= of computational complexity proposed algorithm outperformed the

=

 

                                                                      ( ),                                                   (5)

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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.

 

 

 

 

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