D044021420

K. Sindhu et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 2), April 2014, pp.14-20
www.ijera.com 14 | P a g e
Traffic Differentiation and Scheduling In Vehicular Sensor
Network Using 802.15.4
K. Sindhu*, Dr. T. P. Saravanabava**
*(Department of Embedded System Technologies, College of Engineering, Guindy, Chennai-25)
** (Deputy Director- Knowledge Data Centre, Anna University, Chennai-25)
ABSTRACT
The IEEE 802.15.4 is the widely used next generation standard protocol in many applications utilizing wireless
sensor networks (WSN) especially in vehicular sensor network (VSN). However, currently differentiation and
scheduling mechanisms are not provided in IEEE 802.15.4 specification to improve the quality of service (QOS)
for delay sensitive and critical events. In this paper, multiple scheduling algorithms using FIFO, Priority queue,
RED, WRR and DRR are integrated in compliance with IEEE 802.15.4 to improve the throughput, enhance
bandwidth utilization rate, perform fast processing and delivery of urgent data traffic. NS2.35 has been used for
simulating the VSN and different types of traffic like CBR, poisson and exponential traffic have been
simulated.
Keywords – Vehicular sensor network, scheduling, quality of service, differentiation, IEEE 802.15.4, zigbee.
I. INTRODUCTION
Sensor networks are recently rapidly
growing research area in wireless sensor networks.
Wireless sensors are of small size and low cost are
deployed to establish a sensor network. Vehicular
networks are considered as mobile sensor networks
and characterized by several basic and special
characteristics such as no limited energy and storage
capacity, high node mobility and fast topology
changes. The vehicular sensor network can sense
several types of data in its surrounding area to
provide wide variety of services like traffic
monitoring, crowded streets identifying, speed
controlling, lost vehicle locating and environmental
monitoring since it covers permanently a wide
geographical area [1,2,3].
For wireless sensor networks (WSNs), IEEE
802.15.4 is used as de-facto standard. However, the
behaviour of CSMA/CA results in collision at heavy
load which reduces the throughput and energy
consumption performance of WSN. These problems
demand MAC layer solutions to be proposed to
achieve the better performance of WSN.
Scheduling aids in providing quality of
service (QoS) support to the prioritized and
categorized communication in wireless sensor
networks.This research aims to enhance QoS in a
Vehicular Sensor Networks (VSN) by integrating
traffic differentiation and scheduling mechanisms in
order to reduce the end-to-end delay, improve the
throughput, enhance the bandwidth utilization rate
and perform fast processing and delivery for urgent
data traffic..
The rest of this paper is structured as
follows. Section 2 gives a summary of related works
and Section 3 gives a brief overview of service
differentiation and prioritization methodology used in
our scenario. Hence research constraints used by our
model and generated simulation results are provided
in Section 4. Finally, concluding remarks and future
work are presented in Section 5.
II. PREVIOUS WORK
The MAC layer includes a very important
processing level. since it rules the sharing of the
medium which affects the performance of all the
upper layer protocols. MAC protocol support QoS
provisioning and determining the QoS support
performance by solving the medium sharing
problems and reliable communication.
[4] Proposed a service differentiation
algorithm with slight modification on the protocol to
enhance the achievement of slotted CSMA/CA for
time-critical events. The service differentiation
algorithms were particularly based on various
parameters such as the macHinE, aMaxBE and the
Contention Window (CW). They differently process
the command and data frames since they are affected
by high and low priority levels (service class),
respectively. In other terms, different attributes have
been defined and assigned for different service
classes. This algorithm keeps slotted CSMA/CA in
its original form and focuses on tuning related
parameters effectively in keeping the criticality of
messages. Some existing works [5,6,7] are interested
in controlling over CW depending on the changes in
the network status. In [5], the Sensing Back off
Algorithm (SBA) has been addressed to maximize
RESEARCH ARTICLE OPEN ACCESS

ISSN : 2248-9622, Vol. 4, Issue 4( Version 1), April 2014, pp.
channel throughput with impartial access to shared
channel. When packet collision occurs, it multiplies
its back off interval by α while on a successful
transmission, both sending and receiving wireless
sensors multiply their back off interval by β, and the
others overhearing(sensing) a successful transmission
decreases their back off intervals by θ. α, β and θ are
defined in [5]. However, on the basis of p-persistent
CSMA/CA protocol, [6,7] addresses dynamic IEEE
802.11 wireless networks. Their approaches assume
having a precise number of the active wireless
sensors, to estimate the network state, while they do
not consider QoS for real-time traffic.
