Fitness which is effective to send packets is

     
Fitness Function for Energy Efficient     

              Multipath Routing Protocol

                             in MANETs

     

 

                 
T.RADHAKRISHNA                                                    V.V.RAMAPRASAD

 II.M.Tech student, Department of Computer
Science,                      Professor, Department
of Computer science,

Sree Vidyanikethan Engineering College
Tirupati, India.      Sree Vidyanikethan
Engineering College Tirupati, India

 

Abstract:   Mobile
Ad hoc network (MANET) is group of self-routing enabled devices that transmit
among themselves without any certain network infrastructure. Routing in MANETS
has routes between nodes in a topology with many unidirectional links using
minimum resources. Since routing protocols have  role in MANETS, their energy-awareness make
greater network lifetime by efficiently using of the available energy. In all
existing single path routing schemes a new path-discovery process is meant once
a path failure is detected and it causes 
wastage of node measure. A multipath routing scheme is the alternative
to maximize the network lifetime. Energy, distances are the fitness values used
in the previous work to find the optimal path in multipath routing. In this
work, it is proposed to use the network resource bandwidth as a fitness value.
The calculations for selecting routes towards the destination will be according
to energy, distance and also bandwidth. The proposed work is expected to
improve the performance of mobile ad hoc networks by prolonging the lifetime of
the network. The performance will be evaluated in terms of throughput, packet
delivery ratio, end-to-end delay, routing overhead ratio, energy consumption
and then compare with the results of existing  
AOMDV protocol

 

Keywords:
 Mobile Ad hoc network, routing protocol,
multipath routing, fitness value

 

1.INTRODUCTION:

At present
computer performance and technologies in mobile system to communicate are being
advanced. Nodes communication can be done through links in the ad hoc networks.
Battery capacity of node is depleted which means network security is needed. Routing
protocol made the node energy effective that represent the lifetime of network.
Lifetime of a network must be maximized. There are 3 generations in MANETs: first
generation is the Packet Radio Network in 1970’s.Survivable Adaptive Radio
Network is developed by PRNET in 1980’s.To maintain MANETs there are  standards like Bluetooth, IEEE 802.11.The path
which is effective to send packets is taken and the route that is efficient can
be find using Route Request. Route reply gives the view about the hop, residual
energy and bandwidth. Link breakage can be find by the Route Error. These are
the control packets in the protocol to get the required information about the
route. First the route selection is done based on the control packets. The path
with less distance and the residual energy of the node can be  considered. When this occurs the source
transmit the package over the path to the destination without any interruption.
This can be done with the multipath routing protocol which are referred to the
one path routing protocol. In one path routing once the link splits the packets
will not transmit Whereas in multipath, path are

 

made to send
the data packets. Fitness function is derived from Particle Swarm Optimization
(PSO) algorithm. Fitness Function is mostly used to find the ideal route. The
optimal path is the one with:

·       
Less
distance and

·       
Exhaust
less energy.

The optimal path
minimizes the energy loss and increases the network period. Thus the proposed
FF-AOMDV performance in maximizing the network lifetime is possible in
comparison with the AOMDV.

 

1.1.Existing system:

Here AODV (ad hoc on-demand
distance vector) is the protocol from which AOMDV can be taken i.e.,AOMDV
creates the multipath between the source and destination. AOMDV has route _list
which is not present in AODV and it has advertised_hopcount. As in AODV the
route reply contains the information regarding the node in AOMDV. Damage in
link happens by which multiple paths are required to send the data packets. All
the process in AOMDV is done through control packets (RREQ,RREP and
RERR).Protocol can be designed based on distance, energy and bandwidth factor.

 

 

2. LITERATURE SURVEY:

Energy
Efficiency:

The authors
Tejpreet Singh et al. 1 demonstrates that Energy efficiency and security are
the challenging tasks in the design of a routing protocol. Energy–efficient
secured routing protocol is proposed to get away from this challenge. Secure
optimized link state routing protocol is used to supply security to the
protocol. Node Identification to the network is declared and nodes are approved
by the access control. Access control entity signs a private key Ki, public key
Ki and the certificate Ci needed to obtain the group key by an authorized node.
Group key distribution accepting the generated keys with messages support
reducing energy consumption. The group key distribution mechanism allows
substitute of the group key periodically or when a node is removed. The cyclic
distribution suspends adversaries with the group key, but not a private key. In
community networks, an authorized user may send the group key to a
non-authorized friend so as to the friend accesses network resources. An intrusion
detection system (IDS) also triggers the group key distribution.

