Remaining-Energy Based Routing Protocol
for Wireless Sensor Networks
Millad Ghane and Amir Rajabzadeh
Department of Computer Engineering, Razi University, Daneshgah Ave., Kermanshah Iran
[email protected], [email protected]
Abstract—Consumption of energy in sensor network is one
of the most important goals of designing a routing protocol.
An energy efficient routing protocol will decrease number of
transmitted and received packets, because main consumption
part of routing is using antennas and signals.
This paper presents an energy efficient routing protocol,
called Remaining-Energy Based Routing (REB-R). The idea
behind REB-R is broadcasting remaining energy along with
data in data packet instead of calculating a parameter, related
to remaining energy, and broadcasting it.
REB-R is compared to two well-known protocols, AODV
and T-ANT, and simulation is done by NS-2 framework. In
order to compare REB-R with both of them, T-ANT has been
implemented inside NS-2 and an add-on has been added to
current AODV implementation to support energy
consumption.
For 200 nodes in area, REB-R uses less than 50% of energy
consumption of the others, and number of saved nodes is less
than 20% for T-ANT and AODV but REB-R saves all of them
in simulation time.
Keywords—Routing, Wireless Sensor Networks, Remaining
energy, ns-2, WSN
I. INTRODUCTION
Role of sensor network in monitoring and gathering
information from environment is significant. Volcano
monitoring [1, 2, 3], keep tracking of vital signals of
patients [4, 5], monitoring a bridge [6] and measuring
environmental properties like temperature, pressure,
humidity and etc. are among sensor networks applications
in monitoring and measuring issues. Mobile nodes are also
an interesting issue in sensor networks. These nodes move
across environment and after gathering information form
environment, they send it to workstation for further
processing.
Event detection in environment is another interesting
application of sensor networks. Surveillance of entities is an
example in this field. In this issue, nodes are listening to
environment and wait for an event to take place, and then
they will report position of event to workstation. A good
implementation of this application field is CodeBlue project
[4]. They used event detection mechanism to track location
of individuals, such as patients, nurses, doctors or even
medical equipments in hospitals. This location tracking is
done in three dimensional (3D) to precisely find location of
each entity. To achieve this precise localization, a number
of radio beacons are fixed in locations around environment
to find 3D position of entities. Physicians and emergency
department personnel are able to access entities’ location by
interfaces like handheld PDAs and Web-based clients
through Hourglass system [4].
A different approach to application of sensor networks is
their usage in battlefields. Simon et al. [15] build a sensor
network system consist of scattered nodes around the field,
forming a sensor network. This system reveals the sniper’s
location at the moment sniper begins shooting. Average
accuracy of location of sniper and latency time of finding
are 1 meter and 2 seconds, respectively.
Nodes in sensor network are tiny, low-cost, low-power,
multi-functional [7] and low memory capacity sensors, thus
important factors have to be considered when designing
routing protocols. Security [13, 16], data aggregation
methods [12], reliability [14, 13] and energy consumption
are among important factors to be considered.
Activities of energy consumption of sensor nodes are
sensing, computation and communication [14, 11].
Communication consumes more energy in compare to other
two activities [17, 11], so effect of computation and sensing
is almost disregarded and minimization of number of
packets across network can take us to our goal.
Because of equipping sensors with batteries for energy
source, the energy factor plays a crucial role. These batteries
usually are not replaceable [9] (solar energy is not always
an option [9]), so when battery goes off, node dies and will
be disconnected from network. It’s the reason why energy is
most considerable factor in sensor network and life of
network depends on it [7]. Disconnection of a critical node
from network causes network to come near to its lifetime.
Since these critical nodes are living in bottleneck of many
routing paths to sink, and consume a lot of energy for
relaying packets [18, 11], their death will put out of action
those paths [11]. Also dying non-critical nodes will reduce
precision of measuring parameter which network is trying
to collect data.
