Energy Mapping Approach for QoS in MANETs

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© 2017, IJCSE All Rights Reserved 273
International Journal of Computer Sciences and Engineering Open Access
Review Paper Volume-5, Issue-10 E-ISSN: 2347-2693
Energy Mapping Approach for QoS in MANETs
Bura Vijay Kumar1*, Srinivas Aluvala2, K. Sangameshwar3
1*Computer Science and Engineering, S R Engineering College, Warangal, India
2Computer Science and Engineering, S R Engineering College, Warangal, India
3Computer Science and Engineering, S R Engineering College, Warangal, India
*Corresponding Author: vijaykumar.bura@gmail.com, Tel.: +91- 9849121658
Available online at: www.ijcseonline.org
Received: 23/Sep/2017, Revised: 05/Oct/2017, Accepted: 14/Oct/2017, Published: 30/Oct/2017
Abstract— The mobile ad-hoc networks are the mobile wireless networks which have no fixed infrastructure and routers,
each node act as a router such that the end to end quality of service (QoS) is unpredictable or the single node. The end to end
quality of service metrics is not changeable or fixed when the mobile networks have seen in the whole formed by combining
several different nodes. Logical time (coherence time) is the time taken to send the information or a file to all the nodes is
max and the end to end quality of service metrics is constant. The spreading period is the area covered or extended over a
wide area over a period of time, it’s the time duration to send the information or file to the mobile network of the individu al
nodes. If found that the logical time is more than the spreading period the quality of service metrics is followed a particular
path. The objective of this paper is to calculate or measure the end to end quality of service of each node in a mobile network
and describe how energy map is constructed in a mobile wireless network.
Keywords— QoS, Logical Time, Spreading period, QoS metrics.
I. INTRODUCTION
The mobile wireless networks are the collection of mobile
nodes which have no fixed infrastructure or no fixed access
points. In the mobile wireless networks, each individual node
act as a router and these nodes also acts as an access point for
sending and receiving information from source node to
destination node. In mobile wireless networks the routing
protocols such as geographical routing unable to provide the
end to end quality of service [2]. To maintain the end to end
quality of service in mobile networks we use the path
integration algorithm such that the end to end quality of
service guarantees from source to destination.
The unpredictable mobile networks at individual node scale
can tight with the end-to-end quality of service (QoS) metrics
can be rigid when the network is viewed in aggregate [5].
Coherence time is defined as the maximum duration for
which the end-to-end QoS metric remains roughly constant,
and the spreading period as the minimum duration required
to spread QoS information to all the nodes. If the coherence
time is greater than the spreading period, the end-to-end QoS
metric can be tracked [1].
The objective of this is to focus on the energy consumption
as the end-to-end QoS metric by which an energy map can be
constructed and refined in the joint memory of the mobile
nodes so that energy maps can be utilized by an application
that aims to minimize a node’s total energy consumption
over its near future trajectory [8]. An Ad Hoc network is a
collection of wireless mobile hosts forming a temporary
network without the aid of any established infrastructure or
centralized administration. The absence of any fixed
infrastructure, such as access points, makes Ad-Hoc
networks prominently different from other wireless LANs. In
such an environment each node may act as a router, source
and destination, and forwards packets to the next hop
allowing them to reach the final destination through multiple
hops. With the proliferation of portable computing platforms
and small wireless devices, Ad Hoc wireless networks have
received more and more attention as a means for providing
data communications among devices regardless of their
physical locations.
II. MAJOR CHALLENGES TO QOS
Meeting QoS requirements in sensor networks are the
difficult task. There are major limitations to this. Some
challenges limitations are described below [4].
Resource limitations: The most severe constraint node
restricted by is the limited access to battery power, the
limited bandwidth of the wireless channel, limited processing
capabilities, limited memory size and buffering, as well as
the transmission power the nodes. So we can clearly see that
under such resource restricted properties traditional QoS of

International Journal of Computer Sciences and Engineering Vol.5(10), Oct 2017, E-ISSN: 2347-2693
© 2017, IJCSE All Rights Reserved 274
routing and MAC protocols are not applicable in WBAN
applications [4].
Unpredictable traffic patterns: QoS support in traditional
networks is often dependent on a certain periodicity of the
data traffic. But we all know that in WBAN, it is entirely
different. Sometimes we might experience data burst,
sometimes no traffic and sometimes real-time responses
required such as in the emergency case of heart failure
scenarios [4].
