Int. J. Communications, Network and System Sciences, 2010, 3, 625-630
doi:10.4236/ijcns.2010.37084 Published Online July 2010 (
Copyright © 2010 SciRes. IJCNS
A QoS-Based Multichannel MAC Protocol for Two-Tiered
Wireless Multimedia Sensor Networks
GholamHossein EkbataniFard, Mohammad H. Yaghmaee, Reza Monsefi
Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
E-mail: {Ekbatanifard, Yaghmaee},
Received March 8, 2010; revised April 24, 2010; accepted May 27, 2010
Rapid penetration of small customized wireless devices and enormous growth of wireless communication
technologies have already set the stage for large-scale deployment of wireless sensor networks. Offering pre-
cise quality of service (QoS) for multimedia transmission over sensor networks has not received significant
attention. However offering some better QoS for wireless multimedia over sensor networks raises significant
challenges. In this paper, we propose an adaptive Cross-Layer multi-channel QoS-MAC protocol to support
energy-efficient, high throughput, and reliable data transmission in Wireless Multimedia Sensor Network
(WMSNs). Our proposed protocol uses benefit of TDMA and CSMA/CA to adaptively assign channels and
timeslots to active multimedia sensor nodes in clusters. Simulations show that the proposed system achieves
the performance objectives of WMSNs with increased network throughput at the cost of a small control and
energy overhead.
Keywords: Wireless Multimedia Sensor Networks, MAC, Multichannel, Cross-Layer, Cluster, Adaptive
1. Introduction
The main component of wireless (multimedia) sensor net-
work, are the sensor nodes, which are small in size, capable
of self-organizing, sensing, processing data and communi-
cating with other nodes. The availability of inexpensive
hardware such as CMOS cameras and microphones that
can ubiquitously capture multimedia content from the en-
vironment has fostered the development of Wireless Mul-
timedia Sensor Networks [1], i.e. , networks of wirelessly
interconnected devices that can retrieve video and audio
streams, images, and scalar sensor data.
The major objectives behind the research and deploy-
ment of sensor networks [2] lie in the following two broad
1) Event detection and possible data acquisition by
sensing, data processing and communication through node
coordination and data transmission [3,4] to the sink or to
the interested user.
2) Conservation of energy [5] to maximize the post dep-
loyment, active lifetime of individual sensor nodes and the
overall network. The reason is that replenishing the energy
of sensor nodes by battery-replacement is clearly not feasi-
ble for a large network consisting of hundreds of nodes.
Moreover, wireless sensors are often deployed in an area
which is in-approachable to humans and away from any
sustained power-supply.
On the other hand, today’s wireless communication is a
gradually changing paradigm from its existing voice-alone
services to a new world of real-time audio-visual applica-
This ever-increasing popularity of multimedia applica-
tions has already started penetrating the domain of wireless
sensor networks―thereby giving birth to the new termi-
nology wireless multimedia sensor networks [6].
Video surveillance, telemedicine and traffic-control are
going to be the killer-applications of these emerging
WMSNs. While the need to minimize the energy con-
sumption has driven most of the existing research in wire-
less sensor networks, these new applications require the
sensor network paradigm to be re-investigated in view of
application-specific quality of service (QoS).
A quick look into the existing MAC protocols for sensor
networks reveals that lack of standardization and applica-
tion-specific diverse requirements has deprived wireless
sensor networks from having a single de-facto standard
MAC protocol. Most of the existing MAC protocols for
wireless sensor networks can be divided into two catego-
ries: 1) time division multiple access (TDMA)-based and 2)
carrier sense multiple access (CSMA) based with (possible)
collision avoidance (CA) [7].
TDMA protocols have a natural advantage of colli-
Copyright © 2010 SciRes. IJCNS
sion-free medium access; CSMACA protocols have a
lower delay at varying traffic loads. However, transmitting
multimedia applications with QoS offers significant new
challenges over these energy-constrained sensor networks.
Design of an efficient sensory MAC protocol, satisfying
QoS requirements, is one major step in end to end QoS
provisioning over WMSNs.
Current sensor nodes, such as MICAz and WINS, al-
ready support multiple channels for communication, for
example, 40 channels in WINS [1].
Thus, by developing a multichannel MAC protocol,
which can effectively utilize the available channel capacity
through the cooperative work from other sensor nodes, we
can achieve a better support for multimedia applications
which demand for high data rates [8].