Remaining-Energy Based Routing (REB-R) protocol
leads nodes to broadcast their energy level alongside the
data to their neighbors and let nodes choose their parent
with highest energy level and forward data to it.
Simulations have been done by an event-based framework
called, Network Simulator 2 (NS-2). REB-R is compared
against AODV, a MANET protocol, and T-ANT, an antcolony protocol for sensor networks. Both of the above
protocols are among distributed type just like REB-R.
Results are compared with average energy consumption of
nodes and number of alive nodes. When number of
scattered nodes in area is 200, REB-R uses 42.77% and
42.5% of total average energy of T-ANT and AODV
respectively. Percentage of number of alive nodes of T-ANT
and AODV are 18.47% and 17.74% respectively, but REBR saves all of nodes in simulation time.
Rest of paper is organized as follows. Section 2 describes
related works. In Section 3, proposed protocol, REB-R, is
presented in details. First subsection explains packet
structure. Second subsection in Section 3, describes the
REB-R. Section 4 represents simulations and experimental
results. In this section, results of simulation for all of
protocols, AODV and T-ANT and REB-R, are compared to
each other and shown in figures, and finally Section 5
reveals some conclusions.
II. RELATED WORKS
Distinguished aspect of designing protocols in sensor
networks field is minimizing consumed energy. If number
of exchanged packets is minimized, consumed energy will
be at its minimum value [18]. Many methods have been
introduced to reduce transmitted packets. Two general
categorizations of introduced protocols are distributed and
non-distributed protocols.
In non-distributed protocols, main job of protocols is
done in a center. Running an algorithm on workstation and
broadcast routing results to network is main subcategory of
non-distributed protocols. Bari et al. [7] have implemented
a protocol which uses genetic algorithm to find optimal
parent for each node. After calculations, it broadcasts to
each node a message about their routing information.
Classical MANET and wireless routing protocols like
AODV, DSDV and so forth can be used for sensor networks
but overhead of control packets and enormous routing table
is a huge disadvantage. In these protocols, computations
and protocol codes are so much hard and big, that are
considerable. Wireless routing protocols are designed to
work in environments that energy issue is not worried.
Using these protocols in routers has huge impact on
performance of network and packets are routed faster.
In distributed protocols, core of protocol is not centered
and it is distributed among nodes. Hierarchical is a wellknown subcategory of distributed protocols. In hierarchical
algorithms, clustering techniques are used to group nodes
and route packets toward sink [8, 9, 18]. Some clustering
protocols are based on ant-colony principles and use their
behavior to achieve their energy efficient goals [10].
Hierarchical protocols are categorized to dynamic and static
classes. In dynamic class, routing paths are changed during
lifetime of network. This changing in routing is done in
order to remove centrality of gathering information from a
node, and balance work load among nodes [8, 9]. For static
class, topology of network is usually static and therefore
nodes are immobile. Leu and Li [18] have proposed an
algorithm where it deploys a polar coordinate system for
routing packets. In [18], some fixed and immobile nodes
known as manager nodes are placed in area and their duty
is to collect data from sensors and send it to sink through
their intra-network. Location of manager nodes is expressed
by the protocol.
In hierarchical protocols, overall measuring of a property
is our aim. The main disadvantage of these type of protocols
is aggregating data from whole network, and workstation
could not ask for a specific node’s data, although it is
possible to collect information from a specific node, but it
needs a complex mechanism. Least battery energy
consumption is its advantage.
Hierarchical protocols partition network into levels and
each node belongs to one. Lower nodes only communicate
with one-level higher ones. Partitioning will reduce amount
of packets transmitted through network. As an example, in
T-ANT [10], some nodes gather around each other and
form a cluster, and then they will select one among them as
cluster head and try to aggregate data and send it to sink
through cluster head. Similar to T-ANT, LEACH [8] and
HEED [9] protocols have been proposed which T-ANT
outperforms them.
