International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
Minimize Service Interruptions and Power
Consumption in Handoff
Ankur Soni#1,S.N. Jarholiya#2
1
2
PG scholar, Gyan Ganga Institute of Technology & Science Jabalpur
Asst. Professor, Gyan Ganga Institute of Technology & Science Jabalpur
Abstract- In Wireless Access the terminal devices
are usually highly mobile vehicles or transportation
tools, which handoff much more frequently than those in
wireless networks. However, the quality of network
services like VoIP and multimedia streaming will be
seriously influenced by too frequent handoff or too long
handoff latency. Due to this reason traditional handoff
mechanisms no longer satisfy the need of vehicular
communications. At present, most existing fast handoff
mechanisms are based on the signal strength of the APs,
which does not suit vehicular communications fully.
Generally speaking, the regional traffic in Vehicular Adhoc Networks (VANETs) keep changing and varying.
This paper presents the Quality Scan scheme, an
efficient pre-scanning method to improve the handoff
performance in VANETs. With the attempt to reduce the
handoff latency and maintain the load balance among
the APs, our proposed scheme regularly collects the
loading states of the nodes by the pre-established AP
controller (APC), and predicts the network traffic of the
next moment. Moreover, Mobile Nodes (MNs) can select
the most suitable AP for the best QoS according to the
parameters received by passive scanning, while more
QoS parameters should participate in cooperation. The
simulation result proves that our proposed scheme not
only maintains the QoS and the load balance among the
nodes, but also further enhances the handoff
performance in VANETs.
Keywords: Wireless Access in Vehicular Environments
(WAVE), Vehicular Ad-hoc Networks (VANETs), AP
controller (APC), Mobile Nodes (MNs), Quality Scan,
Cooperative Networks, APC, QoS
I.
INTRODUCTION
The development of the Internet from wired to
wireless has far exceeded the original estimations.
People nowadays are able to access to the Internet
via various web-based devices at anytime and
anywhere. Thanks to the decrease of infrastructure
deployment cost and the increase of the available
bandwidth, the advancement of wireless networks
never ceases. The network has been widely applied to
different fields and VANET is one of them. In recent
years, automobile manufacturers and the academia
have made their great effort in integrating wireless
network with Intelligent Transportation System (ITS)
with the aim to increase driving safety, to assist
ISSN: 2231-5381
drivers, and to adjust and optimize the vehicles. Such
applications include driving safety, transportation
efficiency,
information
and
entertainment
acquirement. [1][2][3].
However, in VANETs, the terminal devices are
usually highly mobile vehicles or transportation tools,
which handoff much more frequently than those in
wireless networks. In addition, the quality of network
services, like VoIP and multimedia streaming, will be
seriously influenced by too frequent handoff or too
long handoff latency [6]. Consequently, traditional
handoff mechanisms cannot satisfy the VANETs any
more [7].
On busy roads or in rush hours in VANETs, the
increase of vehicle number will cause the network
congestion and degrade the performance of vehicular
communications. The simplest solution is usually to
deploy extra APs in this road section to distribute the
traffic. However, most of the present fast handoff
mechanisms are based on the APs’ signal strength,
which is not efficient for the VANETs. Therefore, to
reduce the handoff latency and maintain the load
balance of the nodes, we propose an efficient prescanning
scheme
for
handoff
performance
improvement in Cooperative Vehicular Networks.
II. RELATED WORKS
[10]Has defined the connection between voice quality
and delay in VoIP service. The delays below 150ms
are transparent to users while the delay ranging from
150 to 400ms still can be tolerable. However, the
delays above 400ms are absolutely unbearable and
unacceptable. Too long handoff latency may degrade
the QoS of the MNs, and terminate the connections.
[8][9] For this reason, the handoff latency tests on
different brands of APs and MNs display that the
probe delay is the biggest part in the handoff delay
time, and the handoff latency resulted from the
scanning occupies more than 90%. Two probe timers
of the active scanning can be given by Min Channel
Time and Max Channel Time, whose definitions differ
with different devices. If the probe timer is TA and the
number of available channels is N, the probe timer of
TA can be expressed by (1).
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International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
NMinChannelTim≤ TA ≤ N MaxChannelTime
(1)
Many mechanisms have been proposed to reduce the
probe delay, which causes most of the link layer
