Providing Some Minimum Guarantee for Real-time
Secondary Users in Cognitive Radio Sensor
Networks
Michael Okopa
Changa Andrew
Tonny Bulega
International University
of East Africa,
Faculty of Science and Technology,
Kampala, Uganda
[email protected]
Makerere University,
Kampala, Uganda
[email protected]
Makerere University,
Kampala, Uganda
[email protected]
Abstract—Previous studies of real-time traffic in Cognitive
Radio Sensor Networks(CRSNs) were done using reservationbased method and an absolute priority method. In the absolute
priority method, the traffic is classified as primary, real-time
secondary user, and Best Effort (BE) secondary user. These
studies were done under the assumption that primary user’s
traffic arrive at a low rate. However, under high arrival rate of
primary user traffic, real-time secondary user traffic is starved.
In this study, we developed an analytical model of source-tosink delay in a CRSN that differentiates the service of users into
primary and secondary and further partitions secondary user’s
traffic into real-time and BE. During service, primary traffic is
given higher priority over real-time secondary user which in turn
has higher priority over BE secondary traffic. In this model, a
threshold is introduced on the number of primary user packets
served to reduce the starvation of real-time secondary users under
high arrival rate of primary user traffic. The numerical results
obtained from the derived models show that real-time secondary
users experience a reduction in source-to-sink delay as a result of
introducing a threshold on the number of primary user packets
served. On the other hand, the primary user packets experience
a higher source-to-sink delay. However, the reduction in sourceto-sink delay experienced by real-time secondary user is lower
than the increase in source-to-sink delay experienced by primary
user traffic.
Index Terms—absolute priority; best effort;real-time; sourceto-sink delay;
I. INTRODUCTION
Wireless sensor networks (WSNs) are special networks with
large number of nodes consisting of embedded processors,
sensors, and radios. A typical sensor network consists of
a large number of sensor nodes deployed either inside the
phenomenon of interest or close to it. Romer et al. [1] defines a
wireless sensor network as an ad hoc multi-hop network which
consists of large number of tiny homogeneous sensor nodes
that are resource-constrained, mostly immobile and randomly
deployed in the area of interest.
The main purpose of sensor networks is to provide users
access to the information gathered by spatially distributed sensors, rather than enabling end-to-end communication between
pairs of nodes as in other large-scale networks such as the
Internet or wireless mesh networks.
Sensor nodes collaborate on real-time monitoring, sensing,
collecting network distribution of the various environments
within the region or monitoring object information [2]. Due to
limited transmission range of sensor nodes, the sensory data
are delivered to a processing center, called sink node, through
multi-hop communication meaning that each sensor node plays
the role of being a data originator and a data router. This
information is then processed to obtain useful data, which is
then sent to the user [3].
To reduce delay in data delivery that happens through multihop communication, sensor nodes form clusters and a cluster
head is selected for each cluster [4]. Instead of the data being
relayed by every node along a particular path, data is passed
from the source node to the cluster head, thus reducing the
number of nodes through which data is relayed.
WSNs work in the license-free band, hence vulnerable to
suffer from heavy interference caused by other networks sharing the same spectrum [2], [3]. To overcome this challenge,
cognitive radio network has been suggested as a promising
approach to reduce interference [5]. Cognitive Radio (CR)
networks provide efficient utilization of the radio spectrum and
highly reliable communication to users whenever and wherever
needed. In the event that the licensed owner also referred to
as primary user (PU) is not utilizing the channel, the channel
can be used by the unlicensed user also called secondary user
(SU) without jeopardizing the service quality of the primary
user [6].
Combining WSN and cognitive radio can overcome the
challenge of providing users access to information as well
as reducing interference caused by other networks sharing the
same spectrum. There has been some efforts on combining
WSNs and cognitive radio technology [7], [8].
Providing minimum guarantee in CRSNs is of great importance for example in health care, a packet indicating an
abnormal event of a patient should reach the doctor as soon
as possible. In environmental monitoring, a wireless smoke
sensor should provide real-time recognition of the smoke.