Network instability: The network topology might change
frequently due to link failure, power failure, and mobility of
the nodes. Also for the fact that sometimes certain devices
need not be in operational mode and hence are in sleep mode
to save energy. This changes the network structure. The
network topology might change frequently. Routing and
medium access under these unstable conditions is
challenging [5].
Network dynamics: It may arise from dynamic topology
changes and unreliable nature of wireless networks. Dynamic
topology can change in WBAN due to node mobility, failure,
and an addition of new nodes. Unreliable nature of wireless
links may cause havoc in emergency data transfer. Hence it
increases the complexity of Quality of Service.
Energy balance: This is always a key concern in all sensor
networks application areas. Hence it always requires careful
management of the energy resources. The energy load must
be equally distributed among the sensor nodes and the
devices [5].
Data redundancy: There might be possibilities of data
redundancy in the sensor nodes. It causes the energy loss.
The solutions can be in the form of data fusion and data
aggregation. These techniques help decrease redundancy in
the data.
Heterogeneous traffic types: The Heterogeneous data of
different sensors has different sampling rates.
Packet criticality: Some data in WBAN needs attention.
Priority structure is set up to maintain a quality of service.
Unbalanced traffic: Data may flow from many sensor devices
(many patients in a hospital along with outside people) to a
small number of sinks. Hence it is argued that QoS
mechanisms should be designed for an unbalanced QoS-
constrained traffic [6].
Multiple sinks: WBAN applications prefer central
coordinator which acts as the sink too. It is not usual to have
multiple sinks. But it may happen that due to different nature
of sensors for different types of data in WBAN multiple
sinks are used. It arises from the need of different
requirements on the network. For example, one sink may ask
data from heart monitoring sensors in every minute, while
another sink node may need the only emergency event from
heart sensors or blood pressure monitoring sensors etc.
Hence it is necessary that WBAN supports different QoS
levels in such cases of multiple sinks.
III. METHODOLOGY
In mobile networks to send information from one node to the
fixed base station, it takes lots of energy and time, for this till
now proactive and reactive routing is used. To send the
limited information among the nodes large amount of energy
is consumed and importantly end to end quality metrics is not
implied in the system [7].
Deficiency of an Existing System
• No geographical routing protocol/protocols.
• End-to-end QoS metrics is not implied.
IV. PROPOSED SYSTEM
To send the information from one location to a fixed base
station in mobile wireless networks and to maintain the end
to end quality of service metrics using different paths is
adopted by the path integration algorithm.
A. Modules
In this approach, here we come up with three modules they
are: 1. Node Module
2. Energy Calculation
3. Time Calculation
 Node Module
Here we take five nodes named as Node A, Node B,
Node C, Node D, and Node E from these nodes we
transfer data to destination node in which after
receiving the data in that node data will be displayed
 Energy calculator
In this module, we calculate the energy used by
nodes to receive the data, to calculate the energy
using the path integration algorithm, firstly we
consider the size of the file and find the distance
between the nodes as follows: Air = 1100, Energy
T1= size of the file / Air. Data energy is calculated
using the formula data energy = size of the file / 60
(time), total energy = Energy T1/ Data energy.
 Time calculation
Time calculation is done using Energy T1= size of
the file / Air.

International Journal of Computer Sciences and Engineering Vol.5(10), Oct 2017, E-ISSN: 2347-2693
© 2017, IJCSE All Rights Reserved 275
B. Algorithm
To calculate the energy used by nodes to receive the data,
here we use the path integration algorithm.
The Path Integration Algorithm as follows
Step 1: Read the file.
Step 2: Find the Size of the File.
Step 3: Fix the Air as 1100. Air=1100
Step 4: Calculate the Energy in Time T1 Energy
T1 = Size of the File / Air
Step 5: Time1 = Energy T1
Step 6: Find the Data Energy,
Data Energy = Size of the File / 60
Step 7: Calculate the Total Energy,
Total Energy = Energy T1 / Data Energy
V. CONCLUSION
The dire need of quality of service in MANETs as emerged
as it as a vital role in the modern-day ad-hoc communication,
In this paper we focused to calculate the logical time versus
the area covered or the external area of a wide area over a
period of time (Spreading period), this analysis resulted in
that the logical time is greater than the spreading period, with
the conclusion that the end to end quality of service metrics
are tracked. We also calculated the energy map between the
mobile nodes in the mobile network, future scope of this
paper is to enhance the quality of service by mapping the
individual node energy with the network.
