QoS Oriented Coding For Mobility Constraint in Wireless Networks

IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 6, Ver. III (Nov - Dec .2015), PP 55-66
www.iosrjournals.org
DOI: 10.9790/2834-10635566 www.iosrjournals.org 55 | Page
QoS Oriented Coding For Mobility Constraint in Wireless
Networks
Sudha Arvind1
, Dr.V.D.Mytri2
1
(Assoc. Prof. ECE Dept., CMRTC Hyd. / JNTU Hyderabad, India)
2
(Principal, Appa Institute of Technology, Gulbarga/ VTU, Karnataka, India)
Abstract: With the increase in service demand in wireless network, new quality oriented communication
approach are in need. In the process of wireless communication, user mobility is of greater importance. As the
users are moved at a random fashion, the impact of offered quality of service is degraded. Towards providing
quality of service in mobility scenario an improved QoE based communication model, under user mobility in
random mode is proposed. Wherein QoE coding was used as a traffic control mechanism for service level
coding, the offered blockage is neglected. Under heterogeneous condition with node mobility, is highly affected.
The effect of mobility on the offered quality of service under heterogeneous nodes is developed. The offered
quality of service for the communication network, under variant data traffic forwarding condition is observed to
be improved.
Keywords: Quality of service, wireless network, random mobility, traffic blockage control
I. Introduction
Communication technology has become constraint for future requirement as the growth of the
communication industry is larger and growing complex. Different modes as mobile, wired, wireless, adhoc,
supports growth of the communication industry but with certain limits. Mobility plays a vital role in wireless
networks, where it is necessary to satisfy the requirements of the modern world. It is now a challenge to provide
the seamless flow of information without re-modifying the existing infrastructure. The most challenging aspect
is to provide mobility management for real time communication services in wireless networks with demanded
quality of service. Various approaches are proposed in earlier to manage the mobility in wireless networks. In
[1], the current state of the art for mobility management for next generation IP-based wireless systems with new
wireless network architecture for mobility management were introduced. Towards provisioning of quality of
service in such network [2] outlines a review for QoS based coding for wireless network. Various quality
parameter such as the Mobility Management, Security and Reliability, and Power Consumption etc, were
presented. The MAC control operation for mobility constraint was presented. The basic issues involved in
handoff management [3] aspect of general mobility management in wireless communication systems is focused.
The issue of delay, security and its management is derived. Towards the enhancement in mobility management
for current and future communication networks, and the integration of heterogeneous networks for a smooth
handoff and better Quality of Service (QoS), in the context of next evolutionary step for wireless
communication networks a macro and micro mobility solutions [4].for Mobile IP is presented. In the focus of
providing higher network performance in next generation communication, an adaptive resource allocation wrt.
Bandwidth allocation [5] was proposed. Towards providing seamless mobility of user with higher degree of
mobility an overview of the “off-path” QoS model to supplement PMIPv6 was observed in [6]. With the
objective of heterogeneity, in [7] three alternative architectures for an all-IP network integrating different
wireless technologies using IP and its associated service models were presented. The first architecture, called
ISB, is based on a combination of DiffServ and IntServ models appropriate for low-bandwidth 3G cellular
networks with significant resource management capabilities.
The second architecture, called DSB, is purely based on the DiffServ model targeted for high-
bandwidth wireless LANs with little resource management capabilities. The last architecture, called AIP,
combines ISB and DSB architectures to facilitate the integration of wireless LAN and3G cellular networks
towards a uniform architecture for all-IP wireless networks. [8], investigate the integration of RSVP and
aRSVP-like flow reservation scheme in wireless LANs, as an end-to-end solution for QoS guarantee in wired-
cum-wireless networks. A RSVP-like flow reservation and admission control scheme for IEEE 802.11 wireless
LAN. Using WRESV, wired/wireless integration for supporting multicast session, mobility management, and
admission control is proposed. A seamless service on practical application such as multimedia and voice transfer
were observed in [9, 10 11]. To describe a protocols for mobility management for PLMN-based networks,
Mobile IP, wireless ATM, and satellite networks an integration of these networks is discussed in [12,13], in
context to next evolutionary step of wireless communications networks. The advantage of the heterogeneous
network is outlined in this approach. As heterogeneous networks are more advantageous due to variant offered