III. REB-R PROTOCOL
Assume we have a network with sensor nodes spread in
the area. Sink node which is a special node with unlimited
energy is positioned in the center. Positioning sink node in
center is just another assumption and does not interfere with
REB-R in any way. For network model we have following
assumption [9, 10]:
· Nodes in network are stationary.
· Nodes are unaware of their location.
· All nodes have similar capabilities, i.e. processing
and communications capabilities.
· Nodes are left unattended in area after deployment.
· Each node has fixed transmission power capability,
i.e. radio ranges of nodes are equal, and
transmission energy of one bit from node u to node
v is the same as from node v to node u.
· Nodes are scattered with homogeneous spatial
Poisson process (it does not really affect protocol).
Nodes sent packets to each other simultaneously and
conflicts of packets may occur in the network.
A. Packet Structure
Figure 1 shows packet structure which REB-R uses.
Simplicity and being short are two main advantages of this
structure.
Type (1 bit)
Energy (32 bits)
Data (32 bits)
Figure 1 - Overhead of packet structure for REB-R
A node broadcasts two types of packets, one is
FWD_ROUTE and the other is DATA. For these two types,
we use a bit to represent packet’s state and the mentioned is
“type” bit, and “type” bit is 1 for FWD_ROUTE and 0 for
DATA. For “energy” part, we can use single precision
floating number or number of residual packets, where it is
calculated by dividing battery energy level at the beginning,
to energy value required to send/receive packets.
For “Data” part, we could use integer values or double
values. Integer values are simple and their computation
takes less time, but on the other hand double values are
complex and have heavy computation time. It is a tradeoff
between speed and precision. For our simulations, we have
used double values to achieve better precision.
In Algorithm 1 and Algorithm 2, highest energy
represent energy of current parent. PARENT_NODE points
to current parent, and FIRST_TTL is a variable stored in
node describing value of the first received TTL.
B. REB-R algorithm
After nodes being placed, sensor nodes wait for a packet
from sink. Sink starts network activities by broadcasting a
FWD_ROUTE packet to all of its neighbors. When packet
received by each node, its TTL (time-to-live) is stored as
FIRST_TTL and this node will broadcast a fresh new
FWD_ROUTE packet with previous packet’s TTL
incremented by one, as new TTL. By this action, forwarding
route shapes in levels. Level-one nodes are nodes neighbor
to sink, level-two nodes are nodes neighbor to level-one
nodes and other levels are shaped liked this pattern. From
now on, each node that receives a FWD_ROUTE packet
with TTL smaller than or equal to FIRST_TTL is recorded
as nominee for parent node of this node in future. If TTL is
greater than FIRST_TTL packet will be dropped to avoid
retransmission of extra packets. If this packet is
broadcasted, it has no usage for nodes; as explained above,
we don’t have interest in packets with greater TTL than the
recorded FIRST_TTL and they are discarded. During
making future nominee list, if a packet is collected and it
has minimum TTL and maximum energy, sender will be
selected as current forwarding node. Afterwards, during
steady state, current node will be changed to a node among
future nominee list which has maximum energy. Sink’s job
ends at this point and from now on, it just waits for
information to come along by sensor nodes.
As mentioned previously, sensor nodes are using batteries
to operate and if this battery dies, node will die. Stopping
operation of a node will cause network topology to change,
so every fixed intervals sink broadcasts a FWD_ROUTE
packet to refresh current forward route. Refreshing
forwarding route has benefits for new nodes which have
been added to system. When a new node is added to
network, it just waits for next round of interval to happen.
When sink starts forwarding route phase, this node catches
the FWD_ROUTE packets and it start to participate in
network’s activity. We must have a timer in each node to
clear each nodes neighbor’s list at beginning of every
interval and reset node’s state to start state. In node’s life,
there are some times that node has no parent. During these
times, the node simply does nothing and waits for a
FWD_ROUTE packet. These times occur in two situations.
