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Load-Balancing Geographic Routing Algorithm (ELBGR) For Wireless Sensor Networks

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Load-Balancing Geographic Routing Algorithm (ELBGR) For Wireless Sensor Networks Powered By Docstoc
					                                                 (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                      Vol. 9, No. 5, May2011

    LOAD-BALANCING GEOGRAPHIC ROUTING ALGORITHM
        (ELBGR) FOR WIRELESS SENSOR NETWORKS
                    Nazia Perwaiz                                            Dr. Muhammad Younus Javed
Department of Computer Engineering, NUST College of              Department of Computer Engineering, NUST College of
    Electrical & Mechanical Engineering, National                    Electrical & Mechanical Engineering, National
  University of Sciences & Technology, Islamabad,                  University of Sciences & Technology, Islamabad,
                      Pakistan.                                                        Pakistan.
             missnazia@ceme.nust.edu.pk                                        myjaved@ceme.nust.edu.pk


Abstract: The major challenges faced by wireless                 applications, communicate wirelessly and cooperate with
sensor networks are energy-efficiency and self-                  the neighbor nodes to route the sensed information
organization. .A thorough literature study of routing in         towards the destination. This type of communication has
WSNs shows that there exist so many routing protocols            different characteristics as compared to cellular networks
for WSNs, each of which has the common objective of              or single hop wireless networks as they don’t rely on a
trying to get better throughput and to extend the                fixed infrastructure.
lifetime of the sensor network. In this research work            Many routing strategies have been introduced and
the Location-based or Geographic routing in WSN is               developed for the WSNs. These strategies used in WSN
mainly focused for energy issues and a location based            are different from wired networks routing mechanisms.
WSN routing protocol ELBGR (Energy aware & Load                  The schemes used for routing in WSN must not ignore
Balancing Geographic Routing) is proposed that                   the unique inherent features of the WSNs, the main
features the energy efficiency and self organization of          critical issue in WSNs routing strategies development is
the wireless sensor networks. This protocol extends the          to deal with the energy constraints and to cope with the
lifetime of the network and balances the energy                  nodes status changes (e.g. failure) that occur suddenly
consumption of the nodes within the network. ELBGR               resulting unpredictable changes in the network topology.
considers neighbor’s energy levels, packet reception             The communication protocols governing the network
rate and the locations of the nodes for data forwarding          must be able to cope with all topological changes
purpose. Each node knows geographic location, energy             without human intervention. Most of the nodes are too
levels and PRR of its neighbors. The proposed                    far away from the sink node (node that finally collects
algorithm selects a set of relative optimal nodes from           the sensed information) to communicate directly.
all neighbors called Forwarding Nodes Set (FNS) in               Intermediate nodes are hence used to relay the message,
the first phase and in the next phase from FNS, the              that’s why this type of communication is also called ad
Optimal Forwarding Node (OFN) is finally selected for            hoc multi-hop communication [19].
forwarding purpose. The proposed algorithm balances              The major purpose of this research is to develop an
the energy levels among all the neighbors. A                     energy aware and energy efficient geographic routing
comparison has been made between pre-existing                    algorithm for the WSNs that can play an important role
routing algorithms Greedy routing, EAGR, EEAR,                   in maintaining the energy balance within the network
HHEAA and the proposed one ELBGR. The                            causing the prolonged life-time of the network. A
Simulation results (in OMNET++) show that the                    geographic routing mechanism, ELBGR (Energy &
proposed algorithm gives better performance in terms             Load Balancing Geographic Routing) is proposed that is
of higher success rate, throughput and less number of            much efficient for energy efficiency, it splits the work
dead nodes and it effectively increases the lifetime of          load equally among all network nodes and avoids the
the sensor networks.                                             holes formation than the greedy forwarding in WSNs.
Keywords: WSN; Geographic routing; Energy                        The proposed algorithm depicts the node that will act as
efficient; Load balancing                                        the relaying node among all one-hop neighbors of the
                                                                 sender node on the basis of some relative measures
               I.       INTRODUCTION                             rather than some specific threshold values. This way the
    The technological progress in the embedded systems           over burden of relaying on some nodes is abandoned and
emerged a new network class called Wireless Sensor               the load is divided among all the network nodes,
Networks (WSN) [1]. A WSN consists of many small                 resulting energy efficient long life network.
autonomous systems, called sensor nodes or motes.                The paper is organized as follows. The Related work is
Sensors are the devices that make sense to some physical         presented in section 2. The research motivation,
change for which they are deployed for different                 objectives, assumptions and the proposed algorithm
                                                                 ELBGR is described in Section 3. In Section 4 the