QoS Oriented Coding For Mobility Constraint in Wireless Networks
resources, the traffic overhead is a major issue. Due to the traffic blockage per node, the quality of the service
provided is minimized. In this paper, to offer quality of service under dynamic node movement, following
heterogeneous condition, a new dual bound control approach is proposed. In the recent development of
providing quality of experience (QoE) based coding was outlined in [14]. This approach provides a higher
probability of data transmission based on demanded quality of service. The offered approach is simpler and
quite robust in variation of traffic conditions. However the control operation was observed to be controlling the
upper bound limit of the node buffering. Under the heterogeneous network with node mobile, incoming traffics
are not fixed, hence a upper bound controlling of traffic will lead to node failure condition. With this objective
in this paper a new dual bound control logic is proposed. The approach presents a new controlling of offered
traffic under dynamic variation of node resources due to heterogeneity condition. This suggestive approach
present a novel approach of providing a traffic buffering controlling under node dynamicity and mobility
dynamicity condition. To present the stated approach, the rest of the paper is organized as follows: section II
gives the details about the system model. The details about the earlier QoE-aware framework was given in
section III, section IV provides the details about the proposed approach, section V provides simulation analysis
and finally section VI concludes the paper. (10)
II. System Model
To develop a wireless network with heterogeneous characteristic a system model is developed. A
wireless network considering a topology of randomly distributed nodes, defined by a set of nodes , with a set
of links, . The source node link , is defined as , and the Destination node as . For a link,
and representing sets of
edges corresponding to incoming and outgoing links for a node n, a network is defined, assuming to carry a set
of traffic data, where data „i' generated by a source node Si is sent to the destination node . In general, a
source node generates a data i traffic, and intermediated node realy to its neighbors following it to the
destination node. Let denote the set of packets forwarded by node n, and
the set of data not forwarded by node n. obviously , for the nodes that are not
the sinks of any data. Due to the broadcast nature of the wireless interface, a link whose Destination is in the
proximity of an unintended co-channel transmitter is interfered if the corresponding reception and transmission
occur simultaneously. Let be the set of nodes whose transmission interferes with (the reception of)
link l. Similarly, let denote the set of links that are interfered by the transmission of node n. Note
that if , then , and vice versa.
20 30 40 50 60 70 80 90 100 110 120
20
30
40
50
60
70
80
90
100
110
120
1
2
3
4
5
6
Network in random topology with links
x--->distance in meters
y--->distanceinmeters
Fig.1. Network topology with probabilities of data flow.
For the given network in figure 1, with a network consisting of six nodes, with all links working in
bidirectional mode, the communication and the interference distances are assumed to be random. The

