Abstract Sensor Nodes, energy efficient, multipath routing.

Abstract — To carry multiple routing in
order to make mobile ad-hoc network more energy efficient and to perform
swapping of nodes in order to make network more reliable and balance the load
on to node ,here best fit function will be used in order to carry out swapping of
two perfectly suitable nodes. System initially generates self-configuring
network, which contained mobile nodes without any fixed infrastructure. After
generating the network, multipath source and destination is selected for
sending the data. After that multipath is found for sending the data, shortest
path is found on the basis of energy of nodes and distance of nodes.  After that energy consumption of each node is
calculated, the node which is in ideal state (node which is not in working
condition or node with low energy) are swap by using the swapping algorithm to
the node with high energy and data is send to the destination node.


Wireless Sensor Networks, MANETs, Sensor Nodes, energy efficient, multipath

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Wireless sensor networks (WSNs)
have many uses in tracking and monitoring, where they have attracted more
attention in recent years. The applications of WSN can be classified into
industrial, biomedical, environmental, military, agricultural, domestic, and
commercial fields. In the last few years, WSNs have received considerable
attention in sports applications for monitoring athletes’ performance during
training sessions as well as in international competitions. Cycling is one of
the sports that has recently attracted significant attention in this respect,
and WSNs are widely used for monitoring the physiological and biomechanical
parameters of the athlete and bike, respectively, in order to assess cycling
performance. Cycling performance can be monitored by using unobtrusive sensor
nodes; these nodes comprise different components such as a sensor, data
processor, transceiver module, and power unit.

Mobile  ad  hoc 
network  is  a 
group  of  wireless 
mobile nodes  in  which 
nodes  collaborate  by 
forwarding  packets for each other
and allow them to communicate outside the direct wireless range. These networks
are fully distributed, and   can   work  
at   any   place  
without   the   help  
of   any infrastructure 8.  Ad-hoc 
network does  not  require 
any fixed network infrastructure such as base stations, and can be  easily 
set  up  at 
low  cost  as 
needed.  The  routers, 
the participating  nodes  act  as  router, 
are  free  to 
move  in network   randomly  
and   manage   themselves  
arbitrarily;  thus,  the 
network’s  wireless  topology 
may  change rapidly and  unpredictably.  Each 
of  the  mobile 
nodes  is  operated by a 
limited energy battery and usually 
it is impossible  to recharge  or 
replace  the  batteries 
in  a  remote 
area.  Since wireless  communications  consume 
significant  amount  of battery 
power  1,  this 
limited  battery  lifetime 
imposes  a severe constraint on
the network performance. Routing is a process  
of   detecting   various  
routes   from   source  
to destination nodes.  All the
routes are calculated and then restored in a network.  Routing tables are of two types: Static-  Routing and 
Dynamic  Routing.  Static routing is a type of a  network routing  technique. 
Dynamic routing is  a
networking  technique  that 
provides  optimal  data 
routing. The routing table is not affected by addition or deletions of
routers in case of static routing but it is affected in dynamic routing.   Due   to
changing positions of nodes 3   and
connections, the energy and lifetime of network degrades.

In this paper we
study about the related work done, in section II, the proposed approach modules
description, mathematical modeling, algorithm and experimental setup in section
III .and  at final we provide a conclusion
in section IV.  



In 1 highlights the 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 AOMDV with the fitness
function (FF-AOMDV). The fitness function is used to find the optimal path from
source node to destination node to reduce the energy consumption in multipath
routing. The performance of the proposed FF-AOMDV protocol has been evaluated
by using network simulator version 2, where the performance was compared with
AOMDV and ad hoc on demand multipath routing with life maximization (AOMR-LM)
protocols, the two most popular protocols proposed in this area. Te comparison
was evaluated based on energy consumption, throughput, packet delivery ratio,
end-to-end delay, network lifetime and routing overhead ratio performance
metrics, varying the node speed, packet size, and simulation time.


MANET (Mobile Ad-hoc Networks) is
useful in many practical scenarios since it provides multi-hop communication
without wired infrastructure. However, there is a problem that the
communication performance of a flow may be easily degraded by even a single
local congestion on the whole path. A solution for the problem is to use a
detour path that avoids the local congestion. However, to this end, the detour
paths should not use the nodes in the congested area, which is in fact
relatively large due to the nature of radio waves. In the current state of the
art, we do not have such alternative-path computation algorithms. In this
paper, we propose an algorithm and a routing scheme to compute and utilize
detour paths adaptively according to the network traffic conditions. Through
evaluation, we show that the proposed scheme improve the communication
performance by using the detour paths in practical network scenarios 2.


In 3 designed a new algorithm
using the combination of Ad-hoc on Demand Distance Vector (AODV) and Cross
layer design approach. It is referred as Congestion Control AODV (CCAODV)
approach. It is used to avoid link break in MANET. Received signal strength is
used as cross layer design parameter. The CCAODV protocol creates strong and
stable route by using signal strength of node. The signal strength mainly
depends on the parameters like transmission power of node and distance between
two nodes. The cross layer design approach is tested by using Ns 2.35 simulator
and compared with the AODV routing protocol.


Wireless Sensor technology is one
among the fast emerging technologies in the current scenario and it has wide
range of application also which has small sensors with minimum communicational
and computational power. Depending on the overhead of a node, the energy
consumption varies with each other. This leads to the non-uniform distribution
of the energy which in turn degrades the performance of the whole network. Swap
Rate algorithm (SRA) is used for detecting the low level energy node. In
addition, the nodes are detected even during the other network interruptions.
In the recovery method, the node in its vicinity will detect the low level
energy node position and it will update to sink node which in turn sends nearby
node that has good energy level to recover the node. It will replace the node
and finally the data transmission will be taking place without any obstacles to
achieve the reliability in the network 4.