Two methods have been proposed for prioritizing the realtime traffic over the best effort (BE) traffic in the Cognitive Radio Sensor Network (CRSN), that is reservation-based
method [9] and an absolute priority method [10], [11]. In the
reservation-based method [10], each type of traffic can only
be served when a channel is available during the pre-allocated
time intervals. On the other hand, in the absolute priority
method [11], the real-time traffic can be served whenever a
channel is available (when the primary user is not using the
channel), and the BE traffic can only be served when there
is no buffered real-time traffic. However, under high arrival
rate of primary user traffic, real-time secondary user traffic is
starved.
In this paper, we derive models for source-to-sink delay in
CRSNs while delaying primary user so as to serve real-time
secondary user.
The contributions of this paper are two fold. Firstly, this
research proposes an analytical model that differentiates the
service of users in a CRSN based on absolute priority. In
addition, this model provides minimum guarantee to realtime secondary users under high arrival rate of primary users.
Secondly, this research evaluated how the proposed model
improves the performance of real-time secondary user without
appreciably degrading the performance of primary user.
The rest of the paper is organized as follows: in the
next section, we present the system models. We evaluate the
proposed models in section III, and finally conclude the paper
in section IV.
II. SYSTEM MODELS
We consider a cognitive radio sensor network. When a
frequency channel is available, all transmissions between the
sensors and the Channel Head (CH) are assumed to be errorfree. Co-channel interference between different clusters can
be avoided in different ways. First, a different set of candidate channels can be assigned to neighboring clusters if the
number of candidate channels is sufficiently large. Secondly,
if neighboring clusters have to share the same set of candidate
channels, their CHs may sense the channels in different orders
so that they will find different available channels with a high
probability. If neighboring clusters have to share the same
frequency channel, simultaneous transmissions can be avoided
by carefully coordinating the timelines of the clusters using
similar models as in [12], [13].
Figure 1 and figure 2 shows the single-cluster queue model
and multi-cluster queue model respectively. For the case of
single-cluster, traffic traverses only one cluster head before
reaching the sink, while for the case of multi-cluster, traffic
traverses two or more cluster heads before reaching the sink.
Fig. 2.
Multi-cluster queue model
In multi cluster transmissions, both real-time and best effort
data collected by the CH from the sensors are forwarded
to the next hop CH and further to the sink. The CRSN
opportunistically accesses vacant channels in a spectrum. Each
cluster requires only one available frequency channel at any
time due to the fact that the CH has only one radio for data
communications. The CRSN is assumed to have a primary
user which has one type of traffic, whereas the secondary user
has both real-time and best effort traffic. In order to keep short
the source-to-sink delay, real-time traffic collected by the CH
will be given higher priority over best effort traffic. We assume
the prioritization of real-time traffic is only done at the first
cluster-head and the rest of the cluster-heads perform only the
forwarding function along a particular path to the sink.
In addition to collecting data from the sensors, the CH is
also responsible for sensing available channels from a number
of candidate channels, allocating radio resources, and sending
control signals to the sensors. The sensor nodes have to switch
between different channels depending on channel availability.
The arrival of all traffic to each node is assumed to follow a
Poisson process with mean arrival rate, λ per node. The service
time at each node is assumed to be exponentially distributed.
Buffer capacity of each node is assumed to be finite. Both
real-time traffic and best effort (BE) data traffic can be served,
but the real-time traffic is given a higher priority. In order to
achieve small transmission delay, the real-time traffic is served
with contention-free transmissions using the IEEE 802.15.4
MAC protocol, which is commonly used for WSNs [9].
In the next section, we derive the expression for source-tosink delay.
A. source-to-sink delay models
Fig. 1.
Single-cluster queue model
Source-to-sink delay consists of the following delays; transmission delay, propagation delay, and queuing delay. Of these
components, queuing delay is the most difficult to model. We
use Jackson’s theorem [2] to model the delay experienced
by the Cluster-Heads (CHs) in a multi clustered network.
Jackson’s theorem states that in a network of queues, each
node is an independent queuing system, with a Poisson input
determined by the principles of partitioning, merging, and
tandem queuing. The theorem is based on three assumptions:
(i) The queuing network consists of m nodes, each of which
provides an independent exponential service.
(ii) Items arriving from outside the system to any one of the
nodes arrive with a Poisson rate.
(iii) Once served at a node, a packet goes to one of the
other nodes with a fixed probability, or it goes out of
the system.