interference model is considered to be , and
. It is also assumed that the channel between any pair of nodes is quasi-static which is
observed in the case of mobility scenario. Thus, the interference relations are time-invariant, and packet drops
are entirely due to collisions at the MAC layer and buffering over head at the link node. With a persistence
probability of node n; that is, node n transmits with probability of , with packets buffering in its queue.
For a given node n, the probability of forwarding a packet through link , is denoted by where
. Thus, the access probability of link l is given by with
. Considering the mobility of each node at a rate of r
from its direct neighbor, this buffering overhead is dynamic. Over such communication model, with a
source with link and mobility factor Rm a QoS based communication is focused.
III. QoS Communication Framework
It is required to achieve the demanded quality of service for each communication in a wireless network.
To achieve the objective of quality of service in wireless network, various resource control operations were
developed in past. Whereas in heterogeneous network each node is offered with a dynamic forwarding rate,
resource scheduler were used to control the available resources. The goal for a packet scheduler, which runs at
every node, is to maximize the overall achievable user perceived QoE and fairness among competing flows
under given resource constraints. The scheduler minimizes a cost function which denotes the impact of packet
drop decisions on the QoE of individual flows. Such packet drop decisions will be triggered by an active queue
management algorithm in case it is required to achieve the demanded quality of service for each communication
in a wireless network. To achieve the objective of quality of service in wireless network, various resource
control operations were developed in past. Wherein in heterogeneous network each node is offered with a
dynamic forwarding rate, resource scheduler were used to control the available resources. The goal for a packet
scheduler, which runs at every node, is to maximize the overall achievable user perceived QoE and fairness
among competing flows under given resource constraints. The scheduler minimizes a cost function which
denotes the impact of packet drop decisions on the QoE of individual flows. Such packet drop decisions will be
triggered by an active queue management algorithm in case resource limitations are discovered (e.g., once the
buffer utilization reaches a pre-defined threshold), and they are triggered when the buffers at each node reach a
certain threshold. The scheduler then determines, based on the out-going data rate and buffer fill rate, how many
packets need to be dropped and the combination that minimizes the QoE impact on each flow. Under the
constraint of mobility, these factors are scheduled with respect to the node deviation from its location which
variate the traffic flow offered over each node.
A. Operation overview
For the communication of forwarded data with dynamic resource constraints, Fig.2 illustrates a packet
scheduling strategy. The scheduler calculates all packet drop combinations that would satisfy the required buffer
reduction. For each of these combinations, an estimation of the QoE reduction for each flow is calculated. To
obtain these QoE estimates, each node keeps local statistics of the packet drop rate and experienced delay at the
buffer, and makes use of the QoS-related metrics sent by its neighbors according to the mechanism described in
[14]. The scheduler then drops the packets whose combination results in the smallest total QoE decrease. In
order to provide fairness without dropping packets from some flows excessively, we add an additional constraint
using the standard deviations of QoE decrease resulting from the drop operations. Thus, the scheduler favors
packet drop combinations that contribute to the smallest variation of QoE changes across the various flows.

Fig.2.Impact of a packet combination in the buffer of a node.
B. QoE-aware cost function
In order to obtain a set of suitable packet drop combinations from the buffer, an amount of B bits were defined to
be dropped from the queue based on the buffer fill rate , the outgoing data rate , and the buffer fullness,
: (1)
For a given range of [ , ] bits, a set Si of i packets is selected, with the constraint following,
(2)
i.e., the total size of all packets in a set must be within a range of and around B. The optimal packet drop
combination can be found by minimizing a ccost function, Q(p), calculated for each packet drop combination p. Specifically,
for n flows, Q(p) is given by,
(3)
Where is the QoE reduction for flow I due to the dropping of p, and is a weighting coefficient for
preferential packet dropping from specific flows. Through , the scheduler can be configured to drop more packets from
certain classes of traffic, such as data streams. Furthermore, is the rate reduction of flow I due to the dropping of p,
while λ is a weighting factor that control show aggressively the scheduler drops packets. Finally, is the
standard deviation of the QoE reduction over all flows, while μ determines the weight of the standard deviation in expression
(3). The generic model in expression (3) can be applied directly to all packets present in the buffer. However, to reduce the
processing time, a pre-selection of packet combinations can be done based on a target buffer size. The rate reduction
component is thus unnecessary, resulting in the following minimization function,
(4)
C. Scheduling scheme
To achieve the objective of quality of service under this queue constraint a queue management algorithm was
developed to activate the operation in the case of the following two events: (i) the buffer size exceeds a defined threshold;
(ii) a packet is to be dropped due to in sufficient buffer space. Once the queue management algorithm is invoked, it first
begins by profiling the packets in the buffer, to determine their size, the distortion impact to f video packets (quantifying
how much they will affect the perceived QoE), and to make a list of which flows are traversing the node. Then, expression
(1) is applied to determine the amount of bytes that need to be dropped from the buffer. Next, the scheduler determines those
packet combinations that satisfy expression (2). The number of combinations, in a full-lookup method, is equal to 2n, where
n is the number of packets in the buffer. With the list of packet drop combinations satisfying expression (2), the QoE
reduction per combination is calculated for each flow using the QoE expressions described in [14]. To this end, the algorithm
gathers all packets that belong to each flow, and computes the new flow parameters that would result from dropping those

