Energy Aware Routing Algorithm For WSN Applications in Border Surveillance
Energy Aware Routing Algorithm For WSN Applications in Border Surveillance
Energy Aware Routing Algorithm For WSN Applications in Border Surveillance
) , , , (
) (
K H n m N
t N
s
ac
scheduling sets, where ) (t N is the
number of active nodes at time t, with each node having at
least n routing paths to the sink node. During the data
transmission phase, the scheduling sets are activated
periodically and transmit the sensed data to the sink node.
Each set checks its member nodes energy levels right after it
finishes one periods sensing and updates each sensor nodes
routing table.
The routing setup phase is divided into three steps. To
distribute sensor nodes power consumption most efficiently,
the sink nodes are evenly distributed in the region of interest.
In the first step of routing setup, the total number of
scheduling sets is calculated from the m-coverage ratio H and
n-connectivity probability K .
Step 2 determines the hop count and creates a routing table
for each node as in [10].
Step 3 constructs the n-connectivity paths for each node.
Since the sink nodes are evenly distributed in the region, the
number of nodes with highest hop count will be very small.
To make sure that each scheduling set can fulfill the m-
coverage and n-connectivity ratios after the transmission
paths are setup, we assign random scheduling set numbers to
the nodes that have local maximum hop counts. To do this,
each node checks its vicinity in its sensing range. If it has the
biggest hop count among those nodes, it is recognized as the
local maximum hop count node and assigned a random
scheduling set number between 0 and s-1 (0 and s-1
included).
After all local maximum hop count nodes are assigned a
scheduling set number, they start to propagate the scheduling
set numbers to the nodes with lower hop count numbers.
When choosing next hop nodes, we choose them according to
the P value as below.
EL w
CurrHC
HC
w P
e h
u
'
u , where CurrHC is the hop
count of current node, HC ' is the difference between the
hop count of current node and its neighbor node, EL is the
percentage of remaining energy of the neighbor node, and
h
w and
e
w are the weights ( 1 0 d d
h
w , 1 0 d d
e
w ) we
give to the two metrics.
After these three steps, the network is divided into s
scheduling sets with both transmission latency and energy
consumption taken care of. These s scheduling sets run
alternatively in the following data transmission phase.
During the data transmission phase, the scheduling sets
check the energy levels of their member nodes when they
finish working for a period. Each node broadcasts its current
energy level with the transmission range
t
r , and the nodes
who have it as a neighbor update their routing tables. If
there is any node whose next hop nodes are all with energy
levels below a threshold value
thres
EL , the corresponding
scheduling set is eliminated from the list. Periodically, the
network constructs new scheduling sets with all the available
nodes unless it cannot make even one scheduling set with all
the available nodes. The network lifetime is said to be
expired if there are not enough nodes that can form even
one scheduling set.
C. An Example Network
We illustrate our algorithm here with an example network
as shown in Fig. 4. We have 21 sensor nodes and two sink
nodes. The sensor nodes are labeled from A to U. The sink
nodes indicated by SK are evenly distributed in the network.
Assume after first step of routing setup phase, we know
the sensor nodes can be divided into 3 scheduling sets.
In the second step, each sensor node determines its own
hop count and maintains a routing table with its neighbor
nodes information. Assume after this step the largest hop
count is 3. But with two sink nodes, at some areas, hop count
number 1 and 2 can also be the local maximum hop count. So
as shown in Fig. 4(a), the local maximum hop count nodes
assign themselves scheduling set numbers between 0 and 2.
At the beginning of the third step, the local maximum hop
count nodes first started to find their n next hop nodes based
on the P values of their neighbor nodes as in Fig. 4(a).
Assume our application here is to build an m-coverage 2-
connectivity network. So each node seeks for two next hop
nodes. Upon receiving the notifying messages from local
maxi mum hop count nodes, the selected nodes update
their scheduling number sets. These selected nodes do not
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necessarily have lower hop count than their upstream
nodes, because of the energy level we take into
Figure 4. An Example Network.
account when selecting next hop nodes. This process goes on
until we reach the sink nodes. A possible result of the routing
setup phase is shown in Fig. 4(b). Its not possible for each set
to fully cover the whole region in the example here because the
available number of nodes is small. In real life with border
surveillance applications, we deploy much denser sensor nodes
which give greatly improved results. The simulation results in
the next section prove it.
Compare to the EECCR algorithm in [1], our algorithm
considers both packet transmission latency and the nodes
energy consumption, thus achieves better energy efficiency
while maintaining short transmission latency.
In the following data transmission phase, the three
scheduling sets start to work alternatively.
IV. SIMULATION RESULTS
Simulation is conducted in C# to demonstrate our
algorithm and compare it with the EECCR algorithm in [1].