[8] Uses CSMA/CA as access protocol to
provide service differentiation in WSN. The Collect
then Send burst Scheme (CoSenS) is developed to
facilitate implementation of scheduling policies and
primarily to handle its weaknesses. A earliest
deadline first and fixed priority are implemented on
the top of CoSenS. The results present that the
proposed solution enhances reliability and end-to-end
delay by adapting traffic variations automatically.
Authors claim that proposed solution does not affect
best effort traffic while meeting deadline
requirements for urgent traffic. Moreover, motes are
used for testing and implementation of CoSenS.
Diff-MAC is a QoS aware MAC protocol
based on CSMA/CA access method to support hybrid
prioritization and differentiated services. Diff-MAC
integrates an effective service differentiation
algorithm in order to increase the channel utilization
and provide fair and fast data delivery. Diff-MAC is
needed in WSN supporting QoS-constrained
heterogeneous traffic such as multimedia
applications. To provide QoS, Diff-MAC consists of
(1) Reducing the retransmission using fragmentation
of the long frames into small manageable packets and
transmitting them in form of burst, (2) Decreasing
collisions and minimizing the packet latencies by
adjusting its contention window size as per traffic
requirements and (3) Providing fair and reliable data
delivery among sensor nodes based on intra-queue
prioritization feature [9].
In [10], author has proposed a system called
VASNET (Vehicular Adhoc and Sensor Networks)
which provides safety on highway roads, since many
accidents and injuries have occurred due to car
accidents. Two types of sensor nodes are suggested in
VASNET, one is embedded inside the vehicle called
Vehicular Nodes(VN) and other is deployed in
predetermined intervals on roads called Road Side
Sensor Nodes(RSS). There is a Base Station(BS) acts
as police traffic station, firefighting group and rescue
team. The VN collect the vehicles velocity and send
it to BS via RSS.
[11] Developed a novel cross-layer
integrating an asynchronous Energy Efficient and
Fast Forwarding (EEFF) protocol for WSNs is
resulting to energy efficiency and low latency. EEFF
implements new approaches improving dynamic
routing selection and low power listening which leads
to reducing the latency.
Node-based scheduling and level based
scheduling, proposed in [12], are two centralized
heuristic scheduling algorithms. The first algorithm is
inspired from the classical multi-hop scheduling
using direct scheduling of the nodes given in an ad
hoc mode. The second algorithm uses a routing tree
to schedule the levels before scheduling the nodes.
This algorithm is more suitable for wireless sensor
networks since it supports many-to-one
communication model. A nodes distribution across
levels affects the performance of these algorithms.
In [13], the authors proposed at the MAC
level a scheduling algorithm that is able to support
assorted connections with different QoS necessities.
At the physical (PRY) layer, each connection utilize
an adaptive modulation and coding (AMC) scheme
over wireless fading channels. The scheduling
algorithm assigns a certain priority level based on the
QoS requirements of each connection. Then, it
adjusts dynamically the priority level according to the
channel and service status.
[14] Proposed a Real-Time Query
Scheduling (RTQS) algorithm for conflict-free
transmission scheduling in order to support real-time
queries in WSNs. In this context, in conflict- free
query scheduling [14] showed relatedness between
prioritization and throughput. Then, it proposed non-
preemptive, preemptive and slack stealing query
scheduling algorithms as novel approaches for real-
time scheduling. As a result, the first algorithm
achieves a better throughput by inverting priority.
This problem has been solved by the second
algorithm with trade-off of reduced throughput.
Finally, the third algorithm combined the
remuneration of preemptive and non-preemptive
scheduling algorithms to improve the throughput and
meet query targets.
Current WSN applications generate different
types of traffic with various requirements such as
delay-bounded, bandwidth and reliable data delivery.
Consequently, Quality- of-Service (QoS)-based
mechanisms can improve efficiently the traffic
delivery in WSNs. This work introduces new
differentiated service approaches and tasks
accomplished by scheduling disciplines and
highlighting the impact of these techniques on the
QoS support in mobile sensor networks.
III. SERVICE DIFFERENTIATION
AND PRIORITIZATION
METHODOLOGY
Different types of traffic with various

requirements such as delay-bounded, bandwidth and
reliable data delivery are generated. Consequently,
Quality-of-Service (QoS)-based mechanisms can
decrease end-to-end delay and improve efficiently the
traffic delivery in a wireless sensor networks. We
used differentiated service approach with scheduling
mechanism to improve the overall network
throughput.