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                                                                                           ISSN 1947-5500
                                                  (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                       Vol. 9, No. 5, May2011
proposed algorithm’s design and implementation along              energy efficient, has low latency and QoS. It
with the algorithm and flow chart is presented. The               performs better for immobile networks but
simulation results, comparison of proposed algorithm
with pre-existing algorithms Greedy, EEAR, EAGR,
                                                                  inefficient for mobile networks. Energy aware
HHEAA and the results analysis is explained in Section            greedy routing (EAGR) [15] uses the location
5. Conclusions along with some suggestions for future             information of nodes and their powers
research are provided in Section 6.                               available for routing purpose. In EAGR, high
               II.      RELATED WORK                              energy nodes are used for forwarding and
     In the location-based or geographic routing [7], the
location information is basic requirement for the
                                                                  packets are dropped when no neighbor is alive
neighbor nodes identification and routing. Many routing           to forward the data. EAGR protocol is much
algorithms are defined that use only local information of         more energy efficient and balances the network
neighbors for route determination, these are much energy          loads very well. Holes Healing Energy Aware
efficient than the algorithms using global routing table,         Algorithm (HEEAA) [16] is location based and energy
that depict the whole picture of the network.                     aware routing protocol that works on average energy and
Greedy Perimeter Stateless Routing (GPSR) [8] is                  distance of nodes to overcome weak node problem in
designed to minimize the number of hops,                          WSN. HHEAA has high throughput with reliable packet
which follows greedy forwarding algorithm                         delivery and long lived network. Efficient Energy Aware
                                                                  Routing (EEAR) [17] algorithm is a location based & a
along with the perimeter forwarding strategy to                   power-aware routing technique, it uses efficient energy
achieve  successful  routing. Using greedy                        aware routing mechanism to choose the neighbor node
forwarding when optimal next-hop nodes are not                    that has sufficient power-level and meets the distance
available, the perimeter forwarding algorithm is used on          criteria to determine the receiving node for forwarding
the planar graph to choose the next-hop. Geographic               the packet EEAR gives higher packet delivery rate, less
adaptive fidelity (GAF) [10] is a geographic                      energy consumption with the maximum network life
                                                                  time as compared to traditional routing mechanisms for
and energy-aware routing protocol, used both                      the wireless sensor networks.
for ad-hoc networks and WSNs. It decreases
the redundancy in the network and turns off all                   III.     ENERGY AWARE & LOAD BALANCING
unused nodes of the network. Greedy other                                  GEOGRAPHIC ROUTING (ELBGR)
adaptive face routing (GOAFR) [11] is a
                                                                  A.        MOTIVATION
geographic routing scheme that uses the greedy                         Many of the geographic routing techniques designed
routing along with the face routing called other                  & developed for the WSN uses the basic idea of greedy
adaptive face routing. When GOAFR reaches                         technique for forwarding the data, the minimum distant
the local minimum point it adopts the face                        node (from destination) is used for this purpose to
routing (FR) mode to get routing efficiency for                   choose shortest path and early delivery of the packet.
                                                                  Hence the main problem in routing algorithms designed
both worst case & average case scenarios.                         and used for the WSNs is that how much energy efficient
GOAFR protocol is much better in energy                           [25] they are and their role to enhance the life of the
efficiency. It’s time delay in data delivery and                  network. Very important feature of the WSN is that the
network lifetime is also much better.                             nodes have small sized batteries, limitedly-powered &
Geographic and energy aware routing (GEAR)                        these batteries cannot be changed in practical therefore it
                                                                  is required to uses some ways to save the batteries power
protocol [12] uses the nodes’ location information                to prolong the network life so the WSN is taken as
and the remaining energy level to select the neighbor             energy-constrained. Because batteries cannot be
node with the least overall overhead. The estimated               replaced, communication protocols ought to be as
cost is used for simple routing and based on                      energy-efficient as possible. Energy-efficiency is of
nodes' remaining energy levels and its distance                   utmost importance, as it has very visible impact on the
to the destination node & the learned cost is                     network lifetime. It is required to develop some energy
                                                                  efficient routing mechanism which maintains a balance
used for routing around the holes. GEAR uses                      to the overall network power and holes appearance be
the recursive geographic forwarding (for dense                    prevented consequently.
network) and the restricted blind flooding                        The motivation behind this research work is to design an
(network is not dense) to disseminate the                         energy-aware & energy-balancing geographic algorithm
packet within the region. GEAR is not highly