 

 

 

Fig1. illustrates the group key
distribution mechanism

 

 

Sudhakar Pandey et al 2 Network
accomplishment can be enriched by using cross-layer approach.Application of sending
power charge method to arrange communication power issues in decline of energy
consumption. ED is examined to consult the weight   assisted with each node. D views for degree
and E views for energy. Energy consumption is reduced and network accomplishment
is enriched by Control overhead reduction while route discovery and dynamic
improvement of transmission power is done. The energy model of wireless sensor network
can be stated as the total energy consumption of the network, arrange all its
units, be it sensor device components, energy used in routing or route
maintenance, topology maintenance or whosoever it may be. Creating an energy
model is an vital part of any protocol growth and its performance estimation.
Here a network is treated with n mobile sensor nodes and single sink node that
is static. Energy consumed by sensor device: The sensor device consists
of processing units, sensing unit, memory unit and transceiver unit. So, energy
consumption of each unit made considered as:

E Sensor Device = E processor + E sensor +

                              Ememory+Etransceiver            (1)                                                                                      

Where E Sensor Device is the energy consumed by a
sensor device, E processor is the energy depleted by the processing units, E sensor
is the energy use up by the sensing unit, E memory is the energy spent by the
memory unit and E transceiver is the energy consumed by the transceiver unit. Since
network lifespan is an vital aspect criterion Sensor nodes perform for years.
clearly 70% of network’s energy is used  in data communication. By getting average of
Received Signal Strength (RSS) values, transmission power is improved by
Cross-Layer design approach for Power Control.

 

S.Muthurajkumar 
et al 3 Two important aspects of Mobile Ad Hoc Networks (MANETs) are
Energy consumption and security. Using trust management, key management,
?rewalls and intrusion detection security is provided in MANET. It is essential
to consider the energy and security aspects in routing algorithms since energy
and security are important for communication. Energy consumption can be reduced
automatically by the prevention of security attacks on routing protocols and
cluster based routing. Trust score evaluation, routing and
threshold setting using the trust values are the phases in trust based secure
routing algorithm. In trust score evaluation process the trust score for
individual nodes are calculated based on constraints like nodes which are
genuinely sending their acknowledgement to neighbors when they received the
packets are treated as first group and 
the nodes which drop more packets are considered as and  the nodes which drop more packets are
considered as group two nodes. Now, the initial trust score is computed using
the Eq that represents the percentage of 
acknowledgements.

 

 TS1i=(ACK/RP)*100                                    (2)

                                

ACK = No. of acknowledgements sent to the neighbors , TS1i = First trust
score in percentage for ith node, RP = No. of packets received from 
neighbors second trust score is computed using Eq (3) which calculates
the dropped packets

 

TS1i=100-((DP/TDP)*100)                           (3)                                   

 

DP = No. of packets dropped, TDP = Total number of
packets dropped in network. TS2i = Second trust score percentage
for ith node. The overall trust score of the particular node
is calculated using Eq. (4)

 

    TSi=(TSli+TS2i)/2                                      (4)                                  

 

TS1i = First trust score for node i, TS2i = -Second
trust score for node I, TSi = Overall trust score for node i.

 

 For
developing a cluster based network a clustering scheme is developed with clusters.
A Cluster based Energy Ef?cient Secure Routing Algorithm (CEESRA) is proposed for
providing effective routing. Malicious nodes can be avoided and detected using
the trust score. A dynamic clustering technique not only uses low mobility
nodes, energy consumption, trust values and distance parameters for providing
the energy ef?cient secure routing algorithm. The proposed algorithm provides
better performance in terms of packet drop ratio, residual energy, security and
throughput when compared to the existing techniques.

