Fitness Function for Energy Efficient     

               Multipath Routing Protocol

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                            in MANETs



                  T.RADHAKRISHNA                                                                       V.V.RAMAPRASAD

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

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



Abstract:   Mobile Ad hoc network (MANET) is collection
of self-routing enabled devices that communicate among themselves without any
specific network infrastructure. Routing in MANETS maintain routes between
nodes in a dynamic topology with possibly unidirectional links using minimum
resources. Since routing protocols have central role in MANETS, their
energy-awareness increases network lifetime by efficiently using of the
available energy. In all existing single path routing schemes a new
path-discovery process is required once a path failure is detected and it
causes delay and wastage of node resources. 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


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



 In recent years there is advancement in the
computer performance and technologies in wireless   communications. Wireless networks need ad hoc
networks in which mobile nodes can communicate over links. In MANETs, network
survivability is must because the battery capacity of the nodes get exhausted.
Thus to prolong the network lifetime the routing protocol is required to
enhance the energy of the node. Multipath routing protocols maintain routes to
flood the packets i.e., route request is sent by the source to get information
regarding the paths. MANETs can be categorized into three generations: first,
second and third generations. In 1970’s the ad hoc network first generation are
called Packet Radio Network (PRNET). In early 1980’s Survivable Adaptive Radio
Network (SURAN)is evolved from PRNET. The function group of MANETs made the
routing protocols standardized and implemented the devices like PDA’S,
palmtops, notebooks. Some standards like Bluetooth, IEEE 802.11(WLAN’S) are
developed to maintain the MANETs. For several years from 1970’s to 1990’s there
are changes in the generations of MANET i.e., finally some standards are made
to maintain the MANET. Energy efficiency is to be regarded as a factor in
MANET. Mainly the packets are transmitted based on the:


of the route

Energy of the node.

                     Thus the route that is
efficient and possible in transmitting packets can be identified .Route Request
is send by the source to find the route that is efficient. All the routes   that are possible   can be find .The information about the nodes
energy level and the links are sent through the Route Reply. Whenever the link breaks
the Route Error is send. When this happens the source sends the packets through
the route to the destination without any delay. This can be done with the
multipath routing protocol which are preferred to the single path routing
protocol. In single path routing once the link breaks the packets cannot be
transmitted. Whereas in multipath routing other routes can be preferred to send
the data packets. Particle Swarm Optimization (PSO) is the algorithm from which
the fitness function is derived. Fitness Function is mainly used to find the
optimal path.The optimum route is nothing but the route with:

distance and

less energy.

              The optimal path minimizes the
energy consumption and maximizes the network lifetime. Thus the proposed
FF-AOMDV performance in maximizing the network lifetime is possible compared to
the AOMDV.


1.1 Existing system:


The research proposed highlights
the problem of energy consumption in MANET by applying the Fitness Function
technique to optimize the energy consumption in Ad Hoc on Demand Multipath
Distance Vector (AOMDV) routing protocol. The proposed protocol is called Ad
Hoc on Demand Multipath Distance Vector with the Fitness Function
(FF-AOMDV).The fitness function is used to find the optimal path from the
source to the destination to reduce the energy consumption in multipath


1.2 AOMDV Routing protocol:


An on-demand routing protocol,
AOMDV has its roots in the Ad hoc On-Demand Distance Vector (AODV), a popular
single-path routing protocol. AOMDV offers two key services: route discovery
and route maintenance. Compared with AODV, AOMDV’s additional overhead is extra
RERRs and RREPs intended for multipath  maintenance and discovery, along with extra
fields to route control packets . Route discovery and route maintenance involve
finding multiple routes from a source to a destination node. AOMDV utilizes
three control packets: the route request (RREQ); the route reply (RREP); and
the route error (RERR).A new multipath routing protocol called the FF-AOMDV
routing protocol is proposed which is a combination of Fitness Function and the
AOMDV’s protocol. The route, which consumes less energy could possibly be (a)
the route that has the shortest distance; (b) the route with the highest level
of energy, or (c) both











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 overcome this challenge. Secure
optimized link state routing protocol is used to provide security to the
protocol. Node Identification to the network is announced and nodes are
authorized by the access control. Access control entity signs a private key Ki,
public key Ki and the certificate Ci required to obtain the group key by an
authorized node. Group key distribution using the generated keys with messages
helps reducing energy consumption. The group key distribution mechanism enables
replacement of the group key periodically or when a node is excluded. The
periodic distribution excludes 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.