Below are the metrics to be considered.
1) Standard deviation of nodes usage over hop count.
We check the energy level of all nodes periodically
and calculate the standard deviation of the energy
used among all nodes with the same hop count. We
can check the algorithms efficiency of distributing
power consumption among nodes by checking the
standard deviation values.
2) Network lifetime. Here the network lifetime is
defined as the time from the sensor network is first
established until there are not sufficient available
sensor nodes to achieve the m-coverage and n-
connectivity network.
3) Network m-coverage ratio. We loop over the whole
region, search in a radius
s
r circular area centered
at each point to see how many active nodes are in
that area for each scheduling set and get the average
m-coverage ratio among the scheduling sets. We
calculate the network m-coverage ratio with
different number of nodes per scheduling set, and
compare the results of our algorithm with the
EECCR algorithm.
In order to compare with the EECCR algorithm, the
monitored area in our simulation is a circular region with a
radius m R 200 . In a large scale sensor network,
homogeneous sensor nodes are usually used. In the simulation,
5000 sensor nodes with a sensing range of 20m and a
transmission range of 40m are randomly distributed in the
area.
In Fig. 5(a) (b), we plotted the average number of
active nodes in the network versus network m-coverage ratio
of both the EECCR algorithm and the EAR algorithm
(our algorithm with 1 sink node [10] and with 4 sink nodes).
From the plots we can see that by using our algorithm,
there are always fewer nodes that are active in the network
than using the EECCR algorithm while keeping the high
network m-coverage ratio. This is because in our algorithm,
the sensor nodes are included in fewer scheduling sets. So
there are fewer nodes running at anytime.
(a)
(b)
Figure 5. Number of Active Nodes vs. Coverage Ratio.
Fig. 6(a) and (b) plotted the standard deviation of energy
consumption among the nodes with the same hop count.
Here we used equal weight for both the energy level and hop
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count when setting up the n-connectivity paths. The standard
deviation values of our algorithm are smaller than the
EECCR algorithm for most hop counts. This indicates that
our algorithm tends to spread the power consumption among
the nodes with the same hop count. The EAR algorithm with
4 sink nodes has even smaller standard deviation values,
which indicates that with 4 sink nodes, the power
consumption is further distributed among all nodes. Note that
with more sink nodes, the largest hop count is smaller than
with 1 sink node, because it is easier for the sensor nodes to
reach the sink nodes.
( a )
( b)
Figure 6. Nodes Usage vs. Hop Count.
While if we set 1
e
w and 0
h
w , our algorithm is
actually a pure energy saving algorithm. In the other hand,
the EECCR algorithm only takes hop count into
consideration which makes it a greedy algorithm.
To check if our algorithm really prolongs the network
lifetime, we did simulations with low initial energy level
for all the sensor nodes. With an initial energy level of
120, for simplicity, assume each nodes sensing power is
2, packet sending power is 2, and packet receiving power is
1, when using 50% weights, the 2-connectivity network
using our algorithm ran 249 seconds with 1 sink node, 283
seconds with 4 sink nodes. While the 2-connectivity
network using the EECCR algorithm only ran for 174
seconds. That means that our algorithm has more than 40%
improvement on energy saving over the EECCR algorithm.
Because of the concentrated node usage of the EECCR
algorithm, some areas die sooner which causes the shorter
network lifetime.
V. CONCLUSION AND FUTURE WORK
In border security wireless sensor networks, the small
sensors are randomly distributed in great volume. It is very
hard to locate each sensor node correctly. So battery
recharging or replacement is impossible in this circumstance.
Energy conservation becomes the only solution to
prolonging the lifetime of this kind of network. The EECCR
algorithm in [1] divides the whole network to s scheduling
sets and let different sets work alternatively to distribute
power consumption among nodes. However, when setting up
the scheduling sets, the EECCR algorithm did not take into
account nodes energy level which may cause some nodes
deplete very soon. In this paper, we proposed an improved
energy aware routing algorithm to distribute data traffic
among sensor nodes. We reduced network power
consumption by activating fewer nodes than in the EECCR
algorithm. Simulation results verified that our algorithm
prolongs network lifetime much more than the EECCR
algorithm while maintaining better network m-coverage and
n-connectivity ratios.
With multiple sink nodes, the network power consumption was
further improved. But the nodes that are closer to the sink
nodes still carry most data traffic. These nodes will be the
first nodes that deplete their energy. In order to further
distribute power consumption, we could use moving sink
nodes to improve energy efficiency.
ACKNOWLEDGMENT
Our thanks to Andrew Kim for providing help with
editing the text, to Almir Davis, Nicole Ng, Nick Sullivan,
and Clint Epperson for discussing and sharing ideas with us.
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