Quality of Service for Differentiated Traffic Using Multipath in Wireless Sensor Networks

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International Journal of Inventive Engineering and Sciences (IJIES)
ISSN: 2319–9598, Volume-3 Issue-1, December 2014
61
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication Pvt. Ltd.
Quality of Service for Differentiated Traffic using
Multipath in Wireless Sensor Networks
Prabha R, Shivaraj Karki, Manjula S. H, K. R. Venugopal, L. M. Patnaik
Abstract- Providing Quality of Service in wireless sensor
networks refers to a set of service requirements to be satisfied
when transmitting a packet from source to destination. The main
challenge involved in quality of service based data transmission
is to select the efficient path from source to destination. Quality
of service in wireless sensor networks is an important factor. The
two most important parameters that hinder the goal of
guaranteed event perception are time-sensitive and reliable
delivery of gathered information, while minimum energy
consumption is desired. In this paper, a multi-traffic, multi-path
and energy aware data transmission mechanism is proposed for
improving Quality of Service in Wireless Sensor Networks. The
simulation results demonstrate that, the algorithms efficiently
improve quality of reception ratio, satisfying the required quality
of service metrics.
Index terms- Differentiated Traffic, End-to-End Delay, Energy,
Reliability, Wireless Sensor Networks.
I.
INTRODUCTION
The Wireless Sensor Networks (WSN) is a kind of self-
organizing wireless network consisting of a group of
randomly distributed embedded sensor node. These nodes
integrate sensor, data processing unit and communication
module. WSN serves a large number of applications that
are critical to the extent of saving human life. Serving
reliable and timely information is utmost important to any
WSN. Quality of Service (QoS) in WSN enables techniques
and requirements to provide reliable and trusted service.
QoS is a set of service parameters to be fulfilled when
transmitting a stream of packets from source to destination.
Reliability, timeliness, energy, robustness,
availability,
security, throughput, end-to-end delay, jitter and packet
loss rate are the most fundamental parameters of QoS in
WSN. Certain applications of wireless sensor networks like
biomedical and vehicular have different QoS requirements.
In wireless sensor network the data traffic type is classified
into (i) Regular traffic which does not require any data
related QoS requirement. (ii) Reliable traffic which requires
data delivery without any loss, can withstand a certain
amount of delay. (iii) Delay sensitive traffic which requires
data delivery within certain deadline. (iv) Critical data
which is delivered within the deadline time [1].
Manuscript Received on December 2014.
Prabha R, Department of Computer Science and Engineering,
University Visvesvaraya College of Engineering, Bangalore, India.
Shivaraj Karki, Department of Computer Science and Engineering,
University Visvesvaraya College of Engineering, Bangalore, India.
Dr. S. H. Manjula, Department of Computer Science and Engineering,
University Visvesvaraya College of Engineering, Bangalore, India.
Dr. K. R. Venugopal, Department of Computer Science and
Engineering, University Visvesvaraya College of Engineering, Bangalore,
India.
Dr. L. M. Patnaik, Honorary Professor, Indian Institute of Science,
Bangalore, India.
Motivation: Quality of Service based data forwarding in
wireless sensor network has major challenges and
constraints. Localized Multi-Objective Routing
LOCALMOR [1] implements a localized QoS routing
protocol based on different traffic types and routing decision
is based on latency, packet reception ratio, packet delivery
time and energy criteria. LOCALMOR [1] considers CBR
traffic type, uses multisink and single path hence priority
queue is required.
Contribution: The main contribution of this work is of QoS
based data forwarding techniques in wireless sensor
networks for differentiated traffic. The proposed algorithms
handle different traffic categories namely, Constant Bit Rate
(CBR) traffic for regular traffic, Variable Bit Rate (VBR)
for delay sensitive traffic. The packet is forwarded from
source to destination considering QoS metrics namely,
delay, reliability, residual energy and link quality.