3.1 PROBLEM FORMULATION
A specific problem that arises as a result of
the collected traffic diversity is how to differentiate
and process the diversified traffic in a suitable way to
their requirements. The traffic diversity is caused by
multidisciplinary supported applications. It is
controlled at the roadside unit (or base station) acting
as routers and coordinators. Traffic diversity poses
challenges that need to be resolved by integrating
new mechanisms to (a) classify packets according to
their types of service and (b) schedule them
appropriately to their requirements.
Grade of service is one of crucial parts of
QoS in mobile communications which involves
outage probability and blocking probability and
scheduling starvation. Various mechanisms such as
mobility management, fair scheduling, radio resource
management, channel-dependent scheduling etc are
affected to measure the above said performance
measures.
3.2 POSSIBLE SOLUTIONS
It includes the use of message relay boxes
for collection, classification and scheduling messages
and specific roadside gateways for proper data
propagation. Moreover, maintaining Quality-of-
Service (QoS) in VSNs is challenging while nodes
are mobile. IEEE 802.15.4 defines unslotted
CSMA/CA channel access protocol which enables
contending wireless sensors to access the shared
channel without providing service differentiation at
the MAC layer. This lack of providing service
differentiation has hindered the development of
service differentiation model for rate-sensitive
applications.
In this paper, a suitable scheduling scheme
among various scheduler schemes is selected at MAC
layer for assorted connections with varied QoS
requirements. Therefore, a priority or weighted
function is requested for every link established in the
system and depending on wireless channel quality,
service priority across layers and QoS satisfaction
every connection is updated dynamically. The
proposed scheduling model is flexible, scalable,
easily implementable, guarantees QoS and utilizes
the wireless bandwidth efficiently.
MAC layer controls medium sharing and all upper
layer protocols related to that for QoS provisioning.
QoS cannot be achieved at network, transport or
higher layers without support of MAC protocol. The
aim of this research consists of supporting Quality of
Service (QoS) in a vehicular sensor environment by
integrating traffic differentiation and scheduling
mechanisms. To address QoS provisioning, the
research uses the model of Service Differentiation.
Service differentiation has two stages: (i) assigning
priority, and (ii) differentiation between priority
levels. The QoS is ensured using Queue Scheduling.
A better performance is achieved by assigning
appropriate priority to the traffic since higher priority
is always served first.
3.3 DIFFERENTIATION IN VSN
The first step for supporting Quality-of-
Service (QoS) in VSNs consists of including
differentiation mechanism in theMAC layer, since
several types of events with different significance and
severity may happen in the roads. Moreover, other
non-related road traffic is to be supported by the
sensor network such as pollution control, urban
application etc. The differentiation mechanism will
not retransmit packets as they arrive but it consists of:
 Collecting and classifying data from cars and
other neighbor platforms
 Marking and storing data in different queues
characterized with different priority levels.
3.4 SCHEDULING IN VSN
The scheduling in VSN is achieved and
tested using the queuing methods such as FIFO,
priority queue, RED, WRR, DRR. The proposed
solution is evaluated by multiple scenarios using
NS2.35 simulation. The simulation results show the
proposed system improves the QoS when compared
with standard system. The proposed system can
achieve fast categorization of incoming traffic at
RSU from the vehicles and treat them according to
their prioritization assigned for each traffic type. The
extensive simulation results further justify the
usefulness of proposed system to get better QoS in
VSN.
IV. SERVICE DIFFERENTIATION
AND PRIORITIZATION
4.1 METHODOLOGY
A low-cost and energy efficient IEEE
802.15.4 radio technology is used in nodes. These
nodes communicate with road side units positioned
over small distances along road side. In this
simulation, FIFO, priority queue, RED, WRR, DRR
scheduling algorithms are used to determine how
quality of service can be enhanced.
4.2 RESEARCH CONSTRAINTS
The research has been simulated using

NS2.35 on 6 lanes with 2 Coordinators and 4 Routers
along the road. For simplicity, straight roads are
considered and turns, comers and exits are omitted in
our proposed high-way model. Each vehicle in the
system is assumed to be equipped with a vehicle
sensor system to send vehicle's information request to
the RSU. For thorough testing the proposed scheme
has been applied on packets of different sizes such as
500, 1024, 500, 2500 as shown in Table. 1It is
assumed that vehicles running on the road with
constant driving behaviors, such as lane change,
acceleration, and overtaking, deceleration. Vehicles
are moving in constant speed and moving in their
lane. After the distance d1, d2, d3 is reached, the
vehicle may wait for constant time period for signals
on the road. Multiple scenarios are simulated
concurrently and compared.