packets (e.g., the new packet loss or the new total distortion). With these new parameters, the QoE of that flow is updated,
and is determined as the difference between the old and new flow‟s QoE values. The overall total and mean
reduction in QoE is then calculated from these QoE values, as well as their standard deviation (σ). Expression (4) is applied
to find the packet combination with the lowest impact on QoE. The scheduler then drops this combination from the buffer.
Wherein this approach of packet scheduling in observed to provide higher degree of quality of service in wireless network,
the mobility of network node in the network, impacts the offered traffic per node. Hence, to incorporate a mobility constrain
to the offered QoE in provision to quality of service in wireless network, a mobility constraint QoE is proposed.
IV. Proposed Approach
In the approach of traffic management, each node routes the packet based on the offered link and the allocated
data rate R. wherein in the conventional model of QoE based communication for quality provisioning, it is observed that for
each node buffer status B, the forwarding rate is defined by the current buffer level of each node defined by Bcurr. The
traffic rate is allocated based on state of this buffer level. However with the node in heterogeneous characteristic and
mobility allocation of rate is not effective for such approach. Hence in this proposed work a probabilistic based rate
allocation approach is proposed to constraint the quality of service in wireless network. In the process of rate controlled
traffic controlling approach, the flow control is derived by referring to the back pressure at a given node defined by the
QoEBp-signal as,
(5)
Based on the QoEBp status, the allocated data rate is then set as,
(6)
In this approach QoEBp is set as „1‟ if traffic blockage is observed. A packet dropping is observed when the buffer
queue length has reached to higher limit value i.e. . When the stated condition satisfies the control
operation is activated in accordance to QoEBp value set. However the status signal is set high at the upper bounding limit of
the traffic blockage. Once the traffic blockage level reaching to Lmax. This approach, hence give a higher probability of total
node blockage in the network. This blockage rate would be very high under mobility scenario as nodes with different offered
traffic enter into the node communication range. Hence to avoid such node failure probability, an adaptive rate controlling
approach based on the computed probability of traffic blockage at buffer, rather to direct queue length correlation is
proposed. To develop the proposed probabilistic rate allocation a probability on traffic blockage is computed over the two
limiting values of and , with the node mobility rate Rm. As in consideration to two factors for traffic control,
the buffering value and the mobility factor we call this as dual bound control approach. The approach of dual tolerance limit
reduces the traffic blockage probability, and provides an initialization of traffic blockage controlling at an lower stage of data
buffering rather to upper limit as in conventional case. In such a coding approach the data rate for the flow of data from the
buffer is Ru. Considering the node mobility at Rm from the neighbor node, two cases arises, a) Node entering to a neighbor
node
b) Node moving away from a neighboring node
The incoming data are buffered into the buffer till the lower limit Limn is reached. Once the lower limit Limn is
reached the allocated data rate is controlled on the probability of traffic blockage at the buffer logic. The average level of
probability of traffic blockage at the node level is defined by,
(7)
Where,
The blockage rate for the buffer logic is defined as,
(8)

Where,
The current blockage at the buffer level is computed as,
(9)
Where,
L – Total Buffer length and,
For such buffer control operation the traffic blockage control signal QoEBp is defined into three logical values rather to two
values as stated in [15]. The QoEBp signal is assigned with, QoEBp=(-1, 0, 1), eqn. (5) then becomes,
QoEBp=0 indicates no mobility.
QoEBp= 1 indicates node moving away from node (10)
QoEBp=-1 indicates node moving towards the node.
In this case the allocated rate updated by the probability of traffic blockage and eqn.(6) is then updated as;
(11)
The data rate allocated for traffic blockage control based on probability of traffic blockage, leads to minimization
of node overhead. Due to initiation of traffic blockage controlling under node moving into the neighboring node offer a
higher rate of traffic flow under high traffic condition as well. The buffer queuing is reduced, which results in higher traffic
flow through each node. This improvises the overall network throughput, and intern improves the network performance. An
evaluation for the network performance based on the suggested control mechanism is carried out and obtained observations
are outlined in following section.
V. Simulation Results
To evaluate the operational efficiency of the proposed approach, a randomly distributed wireless sensor network is
simulated with control mechanism of rate control approach [15] and the proposed probabilistic control approach. A network
is defined with the following network parameter under consideration;
Network parameter Values
Node placement Random
Mobility Random
MAC protocol IEEE 802.11
Power allocation Random
Transmission range 40 units
Network area 200 x 200
Number of nodes 50-1000
Memory size / node, (M) 3M
Lmin 0.15xM
Lmax 0.75xM
Initial blockage probability 0.1
The simulation of the developed approach is iterated for three runs with the source moving and all other nodes
been kept constant. The impact of source mobility over the offered quality parameters were evaluated.