Organization: Section II discusses the Related Work,
Section III presents the Network Model and
Assumptions, Section IV gives the Problem Definition,
Section V Explains the Algorithms for the various data
traffic types. Section VI deals with Performance Analysis
and simulation study followed by Conclusions and
References.
II.
RELATED
WORK
Localized routing protocols makes use of localization
information in order to select the next forwarding node
among the neighbors. Djamel and Ilangko proposed a
multi-objective Quality of Service (QoS) protocol for
wireless sensor networks (WSN). The protocol takes into
account the traffic diversity typical for many applications. It
ensures several QoS metrics for different traffic categories,
and attempts for each packet to fulfill the required metrics in
a power- aware and localized way [1]. Lim and Mohan [2]
addressed three energy aware geographical data forwarding
schemes. The proposed three energy aware forwarding
schemes are namely, Energy Aware Geographical
Forwarding Scheme (EAGFS), Highest Energy Forwarding
Scheme (HEFS) and Above Average Energy Forwarding
Scheme (AAEFS), this schemes Considers residual energy
in their decision of the next hop. The aim is to delay the
death of the first node in the sensor network so as to achieve
a longer network lifetime thus enhancing the QOS in the
network. Navid and Turgay [3] discussed the reasons behind
failure in packet reception ratio. Analyzed the concept of
multiple receiver radios in mobile sinks. Actual
experiments were conducted to gain performance using
multi-radio sinks. Multiple sinks significantly improved
packet reception ratio with less number of retransmission.
Minimizing number of retransmissions improves QoS
performance metrics delay, efficient use of energy and
network lifetime. Energy efficiency, network

Quality of Service for Differentiated Traffic using Multipath in Wireless Sensor Networks
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communication traffic and failure tolerance are important
QoS factors related with performance of WSN. He et al.,
[4] forwards packets by selecting routes that ensures a given
speed. Exponential Weighted Moving Average for link
latency estimation is used. The aim of this protocol is to
reduce QoS metric delay. It probabilistically chooses the
node among the ones that fulfill the required speed, which is
energy efficient and balances the network load.
Jain et al., [5] came up with centralized and distributed
solution for QoS topology control, by employing
opportunistic transmission to catch the best transmission
opportunities on transitional links. A unique contribution of
this work is consideration of link quality and applies
opportunistic communication in topology control for
wireless sensor networks.
Muhammad et al., [6] addressed an energy-aware, multi-
constrained and multi-path QoS provisioning mechanism for
WSN based on optimization approach. Detailed analytical
analysis of reliability, delay and energy consumption is
presented to formulate the optimization problem in an
analytical way. A greedy algorithm is proposed to achieve
the desired QoS guarantee while keeping the energy
consumption minimum. A simple but efficient
retransmission mechanism is employed to enhance the
reliability further, while keeping the delay within delay
bound.
Felemban et al., [7] presented a novel packet delivery
mechanism called Multi-Path and Multi-SPEED Routing
Protocol (MMSPEED) for probabilistic QoS guarantee in
wireless sensor networks. The QoS provisioning is
performed in two quality domains namely, timeliness and
reliability. Multiple QoS levels are provided in the
timeliness domain by guaranteeing multiple packet delivery
speed options. In the reliability domain, various reliability
requirements are supported by probabilistic multipath
forwarding. These mechanisms for QoS provisioning are
realized in a localized way without global network
information by employing localized geographic packet
forwarding augmented with dynamic compensation, which
compensates for local decision inaccuracies as a packet
travels towards its destination.
Applications like target tracking require some QoS
guarantees. Some factors limit the ability of multi- hop
sensor networks to achieve desired goals such as the delay
caused by network congestion, limited energy and
computation of sensor nodes, packet loss due to
interferences and mobility. Shanghong et al., [8] designed
an adaptive QoS and energy-aware approach using an
improved ant colony algorithm for WSNs. Belghachi et al.,
[9] focused on an idea to ensure QoS, by detecting paths
which meet QoS requirements based on Ant Colony
Optimization through a routing process, which can detect
path based on ant colony optimization.
