AN ENHANCE APU FOR GEOGRAPHIC ROUTING BASED ON MOBILITY
FORWARDING NODE SELECTION
Mr. Kailash Kumar Baraskar
Mr. Dhirendra Jha
MTRI, RGPV, Bhopal
MTRI, RGPV, Bhop al
ABSTRACTAdaptive Position updates are costly in many ways. Each update consumes node energy,
wireless bandwidth, increased beacon overhead and increases the risk of packet collision at
the medium access control (MAC) layer. Packet collisions cause packet loss which in turn
affects the routing performance due to decreased accuracy in determining the correct local
topology (a lost beacon broadcast is not retransmitted). A lost data packet does get
retransmitted, but at the expense of increased end-to-end delay. Clearly, given the cost
associated with transmitting beacons, it makes sense to adapt the frequency of beacon updates
to the node mobility and the traffic conditions within the network, rather than employing a
static periodic update policy. In this project, proposed the algorithm to make efficient
geographic forwarding path to data transmission. Existing mechanisms i.e. Periodic
Beaconing and Adaptive Position Update for geographic routing in MANETs could not to
reduce the beacon overhead completely; energy consumption and better packet delivery ratio
are also important concern to this project.
1.INTRODUCTION
1.1 BACKGROUND
Ad hoc networks consist of mobile or stationary nodes that communicate over wireless links.
There is neither fixed infrastructure to support the communication nor any centralized
administration or standard support services. Mobile Ad-hoc Network (MANETs) is an
autonomous and decentralized wireless system. MANETs consist of mobile nodes that are
free to move in and out in the network. The term node is used for the system or device, e.g.
Mobile phone, laptop or personal computer, etc., which are participating in the network. This
node can act as host/router or both at the same time. They can form arbitrary topologies
depending on their connectivity with each other in the network and can also be deployed
instantly without the need of any infrastructure.
Geographic routing protocols are one of the challenging and interesting research
areas. Many geographic routing protocols have been developed for MANETs, i. e. Greedy
Perimeter Stateless Routing (GPSR), Dynamic Source Routing (DSR), and Ad-hoc On
Demand Distance Vector (AODV) protocols etc.
But, the problems such as overhead, bandwidth consumption, energy consumption,
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end to end delay and packet collision at the medium access control (MAC) layer are still
persisting in geographic routing to some extent and various papers have been appeared in the
literature to minimize this protocol employed for forwarding data packets in geographic
routing requires the following information: (1) the position of the final destination of the
packet and (2) the position of node’s neighbours. Each node exchanges its own location
information with its neighbouring nodes. This allows each node to build a local map of the
nodes within its vicinity, often referred as local topology. The location information of
neighbouring nodes is obtained using the packets generally known as beacons. Recently, the
schemes in which the frequency is made adaptive like APU scheme have appeared in the
literature.
The APU scheme follows two rules: (1) Mobility Prediction (MP) rule and (2) OnDemand Learning (ODL) rule to improve the performance of the network. The MP rule tries
to maximize the effective duration of is beacon by broadcasting a beacon only when the
predictive position information (based on the previous beacons) inaccurate [5]. The MP rule
solely is not ample enough for maintaining an accurate local topology. So ODL rule is used
with MP rule. According to ODL rule whenever a node overhears data transmission from a
new neighbour (the neighbour who is not present in the neighbour list of this node), it
broadcast a beacon as a response. Thus, it is ensured that nodes involved in forwarding data
packet maintain up-to-date view of local topology in MANETs.
In this project, a novel scheme is proposed for forwarding node selection based on
nodes mobility. In this technique, first lowest mobility node i.e. Highest stable node is
detected. This highest stable node is used for forwarding the data packet. The proposed
mobility based forwarding node selection scheme improves beacon overhead, packet delivery
ratio, and energy consumption.
1.2 Motivation and Objectives
 To adapt the beacon update policy employed in geographic routing protocols to the
node mobility dynamics and the traffic load using modified APU scheme.
 To estimate the accuracy of the location estimate and adapts the beacon update interval
accordingly, instead of using periodic beaconing using MP rule.
 To maintain an accurate view of the local topology by the nodes along the data
forwarding path through the exchange of beacons in response to data packets that are
overheard from new neighbors using ODL rule.
