forward gradient is to direct the data propagation direction
with the minimum cost principle. It reflects that the
intermediate network nodes can make similar judgment on the
required matching condition of the data source. When the
node receives the query interest message from the neighbor
node, if there is no the same query record in the present cache,
a new record will be added, in which data transmission speed
set by neighbor node----gradient, is included as shown in
Fig1(b). Actually, initial gradient setup goes hand in hand
with interest propagation in the two-way approach. Sink node
delivers interest message, while source node delivers data
message. When any node of the network receives interest
message and data message, query is regarded successful.
Adopting the same transmission approach as the one-way
query can improve the path quality by increasing the amount
of the interest message and data message.
In the conventional DD, when the intermediate node
receives the retransmitted interest message for the first time, it
will propagate the interest message to all of the neighbor
nodes by flooding. Exponential growth of the retransmitted
interest message in the network comes with the increasing of
the propagation depth. When the density of node becomes
higher, the network load will swell greatly, resulting in the
sharp declining of the network performance. In addition,
sensor network data is always flowing in many-to-one
approach. While the number of source node is limited, so the
propagation to the entire network can bring about great waste.
Thus, at the interest propagation stage, when each node
retransmits interest message, it is very necessary to properly
reduce their propagation scope. Furthermore, in the
conventional DD, interest message determines transmission
speed, which can easily result in the running out of energy of
some nodes which are close to sink node or located in the
critical position. But some other nodes still possess the
remaining energy. In order to improve the network load
balance, it is also necessary to add the minimum remaining
energy. GRE-DD proposed in this paper, can help to reduce
the times of retransmitting interest message at the interest
propagation stage, leading to the reduction of transmitted data
by setting the maximum gradient diffusion depth, and can
help to increase the each node’s probability of being selected
to perform retransmission task, prolonging the node average
working time and improving the network load balance by
setting the minimum node remaining energy.
C. Data Delivery along Reinforced Path
A. GRE-DD Overview
At the stage of data propagation, source node sends data
message to sink node along the initially setup gradient
direction. Sink node sends a reinforced message to the
neighbor node which is the first one receiving the target data.
The neighbor node which receives the reinforced message can
also reinforce and select the neighbor node which can receive
the new data first. Consequently, a path with maximum
gradient is formed, so that the future received data message
can be transmitted along this best reinforced path as shown in
Fig.1(c).
1) During the propagation of interest message, gradient is
set up by utilizing the remaining energy of the nodes
executing retransmission task and the min hop count to the
neighbor node. As to the source node, the gradient level of the
neighbor node indicates that the min hop count of
retransmitting interest message from this neighbor node to the
sink node. The min hop count is the minimum count of the
multiple paths from sink node to source node, reflecting the
optimal delay metric of the path. The remaining energy of the
node embodies the minimum level of the energy of the node
executing retransmission or the longest duration of the path
with the working node.
B. Initial Gradient Setup
In the DD, when the interest message is propagated, the
configuration of the network is performed. The transmitting
path of interest message from sink node to source node is
established. Besides, the transmitting path of data message
from source node to sink node is also established. The rule of
interest propagation is based on local information and it is
unnecessary to have a complete knowledge about network
topology. When the interest message is propagated to the
entire network, the gradient from source node to sink
node----reinforced path is set up.
2) The process of gradient setup: when the interest
message carrying the min hop count of the previous hopping
node and the node remaining energy etc. goes through
multiple paths to get to one node, if this node can meet the
following three conditions simultaneously, it can be selected
as the next hopping node, that is to say, to establish gradient
with sink node:
a) The energy possessed by the node itself is higher than
the set minimum node remaining energy.
b) The hop count of the node path should be the lowest.
c) The interest message propagation depth should be lower
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than the set maximum gradient diffusion depth.
IV.
SIMULATION
3) The gradient setup process by nature is to reduce
interest message propagation scope or retransmission times by
setting the maximum diffusion depth, and to increase each
node’s probability of being selected to perform retransmission
task, prolonging the node average working time and
improving the network load balance by setting minimum node
remaining energy. At the same time, the gradient requirement
in the conventional DD should be satisfied.
Simulation is based on NS-2 platform of CMU. In the
network simulation quality evaluation metrics, GRE-DD is
evaluated from two aspects: the average end-to-end delay and
the remaining energy of the entire network. The average
end-to-end delay refers to the average time spent when a piece
of data message is transmitted from source node to sink node.
The entire network remaining energy can indicate the lifetime
of the sensor network.
4) In GRE-DD, each interest message will stop its
propagation when reaching the maximum gradient diffusion
depth. That is to say, if the maximum gradient diffusion depth
is 4, the interest message can be retransmitted for 4 times at
most before it shifts away from the network. While before the
retransmission times reach 4, if the node remaining energy is
lower than the set minimum energy, the node’s retransmitting
of the interest message will be stopped in advance, and
another node which can meet the requirement will be selected
as the next hopping node.
In the simulation environment, nodes are randomly
scattered in a rectangular field (670m×670m) with one sink
node and 8 source nodes. The maximum gradient diffusion
depth is 4, the minimum node remaining energy is 1/32 of the
initial energy. The propagation scope of the configuration
node is R=20m. The S-MAC protocol is adopted (USC/ISI has
successfully realized S-MAC in NS2 in the year of 2005), and
comparatively real node energy consumption mode is utilized.