Jeya [10] analyzed energy-aware QoS protocol for adhoc
wireless networks. The performance metrics considered
were average lifetime of the node, average delay per
packet and network throughput. The parameters considered
are end-to-end delay, real time data generation or capture
rates, packet drop probability and buffer size. Mirela et al.,
[11] focused on QoS based protocol for wireless sensor
networks application. Packet forwarding was done based on
geographic routing mechanism with QoS support.
Forwarding node selection is based on high residual energy
at the nodes, high link quality and low load. Congestion
control was incorporated using ring or barrier mechanism
combined with QoS support is used to forward packets in
the network.
Adel et al., [12] designed a data forwarding protocol for
WSN which aimed at extending the network lifetime.
Position information and remaining energy of nodes are
parameters for forwarding packets from source to
destination. The protocol is an efficient and energy
conservative technique for wireless sensor networks.
III.
NETWORK
MODEL
AND
ASSUMPTIONS
Nodes are aware of their positions, through an internal
Global Positioning System (GPS) device. Each node is
aware of its battery state E
vi
, all nodes have same initial
energy and spherical transmission power range E
range
. The
set of nodes in n
i
vicinity represented by S
ni
consists of n
i
’s
neighboring nodes which are within the power range and
within one hop distance, S
ni
= { n
j
: Ln
i
, n
j
<= E
range
and
one hop distance } i.e, forward set for node n
i
towards
destination which provide positive advance towards the
destination Regular Sink (RS), Normal Sink (NS).
E = 2 Eelec +βdα (1)
The energy consumed for transmitting one bit from
source to destination is as given in equation (1) [1]. Where
Eelec is the energy utilized by transceiver electronic, which
is independent of the distance. βdα is the power utilized in
transmitting one bit over destination d. Where α is the
path loss ( 2 ≤ α ≤ 5 ) and β is a constant given in
Joules/bit × m
α
. Equation (1) is for unicast packets. For
broadcasting messages energy consumed is given by
equation (2).
E = (( ║N(vi)║ + 1 ) E
elec
)
+ β d
α
(2)
Like other geographical routing protocols nodes determine
their neighboring nodes other related parameters via the
execution of Hello Protocol.
IV.
PROBLEM
DEFINITION
The objective of proposed algorithms is to ensure required
quality of service for differentiated traffic types namely
CBR and VBR traffic types. The QoS metrics considered
are reliability, timeliness and residual energy of the node.
Multiple sinks and multi path are employed for improving
packet reception ratio and reliability.
V.
ALGORITHMS
This section presents the various algorithms designed for
providing QoS for different traffic types in WSN. The
algorithm for obtaining Neighbor information is given in
Table I. The algorithm extracts up to date network
information viz., node location, packet reception ratio,
velocity or speed with which packet can be forwarded to
destination and residual energy of the nodes. Updates the
number of neighbor nodes available for given node and
their respective QoS metrics. These QoS metrics are
periodically updated by Hello protocol. Each Hello
packet carries the location of sending node, node id, node
residual energy, hello packet sequence number, global
synchronous clock, time stamp. At every node on
reception of hello packets, nodes calculate packet reception

International Journal of Inventive Engineering and Sciences (IJIES)
ISSN: 2319–9598, Volume-3 Issue-1, December 2014
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ratio using Weighted Mean Exponential Weighted Moving
Average (WMEWMA) [1].
A. Neighbor Management Algorithm:
Table I : Algorithm for Neighbor Management
using Weighted Mean Exponential Weighted Moving
Average (WMEWMA) [1].
B. Algorithm for Delay Updating
Algorithm for updating delay process is given in Table
II. Updating delay process involves following procedure.
Initially delay of all nodes are set to zero in line 1, after
receiving each Hello packet delay is calculated by taking
difference between sent time and current time, size(ack)/bw
(considers time of receiving entire packet). For successive
reception of Hello packet from same node Exponential
Weighted Moving Average (EWMA) is used in calculating
Delay.