Suppose a node is isolated and deserted far from network
that its signal is not received by any nodes. In this situation
FWD_ROUTE packets are not received by them and they
just do not do anything. The other situation is when a new
node is joined to network and it have to wait to receive
FWD_ROUTE packet.
Figure 2 - Starting point of network. Sink sends FWD_ROUTE packet.
Figure 3 - Forming reverse forwarding path to sink for level one nodes
From energy level encapsulated in packets, forwarding
route will change. In wireless communications, when a
node broadcasts a packet, it will be collected by its
neighbors, and we make sure around neighbors take energy
level of all possible parents with only one packet
transmission which holds data too. From time to time,
current parent of a node has an energy level which is lower
than the node broadcasting its energy level in data phase.
sends its current energy level along with data. This behavior
will help other nodes which select this node as nominee to
know energy level at every moment and to change their
parent node to a node with maximum energy level at any
time. Selecting node with maximum energy will balance
energy in a specific area of environment.
Algorithm 1 – Parsing forwarding packets
// When a FWD_ROUTE packet is received,
// this method is called
Parse_FWD()
{
if FIRST_TTL has not been initialized
{
FIRST_TTL is initialized to packet’s TTL.
PARENT_NODE is initialized to sender.
Record sender’s energy as highest energy.
Add sender to future nominees list.
Increment TTL of received packet by one.
Integrate current energy level with incremented
TTL to a FWD_ROUTE packet and send
the packet.
Figure 4 - Forming reverse forwarding path to sink for level two nodes
}
else if packet’s TTL is less than or equal to
FIRST_TTL
{
Add sender to future nominees list.
if packet’s TTL is less than the smallest received
TTL till now
{
PARENT_NODE is set to sender node.
Record sender’s energy as highest energy till now.
Increment TTL of received packet by one.
Integrate current energy level with incremented
TTL to a FWD_ROUTE packet and
send the packet.
Figure 5 - Reverse forwarding route to sink node after forward phase
As shown in Figure 1 and described previously, node
sends its current energy level along with data. This behavior
will help other nodes which select this node as nominee to
know energy level at every moment and to change their
parent node to a node with maximum energy level at any
time. Selecting node with maximum energy will balance
energy in a specific area of environment.
From energy level encapsulated in packets, forwarding
route will change. As you know, in wireless
communications, when a node broadcasts a packet, it will
be collected by its neighbors, and from this characteristic we
make sure neighbors around found energy level of all
possible parents with only one packet transmission which
holds data too. From time to time, current parent of a node
has an energy level which is lower than the node
broadcasting its energy level in data phase.
As shown in Figure 1 and described previously, node
}
}
Drop packet.
}
Nodes and formation of their paths are shown through
figures. Figure 2 shows node formation within environment
and starting point of network that at this time no reverse
forwarding route is formed and sink starts network activities
by sending a FWD_ROUTE packet. At Figure 3, level-one
nodes are forming around sink and their reverse forwarding
path is shown. In this point, each node in level one knows
who its parent is and following that, they are sending a
FWD_ROUTE packet to their neighbors to form level-two
nodes. Figure 4 demonstrates how level-two nodes and their
routes are taking shape. These routes are constructed by
level-one nodes where they broadcast a FWD_ROUTE
packet to their neighbors. When all forwarding paths are
Algorithm 2 – Parsing data packets
// When a data packet from a node arrives to a node,
// this method is called.
Parse_DATA()
{
if sender of packet has energy level more than
highest energy and is a member of the
future nominees list
{
PARENT_NODE is initialized to sender.
Record sender’s energy as highest energy till now.
}
if packet’s destination is this node
{
Aggregate received data.
}
nominees list and that node would have chance to be
selected as this node’s parent, and when TTL has minimum
value among received TTL, beside of adding sender node to
future nominees list, sender is selected as parent.
We used FIRST_TTL to build a list of neighbor nodes.