 

 N.Magadevi et al 4 The wireless nodes have
limited power resource in Wireless Sensor Networks. To recharge the batteries
of the wireless nodes Wireless charging is an alternative. Using a single
mobile anchor a wireless recharging and also localization are proposed.
Localization provides the position information. Static node is located by the
mobile anchor first and then it receives the battery level. Later static nodes
are recharged if the static node battery is lesser than the threshold limit. Fundamental
unit of sensor network is sensor node. It comprises of   sensors, microprocessor, transceiver , memory
and power supply. An Adhoc network with a collection of number of sensor nodes
is Wireless Sensor Network. It is used in many ?elds like disaster rescue, intrusion
detection and in health care applications. Gateway between the WSN and the
other network is sink node. Noise Ratio (SNR), increased ef?ciency, improved
robustness and scalability are the advantages in WSN. In designing WSN there
are several challenges like software development, deployment, localization,
hardware design, routing protocol and coverage. For effective data
communication and computation sensor node must be accurate. In the advancement
of wireless sensor networks effective localization system must be developed.Range
free localization algorithms do not require distance or angle measurements.
Along with the wireless charging localization problem is addressed here. Sensor
senses the data and communicates with the base station through Multi hop
communication. In Wireless Rechargeable Sensor Network an effective and
controllable energy harvesting scheme is to be adopted. Thus proposed method
improves the network’s lifetime.

 

Wen-KuangKuo
et al 5 The energy consumption of battery-powered mobile devices can be
increased by measured in bits per Joule for MANETs. By jointly considering
routing multimedia applications the energy ef?ciency (EE) is an essential
aspect of mobile ad hoc networks (MANETs). Based on the cross-layer design
paradigm EE optimization is, traf?c scheduling, and power control a non convex
mixed integer nonlinear programming is modeled as a problem. Branch and bound
(BB) algorithm is devised to ef?ciently solve this optimal problem.

EE
OPTIMIZATION PROBLEM:

A MANET comprised of one set of stationary nodes N connected by a set L of
links. We consider every

link l = nt
-> nr to be directional,
where nt and nr are the
transmitter and receiver of l,
respectively

MATHMATICAL MODEL FOR THE
EE OPTIMIZATION PROBLEM:

For every link l at every time slot t,
binary variable  as

 

  (),                                                (5)                  

Where ? = 
(1 ,…., T) and T is the total number of scheduled time slots. Transmission
power on link l at time slot t, i.e., , is continuously adjusted
in given interval 0, pmax.

constraint      

 

        

 (                                                    (6)

Note that
being allowed to transmit does not necessarily mean a transmission actually
occurs, which is decided by the optimization algorithm. With recent advances in
information and communication technology (ICT), MANETs become a promising and
growing technique. Multimedia services like video on-demand, remote education,
surveillance, and health monitoring are supported using MANETs. Energy is a
scarce resource for mobile devices, which are typically driven by batteries.
Using cooperative multi-input-single-output transmissions authors maximized EE
for the MANET. By designing resource allocation mechanisms cross-layer
optimization can substantially enhance EE. By jointly computing routing path,
transmission schedule, and power control to the network, link, and PHY layers
across-layer optimization framework is proposed to enhance EE. The transmission power of every active node in
each time slot is specified by the power control problem. To globally optimize ,a
novel BB algorithm is developed. In terms of computational complexity proposed
algorithm outperformed the reference algorithm. By exploiting the cross-layer
design principle a solution to determine the optimal EE of the MANET is
provided. Distributed algorithms and protocols are designed to find the optimal
EE. Any technique which can optimize non convex MINLP problem in a distributed
manner is not proposed. Thus distributed algorithms and protocols are developed
using approximation algorithms. The guarantee for acquiring the optimal
solution is the disadvantage of approximation algorithm. A customized BB
algorithm for the optimization of the problem is proposed. A novel lower bounding
strategy and branching rule is designed and incorporated in the proposed BB
algorithm. To optimize EE of MANETs distributed protocols and algorithms are
implemented. To improve EE of MANETs novel distributed protocols and algorithms
are developed.

 

3. PROPOSED SYSTEM:

A new
multipath routing protocol called the FF-AOMDV routing protocol is a
combination of Fitness Function and the AOMDV’s protocol. When a RREQ is
broadcast and taken, the source node will have three types of information in
order to find the shortest and optimized route path with minimized energy
consumption. This  include:

1.Information about network’s each node’s energy level

2.The distance of every route

3.The
energy consumed in the process of route

     discovery.

 

The source node will then sends the data packets via
the route with highest Energy level, after which it will calculate its energy
consumption.In this simulation, an OTcl script has been written to define the
network parameters and topology, such as traffic source, number of nodes, queue
size, node speed, routing protocols used and many other parameters. Two files
are produced when running the simulation: trace file for processing and a
network animator (NAM) to visualize the simulation.