0 20 40 60 80 100 120
0
20
40
60
80
100
120
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Network with its coverage range
Fig.4 Network with its coverage range
The coverage region for each node is illustrated in figure 4. The bounded region for each node is computed by the
possible range coverage of each node. The communication range of 30 units is defined for each node, and the nodes in this
coverage range are used for the computation of a 1-hop neighbor.
0 20 40 60 80 100 120
0
20
40
60
80
100
120
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Cell links
Fig.5 cellular links
The network with 1-hop neighbor is illustrated in figure 5. Each node computes the distance of node with all other
nodes and distance below the communication range in defined as a 1-hop neighbor for the refereeing node. In the process of
neighbor discovery, the Nodes distance falling below to communication range are declared as direct neighbor, and each node
exchange their one hop neighbor details to formulate a link network.
0 20 40 60 80 100 120
0
20
40
60
80
100
120
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Communcating Path
Fig.6 Communication Path

With the one hop neighbor list, an routing protocol is developed to evaluate the route from a given source to the
destination. The routes are generated via the data exchange from source to destination in a broadcasting manner. A forward
and backward tracing method is applied to obtain the route parameters used for forwarding packets. Among generated
routes, path with minimum hops (i.e. shortest path) is selected for communication. The selected path for the developed
network is as illustrated in figure 6.
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
0
10
20
30
40
50
60
70
80
Forwarding
Overhead
Network Overhead Plot
Dual Bound Control Approach
QoE Approach
Fig.7 Network Overhead Plot
On the process of communication, data are forward for the path selected from source to destination via
intermediated nodes. During the process of data exchange the packets are buffered and based on the computed
traffic blockage level, packets are forwarded. To transfer the packets per node forwarding data rate is computed
and based on the traffic blockage level and allocated data rate packets are released or queued up in the node. The
queued up packets intern builds a overhead in the network. This overhead is defined as the number of packets
queued up for processing at each node. With the forwarding of packets from source to destination, at each
intermediate node, packets are buffered; the overhead hence observed is presented in figure 7. Due to
probabilistic coding, the traffic blockage is controlled over a limit of buffering as in contrast to buffering to a
higher level of buffer queue. Due to early controlling and probabilistic estimation, the overhead to such network
is observed to be minimized with the forwarding of packet.
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
0
10
20
30
40
50
60
70
80
90
100
Forwarding
Throughput
Network Throughput Plot
QoE Approach
Fig.8 Network Throughput Plot
The network throughout for the developed network is illustrated in figure 8. Due to higher data rate of
packet forwarding in the proposed approach, it is observed that more number of packets are received and hence
resulting in higher throughout tit e network. The throughput of these simulated systems is defined as the number
of packets generated over packets received for a observing communication time period. The thorug0ut is
observed to be same for the first 1-hop forwarding and as in such case the buffering is observed to be minimized
and hence allocated data rate is improved. However as the number of packet forwarding increases the traffic