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                                                                                            ISSN 1947-5500
                                                    (IJCSIS) International Journal of Computer Science and Information Security,
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for WSNs which will be simple, easy to implement and                packets received by node A). Based on this all
efficient in terms of energy consumption.                           information each node contains, the ELBGR extracts the
                                                                    relatively optimal node among all neighbors, first of all
B.        RESEARCH OBJECTIVE                                        finds the average distance of all nodes' distances from
     Greedy mechanism used in many geographic                       the sink/destination node, & then it calculates the
routing has a major drawback that the construction of the           average energy and average PRR of all neighbor nodes.
routes is based only on the node’s distance from the                After taking these measures, this algorithm selects the
destination and the same group of nodes is repetitively             neighbor nodes for FNS which have average or greater
used for sending data from the source to the destination            remaining energy as well as average or greater PRR
if source & destination are constant. The energy-level of           value. It is obvious that greater energy nodes selection
a node is not taken into account for data transmission              for forwarding purpose prevents the high rate of nodes
and even if the node has very low energy, the packet can            death in the network. Nodes PRR values are important in
be forwarded to it resulting in the breakdown of that               respect of the holes avoidance, if a node forwards all the
optimal path. It puts bad effect on network connectivity            packets its PRR value doesn’t change (remains 1,
as there may exist some of the nodes in networks which              initially set value) and the routing decision is almost
depend on only these dead nodes for routing the packet.             energy dependent. But when a node drops the packet due
In this research many geographic/location based routing             to any reason i.e. lower energy level or queue fill, the
strategies for WSNs are studied for complete                        packet reception rate lowers down and for next packet
understanding. To address the above mentioned                       transmission that node is avoided due to low PRR value
problems an energy-aware and load balancing greedy                  (lower than average PRR value of all neighbors),
routing scheme ELBGR (Energy & load balancing                       resulting the avoidance of the hole. Neighbors having
Geographic Routing) is designed, implemented and                    energy and PRR below average value are ignored so that
evaluated through simulation. The comprehensive                     the balance of energy can be maintained among all
simulation results are compared with pre-existing                   neighbors and this way of selection prevent some nodes
relevant routing protocols for its energy efficiency,               to be selected frequently resulting in energy depletion.
successful data transmission and for some other                     Therefore FNS contains only nodes having greater
parameters. ELBGR is a simple, easy to implement and                energy and PRR values. In next phase ELBGR considers
energy-efficient algorithm, it splits the work load equally         only FNS for decision making and selects the node
among all network nodes and avoids the holes formation              nearest to the target/ destination node from the set of
resulting energy efficient long life network.                       nodes that lie in FNS. After choosing the optimal
                                                                    forwarding node (OFN), the packet is sent to it, this
C.       ASSUMPTIONS FOR ELBGR                                      current node then makes further decision for next
     For the designing and implementation of the                    forwarding node among its neighbors using ELBGR
ELBGR routing algorithm some assumptions are taken.                 mechanism. This process continues until the destination
Sensor nodes are considered to be static or immobile                node is reached. When packet is sent to a neighbor, the
having fixed coordinates. The location of the nodes is              energy used suppose 0.001joule is deducted from its
determined by some kind of GPS system (some central-                current energy level, this way the remaining energy
location database is used for this purpose). The energy             value and current PRR value of each node is updated
levels and the PRR values of the nodes are known by                 after each time when it forwards the packet. The new
each node; initially both values are set 01 for each node.          values are stored in the node’s memory. For next
The topology used is irregular random topology as in                transmission if any neighbor node considers it for
real the sensors are deployed in random style. Single               forwarding, it will check its energy and PRR values,
destination node is considered with already known                   which are if less than average values then that node will
location by each node. The limited-size buffers/ queues             not be considered for selection in FNS and the nodes
are used at each node for containing incoming and                   which are not selected previously or having greater
outgoing message packets. Packets size is taken fixed for           energy and PRR values are selected in FNS. Using
the proposed system implementation.                                 ELBGR routing scheme the network work load is taken
                                                                    as the data packets which are needed to be sent to the
D.        DESCRIPTION OF ELBGR                                      destination or target node, is equally distributed (all
     The major role of the proposed algorithm is the                nodes are considered equally for forwarding purpose)
avoidance of holes formation in the network by equally              among all nodes and almost an energy balance is also
distributing the load among the nodes, no single node               maintained among nodes. So the path selected may not
depletes its energy very soon as relatively optimal node            be the similar every time & the traffic is spread over
is selected for forwarding purpose. Each node know                  many nodes rather than to the specific nodes only. As a
about its own distance from sink node and its all                   result the sensor network is utilized for maximum time
neighbors' distance from sink node, energy level and                with greater throughput in energy efficient and load
PRR value (PRR = total packets sent by node A/ total                balancing way. ELBGR algorithm tries its best to