This motivates us to look for QoS-based, yet ener-
gy-aware, MAC protocols for WMSNs. The objective of
this work is to develop a new QoS-based, energy-aware
MAC protocol for WMSNs. In this paper, we propose an
adaptive cross-layer multichannel protocol for MAC layer
in WMSNs. This protocol use benefits of TDMA and
CSMA/CA techniques in one MAC protocol.
The rest of the paper is organized as follows. Section II
reviews existing works in sensory MAC protocols. Subse-
quently, in Section III we explain our proposed MAC pro-
tocol at some sub sections. Simulation results in Section IV
corroborate the efficiency of the protocol in achieving the
desired throughput and delay. Section V concludes the
2. Related Work
A good survey of major MAC protocols for wireless sensor
networks is provided in [9]. Provisioning QoS in MAC
layers for wireless cellular and local area networks [10] is
an active research area, QoS-based MAC protocol for
wireless sensor networks have received relatively less at-
tention. While both TMAC [11] and DSMAC [12] attempt
to reduce the latency, little of the other MAC protocols are
developed with an objective to optimize (or improve) some
application-specific quality of service (like delay, through-
put etc).
Protocols, like SPEED [13], cluster-QoS [14] and de-
lay-constrained least cost routing [15] discuss the QoS-
routing issues in wireless sensor networks. Unfortunately,
all these works attempt to optimize QoS in the sensor-
routing from higher layers only. However, end-to-end QoS
in WMSNs cannot be satisfied without designing an effi-
cient QoS-aware MAC protocol. Unfortunately only a
handful of works exist for QoS-MAC in wireless sensor
networks. These include Q-MAC [16], PQ-MAC [17], and
RL-MAC [18]. To the best of our knowledge COM-MAC
[8] is the first one that use multi channel techniques for
MAC protocol in WMSNs. But it uses static time slots at
control channel and doesn’t propose any mechanism for
nodes that do not have data for sending at start of intervals
or for nodes that start sending data between an interval that
these could increase delay and degrades throughput of the
This motivates us to develop a new cross-layer multi-
channel QoS-aware MAC protocol for clustered WMSNs
that adaptively changes the intervals and use dynamic na-
ture at channel and time slot assigning, therefore promoting
the throughput of the network and exploit the high energy
3. Design of the Proposed Protocol
3.1. Network Architecture
As shown in Figure 1, a WMSN consists of several more
powerful nodes as cluster heads that located at the center of
different monitoring area, a number of identical and statio-
nary multimedia sensor nodes surrounding each cluster
head and a remote data sink which stores the multimedia
content locally for later retrieval. Each sensor node can
communicate directly with its cluster head and cluster head
can communication directly with the data sink using an
out-of-band channel. But, if direct communication is not
available, multi-hop routing is also employed.
3.2. Our Assumptions
We make some assumptions with relation to the configura-
tion of the network. These assumptions are:
Topology of network is cluster based.
There are N different channels available for use and all
channels have the same bandwidth except one that has
lesser bandwidth than others and use as reserved channel,
namely channel-R.
Cluster heads can transmit or receive on N channels at
the same time.
Cluster heads will have sufficient power supply and
more processing capacity than other sensor nodes.
All multimedia sensor nodes in a cluster can transmit or
Figure 1. Network architecture.
Cluster member
Cluster Head
Copyright © 2010 SciRes. IJCNS
receive on three channels, namely channel-1 and channel-2
and channel-R. Channel-1 is a contention based channel
that assigned by cluster head at first phase of network dep-
loyment. Channel-2 is contention free and dynamically
assigned by cluster head. Channel-R is a contention based
reserved channel that is share between a cluster members.
Sensor nodes are able to switch among channels dynami-
cally. The channel switching time is less than 224 μs ac-
cording to [1].
The working of a cluster of sensor nodes is synchronized
to the cluster head and each sensor node can communicate
directly with its cluster head.
3.3. Proposed QoS-MAC Protocol
We assume that the clustering process has been completed
by performing a clustering protocol, the assignment of
sensor nodes to clusters can be handled by existing cluster-
ing techniques [2]. Within each cluster, all tasks are done in
time intervals (∆T), which are dynamically changed. ∆T as
Time interval can be varying depending on application and
traffic load of the network.