 To generate less or similar amount of beacon overhead and to achieve better packet
delivery ratio, average end-to-end delay and energy consumption using modified APU
strategy.
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Figure 1.1: Architecture of modified APU
1.3 LITERATURE REVIEW
Heissenbuttel et al. [1] proposed a periodical beaconing scheme for geographic routing in
MANETs. In which each node broadcasts a beacon to their neighbour nodes at certain time
interval, often referred as beacon interval. If a node does not receive any beacon from its
neighbour within a period of time, called neighbour time-out interval, it considers that
neighbour has moved out of the radio range. It is removed from its neighbour list and if not
done so, this causes a major drop in the performance.
Periodic beaconing can cause the inaccurate local topologies in highly mobile ad-hoc
networks which leads to performance degradation in the form of frequent packet drop and/or
costly in timely manner. It is obvious that if the nodes in the network have high mobility rate,
the frequency of beacon packet should be high to keep updated information about its
neighbors’. There is a dilemma regarding the frequency update. If it is kept high, it causes
higher energy consumption, and if it is kept low, it leads to packet loss due to neighbour's
outdated information. Hence it is required to adapt the frequency of beacon updates to the
node mobility and the traffic conditions within the network, rather than employing static
periodic update policy.
Nayebi and Karlsson et al. [9] introduced beaconing in wireless mobile networks.
Beaconing strategy in MANETs is valuable concept to maintain up-to-date routing table at
each node. Due to the frequently changing dynamic topology of mobile nodes in which their
locations and speed rapidly changes, in this situation it becomes very difficult to maintain up
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to date location and speed information. To make efficient packet forwarding to destination
beaconing scheme is very useful in wireless mobile networks.
Chen et al. [2] proposed the Adaptive Position Update (APU) for geographic routing in
MANETs. It employs two mutually exclusive beacon triggering rules e g. Mobility Prediction
rule and On-Demand Learning rule.
Greedy Perimeter Stateless Routing (GPSR), proposed by Karp and Kung [4], uses the
position of routers and the packet’s destination to make packet-forwarding decision. Current
location information is broadcasted by each node through beacon packet. It makes greedy
forwarding decisions using information about a router’s immediate neighbours in the network
topology. Under mobility’s frequent topology changes, GPSR can use local topology
information to find correct new routes quickly. Greedy Perimeter Stateless Routing (GPSR), a
novel routing protocol for wireless datagram networks. When a packet reaches a region
where greedy forwarding is impossible, the algorithm recovers by routing around the
perimeter of the region. By keeping state only about the local topology, GPSR scales better in
per-router state than shortest-path and ad-hoc routing protocols as the number of network
destinations increases. Under mobility’s frequent topology changes, GPSR can use local
topology information to find correct new routes quickly. Under GPSR, packets are marked by
their originator with their destinations’ locations. As a result, a forwarding node can make a
locally optimal, greedy choice in choosing a packet’s next hop. Specifically, if a node knows
its radio neighbors’ positions, the locally optimal choice of next hop is the neighbor
geographically closest to the packet’s destination. Forwarding in this regime follows
successively closer geographic hops, until the destination is reached. With respect to the path
length the main advantage of this protocol has the end-to-end hops of GPSR are the largest
due to the usage of perimeter mode which leads to increased latency. But this protocol cannot
handle the increased beacon overhead over the entire networks.
Perkins et al. [5] introduced Ad-hoc On-Demand Distance Vector (AODV). AODV
protocol follows a pure on-demand route acquision system. It broadcasts route request packet
(RREQ) to the needy the node. This needy node forwards the data packet to destination with
the help of route reply packet (RREP) and destination sequence number. It is a beaconless
protocol and does not require any periodic hello packets (beacons) transmission, which are
used by a node to inform its presence. Some author also developed several geographic
beaconless routing protocols. A pure on-demand route acquisition system, nodes that do not
lie on active paths neither maintain any routing information nor participate in any periodic
routing table exchanges. Further a node does not have to discover and maintain a route to
another node until the two needs to communicate unless the former node is offering its
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services as an intermediate forwarding station to maintain connectivity between two other
nodes. This needy node forwards the data packet to destination with the help of route reply
packet (RREP) and destination sequence number. It is a beaconless protocol and does not
require any periodic hello packets (beacons) transmission, which are used by a node to inform
its presence. The routing tables of the nodes within the neighborhood are organized to
optimize response time to local movements and provide quick response time for requests for
establishment of new routes. The beneficial thing is this protocol, that it broadcasts discovery
packets only when necessary (reactive) thereby reduces the overhead in the network. But this
mechanism to ensure the reliability of data delivery has not been invoked. So that AODV
routing protocol is not necessary for some place.