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                                                                                              ISSN 1947-5500
                                                 (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                      Vol. 9, No. 5, May2011
prevent the formation of holes, but as it is unavoidable           utilization when almost all nodes of the network have
due to energy constraints but this algorithm avoids the            consumed almost their whole resources.
routing of packet towards holes considering PRR values,
using ELBGR a very little number of holes can be seen              IV.      DESIGN & IMPLEMENTATION OF ELBGR
in the network even after a long time span of network




                                                 Figure 1.         Flow chart of ELBGR

5.      SIMULATIONS AND ANALYSIS
                                                                   four different modules defined in our proposed system
A.      THE PROPOSED SYSTEM MODULES                                are network generator, Route generator, proposed
The proposed system is implemented on OMNET++                      ELBGR algorithm and the router module.
Simulator. OMNET++ works on the modules system;




                                        Figure 2. Proposed System Block Diagram




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The Network generator module generates the network,                simulation model, the packets used are of fixed size 562
the total number of nodes is defined in this module, the           bytes, different number of packets are generated by
packet sending rate and time delay between packets                 some source node at some pre-defined time delay, this
transmission are also defined as parameters of this                time delay can be changed to analyze its effect on the
module. The Route Generator uses the addresses of the              proposed algorithm’s performance. Total simulation
nodes for determination of the sending/ source node &              time is also pre-defined. The simulation process is
sink/ destination node for data packet sending and                 repeated with different number of packets generated
receiving respectively. The Proposed Algorithm                     and with different numbers of nodes to evaluate the
ELBGR is very important module that chooses the next               proposed algorithm. A sample network designed in
forwarding node. The proposed algorithm assumes that               OMNET++ with 90 numbers of nodes is shown below
all nodes know their own geographic-locations, their
energy-levels and current PRR values & it implements
the proposed algorithm. The Router module is also very
important in its functionality. It performs actual routing
of packet. When the packet is sent towards the neighbor
node, this module subtracts the energy from the node's
current energy and re-evaluates the PRR value and
updates all neighbors about it. Finally it shows output
of simulations as the successfully delivered-packets,
dropped-packet, node's current power-level, node’s
current PRR value and the status of the node.

B.        ELBGR SIMULATION MODEL
ELBGR Simulation model is designed in OMNET++.
Different numbers of nodes exist in the network so that
the simulation results may be evaluated for variable-
sized network having variable number of nodes. All
evaluation metrics are checked for different models
with different number of nodes (different network size).                     Figure 3. Sample Network with 90 Nodes
In this simulation, the nodes’ locations of network are
taken randomly without any predefined criteria and                 C.        NETWORK INPUT PARAMETERS
irregular random topology is used as in real conditions            For this simulation various input parameters are defined
the WSN sensor nodes are also deployed randomly. As                which are considered same for both routing strategies
per assumptions already described it is clear that the             (Greedy Routing and ELBGR routing) that are
nodes are static and don’t ever change their position.             compared for evaluation of the proposed one’s
The position of each node is determined by the node                performance.
itself using a central location database, this central
database also inform each node with sink/ destination                 Parameters                     Value
node location. In ELBGR system, the sink node is fixed                Network Size (Number           Variable for each
and pre-defined. One node is declared as target node, as              of nodes)                      scenario
each node have knowledge about its location, each node                Traffic type                   Constant
itself measures it’s distance from the target/ sink node                                             (1pkt/microsecond)
(as for number of hops). In start each node has allocated             Nodes type                     Static/ Fixed
same energy level i.e. 01 Joule and same PRR value i.e.               Topology                       Irregular random
1. Before the simulation starts, each node has                                                       topology
information about Node’s location, Sink node location,                Data packet size               562 byte
Node’s distance from sink node, Node’s energy level &                 Buffer/ Queue size             10
Node’s PRR value. This all information is to be                       Initial Energy level of        1 Joule
exchanged among all 1-hop neighbors for maintaining a                 each node
local-table of neighbor's info, which will be used for                Energy Threshold value         0.1 Joule
routing decision making later. In the start a threshold
                                                                      Transmission energy            0.001 Joule
(TH) energy-level is defined & the nodes whose
energy-levels are lower than threshold value are                      Initial PRR value              1
considered as dead nodes. Each node has a limited size                Number of receiver             1 (Predefined)
buffer, a fixed size queue is defined which is used to                              Table 1. Input Parameters
store incoming / outgoing packets temporarily. In this