We model each node using the M/M/1/k queue system
separate from the others. Mean delay at each node is then
added to derive the total node delays.
by packets in one cluster is therefore given as:
⎛
⎞
k−(i+1)
2
λE[τPi ]
λE[τR2 ]
⎠+
E[TRQ ] = ⎝
2(1 − λPi /E[τPi ])
2(1 − λR /E[τR ])
B. Source-to-sink delay with threshold
(4)
The performance of BE secondary traffic is the same,
whether the threshold is applied or not. This is because BE
secondary user traffic packets have to wait for the queue of
primary user and real-time secondary user to be empty before
receiving service.
Under high arrival rate of primary users, real-time secondary
users can be starved. Therefore, we employ a threshold on
the number of primary user packets served before serving
real-time secondary user packets. In doing this, real-time
secondary users receive minimum guarantee, that is, they can
still receive service even when there are primary user packets
in the queue. The result of this is that the primary user delay
is increased while the delay for real-time secondary user is
reduced. However, the delay for non real-time secondary users
remain the same whether a threshold is applied or not.
1) Source-to-sink delay for primary user’s packets: The
Source-to-sink delay of a primary user packet consists of the
following delays: Transmission delay, propagation delay, the
waiting time in queue of a primary user packet, and the waiting
time due to service of real-time secondary user packets which
do not have to wait for the service of other primary user
packets. We take a worst case scenario where the primary
user finds a real-time secondary user in service.
Therefore, the queuing delay experienced by primary user
packets in one cluster is given as:
k
λE[τP2i ]
λE[τR2 i ]
E[TPQ ] =
+
2(1 − λPi /E[τPi ]) i=1 2(1 − λRi /E[τRi ])
(1)
where k is the number of real-time secondary packets that
have to be served before servicing primary user packets.
The source-to-sink delay, E[SSD] of primary packets with
threshold is therefore:
E[SSD] =
L
d
+ +
R
s
k
λE[τR2 i ]
1
λE[τP2 ]
+
+
E[τP ] 2(1 − λP /E[τP ]) i=1 2(1 − λRi /E[τRi ])
i=0
(3)
The source-to-sink delay, E[SSD] of real-time secondary
user packets with threshold is therefore:
L
d
E[SSD] =
+ +
R
s
⎞
⎛
k−(i+1)
λE[τP2i ]
1
λE[τR2 ]
⎠+
⎝
+
2(1 − λPi /E[τPi ])
E[τR ] 2(1 − λR /E[τR])
i=0
C. Source-to-sink delay without threshold
The source-to-sink delay for primary user’s packets without
threshold can be deduced from [14] as:
L
d
1
λE[τP2 ]
E[SSD] =
+ +
+
(5)
R
s
E[τP ] 2(1 − λP /E[τP ])
On the other hand, the source-to-sink delay for real-time
secondary user packet can also be deduced from [14] as:
E[SSD] =
L
d
+ +
R
s
1
λE[τR2 ]
+
+
E[τR ] 2(1 − λR /E[τR ])
i=1
(6)
In the next section, we present numerical results showing
the performance of the source-to-sink delay models.
k λE[τP2i ]
2(1 − λPi /E[τPi ])
III. PERFORMANCE EVALUATION
In this section, we use the derived models to evaluate its
performance. In particular, we analyze the variation of sourceto-sink delay with arrival rate of packets, and number of
cluster heads. In each case, we consider primary user, real-time
secondary user traffic, and secondary user best effort traffic.
A. Model Parameters
(2)
2) Source-to-sink delay for real-time secondary user’s packets: In this case, the Source-to-sink delay of real-time secondary user packets consist of the following delays: Transmission delay, propagation delay, the queuing delay due to
some primary user packets and queuing delay due to real-time
secondary user packets. The total queuing delay experienced
Table I shows the basic mathematical symbols used in the
analysis.
