=

( ), (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, 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:

Optimum

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

(7)

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.

v