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                                                                                              ISSN 1947-5500
                                                     (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                          Vol. 9, No. 5, May2011
The network size is variable for each scenario as this              nodes hence the network remain alive until almost
research focuses that the proposed algorithm ELBGR                  complete consumption of all network resources.
scales very well and performs better with different sized           Along with the greedy algorithm, we have compared
network having different number of nodes. Yet for each              the evaluation parameters of the ELBGR with some
scenario both the Greedy and ELBGR routing                          other proposed algorithms that proved themselves better
algorithms are evaluated for comparison purpose.                    in performance than simple greedy routing i.e. EAGR,
                                                                    HHEAA and EEAR.
D.      EVALUATION METRICS                                          A comparison among above mentioned algorithms is
To make evaluation for the proposed routing scheme’s                shown below for constant values of number of nodes,
performance, some evaluation metrics are analyzed for               number of packets generated and time duration i.e. 90
some pre-existing algorithm greedy routing, EAGR,                   nodes, 45000 number of packets generated and 500
EEAR, HHEAA and the proposed ELBGR routing                          seconds.
mechanism i.e. Packets delivered, Packets Dropped,
Success rate, Alive Nodes, Dead Nodes and
Throughput.

E.        SIMULATION SEQUENCE OF ELBGR
As defined in input parameters before simulation starts
each node has total energy equal to 1Joule. Initially
each node creates a local-table of its neighbors
containing all 1-hop neighbors along with their current
energy levels (initially 1J), PRR values and their
distance to the destination. When a node is used for
transmission purpose and its energy level or PRR value
changes then it informs its 1-hop neighbors to update
their local tables. Nodes initiate the sending of the data
to the destination by selecting a neighbor nodes set                    Table 2. Evaluation of different routing algorithms for
                                                                                      Network size 90 Nodes
(FNS, Forwarding Nodes Set) on the basis of ELBGR
algorithm. Using ELBGR, the current node first of all
calculates the average remaining energy and average                 The above table shows a comparison between different
PRR of its all neighbors. Initially when all nodes have             algorithms the constant number of nodes and packets
same energy levels and PRR values (equal to the                     generated. Each node consumes equal energy for the
average energy level and average PRR value                          transmission purpose, viewing the results of the
respectively), ELBGR works like greedy scheme, the                  simulations; it is vivid that ELBGR algorithm performs
data packets are sent to the node closest to the sink               much better than greedy algorithm as for the success
node (minimum hops to the target node is considered                 rate, throughput, & number of alive nodes. The network
the nearest or closest node), the process carries on until          lifetime using ELBGR is longer than the Greedy
the arrival of the packet to the destination. After                 algorithm. ELBGR also outperforms EEAR, EAGR and
utilizing a node for forwarding purpose, its energy &               HHEAA in terms of throughput and success rate yet the
PRR values are updated and exchanged with neighbors.                number of alive nodes after the simulation is similar to
For the next packet from the same source which is to be             that of EEAR and EAGR. We performed so many
forwarded towards the same destination, the sending                 simulations to attain the complete picture of the results
node will have same neighbor nodes but it will not use              of different algorithms. Our main objective is to
the previously selected neighbor for forwarding purpose             evaluate different algorithms with changing network
as the nodes that have been used in previous packet                 size and the traffic load, all other parameters remains
forwarding have updated their local values, energy and              constant in our simulations. The main aim of our
PRR values are now less than the average energy and                 simulation is to evaluate the performance of the
PRR, due to this those nodes are not selected in next               proposed algorithm that what type of behavior it shows
transmission, some other node will be selected now.                 with increased number of the nodes and how it scales
This is the beauty of ELBGR that it distributes the load            and performs better for different sizes of the networks.
among all neighbors by screening out previously used                Viewing the detailed picture of simulation result, it is
nodes; this procedure has great impact on the overall               clearly seen that the delivered number of packets
energy consumption of WSN and on the network                        increases with increasing number of nodes of the
lifetime. Secondly ELBGR doesn’t use any specific                   network. It is also observed that the number of alive
values, rather it uses relatively optimal node among all            nodes is far greater using ELGBR routing algorithm
neighbors, this way it utilizes almost all resources of the         than in number of alive nodes using greedy routing.
                                                                    This increases the life-time of the network.