Table II: Algorithm for Updating Delay
Begin
1. Initialize delay = 0
2. for each Hello packet received for
first time
3. delay
curr
= Sent Time – ((RevdTime - Size(ack))
/ BW at regular Time
Interval
4. Delay = α delay
curr
+ (1 - α) delay
prev
end
Table III: Algorithm for Updating PRR
C. Algorithm for Updating Packet Reception
Ratio
The algorithm for calculating the Packet Reception Ratio
(PRR) is given in Table III. Updating PRR involves
calculating packet reception ratio. WMEWMA is used in
calculating PRR, initially number of missed packets,
sequence no of last packet received, window size is set and
number of packets received is set to zero in line 1. For each
Hello packet received current window, packet received
count are incremented. Number of missed packets is
calculated using SQL and received packet sequence number.
Prr is calculated in line 3, when current window reaches
window size Prr is calculated using α = 0.6. In line 4
window size is set to 30.
VI.
PERFORMANCE
EVALUATION
The performance of the QoS based algorithms are validated
through simulations. This section describes the simulation
set up and result analysis.
A. Simulation Setup
The QoS parameters considered in the proposed work is
implemented in the network. Implementation of these QoS
based packet forwarding techniques is carried out in
network simulator2 (ns2). ns2 uses OTCL and C++
languages. The proposed QoS algorithms are implemented
in ns2 are rigorously executed to measure various QoS
metrics viz., latency, reliability, energy and throughput. The
network scenario is tested for various time duration
considering different environment and congestion settings.
Comparison of our work is extensively carried out with
LOCALMOR [1], considering the QoS metrics End-to-End
delay, packet reception ratio and on time packet delivery.
The major criteria considered for forwarding the packets
depending on type is time constraint and energy. The
simulation configuration consists of 200 nodes with 500 *
500 simulation area and 1000s of simulation time. This
high number of nodes permits to investigate scalability.
Begin
1. MP (Missed Packet) = 0; SQL (Sequence no of last Packet) =
0; CW( Current Window) = 0; Pr ( no of packets received ) = 0;
2. for each Hello packet received
do
{
CW = CW+1; Pr = Pr+1; MP =P + Pck.sq -
(SQL+1);
SQL=Pck.sq;
}
3. Prr = Pr / (Pr + Mp )
4. If ( CW == W)
{ Prr
ni,n j
= α Prr
ni,nj
+ (1 - α_);
r=(r + f);
MP = CW = Pr = 0 }
End
Begin
Input HelloPacket
1. Neighbour set of node
i
: S
ni
where Sn
i
= { n :
Dist
i, ,
node < P
range
}
2. Set of nodes that belong to Sn
i
which are closer
to destination FW
id
(Dest) = nSn
i :
L - L
next ≥ o
3. Becon message or hello message is sent at regular
Time Interval
4. A Counter/Timer is set to Hello Period and is
Decremented Timer = Hello Period
5. If Timer Expires
6. Hello Protocol Broadcast Packets to all
Neighbor nodes.
7. At Nodes receiving Hello pkt Initial NLP =
0
8. Update Neighbor Link Profile
9. Update Delay
10. Update
PRR

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The nodes are distributed in Poisson point process
manner with node density 0.005 nodes/meter square,
distributed in a grid topology, with approximately 50m of
power range, resulting in an average density of 8 (each node
has seven neighboring nodes, on average). Constant Bit Rate
(CBR) traffic is used to generate regular packets with1kb/s.