Doing so, nodes will select open-handed among their
neighbors which are only a few hops more than selected
parent away from sink node. There is a chance that a node
with maximum hops away from sink is selected, but above
selection is only made if selected node has maximum
residual energy. It’s obvious that selecting such a node uses
more hops therefore more energy of whole network but our
goal is to balance the energy in network. The situation is
described before on Figure 5. Thus, the bigger the future
lists, the more energy-balanced network, the longer lifetime
of network. Step-by-step procedure of reverse forwarding
route formation is shown in Algorithm 1.
Drop packet.
}
constructed, nodes are ready to send data to sink and this
state is shown in Figure 5. At the time the data timer
expires in each node, they send their sensed data to sink by
forwarding their data to their parent node. In Figure 5, as
you see Node A does not select Node B as its parent and
selects a route that is farther than selecting Node B.
Situations like this may occur sometimes based on
network’s delay or maybe packets collision. If you notice to
Figure 6 route is corrected but this option will remain in
nodes record and node looks at it as an extra option to reach
sink. In future longer path may be selected if it has
maximum energy level among nominees. Although this
situation is not encouraged but if it does happen we take
advantage of it.
Two situations will be occurred when facing
FWD_ROUTE packets. When the TTL value of packet is
greater than FIRST_TTL, the packet will be dropped. We
drop these packets for two reasons:
1. Forwarding such packets does not have any useful
information for routing. REB-R uses minimum
values of TTL for building routing path to sink,
thus TTL values greater than FIRST_TTL are not
considered and will be dropped.
2. Forwarding these packets will bring chaos to
network. Unnecessary packets are broadcasted to
whole network and they will not only be
broadcasted forever (because the TTL we use in
REB-R increases crossing each hop) but also
consume the whole energy of every node in
network.
Because such behaviors are not encouraged, we drop
mentioned packets. And about TTL values less than or
equal to FIRST_TTL, we will take action as follow. If TTL
is equal to FIRST_TTL, we just add node to future
Figure 6 - Changing route path a period of time after start of network for
Figure 2
First phase of our protocol has been fully discussed. At
this phase we created reverse forwarding route to sink node
and future parent nominees of each node. This important
phase in REB-R prepares nodes to transmit their sensed
data to sink node. When a node collects data from
environment, it tries to send data through its selected parent
node which is changed in time. As the data arrives to sink
and crosses from nodes, it causes the forwarding route to
change to best available one. Using the energy part in data
packets traveling through network makes this action
possible. Data packets not only contain the data but also
energy level of transmitter. Energy part in data packets
plays a key role. When packet is received by node, node
looks up in its future list for sender node. If it finds node,
nominee list is updated by new energy level, and parent
node could be changed to this node when energy level of
this node is more than current parent node. For transmitting
data to sink node any kind of aggregation algorithm can be
used and REB-R is independent of this algorithm. In our
simulations we used a simple aggregation strategy; we just
make average of incoming and sensed data within a node
and then transmit it through forwarding route to sink.
Figure 6 shows how forwarding route to sink node change
through time as energy of nodes are consumed due to
transmission and reception of packets. As you see from
Figure 3 comparing to Figure 2, some nodes are forced to
select a specific node for their life time, so those nodes are
among nodes which their energy diminishes faster than
other nodes and we lose sensor nodes of that area. Although
the results have shown REB-R performs outstandingly
better than other protocols in number of dead nodes. Parent
nodes of sensors are changed rapidly before network
achieves its optimal structure.
An example about how protocol works in steady state may
help to understand it properly. Suppose time 0.0 is when
sink starts network activities by sending FWD_ROUTE
packet. Time passes and network goes to its steady state,
and during this time nodes have selected their parents. At
time 32.46, node 23 has broadcasted the data to its parent
and data timer is set to be expired again one second later in
33.46. Among nodes in area, suppose nodes 46, 2, 78 and
124 have selected node 23 as their parent. Data timers on
above nodes are set to be expired 32.84, 32.57, 32.89 and
33.23, respectively. When data timer expires on these
nodes, they begin to send their collected or aggregated data
to node 23 as destination. During consecutive timers on
32.46 and 33.46, node 23 captures all incoming packets and
it will collect every data which their destinations are node
23. After aggregating its data with received data at 33.46 it
will send the aggregated data to its parent. It should be
mentioned during this process, future nominee list and their
corresponding energy level is update as protocol considers
this change.