 

 

 

 

Fig. 2 Optimum
route selection

NAM is a
graphical simulation display tool. It shows the route selection of FF-AOMDV
based on specific parameters. The optimum route refers to the route that has
the highest energy level and the less distance. Priority is given to the energy
level, as seen on the route with the discontinuous arrow. In another scenario,
if the route has the highest energy level, but does not have the shortest
distance, it can also be chosen but with less priority. In some other
scenarios, if the intermediate nodes located between the source and destination
with lesser energy levels compared to other nodes in the network, the fitness
function will choose the route based on the shortest distance available

 

Available Bandwidth:

 Bandwidth is also known as the data transfer
rate. It describes the data sent out by means of connection over a specified
time and the bandwidth is expressed in bps. Bandwidth is the bit-rate of the existing
or the consumed information capacity uttered normally in metric multiples of
bits per second. As the bandwidth is kept high the energy consumption is also
high. The data packets send increases and the energy consumed at each node is
also high. The transmission power consumption is high because the packets send
are more. When the bandwidth is taken as a parameter along with the distance
and energy, energy consumption varies as:

1. when distance
increases energy consumption also increases and when the route distance is less
energy consumed will be low.

2. when
bandwidth is high energy consumption  is
also high  and when it is  less energy consumed will be low. Thus
bandwidth is the parameter considered here and the

simulation has
scenarios like node speed, packet size and simulation time.simulations are done
by keeping the scenariosas:varying the packetsize(64,128,256,512,1024) and keep
both the node speed and simulation time fixed. Packet delivery ratio,
Throughput, End-to-end delay, Routing overhead ratio are   the performance metrics used to test the
scenarios. In the proposed system as the bandwidth is the other parameter the
mathematical model is to be find based on the three parameters energy, distance
and bandwidth.Route reply’s are sent from the specified intermediate nodes by
which hop count,residual energy, Qlength,bandwidth values are taken.Let the
formula be

 

                  Ax1+bx2+cx3+dx4/4                     (7)              

 where   x1-> hop count,

              x2->Q length,

              x3->residual energy,

              x4->bandwidth.

  And a,b,c,d are based on priority.By taking
the values of the parameters optimal path can be find.

 

 

4. CONCLUSION:

Energy
ef?ciency (EE) is an essential aspect of mobile ad hoc networks
(MANETs).secured routing protocol is proposed which is energy efficient and
security is provided for both link and message without relying on the third
party. A secure communication among the participating nodes is offered by the
environment of MANETS. Energy consumption plays an important role in network
lifetime. Since network mobility is an important factor and network’s energy is
consumed in data communication, Cross-Layer design approach is used to enhance
the transmission power for power control. Energy consumption can be reduced by
the prevention of security attacks on routing protocols. Here to find the
optimal path in multipath routing, distance and energy are the fitness values used.
It is proposed to use the network resource bandwidth and calculations in
selecting the routes towards the destination will be according to the distance,
energy and also bandwidth .Thus the proposed work minimizes energy consumption
and maximizes network lifetime.

 

REFERENCES:

1.TejpreetSingh,JaswinderSingh,
and SandeepSharma,

“Energy
ef?cient secured routing protocol for MANETs,” in Wireless Networks,Springer,pp-1001-1009,May2017.

2.SudhakarPandeyandDeepikaAgarwal,”AnEDBasedEnhanced
Energy Ef?cient Cross Layer Model for Mobile Wireless Sensor Network,” in National
Academy Science Letters.,Springer, pp 421-427,December 2017.

3.S.Muthurajkumar,S.Ganapathy
and M.Vijayalakshmi, “An Intelligent
Secured and Energy Ef?cient Routing Algorithm for MANETs,” in Wireless Personal
Communications,Springer,pp1753-1769,September 2017.

4.N.Magadevi,V.JawaharSenthilKumar
and A.Suresh, “Maximizing the Network Life Time of Wireless Sensor Networks
Using a Mobile Charger,” in Wireless Personal Communications .,Springer ,pp
1-11,2017.

5.Wen-KuangKuo
and Shu-Hsien Chu, “Energy Efficiency Optimization for Mobile Hoc Networks,”
IEEE Access, pp 928-940,March 2016