Variable Bit Rate (VBR) is used to generate reliable
packets. Hello protocol is executed more frequently for 1
sec, thus the overall traffic of the network, i.e., traffic
overload is more as compared to LOCALMAR [1]. Table
IV summarizes the simulation parameters used in the
simulation. Packet delivery time is foremost important QoS
metric, in this work packet delivery time is considered as
packet deadline time. Unlike the previous work
LOCALMOR each traffic type is assigned different deadline
time i,e., 160 ms for critical and delay sensitive packets. 200
ms for regular and reliable packets. Thus critical and delay
sensitive packets have 80% lesser deadline time than other
traffic type.
B. Performance Analysis
Implementation is carried out in network simulator 2 (ns2)
which uses OTcl and C++ codes to implement the given
scenario. Performance metrics considered here are End-to-
End delay, packet reception ratio and on time packet
delivery. Starting with end to end packet reception ratio,
critical packet rate is varied keeping VBR and CBR
unaffected. Time taken by packets to reach sink is calculated
by destination (sink) node using 'sent time' field information
in received packet (network uses global synchronized time).
End-to-End delay is measured in milliseconds.
Fig 1 shows the comparison of critical packet End-to-End
delay versus critical packet rate. Critical packet rate is
varied keeping VBR and CBR unaffected. Time taken to
reach sink node is measured, and tabulated in milliseconds.
At lower critical packet rate, critical packet End-to-End
delay is lower and increases for next consecutive packet
rates, and remains almost constant for higher packet rates,
this increase is due to increase in overall traffic in the
network. End-to-End delay varies from 40ms to 69.9ms
where as in LOCALMOR End-to-End delay ranges around
155ms.
Table IV: Simulation parameter values for simulation
scenarios
Parameter Value
Number of Nodes 200
Simulation Area 500 × 500
Traffic Regular 1 Kb / s
Critical Packet Rate From 0.1 to 1 kb/s
Deadline for Critical Packet 0.2 sec
Hello period 1 sec
EWMA Smoothing Parameter
α 0.6
EWMA Window 30
Total required PRR 100
MAC Layer 802.11
Bandwidth 200 Kb/s
Propagation Model TwoRayGround
Thus our work outperforms LOCALMOR giving 45 %
lesser End-to-End delay as depicted by the curve MTD. This
comparison effectively justify that availability of minimum
number of nodes during end to end transmission of packets
is been enhanced by EAGFS technique
Figure 1: Critical Packet Rate v/s Critical End-
to-End Delay
Fig 2 shows the comparison of critical packet reception ratio
versus critical packet rate. Our work performs on-par with
LOCALMOR. Critical Packet Reception Ratio (PRR) is
maximum for lower rate and it decreases and remains
constant at higher critical packet rate. Decrease in PRR for
higher packet rate is due to overall increase in traffic rate in
network reception.
Figure 2: Critical Packet Rate versus Packet Reception
Ratio for Critical Packets
Fig 3 shows comparison of End-to-End delay of regular
and reliable packets, considering End –to-End delay of
regular and reliable traffic which constitute CBR and VBR
traffic against critical packet rate. As critical packet rate
increases End-to-End delay increases from 114.6ms to
128.08ms, this gradual increase along critical packet rate
is due to overall increase in the network traffic, whereas in
LOCOALMOR regular packet End-to-End delay
decreases from 280ms to 160ms. Fig 4 shows regular
packet reception ratio against critical packet rate. Packet
reception ratio of regular and reliable packet is maximum
for lower rate and slightly decreases as critical packet rate
increases. Packet reception ratio of regular packet is

International Journal of Inventive Engineering and Sciences (IJIES)
ISSN: 2319–9598, Volume-3 Issue-1, December 2014
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compared with LOCOLMAR our work outperforms
LOCOMAR protocol.
Figure 3: Critical Packet Rate versus End-to-
End delay
Figure 4: Critical Packet Rate versus Packet Reception
Ratio
VII.
CONCLUSIONS
Proposed protocol considers the different traffic types VBR,
CBR and burst traffic which are used for different
application. The protocol provides differentiated QoS based
data forwarding considering different QoS metrics for each
packet type. Protocol ensures required QoS metrics for each
packet type with which it can be routed. QoS metric
considered are energy, reliability, latency and speed.