Johnson et al. [3] proposed Dynamic Source Routing (DSR). It is a typical routing protocol
for MANET. It composed of two mechanisms i.e. Route Discovery and Route Maintenance
which work together to allow nodes to discover and maintain source route to arbitrary
destination in the ad-hoc network. But among the intermediate router, where chances to
breakage the route and to avoid increased overhead,
It is a typical routing protocol for MANET. It composed of two mechanisms i.e. Route
Discovery and Route Maintenance which work together to allow nodes to discover and
maintain source route to arbitrary destination in the ad-hoc network. DSR is a typical routing
protocol for MANETs. When a source node wants to find a route to another one, the source
node initiates a route discovery it broadcasts a Route Request(RREQ) to the entire network
till either the destination is reached or another node is found with a fresh enough route to the
destination and each node appends own identifier when forwarding RREQ. After destination
node received the first RREQ it sends RREP is on a route obtained by reversing the route
appended to receive RREQ. The DSR protocol is composed of the two mechanisms of Route
Discovery and Route Maintenance, which work together to allow nodes to discover and
maintain source routes to arbitrary destinations in the ad hoc network. The use of source
routing allows packet routing to be trivially loop-free, avoids the need for up-to-date routing
information in the intermediate nodes through which packets are forwarded, and allows nodes
forwarding or overhearing packets to cache the routing information in them for their own
future use. But DSR protocol costly in some corner i. e. Stale caches will lead to increased
overhead. DSR enables randomly chooses the intermediate nodes. So it enables the lowtrusted nodes and the nodes having low energy to repeatedly participate in routes which leads
to route breakage.
Zorzi and Rao et al. [7] proposed Geographic Random Forwarding which is based on the
assumption that sensor nodes have a means to determine their location and that the positions
of the final destination and of the transmitting node are explicitly included in each message.
In this scheme, a node which hears a message is able (based on its position toward the final
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destination) to assesses its own priority in acting as a relay for that message. All nodes who
received a message may volunteer to act as relays and do so according to their own priority.
This mechanism tries to choose the best positioned nodes as relays. In addition, since the
selection of the relays is done a posteriori, no topological knowledge or routing tables are
needed at each node, but the position information is enough. Geographic routing is used here
to enable nodes to be put to sleep and waken up without coordination and to integrate routing,
MAC, and topology management into a single layer. MAC scheme based on these concepts
and on collision avoidance and report on its energy and latency performance. The proposed
scheme performs significantly better for sufficient node density. But overhead is still remain
in this scheme.
Blum and Stankovic et al. [6] introduced IGF, A State-free robust communication protocol
for Wireless sensor networks. Wireless Sensor Networks (WSNs) are being designed to solve
a gamut of interesting real-world problems. Limitations on available energy and bandwidth,
message loss, high rates of node failure, and communication restrictions pose challenging
requirements for these systems. Beyond these inherent limitations, both the possibility of
node mobility and energy conserving protocols that power down nodes introduce additional
complexity to routing protocols that depend on up to date routing or neighborhood tables.
Such state-based protocols suffer excessive delay or message loss, as system dynamics
require expensive upkeep of these tables. Utilizing characteristics of high node density and
location awareness, we introduce IGF, a location-aware routing protocol that is robust and
works without knowledge of the existence of neighboring nodes (state-free).
Implicit Geographic Forwarding (IGF), as it makes non-deterministic routing decisions,
implicitly allowing opportune receiving nodes to determine a packet’s next-hop at
transmission time. Specifically, IGF works without prior knowledge of any other node in the
network1, using an integrated Network/MAC solution to identify the best forwarding
candidate at the instant a packet is sent. Aside from providing robust message delivery,
increasing system stability, and reducing control overhead, we utilize the energy remaining at
a node during the candidate election process to ensure nodes do not shoulder vastly unequal
portions of the workload and die out sporadically. The advantages are elimination
communication overhead to maintain system state, To prevent the adverse affects that realistic
factors (mobility and sleep cycles) have on state based routing protocols, Shorter end-to-end
latency compared to schemes that must update system state. But the maintenance of routing or
neighborhood tables is costly.