TABLE I
BASIC MATHEMATICAL SYMBOLS USED IN THE ANALYSIS
Parameter
ρR
ρBE
λR
λBE
ρ
Meaning
Load due to Real-time secondary user traffic
Load due to best effort secondary user traffic
Arrival rate of real-time secondary user traffic
Arrival rate of best effort secondary user traffic
Load
Value
5
260ms
10 packets/second
7.5Mbits
1.5Mbps
2km
3 ∗ 108ms−1
ρ =0.5 and ρ
R
7
6.5
6
5.5
0
Fig. 3.
load
Source−to−sink delay (ms)
Source−to−sink delay (ms)
7.5
8.5
PU without threshold
PU with threshold=1
PU with threshold=2
0.5
Load due to primary user packets
1
PU without threshold
PU with threshold=1
PU with threshold=2
8
7.5
7
6.5
6
5.5
0
0.5
Load due to primary user packets
7.5
Real−time SU without threshold
Real−time SU with threshold=1
7
Real−time
SU with threshold=2
7
6.5
6
5.5
0.5
1
Load due to primary user packets
6.5
6
5.5
0
0.5
Load due to primary user packets
1
Fig. 4. Source-to-sink delay for real-time secondary user as a function of
primary user load
ρR=0.9 and ρBE=0.9
=0.5
BE
8
7.5
5
0
1) Variation with load : In this case we investigate the
increase in delay of primary users and decrease in delay
of real-time secondary users with increase in load. We used
equations 5, 2 and 6 and 4 to plot graphs of source-to-sink
delay as a function of primary user’s load.
8
ρR=0.9 and ρBE=0.9
=0.5
BE
1
Source-to-sink delay for primary user as a function of primary user
Figure 3 shows a graph of source-to-sink delay for primary
user as a function of primary user load when the load due to
real-time secondary user and BE secondary user is fixed. We
observe that source-to-sink delay increases with increase in
load due to primary user. We also observe that the higher the
threshold, the higher the source-to-sink delay. For example for
low load of real-time and BE secondary users, that is ρ = 0.5,
when the load due to primary user is 0.5 the source-to-sink
delay increases by 0.02ms when the threshold is one, while
when the threshold is two the source-to-sink delay increases
by 0.03ms. On the other hand, when the load of real-time
and BE secondary users is put at ρ = 0.9 and the load due
to primary user is 0.5 the source-to-sink delay increases by
0.77ms when the threshold is one, while when the threshold is
two the source-to-sink delay increases by 0.96ms. The sourceto-sink delay of primary user also increases with increase in
load due to real-time and BE secondary users.
Figure 4 shows a graph of source-to-sink delay for realtime secondary user as a function of primary user load when
the load due to real-time secondary user and BE secondary
user is fixed. We observe that source-to-sink delay increases
with increase in load due to primary user. We also observe
that the higher the threshold, the higher the source-to-sink
delay reduces. For example for low load of real-time and
BE secondary users, that is ρ = 0.5, when the load due to
primary user is 0.5 the source-to-sink delay reduces by 0.02ms
when the threshold is one, while when the threshold is two the
source-to-sink delay reduces by 0.08ms. On the other hand,
when the load due to real-time and BE secondary users is put at
ρ = 0.9 and the load due to primary user is 0.5 the source-tosink delay reduces by 0.15ms when the threshold is one, while
when the threshold is two the source-to-sink delay reduces by
0.66ms. The source-to-sink delay of real-time secondary user
reduces further with increase in load due to real-time and BE
secondary users traffic.
2) Variation with arrival rate : In this section, we investigate the increase in delay of primary users traffic and decrease
in delay of real-time secondary users traffic with increase in
arrival rate of primary users traffic. We use equations 5, 2 and
6 and 4 to plot graphs of source-to-sink delay as a function
of arrival rate of primary user traffic.
λ =λ
R
λ =λ
=4 packets/second
BE
R
16
14
18
PU without threshold
PU when threshold=1
PU when threshold=2
Source−to−sink delay (ms)
Number of cluster heads
Packet inter arrival time
Average service rate
Average packet length
Transmission rate
Distance between two nodes
Propagation speed
Source−to−sink delay (ms)
Parameter
R
Source−to−sink delay (ms)
TABLE II
E VALUATION PARAMETERS
ρ =0.5 and ρ
8
Source−to−sink delay (ms)
Table II shows the hypothetical parameters used in the
analysis which is consistent with parameters used in literature
[14].