Congestion is introduced to get realistic readings. Energy
efficiency is considered for all packets and achieved by
selecting the most power efficient candidate among those
offering the required data related QoS (delay and reliability).
Future enhancements, the protocol can be implemented with
real-time devices. Though congestion is not an issue in our
implementation increasing further traffic type and traffic
rate may impose congestion problem.
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Prabha R, is currently working as an Associate
Professor in the Department of Information Science
and Engineering, Dr. Ambedkar Institute of
Technology, Bangalore, India. She obtained her
Bachelor of Engineering degree in Computer Science
and Engineering branch. M.E in Computer Science
and Engineering from Computer Science Department, UVCE, Bangalore
University in the year 2003. She has 22 years of teaching experience.
Currently she is pursuing Ph.D in the Department of Computer Science and
Engineering, University Visvesvaraya College of Engineering, Bangalore
University, Bangalore. Her research interest is in the area of Wireless
Sensor Networks.
Dr. S. H. Manjula, is currently working as an
Associate Professor in the Department of Computer
Science and Engineering, University Visvesvaraya
College of Engineering Bangalore University,
Bangalore, India. She obtained her Bachelor of
Engineering degree in Computer Science and
Engineering branch, Masters of Engineering and Ph
D. in Computer Science and Engineering. She has published a book on
Wireless Sensor Networks. She has published more than 30 papers in
refereed international journals and conferences. Her research interests are in
the field of Wireless Sensor Networks, Semantic web and Data Mining.
Dr. Venugopal K. R, is currently Special Officer,
DVG Bangalore University and Principal, University
Visveswaraya College of Engineering, Bangalore
University Bangalore. He obtained his Bachelor of
Engineering from University Visvesvaraya College of
Engineering. He received his Master’s degree in
Computer Science and Automation from Indian Institute of Science
Bangalore.
He was awarded Ph. D in Economics from Bangalore
University and Ph.D in Computer Science from Indian Institute of
Technology, Madras. He has a distinguished academic career and has
degrees in Electronics, Economics, Law, Business Finance, Public
Relations, Communications, Industrial Relations, Computer Science and
Journalism. He has authored and edited 39 books on Computer Science and
Economics, which include Petrodollar and the World Economy, C

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Aptitude, Mastering C, Microprocessor Programming, Mastering C++ and
Digital Circuits and Systems. During his three decades of service at
UVCE he has over 400 research papers to his credit. He was a Post-
Doctoral Research Scholar at University of Southern California, USA. His
research interests include Computer Networks, Wireless Sensor Networks,
Parallel and Distributed Systems, Digital Signal Processing and Data
Mining.
Dr. L. M. Patnaik, is honorary professor in
Department of Computer Science and Automation,
Indian institute of Science, Bangalore. During the
past 35 years of his service at the Institute he has over
700 research publications in refereed International
Conference Proceedings. He is a Fellow of all the four
leading Science and Engineering Academies in India.
Fellow of the IEEE and the Academy of Science for the developing world.
He has received twenty national and international awards. Notable among
them is the IEEE Technical Achievement Award for his significant
contributions to High performance Computing and Soft Computing. He is
an Ex-Vice Chancellor Defense institute of Advanced Technology, Pune
India. His area of research interest has been Parallel and Distributed
Computing, Mobile Computing, CAD for VLSI circuits, Soft computing
and Computational Neuroscience.
Shivaraj Karki, received his B.E in
Electronics and Electronics Communication
Engineering from Dr. Ambedkar Institute of
Technology Bangalore from Visvesvaraya
Technological University in the year of 2010. M.E in
Computer Networks from Computer Science
Department, UVCE, Bangalore University in the
year2013. His research focus includes QoS and
Routing in WSN and Cloud Networking and Communication.