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F.     RESULTS & PERFORMANCE COMPARISON                                                                                                                    Our proposed algorithm ELBGR considers the current
This Section describes the experimental results of the                                                                                                     energy levels of nodes & don't sends the data towards
simulations & briefs the observations of the strengths                                                                                                    the lower energy nodes that results in increased number
and the limitations of the proposed algorithm ELBGR
in comparison with the Greedy Routing algorithm,                                                                                                                                                             Throughput
EEAR, EAGR and HHEAA to check the proposed
                                                                                                                                                          100
scheme efficiency.
The results that are collected from the experiments                                                                                                             80                                                                                                  GREEDY




                                                                                                                                           T h ro u g h p u t
include the comparison of the total number of packets                                                                                                                                                                                                               EEA R
                                                                                                                                                                60                                                                                                  EAGR
delivered, the total number of packets dropped, the
                                                                                                                                                                                                                                                                    HHEAA
success rate, remaining live and dead nodes after                                                                                                               40
                                                                                                                                                                                                                                                                    ELBGR
simulation and throughput by different routing
                                                                                                                                                                20
protocols. It is observed that the proposed algorithm
utilizes maximum network resources and results far                                                                                                              0
better than above mentioned existing routing protocols.                                                                                                                  0             10   20   30    40    50    60    70        80        90        100 110

It has been observed that in Greedy routing algorithm                                                                                                                                                  No. of Nodes
due to excessive use of few nodes those nodes deplete                                                                                                                                                                                                                          
their energy very soon and results in the formation of
holes, greedy algorithm don’t consider the remaining                                                                                                                                   Figure 6. Comparison of Protocols Throughput
energy of the nodes and the dead node simply drops the
data packet. Hence using greedy algorithm so many                                                                                                      of packets delivery and very low number of packets are
packets are dropped and the data delivery rate is quite                                                                                                dropped using ELBGR. Hence it can be seen from
lower than ELBGR. The proposed algorithm ELBGR                                                                                                         Figure 6 that the throughput of the proposed algorithm
also performs better than EEAR, EAGR and HHEAA                                                                                                         is far better than Greedy algorithm as well as then
in terms of number of packets delivered and dropped                                                                                                    EAGR, EEAR and HEEAA.
(Figure 4, Figure 5).
                                                                                                                                                       Figure 7 provides the comparison of the success rate of
                                                                       Packets delivered                                                               the Greedy algorithm, EAGR, EEAR and HEEAA and
                                                                                                                                                       ELBGR; it is opaque that ELBGR performs very well.
                   50000
P ack et s D elive red




                                                                                                                          GREEDY
                   40000                                                                                                                                                                                         Success Rate
                                                                                                                          EEAR
                   30000                                                                                                  EAGR                                                   100
                                                                                                                          HHEAA
                   20000                                                                                                                                                         80                                                                                  GREEDY
                                                                                                                                                                 S uccess rate




                                                                                                                          ELBGR
                   10000                                                                                                                                                                                                                                             EEAR
                                                                                                                                                                                 60
                                                                                                                                                                                                                                                                     EAGR
                                           0                                                                                                                                     40                                                                                  HHEAA
                                               0       10   20    30   40        50   60    70   80   90    100   110
                                                                                                                                                                                                                                                                     ELBGR
                                                                            No. of Nodes                                                                                         20