In 5 a particle swarm
optimization (PSO)-based lifetime prediction algorithm for route recovery in
MANET has been proposed. This technique predicts the lifetime of link and node
in the available bandwidth based on the parameters like relative mobility of
nodes and energy drain rate, etc. Using predictions, the parameters are
fuzzified and fuzzy rules have been formed to decide on the node status. This
information is made to exchange among all the nodes. Thus, the status of every
node is verified before data transmission. Even for a weak node, the
performance of a route recovery mechanism is made in such a way that corresponding
routes are diverted to the strong nodes. With the aid of the simulated results,
the minimization of data loss and communication overhead using PSO prediction
has been discussed in detail..






A.    Proposed
System Overview



proposed System Architecture


Detailed descriptions of the proposed system are as follows:


1.  Network Generation

Initially random network is
generated and node position in random network is not fixed. 



2.  Select Source and Destination

After the network creation, the
selection of source node and destination node is done.


3. Find the Path

Depending on the source node and
destination node generated, the multiple paths from source node to destination
node are found.




4. Search Shortest Path

Next step is to search the
shortest path among the multiple paths to send data.


5. Energy Value Calculation

After finding shortest path
calculate the energy of each node of shortest path, if node energy is
sufficient to transfer data then data is transferred from source to


6. Swapping of Node

If node energy is not sufficient
to transfer data then it checks the neighboring paths node energy, if there is
sufficient energy to data transfer then swapping of node is performed.


7. Send Data

After selecting the shortest path
with energy efficient node then send the data from source node to destination
node .



B.    Algorithm


1: Proposed Algorithm


1.       Deployment
of Random nodes in the network .

2.       Select
source node S(n) and Destination node D(n).

3.       Find
the multiple paths from source to destination for sending data.

4.       Depend
on the hop(h) value find the shortest path from multiple path.

5.       After
selecting the Shortest path, calculate the energy of each node, e(n1), e(n2).
e(n3)….e(n)  in  path.

If e(n) ?  Te(n) then

of data from source to destinations.


                Checks the neighboring paths node energy

                 If e(n)? 
Te(n) then

the node for swapping.


                 Check another neighboring node.


After swapping the node get appropriate
path to send data from source to destination.

C.    Mathematical


Set Theory


System S is represented as S=
Dn,S(n),D(n), P, PS,



1. Deploy nodes

Dn = {D1, D2, …..,Dn}

Dn is set of all deployed nodes.


2. Select source ad destination

S(n)= {S1}




3. Find Multiple path source to

P= {P1,P2,P3,P4……}

Where P is a set of all Paths


4. Find shortest path

PS= {PS1, PS2, PS3…..}

Where PS is the shortest path


5. Calculate the energy in
shortest path

E = {E1, E2, E3…..En}

Where E is a set of all nodes


6. Swapping the node

N = {N1, N2, ….,}

Where N is a set of all swapping



7. Data sending from cluster
members to cluster Head and from here to base station

F = {f1, f2, f3, ….fn}

Where, F is a set of all data
packets transmitted.






Experimental Setup

The system is
built using Java framework on Windows platform. The Net beans IDE is used as a
development tool. The system doesn’t require any specific hardware to run; any
standard machine is capable of running the application.


Expected Result

In this section discussed the
experimental result of the proposed system.


Following figure 2 shows the time
consumption graph of the proposed system with the existing system.  Comparison graph shows that the time required
for implementing the proposed system is less than the time required for
implementing the existing system.


Fig. 2: Time Graph


conclusion and future scope

energy efficient routing has been simulated by using node rotation concept
which helps in the uniform distribution of the energy throughout the network.
The critical nodes will be disconnected from the data transmission and the neighbor
nodes that have energy level greater than threshold level will be elected as
border nodes after. By determining the location and energy information, the
nodes will be swapped without network interruptions, in
turn enhances the battery life.


1.   AQEEL
TAHA et al .”Energy Efficient Multipath Routing Protocol for Mobile Ad-Hoc
Network Using the Fitness Function” ,date of publication May 24, 2017, Digital
Object Identifier 10.1109/ACCESS.2017.2707537

2.   Kiyotaka Kaji , Takuya
Yoshihiro.”Adaptive Rerouting to Avoid Local Congestion in MANETs”, 978-1-5090-4183-1/17/$31.00
©2017 IEEE Open Access journal.

4.   Mazher Khan, Dhat Shital M,
Dr. Sayyad Ajij D. “Cross Layer Design Approach for Congestion Control in
MANETs ” 2016 (ICAECCT) Rajarshi Shahu College of Engineering, Pune India. Dec
2-3, 2016.

5.   N.G.Pavithra , M.G.Sumithra,
E.Shalini.”Efficient energy management in wireless sensor networks using node
rotation” 2016 IEEE Online International Conference on Green Engineering and

6.   D. Manickavelu and R. U.
Vaidyanathan, “Particle swarm optimization (PSO)-based node and link lifetime
prediction algorithm for route recovery in MANET,” EURASIP J. Wireless Commun.
Netw., vol. 2014, p. 107, 2014.

7.   Eduardo Cerqueira Kassio
Machado, Denis Rosario and Jose Neuman de Souza. “A routing protocol based on
energy and link quality for internet of things applications”.
Sensors,3(2):1942– 1964, Feb 2013.

8. Tanjida Kabir, Novia Nurain, and Md. Humayun Kabir, Pro-AODV
(Proactive AODV): Simple Modifications to AODV for Proactively Minimizing
Congestion in VANETs, Proc. of IEEE Inter. Conf. on Networking Systems and
Security, Dhaka, Bangladesh, 5-7 Jan. 2015.




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