The interest message propagation interval is supposed to be
30s. Other node configurations are the same as reference [3].
The two algorithms GRE-DD and DD will be statistically
compared in the following part.
B. GRE-DD Analysis
Suppose GRE-DD operates on the ideal MAC protocol,
which can provide end-to-end duplex operation without
conflict between the randomly connected node pairs. Hence,
calculating the energy consumption in the network layer can
be done according to the amount of the transmitted data [5].
Suppose the number of sensor node is N and the nodes are
scattered evenly in the field with R as the radius, r as the
propagation scope. Then the number of each node’s neighbor
r2
N −1
2
nodes is R
. The energy consumption for node to
send information is Ps; the energy consumption for node to
receive information is Pr. In the conventional DD, interest
message is propagated to the entire network by flooding. The
total energy consumption of the network is:
( )
§ r2
·
N × Ps + N ¨¨ 2 N − 1¸¸ × Pr = O N 2
©R
¹
A. Average Delay Analysis
Fig.2 presents the influence of the change of the node
number N to the data transmission average delay. Each source
node can produce 1 data message in a time unit. With the
increasing of N, the average delay can also increase in the two
algorithms. When N changes from 50 to 100, the average
delay of GRE-DD will rise from 0.05s to 0.2s, and the
increase margin is 0.15s. While when the average delay of DD
rises from 0.2s to 0.65s, the increase margin is 0.45s. Hence,
it is very clear that the increase margin of the average delay in
DD is comparatively larger. That is because in the
conventional DD, more nodes are needed to set up gradient to
receive data, resulting in larger delay. Therefore, it can be
concluded that in GRE-DD the network quality is more stable
and cannot be easily affected by the network size. With the
increasing of the node number, the advantage of GRE-DD is
more conspicuous.
˄1˅
Accordingly, in the conventional DD, the network energy
consumption is in proportion to the square of the node number.
In GRE-DD, when setting up path, suppose the number of the
message sent by sink node and source node is K, the
maximum gradient diffusion depth is H. Then the maximum
energy consumption of the network is:
KH (Ps + Pr )
˄2˅
That is to say, in the GRE-DD, the network energy
consumption is related to the performance requirement of the
applied program (The number of the sent message is
connected with the set message propagation depth) and the
transmission distance, but it has no direct relation with the
number of network nodes. Hence, it can be concluded that the
more the network nodes, the more noticeable the advantages
of the GRE-DD.
Fig.2
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Comparison of node-to-node average delay
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B. Remaining Energy Analysis
Fig.3 presents the different changes of the entire network
remaining energy with the time changing in the two
algorithms. Suppose the number of network node N is 100,
the initial energy of each node is 10J. Seeing from the Fig.3,
in the same simulation environment, the entire network
remaining energy in GRE-DD is higher than that in the
conventional DD. It is because by setting the maximum
gradient diffusion depth, GRE-DD can help to reduce the
times of retransmitting interest message at the interest
propagation stage, leading to the reduction of transmitted data,
and by setting the minimum node remaining energy, GRE-DD
help to increase the each node’s probability of being selected
to perform retransmission task, prolonging the node average
working time and improving the network load balance and
extending the entire network lifetime.
Fig.3
[2] A. Boulis, S. Ganeriwal, M.B. Srivastava, “Aggregation in sensor
networks: an energy-accuracy trade-off” In Elsevier journal of Ad Hoc
Networks, Volume 1, Issues 2-3, pp. 317-331, September 2003.
[3] C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed diffusion: A
scalable and robust communication paradigm for sensor networks,” in
ACM MobiCom’00, 2000, pp. 56–67.
[4] C. Inlanagonwiwat, R. Govindan, D. Estrin, 1. Heidemann, and F. Silva.
“Directed Diffusion for Wireless Sensor Net working”. IEEE/ACM Trans.
Networking, vol. 1 I , pp. 2-16, Feb. 2002ˊ
[5] Hou Ronghui, Shi Haoshan, Yang Shaojun. RPDDP: An Energy-Efficient
Routing Protocol Supporting Distributed Data Processing for Wireless
Sensor Networks. Journal of Northwestern Polytechnical University, Vol:
24 No.5, pp: 614-618, Otc. 2006.
Comparison of the entire network remaining energy
V.
CONCLUSION
The experiment results indicate that GRE-DD is feasible
and effective. This algorithm retains the function of
conventional DD without any other extra control overhead.
On the basis of this, by setting the maximum gradient
diffusion depth, the influence of interest message flooding on
the interest propagation stage is greatly reduced, which help to
reduce the transmitted data of the network; by setting the
minimum node remaining energy, the probability of each node
to be selected to perform transmission task is increased, which
help to prolong the average working time of node, improve
the network load balance, reduce the average end-to-end delay
of data message and the energy consumption of network, and
help to extend the entire network lifetime.
ACKNOWLEDGEMENT
This research was supported by Doctoral Fund of Ministry
of Education of China under grant number 20050699037 and
China National Science Foundation under grant number
60273009.
REFERENCES
[1] I.F.A kyildiz, W.Su, Y. Sankarasubramaniam, and E.Cayirci, “Wireless
Sensor Networks: A Survey,” Computer Networks, vol.38, pp. 393~422,
2002.
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