                                                                                                                                                                                  0
                                           Figure 4. Comparison of No. of packets successfully                                                                                         0    10   20   30    40    50    60    70        80        90    100   110
                                                               delivered
                                                                                                                                                                                                             No. of Nodes

                                                                                                                                                                                   Figure 7. Comparison of Protocols Success Rate
                                                                        Packets Dropped

                                           20000                                                                                                       Figure 8 shows that the number of alive nodes after the
                                           18000
                                                                                                                                                       simulation duration using the proposed algorithm
                                           16000
                                                                                                                                                       ELBGR is greater than the number of live nodes using
                         Dropped Packets




                                           14000
                                           12000
                                                                                                                            GREEDY
                                                                                                                                                       Greedy algorithm and HHEAA. Number of alive and
                                                                                                                            EEA R
                                           10000
                                                                                                                            EA GR
                                                                                                                                                       dead nodes using EAGR and EEAR are almost similar
                                               8000                                                                         HHEA A                     to that of ELBGR. (Figure. 8, Figure. 9).
                                               6000                                                                         ELB GR
                                               4000
                                               2000
                                                   0
                                                       0         20         40         60        80        100      120
                                                                                 No. of Nodes


                                               Figure 5. Comparison of No. of packets dropped




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                                                                                                                                                                                                                   ISSN 1947-5500
                                                                                               (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                                                    Vol. 9, No. 5, May2011
                                                                                                                that of Greedy algorithm (70.56%). It can also be seen
                                                    Live Nodes
                                                                                                                that ELBGR also outperforms EEAR, EAGR and
                   120
                                                                                                                HHEAA having average throughput percentage 95.4%,
                                                                                                                93.52 and 84.76% respectively.
                   100
                                                                                               GREEDY
      Live Nodes




                   80
                                                                                               EEAR
                   60                                                                          EAGR                                           Routing Protocols Evaluation
                                                                                               HHEAA
                   40
                                                                                               ELBGR                                                     95.4                     98.92
                                                                                                                                        100                      93.52
                   20                                                                                                                                                    84.76




                                                                                                                  % ag e Th ro ghp ut
                    0                                                                                                                    80   70.56
                         0        20        40         60         80     100        120
                                                                                                                                         60
                                                 No. of Nodes
                                                                                                                                         40
                              Figure 8. No. of Live Nodes Comparison
                                                                                                                                         20

                                                                                                                                         0
                                                    Dead Nodes                                                                                GREEDY      EEAR    EAGR
                                                                                                                                                                   1      HHEAA     ELBGR

                   12
                                                                                                                                                           Routing Protocols
                   10
                                                                                                                  Figure 11.                           Throughput Percentage Comparison
      Dead Nodes




                                                                                                 GREEDY
                    8
                                                                                                 EEAR
                    6
                                                                                                 EA GR
                                                                                                                G.        DISCUSSION
                    4                                                                            HHEA A         Using the same input parameters, different experiments
                    2                                                                            ELBGR          have been performed for different sized-network
                    0
                                                                                                                (having different amount of nodes). Same energy is
                         0        20        40          60         80        100         120                    consumed for each transmission by each node. It is
                                                 No. of Nodes                                                   observed that the proposed algorithm ELBGR
                                                                                                                outperforms Greedy routing algorithm in all aspects
                              Figure 9. No. of Dead Nodes Comparison                                            including successful packets delivery, throughput and
                                                                                                                remaining number of live nodes except the data
Figure 10 shows a comparison of time delay in data                                                              delivery time-delay component, for greedy routing
delivery using both Greedy and ELBGR routing                                                                    computation time is lesser than ELBGR as ELBGR has
schemes, ELBGR take more time as it has to compute                                                              to compute multiple parameters. It is also seen that the
multiple parameters before forwarding the data.                                                                 ELBGR also outperform some pre-existing routing
                                                                                                                protocols (EEAR, EAGR and HHEAA) in the success
                                                       Time Delay                                               rate and throughput.
                                                                                                                A thorough picture of the detailed simulation results is
                 0.00002                                                                                        presented using the graphs. Initially the Greedy routing
                0.000018
                0.000016
                                                                                                                algorithm is compared for different parameters with the
                0.000014                                                                                        ELBGR as our main focus is to make a comparison of
 Tim e (S ec)