IV. SIMULATION AND RESULTS
For performance comparison of our proposed protocol
with other protocols, we have used Network Simulator 2
(NS-2) framework which is capable of simulating network
protocols to weigh against other protocols. NS-2 is an
event-driven simulator which simulates a network with a
specific topology for network researches, and it supports
simulation of TCP, routing and multicast protocols over
wired and wireless networks in local and satellite
environments. In sensor network applications, topology
usually is not static form, so results vary. To cover this
variation, we have simulated our protocols with different
topologies of nodes which are distributed according to
homogeneous spatial Poisson process; it scatters nodes
randomly in a manner which nodes are almost near and in
touch to each other.
In Figures 7, 8, 9, 11, 12 and 13, graphs represent
average value of parameter and 90% of simulation results.
In order to eliminate influence of out of order data samples,
5% of high values and 5% of low values are omitted.
Bottom and top of bar represent minimum and maximum
values of this 90% samples.
A. Simulations parameters
For our simulation experiments, we have assumed that
nodes are scattered in a square 670x670 area. Transmission
and reception energy of packets are assumed to be equal and
static values as 10µJ and initial energy of each node is
valued at 1J. Sink node’s initial energy similar to other
experiments and like real applications is set to infinite, thus
neither transmitting nor receiving packets would not change
energy level of sink node. Infinity value for energy level of
sink node comes from this concept that this node is a data
collector node and must be online at anytime to take
delivery of data packets. The only control packet REB-R
uses, FWD_ROUTE, is for building forwarding route from
each node to sink. Constructing this route takes time and
this time is not fixed and it is related to the number of
nodes. For transmitting acquired data to sink node, data
timer is set to 1 second. For AODV parameters, we used the
default ones in NS-2, but for sending our specific data we
added another timer to protocol, to send us data every
seconds, like REB-R and T-ANT. T-ANT parameters are
also changed to reach one second of transmission of data.
Unlike AODV, T-ANT protocol has mechanism of
broadcasting data to sink node as default. For T-ANT
experiments, all parameters except timers’ interval are
those introduced in its paper. Cluster head selection phase
interval is set to 8 seconds, join phase interval is 3 seconds,
and ant releasing takes place each 4 seconds. Radio range of
each node and sink node is set to 200. T-ANT paper
assumes nodes have awareness of their neighbors and
recognize them but we did not apply the supposition. When
network starts, each node sends a FNA (Find Neighbors
Around) packet and asks for neighbors to respond to him by
RFNA (Response FNA) packet, and then neighbor list in
each node is generated. Since AODV does this implicitly by
on-demand routing and REB-R actually uses FWD_ROUTE
packet types to recognize its neighbors so we are not
worried about finding neighbors in those protocols. But TANT needs to know its neighbors before network is started.
In original paper [10], authors assume nodes are aware of
their neighbors from beginning.
Number of nodes is another parameter to system which
along in the simulation, by changing it, we get different
results. This number varies among 50, 100 and 200. The
results are shown in diagrams. Each protocol is simulated
with a specific topology for 2000 seconds, and results
shown here are based on this timing.
B. Experimental results
Within this part, we will demonstrate results of our
experiments and compare them to each other. The features
to compare are average consumption of energy of all nodes
% of average consumed
energy
and average number of alive nodes. In all of them REB-R
surpasses both of T-ANT and AODV.
1) Average consumed energy: We have simulated our
proposed protocols along with other protocols for 50 times
with 50 different topologies in each round. At the end of
every round, consumed energy of each node is determined
and summing all of them together yields total energy
consumed in all of nodes, and dividing it by number of
nodes, concludes average value.