Casari and Zorzi et al. [8] proposed Efficient Non Planar Routing around Dead Ends in
Sparse Topologies using Random Forwarding. Geographic forwarding in wireless sensor
networks (WSN) has long suffered from the problem of by passing “dead ends”. In this paper,
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we approach the problem of dealing with dead ends in a novel way that allows us to guarantee
the delivery of packets to the sink without requiring the overhead and the inaccuracies
incurred by “planar” methods. Our solution, termed ALBA–R for Adaptive Load-Balanced
Algorithm, Rainbow version, is a simple, distributed scheme that is remarkably resilient to
localization errors and independent of whether the network topology is modeled by a unit disk
graph or not. The design of ALBA–R follows recent trends in geographic routing protocol
design that demonstrated that remarkable performance improvements can be obtained by
applying cross–layer techniques. ALBA–R integrates MAC and routing design. Whenever a
node has to forward a packet (a typical network layer duty) all its neighbors are addressed by
a relay selection message (RTS-like, to use IEEE 802.11 terminology) which initiates a
competition for electing the “best” next hop relay. Each eligible node can locally compute its
own suitability to serve as a relay, based on a cross–layer parameter that reflects its current
status (e.g., packet error rate, transmission bit rate, residual energy, current queue occupancy,
capability of fast and reliable packet forwarding, and combinations thereof) and participates
to the competition. The relay node is thus elected based on the value of this parameter that is
based on values and methods typical of the PHY, MAC and routing (network) layers
combined. This node then receives the data packet, and forwards it to the sink (if the sink is
now directly reachable) or to the next best relay. As mentioned, a problem with this simple
and very efficient mechanism occurs when a node is not able to find a relay, i.e., it is a dead
end. ALBA–R is a simple, completely distributed and low overhead protocol. But it is also
create high overhead in this scheme.
Kim and Govindan et al. [15] proposed Geographic Routing Made Practical. Geographic
routing has been widely hailed as the most promising approach to generally scalable wireless
routing. However, the correctness of all currently proposed geographic routing algorithms
relies on idealized assumptions about radios and their resulting connectivity graphs. We use
tested measurements to show that these idealized assumptions are grossly violated by real
radios, and that these violations cause persistent failures in geographic routing, even on static
topologies. Having identified this problem, we then fix it by proposing the Cross- Link
Detection Protocol (CLDP), which enables provably correct geographic routing on arbitrary
connectivity graphs. Cross-Link Detection Protocol (CLDP) that, given an arbitrary
connected graph, produces a sub graph on which face traversal cannot cause a routing failure,
regardless of radio irregularities and localization errors. In CLDP, each node probes the faces
on which each of its links sits to determine if there exists a crossing link. Crossing links are
eliminated only when doing so would not disconnect the resulting sub graph. This algorithm
is qualitatively different from the planarization algorithms used by earlier face routing
protocols, in both its approach and its correctness. The unmodified GPSR algorithm conducts
perimeter-mode forwarding using the sub graph produced by CLDP. In Geographic made
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practical report, CLDP protocol is to prevent routing failures in an arbitrary connected graph.
Geographic routing provides a high delivery rates, low overhead and fast convergence in
wireless network. CLDP’s overhead and robustness on more dynamic topologies, as well as
the effect of localization errors on CLDP’s path stretch in deployment is the disadvantage of
this scheme.
2. AN ENHANCE APU FOR GEOGRAPHIC ROUTING BASED ON MOBILITY
FORWARDING NODE SELECTION
2.1 PROPOSED WORK
The main concept behind our new modified APU scheme is the selection of forwarding path
without actually sending unnecessary beacon packets. When the forwarding node is selected
then the data is transmitted through that path. The modified APU strategy proposed in this
project dynamically adjusts the beacon update intervals based on the mobility dynamics of the
nodes and the forwarding patterns in the network. The proposed scheme is based on mobility
characteristics of the mobile nodes in MANETs. APU employs two schemes, namely mobility
prediction rule and on-demand learning rule, based on the mobility dynamics of the nodes and
the forwarding pattern in the network.