                0.000012
                                                                                                GREEDY
                                                                                                                the proposed algorithm's (ELBGR) performance with
                 0.00001
                0.000008                                                                        ELBGR           the Greedy routing strategy to evaluate the proposed
                0.000006                                                                                        algorithm's performance. Later on a comparison of
                0.000004                                                                                        ELBGR is also made for EEAR, EAGR, and HHEAA
                0.000002
                       0
                                                                                                                for the above parameters to check ELBGR efficiency.
                              0        20         40         60         80         100                          It can be concluded after vivid observation of the
                                                   No. of Nodes                                                 results extracted from the simulations that ELBGR is
                                                                                                                better, more efficient and reliable as compared to
                             Figure 10.                 Time Delay Comparison                                   Greedy algorithm, EEAR, EAGR & HHEAA routing
                                                                                                                schemes and it increases the network life-time with
In the last the above mentioned algorithms are                                                                  maximum utilization of the resources for maximum
evaluated on the basis of their overall performance,                                                            possible duration.
which is measured by taking percentage of their
average throughputs for different sizes of networks and                                                         VI.      CONCLUSION AND FUTURE WORK
different amount of traffic generated. It is clearly seen                                                       The main theme of this research to handle the wireless
from Figure 11 that the performance/ efficiency of the                                                          medium’s broadcast nature along with energy
proposed algorithm ELBGR (98.92%) is far better than                                                            constraints associated with the wireless sensor networks



                                                                                                          216                                              http://sites.google.com/site/ijcsis/
                                                                                                                                                           ISSN 1947-5500
                                                    (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                         Vol. 9, No. 5, May2011
that put the great effect on the network life time,                be extended in some directions in future. For this
scalability and reliability. In this research work an              research work, the static or immobile sensor nodes are
energy aware routing algorithm ELBGR (Energy aware                 considered, a single fixed destination is taken into
and Load Balancing Geographic Routing) is proposed,                consideration and the packets size is assumed fixed. For
implemented and evaluated. The main idea used to                   future research work the mobile sensor nodes may be
propose the routing scheme is to distribute the workload           considered with multiple destination nodes and variable
of the whole network among all nodes evenly so that                size packets. Energy consumption can be calculated
the network resources may be utilized at their                     depending upon the radio ranges and the distances of
maximum capacity. The proposed routing scheme is                   nodes.
compared mainly with the Greedy routing, the main
limitation of greedy routing is that it heavily utilizes           VII.      BIBLIOGRAPHY
few nodes for data transfer that are at optimal distance
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also out performs some other routing protocols                     [14]   J. Chen, R. Lin, Y. Li, Y. Sun, “LQER: A Link Quality
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ELBGR more reliable and efficient, this algorithm may                     Algorithm for Wireless Sensor Networks”, International




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                                                            (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                 Vol. 9, No. 5, May2011
       Journal of Computer Theory and Engineering (IJCTE),                                    AUTHOR’S PROFILE
       (ISSN: 1793-821X), Singapore
[17]   Munazza, Y., Abid, A. M., Javed, M.Y. and Atif, N., “EEAR:
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       Networks”, Proceedings of the 7th IEEE International                                        Degree         (MS        software
       Conference on ICT and Knowledge Engineering (ICTKE-                                         Engineering) from National
       2009), December 1- 2, 2009, (ISBN: 978-1-4244-4514-1),
       Bangkok,Thailand, pp. 57-62.
                                                                                                   University     of   Science     &
[18]   H. Hassanein, J. Luo, “Reliable Energy Aware Routing in                                     Technology, Pakistan and a
       Wireless Sensor Networks”,dssns,pp.54-64, Second IEEE                                       candidate for PhD studies in the
       Workshop on Dependability and Security in Sensor Networks                                   field of sensor networks.
       and Systems, 2006.
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                                                                                                    Professor   Dr.     Muhammad
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       “Energy-efficient communication protocol for wireless sensor                                 computer engineering, NUST
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[22]   D. Estrin, R. Govindan, J. Heidemann, and S. Kumar. “Next
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[24]   G. Acs and L. Buttyabv. “A taxonomy of routing protocols
       for wireless sensor networks,” BUTE Telecommunication
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[25]   E. Ilker Oyman and Cem Ersoy, “Overhead Energy
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       Computer Science, Texas A&M University, Dec 2004.




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