REFERENCES
[1] Min Kyoung Park, Member, IEEE and Volkan Rodoplu, Member
IEEE “Energy Maps for Mobile Wireless networks coherence Time
Versues Spreading Period” in International Communications
Conference (ICC) accepted Jan 5, 2009.
[2] D. Niculescu and B. Nath, “Trajectory based forwarding and its
application” in Proc. ACM/IEEE MOBICOM, 2003.
[3] M.K. Park and V.Rodoplu, “Energy maps for large-scale, mobile
wireless networks” in Proc. International Communications
Conference (ICC), Jun. 2007, pp. 3136-3141.
[4] S. Chakrabarthi and A. Mishra, “QoS issues in ad hoc wireless
networks” IEEE Commnications Magazine, pp. 142-148, Feb.
2001.
[5] A. Iwata,C.-C Chian, G. Pei, M. Gerla and T.-W. Chen, “Scalable
Routing Strategies for ad hoc wireless networks” IEEE Trans. J.
Selected Areas Commun, Vol.17, No. 8, pp. 1369-1379, Aug.
1999.
[6] C. Zhu and M. S. Corson, “QoS routing for mobile ad hoc
networks” in Proc. IEEE INFOCOM 2002, Aug. 2002, pp. 1488-
1505.
[7] S. Subramanian and S. Shakkottai, “Geographic Routing with
limited information in sensor networks” in Proc 4th Int. Conf.
Information Processing in Sensor Networks, Apr. 2005.
[8] P.Pal, R.Pal Observing of Mobility Model against Reactive
Routing Protocols in MANETs of throughput, Average end-to-end
delay, packet delivery ratio” in International Journal of Computer
Sciences and Engineering (IJCSE) Vol.5, Issue.1, pp. 43-52, Jan-
2017.
Authors Profile
Vijay Kumar Bura received his Bachelors
Degree (B.Tech) in Computer Science
Information Technology from JNTUH in
2006 and Masters degree (M.Tech) in
Software Engineering form Jawaharlal
Nehru Techno-logical University,
Hyderabad, Telangana, India in 2011. He
worked as Software Engineer at ITP
Software India Private Limited, Hyderabad
for 2 years. He developed various web applications for different
clients. He worked as Asst. Prof. in the Dept. of IT, SVS Institute
of Technology, Warangal for 2 years. Presently he is working as
Assistant Professor in the Department of Computer Science and
Engineering, S R Engineering College (Autonomous), Warangal
Telangana, India. As a mentor, he represented a team to participate
in CISCO IoT Hackathon 2017 held at Trident Group of
Institutions, Orissa, The team idea was selected for “Best Jury
Award” and secured RUNNER UP position.
Srinivas Aluvala received his Masters degree
in Computer Science and Engineering
(M.Tech) form Jawaharlal Nehru
Technological University, Hyderabad,
Telangana, India in 2010. He is pursuing
Ph.D. degree in the stream of Mobile Ad-hoc
Networks in Computer Science and
Engineering at KL University, Guntur, and
Andhra Pradesh, India. He has 11 years of
teaching experience. Currently he is working
as Assistant Professor in the Department of
Computer Science and Engineering in S R Engineering College
(Autonomous), Telangana, India and Lead, SR CISCO Networking
Academy. He has published Twelve International Journals and
Thirteen International Conference Papers. His Research interest
includes Computer Networks, Network Security and Mobile Ad-hoc
Networks. He is a Life Member of Indian Society for Technical
Education (ISTE).
Sangameshwar Kanugula received his
Masters degree (M.Sc.) in Computer Science
from Kakatiya University, Warangal,
Telangana in 2007 and M.Tech in Computer
Science and Engineering form Jawaharlal
Nehru Technological University, Hyderabad,
Telangana, India in 2013. He developed
various automation tools for Engineering
Colleges and most of them are using since 4
years. Currently he is working as Assistant Professor in the
Department of Computer Science and Engineering in S R
Engineering College (Autonomous), Telangana, India. From 2013
he is promoting Engineering Graduates for various programming
competitions. He has professional membership at ACM. As a
mentor, he represented a team for CISCO IoT Hackathon 2017.