It employs two mutually exclusive beacon triggering rules e g. Mobility Prediction
rule and On-Demand Learning rule. i.e.
(1) MOBILITY PREDICTION (MP) RULE
Nodes in
MANET
Neighbors
Location
difference
Beacon update
This rule determines updated location coordinate of nodes, to maintain node’s
neighbour list. The actual position of node n (𝑀, 𝑁)obtained via GPS and predicted position
of node n (𝑀, 𝑁) calculates using simple prediction scheme,
i
Mpi = Mli + (Tc − Tl )Vm
Npi = Nli + (Tc − Tl )Vni
To determine the deviation between predict location and actual location by
2
2
Didevi = √(Mai − Mpi ) + (Nai − Npi )
Where,
𝐷𝑑𝑒𝑣𝑖 = The deviation of i node
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TABLE 2.1: Notations for Mobility Prediction
Variables
(𝑀𝑙𝑖 , 𝑁𝑙𝑖 )
Definition
The coordinate of node i at Tl time (included i the previous
beacon)
(𝑀𝑝𝑖 , 𝑁𝑝𝑖 )
The predicted position of node i at Tc current time
𝑇𝑙
The Time of the last beacon broadcast
𝑇𝑐
The current Time
(𝑉𝑚𝑖 , 𝑉𝑛𝑖 )
The velocity of node i along the direction of x and y axes at time
Tl
If the deviation is greater than a certain threshold, known as the Acceptable Error Range
(AER), it acts as a trigger for node i to broadcast its current location and velocity as a new
beacon. If the deviation is greater than a certain threshold, then node n broadcasts its current
position and velocity as a new beacon to all its neighbours.
(2) ON-DEMAND LEARNING (ODL) RULE
When a node moves from P1 to P2 at a constant velocity, the node sent a beacon packet, since
neighbour node did not receive this beacon, it is not aware of the existence of that node. In
further condition when a node moves from P1 to P2, then AER will become large and MP
rule will never apply.
Nodes in
MANET
Neighbors
Nodes
overhearing
data
transmission
Beacon Update
Hence, this critical condition handles by ODL rule. According to this rule, a node broadcasts
beacons on-demand, whenever a mobile node overhear a data transmission from a new
neighbour, it broadcasts a beacon as a response. Then that node participates to data
transmission and makes the enriched topology.ODL aims at improving the accuracy of
topology along the routing path from the source to the destination, for each traffic flow within
the network.
In APU, the nodes located in the hotspots, which are responsible for forwarding most
of the data traffic in the network have an up-to-date view of their local topology. Before
actual data transmission the APU scheme manipulates the beacon packet, which contains
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location coordinates and speed of each node. The beaconing mechanism maintains up-to-date
neighbour list and beacon update intervals to take the forwarding decision in low and high
mobility network.
2.2 THE MOBILITY CHARACTERISTICS OF THE MOBILE NODES
CASE 1: LOW MOBILITY
In this case, the effective duration of the beacon update for nodes is extended which reduces
the number of beacons with low mobility.
Figure 5.1: Few long links with low quality
CASE 2: HIGH MOBILITY
Highly mobile nodes can broadcast frequent beacons to ensure that their neighbours are aware
of the rapidly changing topology.
Figure 5.2: Many short links with high quality
2.3 MOBILITY BASED FORWARDING NODE SELECTION (HIGHLY STABLE
GREEDY FORWARDING)
In MANETs, if forwarding nodes have high mobility, then there are chances to make local
topology inaccurate. If the nodes, involved in the forwarding path, move frequently, there is a
need of frequent beacon updates that leads to network traffic which may cause packet
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collision. Hence, it is required to select the node with lowest mobility. This project, with
lowest mobility based forwarding node selection, improves routing performance when
compared with APU.
2.4 ALGORITHM FOR SELECTION OF FORWARDING NODE
Step 1: Find distance d(t1) of each neighbour from source at time t1.
Step 2: Find distance d(t2) of each neighbour from source at time t2.
Step 3: If [d(t2) – d(t1)], select the neighbour as highest stable link.
Step 4: Find distance D between destination and the node having high stable link.
Step 5: Link having minimum D is selected as next hop.