handoff latency. Next, we will introduce several
enlightened mechanisms.
2.1 Introduction to Neighbour Graphs
Mishra et al. [11] established the Neighbour Graph
and collects the topology of the MNs temporarily by
the cache mechanism. The Neighbour Graph can
record the adjacent relations among the APs and the
channel adopted by each AP. In addition, the APs
recorded in the neighbour list are the next candidate
handoff APs. The Neighbour Graph reduces not only
the number of channels scanned, but also the time
wasted in scanning the nonexistent channels.
However, too many adjacent APs will generate a great
amount of data, which influences the performance of
the Neighbour Graph.
2.2 Introduction to Sync Scan
Proposed by Ramani and Savage [12], Sync Scan
synchronizes the announcement of beacon frames for
the APs to send the beacon frames sequentially in
fixed intervals. Sync Scan can track the signal strength
from the neighbouring base stations continuously with
low cost. As a full channel pre scan mechanism, Sync
scan reduces not only the time in searching for
available channels but also the handoff latency.
2.3 Introduction to Deuce Scan
As described in [13], the MNs keep tracking the signal
strength of the neighboring APs by beacons. After the
first full channel prescan, DeuceScan executes a
partial prescan and establishes a spatiotemporal graph
to record the received signal strength (ΔRSS) of each
channel, which implies the possible directions of the
MNs. According to the spatiotemporal information
and the signal strength variation, the AP with the
optimal signal strength will be chosen. DeuceScan
performs well in decreasing the handoff latency and
provides better handoff efficiency than SynScan.
2.4 Enhanced Distributed Channel Access (EDCA)
The contention-based EDCA (Enhanced Distributed
Channel Access) is similar to the DCF mode in
802.11. By eight levels of priority, EDCA supports
four kinds of Access Categories (AC). For different
ACs to have different levels of priority, different types
of traffic use different contention parameters,
including Arbitration Inter-Frame Space (AIFS) and
Contention Window (CW). The data of different ACs
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are mapping to their corresponding queues. The types
of traffic from high to low priority are Voice, Video,
Best Effort and Background.
III. THE PROPOSED SCHEME
In VANETs, high traffic on busy roads or at the toll
stations leads to high network traffic. Usually, the
easiest way to solve the network congestion is to add
extra APs in this region to distribute the network flow.
Most fast handoff mechanisms discover the next AP
based on the signal strength, which might cause the
load imbalance. Owing to the positions, the signal
strength of some APs will be stronger to the MNs than
others. Thus, to consider the signal strength only is not
an efficient method to distribute the network flow.
This paper presents the Quality Scan scheme, an
efficient pre-scanning method to enhance the handoff
performance in VANETs by considers the states of
multiple APs, including the signal strength and the
usage or busy level of the APs. To achieve a network
environment of high quality, we propose to group the
local APs and make good use of them to distribute the
network traffic when necessary. In our proposed
scheme, an AP Controller (APC) that collects the
information of the APs in the subnets regularly is
integrated with Transportation Information System
(TIS) that obtains the velocity and the traffic flow in
this region. With the information gathered for
calculation, we can predict the future network flow.
The APC provides the calculation result for the APs,
and the APs will send the information of the adjacent
APs to the MNs by beacon frames. According to the
information, the MNs can obtain the information of
the neighboring APs by passive prescan, determine the
next handoff AP and maintain the load balance. In this
way, we can omit the active scanning during the
handoff and reduce the handoff latency greatly. In
addition, to choose a suitable AP can distribute the
network flow efficiently and enhance the transmission
quality.
3.1
Choosing the Best AP According to the
Received Scanned Information
As illustrated in Figure 1, every AP periodically sends
the information of its channel, location, queue state,
etc. to the APC. According to the information of the
APs, the APC calculates the current and future loading
states of each AP, which will be attached in the
beacon frames periodically sent by the APs. In
addition to the detail of the neighboring APs, this
information further includes the EDCA parameter set
elements, the busy level and the queue states of the
APs. With the neighbor list, the APC sends suitable
information to the APs at different locations.
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International Journal of Engineering Trends and Technology- Volume4Issue3- 2013