12
10
8
6
4
0
5
10
Arrival rate of PU packets (packets/second)
16
=9 packets/second
BE
PU without threshold
PU when threshold=1
PU when threshold=2
14
12
10
8
6
4
0
5
10
Arrival rate of PU packets (packets/second)
Fig. 5. Source-to-sink delay for primary user as a function of arrival rate of
primary user traffic
We observe from figure 5 that source-to-sink delay for
primary user generally increases with increase in arrival rate
of primary user traffic. We also observe that at low arrival
rate (4 packets/second) of real-time and BE secondary users,
the performance of primary user packets are almost the same
when the threshold is applied and when the threshold is not
applied. This implies that introducing a threshold when the
arrival rate of primary users is low has no significant effect
on the performance of the system. However, when the arrival
rate of real-time and BE secondary users are increased to
9 packets/second, the source-to-sink delay of primary user
traffic increases by 1ms when the threshold is one and by
2ms when the threshold is two at primary user arrival rate of
5 packets/second.
λR= λBE=9 packets/second
=4 packets/second
BE
11
Source−to−sink delay (ms)
Source−to−sink delay (ms)
Real−time SU without threshold
Real−time
10 SU when threshold=1
Real−time SU when threshold=2
14
12
10
8
6
9
8
(a) ρ =0.9 and ρ
7
R
4
6
0
5
10
0
5
10
Arrival rate of PU packets (packets/second) Arrival rate of PU packets (packets/second)
Fig. 6. Source-to-sink delay for real-time secondary user as a function of
arrival rate of primary user
We observe from figure 6 that source-to-sink delay for
real-time secondary user generally increases with increase in
arrival rate of primary user traffic. We also observe that at low
arrival rate (4 packets/second) of real-time and BE secondary
users, the reduction in source-to-sink delay is low. However,
when the arrival rate of real-time and BE secondary users are
increased to 9 packets/second, the source-to-sink delay of realtime secondary user increases by 0.2ms when the threshold is
one and by 0.4ms when the threshold is two. The reduction in
source-to-sink delay is more pronounced at high arrival rate
of real-time and BE secondary user traffic.
B. Tradeoff
In this section, we investigate the tradeoff between the
increase in source-to-sink delay experienced by primary user
traffic and the reduction in source-to-sink delay experienced by
real-time secondary users as a result of introducing a threshold.
We use equations 2 and 4 to plot graphs 7 to 10.
(a) ρ =0.5 and ρ
R
(b) ρ =0.5 and ρ
=0.5
BE
R
PU without threshold
PU with threshold=1
Source−to−sink delay (ms)
Source−to−sink delay (ms)
8
7.5
7
6.5
6
5.5
0
0.5
Load due to primary user packets
1
(b) ρ =0.9 and ρ
=0.9
R
BE
8
=0.5
BE
8
Real−time SU without threshold
Real−time SU with threshold=1
7.5
7
6.5
6
5.5
0
0.5
Load due to primary user packets
1
Fig. 7. Source-to-sink delay as a function of primary user load, at low
secondary user load
Figure 7(a) shows a graph of source-to-sink delay for
primary user as a function of primary user load, when the
PU without threshold
PU with threshold=1
7.5
7
6.5
6
5.5
0
0.5
1
Load due to primary user packets
Source−to−sink delay (ms)
R
Source−to−sink delay (ms)
λ =λ
16
load due to real-time and non BE secondary user traffic are
fixed at low load, ρ = 0.5, while figure 7(b) shows a graph of
source-to-sink delay for real-time secondary user as a function
of primary user load, when the load due to real-time and
BE secondary user traffic are fixed at low load, ρ = 0.5.
We observe that source-to-sink delay generally increases with
increase in primary user load. We also observe that the increase
in source-to-sink delay experienced by primary user is 0.1ms
at primary user load of 0.5 while the decrease in source-tosink delay experienced by real-time secondary user is 0.3ms
at the same primary user load.. Therefore, the reduction in
source-to-sink delay experienced by real-time secondary user
is more than the increase in source-to-sink delay experienced
by primary user at low load.