Nodes in
MANET
Neighbors
High stable
neighbor
Data forwarding
Closer to
destination
Source node finds the distance of each neighbour from itself at particular time. After certain
time, say at, it finds the distance again. If the difference between the two distances is less than
the threshold, the neighbour is considered as highly stable neighbour. To apply highly stable
greedy forwarding distance between destinations, highly stable neighbours are calculated. The
neighbour that is having the minimum distance is selected as forwarder.
3.SIMULATION AND RESULT
3.1 MOBILE NODE: CREATING WIRELESS TOPOLOGY
Mobile node is the basic ns Node object with added functionalities like movement, ability to
transmit and receive on a channel that allows it to be used to create mobile, wireless
simulation environments. The class Mobile node is derived from the base class Node. Mobile
Node is a split object. The mobility features including node movement, periodic position
updates, maintaining topology boundary etc are implemented in C++ while plumbing of
network components within Mobile Node itself (like classifiers, dmux , LL, Mac, Channel
etc) have been implemented in Otcl.
Our simulation begins with node deployment l
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Figure 3.1: Node deployment
3.2 MODULES DESCRIPTION
Network Configuration and Initial Beacon Broadcast is initial stage to build network.
MANET is created with the help of 48 wireless nodes. Nodes are configured with simulation
parameters listed in the simulation model table below. Nodes are deployed in the initial
location. After the deployment, each node identifies its neighbours by sending beacons.
Nodes that are located within the communication range are known as neighbours. Each node
broadcast the beacon to its neighbours.
Initialize Beacon Broadcast in MANETs
Figure 3.2: Beacon broadcast in MANETs
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3.3 SIMULATION MODEL
The proposed work is simulated in NS-2 simulation tool which is discussed in previous
chapter, by using the initial parameters and its value as shown in Table I. Each node
periodically broadcast the beacon which causes high overhead and packet collision and high
energy
consumption.
Figure 3.3: Periodic Beaconing
Source 0 sends data to destination 40. Data is not delivered properly to the destination due to
packet collision caused by periodic beacons.
TABLE 6.1: SIMULATION PARAMETER
Parameter
Value
Simulator
Network Simulator 2
Number of nodes
50
Mac type
802.11
Queue length
210 packets
Routing protocol
DSR
Transmission power
1.0watts
Reception power
1.5watts
Simulation area
1060*860
Simulation time
40 sec.
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Some screen shots shown below during NS-2 simulation,
 Beacon update based on MP rule
Figure 3.4: Apply MP rule
Nodes in blue color are MP nodes which are having the actual location with larger difference
from its predicted location. Hence MP nodes update the beacon packet. Deviation threshold is
fixed as 60m. If there exists difference between actual locations and predicted of a node is
greater than 60m then beacon packet is sent by the node.
 Beacon update based on ODL rule
Nodes overhearing data transmission send beacon packet to nexthop. Nodes in dodger blue
color overhear data transmission. Hence they send beacon packets to update their presence
which can be used by the source node in the case of failure of current next hop.
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Figure 3.5: Apply ODL rule
6.4 MOBILITY BASED FORWARDING NODE SELECTION
Source node finds the distance d (t1) of each neighbour from itself at particular time (t1).
After certain time (t2) it finds the distance d (t2) again. If the difference between the two
distances [ d (t2) – d (t1) ] is less than the threshold, the neighbour is considered as highly
stable neighbour. To apply highly stable greedy forwarding distance between destination and
highly stable neighbours are calculated. The neighbour which is having the minimum distance
is selected as forwarder. But the least distance from destination.
Energy consumption and Beacon overhead in Periodic beacon scheme is high
compared to APU. APU saves energy by avoiding unnecessary beacon update and do the
beacon update adaptively. And APU reduces the beacon overhead by avoiding unnecessary
beacon updates and only does the beacon update process adaptively.
Packet delivery ratio of APU is high when compared to Periodic beacon scheme. Since
network-traffic in APU is reduced due to adaptive beacon update instead of periodic beacons
in the case of periodic beacon scheme. In PB data gets dropped due to high traffic in the
network.