 



Figure 1. System Architecture
3.2 Traffic Anticipation of the Local AP Groups
We propose to group several APs in one zone as the
traffic anticipation scope. As shown in Figure 2, there
are Zone A, B and C, each of which is constructed by
three APs to serve the MNs in their zones. Figure 2
reveals that the cars in Zone B will move into Zone C
in the near future and will be in the service of the APs
in Zone C. By grouping the local APs, our proposed
scheme can predict the possible network traffic in a
zone according to the service of the previous local
group. For example, the traffic of the APs in Zone B
can be the reference value of that in Zone C in the
near future. Also, it is sure there is no traffic from
Zone A to Zone B at the next moment. Our purpose is
to balance the busy level rate (ρ) of the local APs, i.e.
to achieve the load balance of multiple APs.
As for the busy level of the server, Queuing theory
described in [14] has defined several basic parameters
as listed in the following:
Wq : the waiting time in the queue. 
W : the time spent in the system. 
Lq : the number of packets in the queue. 
L : the number of packets in the system. 
The first proof to Queuing theory was published in
1960 by John D. C. Little and therefore called Little’s
Formulas, as given in (2) and (3). When there is only
one server, L-Lq in (4) denotes the number of packets
in the server and we can analyze the busy level of the
server by Little’s Formulas as given in (5). Supposing
there are c servers providing services simultaneously,
the average utilization rate of the servers can be given
by (6). With the attempt to balance the busy level of
the APs and to achieve the load balance of multiple
APs, our proposed scheme therefore considers the
number of packets in the queue, Lq, and the busy
level of each AP, ρ, in determining the next handoff
AP.
L  W
(2)
Lq  Wq
(3)
L  Lq  W  Wq   (W  Wq )   
1 

 
(4)



(5)


c
(6)
3.3 Service-Oriented Plus Traffic-predicted Regional
Load Balance
Figure The local 2. AP groups



λ : the arrival rate (the number of
arrival packets within a timeslot). 

μ : the service rate (the number of
completed services within a timeslot). 
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In our proposed service-oriented mechanism, the
optimal handoff AP is determined based on the
loading states of each AP because the average
processing rate of the queues in each AP is different
and so is the queue length of each AP. Therefore,
besides the QoS, the loading states of the APs must be
taken into account to choose the suitable handoff AP.
In order to achieve the load balance in the region and
guarantee different QoS s for different types of traffic,
we consider the queue length and the state of each
queue. The relation between ρ and Lq is explained in
the following. ρ<1 means that the AP’s processing rate
of the queue is faster than the packet enqueuing rate
and Lq inclines to decrease. ρ≧1 symbolizes that the
AP’s processing rate of the queue is slower than the
packet enqueuing rate and Lq inclines to increase. ρ=0
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International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
denotes that no packet enters this queue. It is possible
that there is no packet in this queue currently, or the
number of packets remains the same because of no
processing. The optimal handoff AP should be the one
with the smaller ρ and the smaller Lq. Therefore, we
use (7) to define a K value, which is the smaller the
better. The definitions of the K value in the queues are
given from (8) to (11).
K = Lq × 
(7)
KVO = Lqvo × vo
(8)
KVI = Lqvi × vi
(9)
KBE = LqBE × BE
KBE = LqBE × BE
KBK = LqBK × BK
(15)
(16)
(10)
Figure 3. Traffic anticipation
KBK = LqBK × BK
(11)
3.5 Handoff Conditions
3.4 Adjusting the K Value According to the Traffic
Anticipation
As shown in Figure 3, because the queues of different
traffic types at the ti moment and the ti+1 moment are
dissimilar, we consider the leaving and entering
vehicles from ti to ti+1, just like the leaving V1 and the
entering V4 and V5. To anticipate the upcoming
traffic and calculate the K value based on the
anticipation, we can enhance the handoff accuracy
greatly. Therefore, the parameter Lq will be adjusted
dynamically. The queue length at ti is defined as Lqti
and the queue length at ti+1 is defined as Lqti+1. Lqti+1
is equal to Lqti plus the number of entering packets
from ti to ti+1, minus the original number of packets
from ti to ti+1, minus the packet number of the leaving
vehicles in the queue and plus the number of packets
that is predicted to enter the queue. The equation can
be expressed by (12).
The APC sends the calculation result to the APs,
which forward the information to the designated MN
by beacon frames. According to its demanded service
type, the MN checks the K value and selects the
suitable handoff AP. When the signal strength is as
weak as the threshold, the active scanning is directly
omitted. If the MN needs various types of service
simultaneously, we regard the service that takes the
longest time in the past T time as the main one and
select the AP with the optimal K value.
To make sure that the received information comes
from effective APs, we use the signal variation to
affirm the directions of the APs. Supposing that RSSti
and RSSti-1 denote the AP’s signal strength received
by V at ti and ti-1, the signal variation can be
expressed by (17).
AP
AP
 RSS tiAP
ti  RSS ti
1
(17)
The handoff condition can be defined as (18).
Lqi 1  Lqti  (   )(t i 1  t i )  LLqti ~ti 1 


PLq ti ~ti 1
N
(12)

Lqti+1 : the expected queue length at ti+1. 