=0.9
BE
8
Real−time SU without threshold
Real−time SU with threshold=1
7.5
7
6.5
6
5.5
0
0.5
1
Load due to primary user packets
Fig. 8. Source-to-sink delay as a function of primary user load, at high
secondary user load
Figure 8(a) shows a graph of source-to-sink delay for
primary user as a function of primary user load, when the
load due to real-time and BE secondary user traffic are fixed
at high load, ρ = 0.9, while figure 8(b) shows a graph of
source-to-sink delay for real-time secondary user as a function
of primary user load, when the load due to real-time and BE
secondary user traffic are fixed at high load, ρ = 0.9. We
observe that the source-to-sink delay generally increases with
increase in primary user load. We also observe that the increase
in source-to-sink delay experienced by primary user is 0.6ms
at a primary user load of 0.5 while the decrease in source-tosink delay experienced by real-time secondary user is 0.2ms
at the same primary user load. Therefore, the reduction in
source-to-sink delay experienced by real-time secondary user
is less than the increase in source-to-sink delay experienced
by primary user traffic at high load.
We observe from figure 9(a) that for low arrival rate of realtime and BE secondary users traffic, the increase in source-tosink delay experienced by primary users is low compared to
the reduction in source-to-sink delay experienced by real-time
secondary users observed in 9(b). For example, at primary
user arrival rate of 5 packets/second, the source-to-sink delay
experienced by primary user is the same with or without
the threshold, while for real-time secondary user, there is a
reduction in source-to-sink delay of 0.1ms at the same arrival
rate.
We observe from figure 10 that for high arrival rate of real-
(a) λR= λBE=4 packets/second
(b) λR= λBE=4 packets/second
PU without threshold
PU when threshold=1
14
Source−to−sink delay (ms)
Source−to−sink delay (ms)
16
12
10
8
6
4
0
5
8
6
5
10
Source-to-sink delay as a function of arrival rate of primary user
λ =λ
R
λ =λ
=9 packets/second
BE
R
PU without threshold
PU when threshold=1
14
Source−to−sink delay (ms)
Source−to−sink delay (ms)
10
Arrival rate of PU (packets/second)
16
12
10
8
6
4
0
5
10
Arrival rate of PU (packets/second)
Fig. 10.
traffic
12
4
0
10
Arrival rate of PU (packets/second)
Fig. 9.
traffic
16
Real−time SU without threshold
Real−time SU when threshold=1
14
=9 packets/second
BE
R EFERENCES
12
10
8
5
ACKNOWLEDGMENT
This work was partially funded by the International University of East Africa, Kampala, Uganda Research Fund.
16
Real−time SU without threshold
Real−time SU when threshold=1
14
6
0
high arrival rate. However, at low arrival rate of packets into
the system, the reduction in source-to-sink delay experienced
by real-time secondary user is higher than the increase in
source-to-sink delay experienced by primary user traffic.
Therefore, we conclude that service differentiation and
inclusion of threshold can improve the performance of realtime secondary user at an appreciably low degradation to the
primary user.
However, the potential bottleneck to the implementation of
service differentiation is the computational overhead necessary
to identify which type of user to give service.
10
Arrival rate of PU (packets/second)
Source-to-sink delay as a function of arrival rate of primary user
time and BE secondary user’s traffic, the increase in sourceto-sink delay experienced by primary users is about 0.8ms
compared to the reduction in source-to-sink delay experienced
by real-time secondary user which is approximately 0.4ms.
Therefore, we conclude that the reduction in source-to-sink
delay experienced by real-time secondary users is achieved
at no cost at low arrival rate of real-time and BE secondary
user traffic. However, the reduction in source-to-sink delay
experienced by real-time traffic at high arrival rate of realtime and BE secondary user traffic is less than the increase in
source-to-sink delay experienced by primary user traffic.
IV. C ONCLUSION
An analytical model of source-to-sink delay is developed for
a CRSN that differentiates the service of users into primary
and secondary and further partitions secondary user into realtime and BE. During service, the first priority is given to
primary user, second priority to real-time secondary user and
third priority is given to BE secondary user.
We observe that real-time secondary users experience a
reduction in source-to-sink delay as a result of introducing
a threshold on the number of primary user packets served. On
the other hand, the primary user packets experience a higher
source-to-sink delay as a result of the introduction of the
threshold. The reduction in source-to-sink delay experienced
by real-time secondary user is lower than the increase in
source-to-sink delay experienced by primary user traffic at
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