 Mobility and distance based forwarding node selection
Figure3.6.: Mobility based forwarding node selection
Node having high stability and less distance to destination is selected as next hop. Source: 0,
Destination: 40
Next hop: 14 – 18 – 28 – 36 – 37 – 41
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3.5 PERFORMANCE EVALUATION
The comparative simulation study between periodic beaconing and modified APU scheme
gives state result in terms of three valuable parameters e.g. Packet delivery ratio, Energy
consumption and Beacon overhead. Each node periodically broadcast the beacon which
causes high overhead and packet collision and high energy consumption.
In the simulation, Data packets transmission from source to destination is taken as
input along the x and y axes. The parameters of the system are shown through NS-2 graph.
Which are plotted with respect to time (second).
3.5.1 PACKET DELIVERY RATIO (PDR)
Packet delivery ratio is defined as the ratio of data packets received by the destinations to
those generated by the sources. PDR is the ratio of the number of delivered data packet to the
destination. Only the data packets that successfully delivered to destinations that counted.
This illustrates the level of delivered data to the destination. The higher mobility of nodes
causes PDR to decrease.
Mathematically, it can be defined as: PDR= S1÷ S2
Where, S1 is the sum of data packets received by the each destination and S2 is the sum of
data packets generated by the each source. The below shown graph between Packet delivery
ratio versus Time, represents the fraction of data packets that are successfully delivered
during simulation. A comparative study between periodic beaconing and modified APU
scheme shows variation (packets begins dropped in periodic beaconing) at 5th second.
PDR (%) = Number of packets received / Number of packets sent.
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Figure 3.7: Packet delivery ratio versus Time
3.5.2 ENERGY CONSUMPTION
It is the amount of energy consumed by the sensors for the data transmission over the
network.
Energy Consumption = ∑ energy consumed by each sensor.
As seen from figure 3 the modified APU consumed energy during the initial time, but after 11
second it becomes saturate where as general PB keep consuming the energy.
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Figure 3.8: Energy consumption versus Time
3.5.3 BEACON OVERHEAD
Overhead = Number of messages involved in beacon update process.
As seen from figure 4, modified APU create more beacon overhead during initial phase but
after 6 second periodic beacon shoot up where as modified APU remain linear.
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Figure 3.9: Beacon overhead versus Time
From the trace obtained from the data transmission from source to destination,
performance metrics such as energy consumption, overhead, and packet delivery ratio are
obtained using the awk script. Awk script processes the trace file and produces the result.
Using the results obtained from awk script, graph is plotted for performance metrics using
xgraph tool available in ns-2.
4.CONCLUSION AND FUTURE WORK
4.1 CONCLUSION
This proposed scheme presented efficient geographic routing based on high stable mobile
nodes with least distance from node’s neighbor to destination in multi hop wireless ad hoc
network. The proposed work implemented on APU scheme to produce more efficient
dynamic geographic routing in MANET. As shown in our detailed simulation studies and
implementation of the proposed approach in a real ad hoc network of moving devices and
routing among themselves, mobility based node selection approach has very low beacon
overhead and is able to efficiently deliver all originated data packets. This project identified
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the need to adapt the beacon update policy employed in geographic routing protocols to the
node mobility dynamics and the traffic load. This work proposed the modified Adaptive
Position Update strategy to address these problems.
The APU scheme employs two mutually exclusive rules. The MP rule uses mobility
prediction to estimate the accuracy of the location estimate and adapts the beacon update
interval accordingly, instead of using periodic beaconing. The ODL rule allows nodes along
the data forwarding path to maintain an accurate view of the local topology by exchanging
beacons in response to data packets that are overheard from new neighbours. This work has
embedded APU within GPSR and has compared it with other related beaconing strategies
using extensive NS-2 simulations for varying node speeds and traffic load.
Our results indicate that the APU strategy generates less or similar amount of beacon
overhead as other beaconing schemes but achieve better packet delivery ratio, average end-toend delay and energy consumption. Performance of modified APU is evaluated using
extensive NS-2 simulations for varying 48 nodes speeds and traffic load. Results indicate the
modified APU strategy generates less amount of beacon overhead as compared to periodic
beaconing scheme and also attain better packet delivery ratio and energy consumption.
4.2 FUTURE WORK
Future work will be the exploring the new techniques to the proposed work to reduce the
overhead and energy consumption further in the network study on analyzing and evaluating
that how the proposed scheme can be used to achieve for more accurate view of local
forwarding topology and evaluating the best performance of the efficient geographic routing
in Mobile Ad hoc Networks.
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