blockage level increased resulting in decrement of throughout, which is maintained to be higher and linear in
case of probabilistic coding.
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
0
5
10
15
20
25
30
Forwarding
delay(Sec)
End to End Delay Plot
QoE Approach
Fig.9 End to End Delay Plot
The observed end-end delay factor for the
developed system is presented in figure 9. The
delay for the rate control approach is higher than
the probabilistic coding, as the buffering of data at
each node is minimized at the node level the
packets are released faster. The traffic blockage
level in such coding is developed in a probabilistic
manner, wherein packets buffered in queue based
on rate control approach, which builds the
forwarding delay for each node.
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
0
10
20
30
40
50
60
70
80
90
100
Forwarding
QueueLength
Queue Length Plot
QoE Approach
Fig.10 Queue Length Plot
The buffered Q length for such system is
presented in figure 10. The Q lengths are measured
as the volume of data packets buffered with
increase in forwarding of data packets. It is
observed that, the Q length of buffering is reduced
for probabilistic coding due to increment in the
data rate. The queuing is however observed to be
equal in the initial communication phase and
gradually increased with forwarding of packets.
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
0
5
10
15
20
25
Forwarding
AllocatedDataRate(B/s)
Allocation Rate Plot
QoE Approach
Fig.11 Allocation Rate Plot
The allocated data rate for the developed
method over the simulating network is as outlined
in figure 11. The nodes are communicated for the
given data packet and each level of buffering these
nodes compute the allocable data rate for
forwarding. This forwarding results in proper
control of traffic blockage and hence results in
efficient network performance. The simulation is
then iterated for two node positions as shown in
following figures
0 20 40 60 80 100 120
0
20
40
60
80
100
120
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Communcating Path
Figure 12 (a)

Node with mobility in a random 1-step
0 20 40 60 80 100 120
0
20
40
60
80
100
120
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Communcating Path
(b) Node with mobility in a random
2- steps
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
0
5
10
15
20
25
Forwarding
QoE Approach
Fig.(a)
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
0
10
20
30
40
50
60
70
80
90
100
Forwarding
Throughput
Network Throughput Plot
QoE Approach
Fig.(b)
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
0
5
10
15
20
25
30
Forwarding
delay(Sec)
QoE Approach
Fig.(c)
1 2 3 4 5 6 7 8 9
0
10
20
30
40
50
60
70
80
90
100
Forwarding
QueueLength
Queue Length Plot
QoE Approach
Fig.(d)
1 2 3 4 5 6 7 8 9
0
20
40
60
80
100
120
140
160
Forwarding
Overhead
QoE Approach
Fig.(e)
Figure 13 (a) allocated data rate for node
with 1-step mobility (b) throughout for
node with 1-step mobility (c) delay for node
with 1-step mobility (d) Queue length for
node with 1-step mobility (e) Overhead for
node with 1-step mobility over variation in
forwarding Packets

1 2 3 4 5 6 7 8 9
0
10
20
30
40
50
60
70
80
90
100
Forwarding
Throughput Network Throughput Plot
QoE Approach
Fig.(a)
1 2 3 4 5 6 7 8 9
0
5
10
15
20
25
30
Forwarding
delay(Sec)
QoE Approach
Fig.(b)
1 2 3 4 5 6 7 8 9
0
10
20
30
40
50
60
70
80
90
100
Forwarding
QueueLength
Queue Length Plot
QoE Approach
Fig.(c)
1 2 3 4 5 6 7 8 9
0
5
10
15
20
25
Forwarding
QoE Approach
Fig.(d)
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
0
10
20
30
40
50
60
70
80
Forwarding
Overhead
QoE Approach
Fig.(e)
Figure 14 (a) Allocated data rate for node with
2-step mobility (b) Throughout for node with 2-
step mobility (c) Delay for node with 2-step
mobility (d) Queue length for node with 2-step
mobility (e) Overhead for node with 2-step
mobility over variation in forwarding Packets
The offered throughput and allocated data rate is observed to be higher, when the node moves into the
network, it is observed that the number of intermediate hops are reduced. This leads to higher blockage
probability as more packets are exchanged over the node, however with the proposed approach, due to faster
data exchange, the offered traffic rate is higher, which results in higher throughout. The offered data rate is
observed to be over 8% higher than the QoE based coding. However as the node moves out a region the
throughput consistence is maintained due to higher offered data traffic rate.
VI. Conclusion
A new dual mode controlling of quality governed communication approach is developed. To control
the flow of traffic for demanded service, in this approach a combined model of node blockage rate and mobility
factor is considered. Wrt., the node mobility within or out of node, the network is defined for improvement in