Lqti

LLqti ~ti+1: the packet number of the leaving
AP
ti > 0 and min {K in neighbour list}
(18)
: the queue length at ti. 
vehicles in the queue from ti to ti+1. 

PLqti : the predicted number of entering
packets from ti to ti+1. 

 N : the number of APs in the region.
To define Lq’=Lqti+1, the K value in each queue at ti+1
can be revised to (14) to (17).
KVO = Lqvo × vo
(13)
KVI = Lqvi × vi
(14)
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IV. PERFORMANCE ANALYSIS
In this section, we use MATLAB7.10 for simulation
and comparison between the existing handoff
mechanisms and our Quality Scan scheme. The steps
and parameters are described below.
4.1 Analyses of Moving Speed and Handoff Latency
According to the simulation results and data given in
[10], we define the parameters, including Min Channel
Time, Max Channel Time, Channel Switch Time and
so on. As defined in IEEE802.11p, the number of
channels is 7 and the number of the adjacent APs is 4.
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International Journal of Engineering Trends and Technology- Volume4Issue3- 2013
The following part explores the relation between the
moving speed, from 18km/hr to 108km/hr, and the
handoff latency. The detailed simulation parameters
are listed in Table 1.
way, we reduce the handoff latency and balance the
load of each AP in the system. Therefore, the
bandwidth of each zone can be utilized more
efficiently.
Table 1 Simulation Parameters
Parameter
Value
MinChannelTime
5ms
MaxChannelTime
11ms
Channel switch time
19ms
Authentication
1ms
Reassociation
1ms
Figure 4. Relation graph of moving speed and handoff latency
Number of channels
7
Number of neighbouring APs
4
Moving speed of vehicles
18~108 km/hr
The relation charts of the moving speed and the
handoff latency are given in Figure 4 and 5, which
compare IEEE 802.11 and other fast handoff
mechanisms with our Quality Scan scheme. While
meeting the handoff conditions, IEEE 802.11 and the
Neighbor Graph have to execute active scanning,
which worsens the handoff latency. As for the
Neighbor Graph technique, the handoff latency differs
with the number of the neighbouring APs in the
neighbour list. The more APs, the more serious the
latency will be. In addition, with the increase of the
moving speed, the signal quality decades owing to the
influence of Doppler Effect, and the bit error rate
increases as well. Therefore, we can conclude that the
handoff latency increases with the increase of the
moving speed.
Figure 5 further makes a comparison between Sync
Scan, Deuce Scan and our proposed Quality Scan. All
the three are passive scanning schemes that determine
the next handoff AP according to the received beacon
frames and thus omit the handoff latency in
discovering channels. It is revealed that that compared
with the partial scanning Deuce Scan and Quality
Scan, Sync Scan, the full channel passive scanning,
has longer handoff latency. This result shows that by
omitting the step in discovering channels in IEEE
802.11, the passive scanning can reduce the handoff
latency and improve the QoS efficiently. The
mechanism we proposed is service-oriented and
considers the loading of each AP in the zone. In this
ISSN: 2231-5381
Figure 5. Relation graph of moving speed and handoff latency
V. CONCLUSION
This paper presents an efficient pre-scanning scheme
for the improvement of handoff performance in
VANETs. To predict the network traffic of the next
moment, the APC gathers the loading states of the
APs in each subnet and obtains the traffic and moving
speed via TIS for calculation. The APs sends the
calculation result and the information of the
neighboring APs to the MNs so that the MNs can
select the optimal AP for the best QoS. This scheme
not only enhances the QoS, but also considers the load
balance of regional APs. By obtaining related
information by passive scanning and determining the
handoff AP in advance, we can abridge the scanning
in traditional handoff procedure and reduce the
handoff latency efficiently. In the future work, we
will discuss the relation of vehicles density to balance
index and do more simulation to verify the
performance in VANETs.
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