120
100
80
AODV
60
T-ANT
40
REB-R
20
0
0
100
200
300
Figure 10 - Comparison of average consumed energy
AODV
T-ANT
REB-R
Figure 7 - Consumed energy for 50 nodes
% of consumed energy
120
100
80
60
40
20
0
AODV
T-ANT
REB-R
Figure 8 - Consumed energy for 100 nodes
% of consumed energy
120
100
80
60
40
20
0
AODV
T-ANT
2) Average number of alive nodes: Along with average
consumed energy simulation, we measured number of alive
nodes in network. Like average consumed energy, numbers
of active nodes are summed together and divided by number
of nodes, expressing average nodes currently active.
Figure 11 reveals comparison of number of alive nodes in
simulation against protocols for 50 nodes. REB-R does not
have any dead nodes in simulation time but AODV
performs well with more than 90% of alive nodes and TANT performs near REB-R performance. In Figure 12
comparison is done for 100 nodes. Like previous results
REB-R does not have dead nodes but T-ANT saves only
80% of nodes, and AODV as expected saves around 30%.
Figure 13 indicates that T-ANT does not perform well and
like AODV less than 20% of nodes are saved for both of
them, but like other results, REB-R achieves perfect value.
Looking for reason for this behavior will guide us to
complexity of T-ANT and amount of control packages
which are traveling in network and consume more energy
just like AODV.
Figure 14 certifies our conclusion about behavior of
converging T-ANT to AODV when number of nodes
increases. In this figure, T-ANT’s result converges to
AODV result. For 50 nodes, they perform like each other
but when number of nodes grow and reaches 100, T-ANT is
behind of REB-R and for 200 nodes, T-ANT perform like
AODV. As it is obvious in results, REB-R protocols leads
network toward long life and high performance.
REB-R
Figure 9 - Consumed energy for 200 nodes
Figure 10 shows average consumption of energy against
number of nodes in environment. This figure shows how
REB-R performs outstandingly when number of nodes
grows. AODV reaches its maximum available value just for
100 nodes and uses 100% of energy of a node in average.
For 200 nodes, REB-R does not even consume half energy
of a node in average. T-ANT for 100 nodes exceeds 50% of
energy a node and keeps it distance from AODV.
Number of alive nodes
% of consumed energy
Number of nodes
80
70
60
50
40
30
20
10
0
60
50
40
30
20
10
0
AODV
T-ANT
REB-R
Figure 11 - Comparison of alive nodes for 50 nodes
Number of alive nodes
REFERENCES
120
[1]
100
80
60
40
20
0
AODV
T-ANT
REB-R
Number of alive nodes
Figure 12 - Comparison of alive nodes for 100 nodes
250
200
150
100
50
0
AODV
T-ANT
REB-R
Average number of alive nodes
Figure 13 - Comparison of alive nodes for 200 nodes
250
200
AODV
150
T-ANT
100
REB-R
50
0
0
100
200
300
Number of nodes
Figure 14 - Comparison of average alive nodes
V. CONCLUSION
Leading nodes of sensor networks toward consuming less
energy is a gigantic problem for designing routing
protocols. Many protocols are offered for minimizing
network’s total dissipation of energy. Some protocols rely
on grouping nodes and therefore routing packets through
one of them, and others are designing routing path by
algorithms which run on sink node. This paper presents a
routing protocol which route packets based on decision a
node makes from energy level that neighbors transmit along
with data.
G. Werner-Allen, J. B. Johnson, M. Ruiz, J. Lees and M. Welsh,
“Monitoring volcanic eruptions with a wireless sensor network”, Proc.
Second European Workshop on Wireless Sensor Networks (EWSN’05),
2005
[2] G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees and M. Welsh,
“Fidelity and yield in a volcano monitoring sensor network”, 7th
USENIX Symposium on Operating Systems Design and
Implementation (OSDI 2006), pp. 381-396, 2006.