Table 1
If mobile node is out of its parent
transmission range, then it connects to the closer
node and it continues with transmission. The network
structure simulated using NS2.35 is shown in figure
1.
Figure 1. Network Scenario
4.3 END-TO-END DELAY
End-to-end delay is used to measure
network delay faced by every packet. It is measured
as time interval from message transmission to the
message complete delivery at receiving end. Figure 2
shows the end-to-end delay result of the simulated
scenario using differentiation and sheduling
mechanisms. The DRR and WRR queue have less
end-to-end delay as compared to others in this
simulation.
Figure 2. End-end delay.
4.4 DELAY:
Delay is measured when packets of data take
more time than expected to reach destination. Figure
3 shows the measurement for overall global delay for
FIFO, priority queue, RED, WRR and DRR
scheduling schemes. Multiple factors contribute to
delay such as network congestion and packet
processing at each link till the final destination
arrives. Their effects can be minimized by selecting a
proper scheduling scheme. It is observed that DRR
and WRR have a minimum values compared to RED,
priority queue and FIFO. FIFO has the maximum
delay as simulated in the scenario.
PARAMETERS VALUES
Transmission band 2.4 Ghz
No.of routers 4
No. of coordinators 2
Traffic types CBR, Poission,
Exponential.
Packet size 512, 1024, 1500,2500.

Figure 3
4.5 DATA TRAFFIC SENT
Data traffic sent is expressed as the total
number of bits sent from source to destination per
unit time. Data traffic sent includes all data bits
irrespective of the condition whether these bits reach
the destination or not. Figure 4 indicates the data
traffic sent for FIFO, priority queue, RED, WRR and
DRR scheduling schemes. It is noticed that data sent
is maximum in case of DRR scheduling scheme as
packets are held back those exceed from the packet
length for the next round of scheduler. Those packets
exceeds from packet length can be calculated by
subtracting maximum packet size number from
packet length. Also DRR scheduling scheme can
handle variable packet size without knowledge of
their mean size. It achieves a better generalized
processor sharing (GPS) approximation without prior
knowledge of mean packet size of each connection
Also it has been noticed that data traffic sent is
minimum in FIFO because it works as first in first
out.
Figure 4
4.6 DATA TRAFFIC RECEIVED
Data traffic received can be expressed as
"number of bits of the data received per unit time".
Figure 5 depicts the data traffic received for the
FIFO, priority queue, RED, WRR and DRR
scheduling methodologies respectively in vehicular
sensor network. It noticeably point out that the data
traffic received is maximum in case of WRR
scheduling scheme because each packet flow or
connection has its own packet queue in a network
interface card. WRR serves the amount of packets for
every nonempty queue.
Also it is noted that data traffic received is
minimum in case of DRR scheduling scheme as
packets are held back those exceed from the packet
length for the next round of the scheduler. Those
packets exceeds from packet length can be calculated
by subtracting maximum packet size number from
packet length. Although DRR scheduling scheme can
handle variable packet size without knowledge of
their mean size.

Figure 5
4.7 THROUGHPUT
Throughput is the actual amount of data
transmitted starting from the source to the destination
within a given time (seconds). The importance of
analyzing this QoS parameter is because the
increased numbers of users of the wireless medium is
the reason for increased possibility of interference.
Throughput is quantified with various factors
including packet collisions, barrier between nodes
and the differentiation and scheduling mechanism
used. It gives a general idea of the overall throughput
of the system. Figure 6 shows that the maximum
throughput is achieved using DRR scheduling
mechanism, the WRR has second highest throughput
and the priority queue has third highest throughput
while FIFO and RED scheduling mechanism has the
lowest throughput. The reason for this is because
DRR scheduling mechanism is communicating more
efficiently as compared to other mechanisms. Also in
DRR mechanism distributed total load of the network
among the ZigBee Routers as a result of which
collisions and packet drops are decreased.
Figure 6
V. CONCLUSION
This work introduces new differentiated
service Approaches and tasks accomplished by
scheduling disciplines and highlights the impact of
these techniques on the QoS support in mobile sensor
networks. We compared the use of different quality
control algorithms for prioritizing and scheduling of
traffic received from vehicles in ZigBee environment.
On the basis of our measurements and results, DRR
and WRR have increased QoS by decreasing the
collision, packet drop rate and delay. This research
can be further extended by implementing existing
modern priority and scheduling mechanism or by
presenting innovative new algorithm for particular
scenario of vehicular sensor networks.
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