quality metrics. The probabilistic control of blockage per node is developed, in consideration with node
position, to allocate dynamic data rate to achieve least blockage overhead per node. The signaling and data
exchange overhead is hence reduced. The suggestive approach, defines the probability of traffic data rate
controlling under user mobility in a random order. Further investigation of the suggested approach for the
controlled node mobility with node speed and demanded quality of service would be focused.
References
[1] Christian Schindelhauer, “Mobility in Wireless Networks”, J. Wiedermann et al. (Eds.): SOFSEM 2006, LNCS 3831, pp. 100–116,
2006. Springer-Verlag Berlin Heidelberg 2006.
[2] Ian F. Akyildiz, Jiang Xie, andShantidevMohanty, “A Survey of Mobility Management In Next-Generation All-IP-Based Wireless
Systems”, IEEE Wireless Communications, August 2004 IEEE.
[3] Chiwetalu Barth. N. &Nwachi-Ikpor, Juliana O, “Handoff Management: A Critical Function in MobilityManagement for Fourth
Generation (4G) Wireless Networks”, Global Journal of Computer Science and Technology: ENetwork, Web & SecurityVolume 14
Issue 2 Version 1.0 Year 2014
[4] Pramod Kumar P1, Thirupathi V2, Monica D, “Enhancements in Mobility Management forFuture Wireless Networks”,
International Journal of Advanced Research in Computer and Communication EngineeringVol. 2, Issue 2, February 2013
[5] Mr. P.Thirunavukkarasu, Ms. P.IreneBerilYazhini, “Efficient Mobility Management in DifferentWireless Network for Continuous
Flow ofInformation”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.
3, Special Issue 3, April 2014
[6] Irfan Ali, “Network-Based Mobility Managementin the Evolved 3GPP Core Network”, IEEE Communications Magazine, February
2009
[7] MajidGhaderi and RaoufBoutaba, “Towards All-IP Wireless Networks:Architectures and Resource ManagementMechanism”.
[8] Ming Li, Hua Zhu, SathishSathyamurthy, B. Prabhakaran, and ImrichChlamtac, “End-to-end QoS Guarantee in Heterogeneous
Wired-cum-Wireless Networks”, TR UTD/EE/11/2003.
[9] Jonatha, “A home-proxy based wireless internet framework in supporting Mobility and Roaming of real time services”, IEICE
TRANS. COMMUN., VOL.E84-B, No.4 April 2004.
[10] Roopali Sood1, AtulGarg, “Mobility Management –Framework, Issues andChallenges”, International Journal of Application or
Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 3, March 2014.
[11] KhaledZeraoulia, “ SM-SIP: Seamless Mobility Management for Heterogeneous Wireless Networks”, I.J. Computer Network and
Information Security, 2014, 9, 58-65
[12] G. Zayaraz1, J. Kanchana Devi2, V. Vijayalakshmi3, V. Hemamalini, “Mobility Management in Heterogeneous Wireless
Networks”, IJRET: International Journal of Research in Engineering and Technology, Volume: 03 Special Issue: 07 | May-2014
[13] Ian F. Akyildiz, Janise McNair, Joseph Ho, “Mobility Management inCurrent and FutureCommunications Networks”, IEEE
Network July/August 1998
[14] NunoCoutinho, “Dynamicdual-reinforcement-learningroutingstrategiesforqualityofexperience-awarewirelessmeshnetworking”,
Elsevier, Computer Networks 88 (2015) 269–285.
[15] Qian Peng, Dong Enqing, Xu Juan, Lan Xing, Liu Wei, Cui Wentao, “Multipath Routing Protocol Based on Congestion Control
Mechanism Implemented by Cross-Layer Design Concept for WSN”, IEEE 17th
International Conference on Computational
Science and Engineering, 2014.

QoS Oriented Coding For Mobility Constraint in Wireless Networks

More Related Content

What's hot (17)

Similar to QoS Oriented Coding For Mobility Constraint in Wireless Networks (20)

More from iosrjce (20)

Recently uploaded (20)

QoS Oriented Coding For Mobility Constraint in Wireless Networks