1 illustrates the group key distribution mechanism


Sudhakar Pandey et al 2 Network
performances can be improved by using cross-layer approach. Application of
transmission power control technique to adjust transmission power results in
reduction of energy consumption. ED is considered to calculate the weight   associated with each node. D stands for degree
and E stands for energy. Energy consumption is reduced and network performance
is improved by Control overhead reduction during route discovery and dynamic
adjustment of transmission power. The energy model of wireless sensor network
can be defined as the total energy consumption of the network, including all
its units, be it sensor device components, energy consumed in routing or route
maintenance, topology maintenance or whosoever it may be. Generating an energy
model is an important part of any protocol development and its performance
evaluation. Here we considered a network with n mobile sensor nodes and one
sink node which is static.

Energy consumed by sensor device:

The sensor device comprises of processing
units, sensing unit, memory unit and transceiver unit. So, energy

consumption of each unit needs to be

E Sensor Device
= E processor + E sensor +

Ememory+Etransceiver                                          (1)                                                    


Where E Sensor Device is the total energy consumed by a
sensor device, E processor is the energy consumed by the processing units, E sensor
is the energy consumed by the sensing unit, E memory is the energy consumed by
the memory unit and E transceiver is the energy consumed by the transceiver
unit.Since network lifetime is an important performance criterion Sensor nodes
operate for years. Energy consumption plays an important role in network
lifetime. In working with network mobility is an important factor. About 70% of
network’s energy is consumed in data communication. By taking average of
Received Signal Strength (RSS) values, transmission power can be enhanced 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 


 TS1i = (ACK / RP )
* 100                                     


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)


= (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.




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,



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.



     (                                                       (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.



A new multipath routing protocol
called the FF-AOMDV routing protocol, which is a combination of Fitness
Function and the AOMDV’s protocol. When a RREQ is broadcast and received, 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:

Information about network’s each node’s energy level

The distance of every route

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. The optimal route with less distance to destination will consume
less energy and it can be calculated as follows:

route 1 = ?(n)rene(v(n)) / ? v Vene(v) 

In this equation, v
represents the vertices (nodes) in the optimum route rand V represent all the vertices in the

network. It compares the energy level among all the routes
and chooses the route with the highest energy level.

The calculation of the shortest route is as follows:

 Optimumroute2=?(n)rdist(e(n))/?eE                     (8)                        


Where e represents
the edges (links) in the optimum route rand
E represent all the edges in
the network.


The pseudo-code for the fitness
function is provided and Simulations are conducted to run the FF-AOMDV
protocol. 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. 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. . Energy, distances
are the fitness values used in the previous work to find the optimal path in
multipath routing.






Fig. 2 Optimum route selection in

same proposed FF-AOMDV protocol
is used along with the bandwidth as a fitness value. Now the calculations for
selecting routes towards the destination will be according to energy, distance
and also bandwidth. The same performance metrics used in the experiments: 

1. Packet Delivery Ratio.

2. Throughput.

3. End-to-end delay.

4. Energy Consumption.

5. Network Lifetime.

are used here to evaluate the
results. Thus 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, energy consumption and then compare with the results of
existing AOMDV protocol.


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
scenarios as: 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.



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



and SandeepSharma,

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

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,pp 1753-1769,September 2017.

and A.Suresh, “Maximizing the Network Life Time of Wireless Sensor Networks
Using a Mobile Charger,” in Wireless personal communications .,Springer ,pp

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










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