[3] G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. B. Johnson, J.
Lees and M. Welsh, “Deploying a wireless sensor network on an active
volcano”, IEEE Internet Computing, Volume 10, Issue 2, pp. 18-25,
March 2006.
[4] D. Malan, T. Fulford-Jones, M. Welsh and S. Moulton, “CodeBlue: An
ad hoc sensor network infrastructure for emergency medical care”, Proc.
MobiSys 2004 Workshop on Applications of Mobile Embedded
Systems (WAMES 2004), June 2004.
[5] T. Gao, D. Greenspan, M. Welsh, R. R. Juang and A. Alm, “Vital signs
monitoring and patient tracking over a wireless network”, Proc. of the
27th Annual International Conference of the IEEE EMBS, Shanghai,
pp.102-105, September 2005.
[6] R.G. Lee, K.C. Chen, C.C. Lai, S.S. Chiang, H.S. Liu and M.S. Wei, “A
backup routing with wireless sensor network for bridge monitoring
system”, Measurement, Elsevier, Volume 40, Issue 1, pp. 55-63,
January 2007.
[7] A. Bari, S. Wazed, A. Jaekel and S. Bandyopadhyay, “A genetic
algorithm based approach for energy efficient routing in two-tiered
sensor networks”, Ad Hoc Networks, Elsevier, Volume 7, Issue 4, pp.
665-676, June 2009.
[8] W.B. Heinzelman, A.P. Chandrakasan and H. Balakrishnan, “An
application-specific protocol architecture for wireless microsensor
networks”, IEEE Transactions on Wireless Communications, Volume
1, No. 4, pp. 660-670, October 2002.
[9] O.Younis and S. Fahmy, “Heed: a hybrid, energy-efficient, distributed
clustering approach for ad hoc sensor networks”, IEEE Transactions on
Mobile Computing, pp. 366-379, October 2004.
[10] S. Selvakennedy, S. Sinnappan and Y. Shang, “A biologically-inspired
clustering protocol for wireless sensor networks”, Computer
Communications, Elsevier, Volume 30, Issues 14-15, pp. 2786-2801,
October 2007.
[11] J.Y. Choi, H.S. Kim, I. Baek and W.H. Kwon, “Cell based energy
density aware routing: a new protocol for improving the lifetime of
wireless sensor networks”, Computer Communications, Volume 28,
Issue 11, pp. 1293-1302, July 2005.
[12] P. von Rickenbach and R. Wattenhofer, “Gathering correlated data in
sensor networks”, Workshop on Discrete Algothrithms and Methods
for MOBILE Computing and Communications, ACM (2004), pp. 6066.
[13] K.F. Ssu, C.H. Chou and L.W. Cheng, “Using overhearing technique to
detect malicious packet-modifying attacks in wireless sensor networks”,
Computer Communications, Volume 30, Issues 11-12, pp. 2342-2352,
September 2007.
[14] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless
sensor network: a survey”, Computer Networks, pp. 393-422, 2002.
[15] G. Simon, M. Maroti, A. Ledeczi, G. Balogh, B. Kusy, A. Nadas, G.
Pap, J. Sallai and K. Frampton, “Sensor network-based countersniper
system”, Proc. of 2nd International Conference on Embedded
Networked Sensor Systems, pp. 1-12, 2004.
[16] S. Madria, J. Yin, “SeRWA: A secure routing protocol against
wormhole attacks in sensor networks”, Ad Hoc Networks, Elsevier,
Volume 7, Issue 6, pp. 1051-1063, August 2009.
[17] A. Bachir, D. Barthel, M. Heusse and A. Duda, “O(1)-Reception routing
for sensor networks”, Computer Communications, Volume 30, Issue
13, pp. 2603-2614, September 2007.
[18] F.Y. Leu and G.C. Li, “A scalable sensor network using a polar
coordinate system”, Signal Processing, Volume 87, Issue 12, pp. 29782990, December 2007.