We suppose three type nodes in a cluster as illustrated
in Figure 2. These nodes are: cluster head, active nodes
which are nodes that have data for sending, and passive
nodes which have not data for sending at present. First of
all, when the network is initially deployed, channel-1 allo-
cation phase begin in each cluster and doing only one time.
Channel-1 will be used as control channel for sending re-
quest message from multimedia sensor nodes to cluster
head. As mentioned before the number of channels at clus-
ter head is limited. So, it may happen that a channel as-
signed to more than one sensor nodes in a cluster. N-1
channels of cluster head could be assigned at this phase as
channel-1 of multimedia sensor nodes. One remained
channel from N channels of cluster head will be used as
channel-R. That is share between all of nodes in a cluster.
The usage of channel-R will be expressed later.
The operations of a cluster on ∆T are organized in three
sequential phases: request phase, scheduling phase and data
transmission phase. We now explain our MAC protocol
details in three phases.
3.3.1. Request Phase
After channel-1 assignment phase, that runs only one time,
the network operations begin at ∆T intervals. At the start of
each ∆T, the network layer of each multimedia sensor node
determines that whether information exists for sending or
not in a cross layer manner. Then nodes that have informa-
tion for sending, active nodes, start request phase on chan-
nel-1, and send a request message (REQ) to the cluster
The REQ message includes QoS requirements, such as
amount of multimedia data to be transmitted, maximum
delay, priority information and traffic class (streaming
video, Non-Real Time (NRT), Best Effort (BE)), and
Packet Error Rate (PER).
Because channel-1 may be assigned to more than one
node in a cluster, so, adaptive contention window protocol
[19] can be used for better performance on this channel.
When active nodes received acknowledgement of its
REQ message, they go to standby mode and waiting for
scheduling message from cluster head.
Request phase time (Tr) determined dynamically,
based on event rate, traffic load and average of previous
∆Tr periods.
3.3.2. Scheduling Phase
After request phase, cluster heads gather REQ messages
and then start scheduling phase. Each cluster head calculate
an appropriate schedule, based on priority and other QoS
requirements that specified in REQ messages, to coordinate
the data transmissions of active nodes. Then cluster heads
broadcast scheduling messages through all N-1 channels.
In scheduling message, cluster head assign a channel as
channel-2 to each active node for data transmission. If the
number of active nodes in a cluster is more than N1, then
a channel should assign to more than one active node. In
fact, a time slot in a channel may be assign to an active
node as channel-2.
Therefore, the scheduling message includes a channel
and probably a time slot in it as channel-2 for each active
node. Moreover new ∆T that calculated based on REQs is
included in scheduling message. New ∆T specifies end of
this interval and start of next interval indeed. A cluster time
intervals have been illustrated in Figure 3 for three inter-
Lengths of time slots that assign to active nodes are de-
pending on amount of data that specified in REQ me-
Figure 3. A cluster operations for three intervals.
Figure 2. Node types in a cluster.
Active node
Cluster Head
Passive node
Request phase (∆Tr)
Scheduling phase (chanel-2 assignment phase)
Data transmission phase
Copyright © 2010 SciRes. IJCNS
ssages and times need for sending acknowledgment mes-
sages if needed. Some traffic types may not require ac-
knowledgement message from cluster head, that it should
be declared in REQ message.
The priority of REQs is essentially based on its traffic
class, low priority for best effort traffic, medium priority
for non-realtime and high priority for streaming video traf-
Figure 4 shows the pseudo-code of scheduling algo-
rithm that assigns channel-2 and timeslots to active nodes
in a cluster. Also announce new_∆T for next interval. The-
reafter, scheduling message broadcasts for sensor nodes in
the cluster at network.
If some nodes that were passive get active after this
phase, send their REQ messages to cluster head on chan-
nel-R. Then, if there is enough unused time on channels, it
could be assigned to these nodes. Otherwise, if there is not
enough time for some of these nodes, then these nodes only
will be announced with new_T. So unsuccessful nodes
will go to sleep mode until starting of next request phase.
The network throughput in an interval is given by
/ ((1))
= −
(1 )
where k is the total number of REQ messages in jth interval,
Pi is amount of bits of data requested for transmission in ith
REQ message. Maximum occupied channel time in jth
interval is Tj seconds. In other word Tj is equal to new_∆T
at jth interval that used in Figure 4.
N – 1 is the number of contention free channels available
at cluster head. And capacity of each these channels are
C bps. The total throughput of network in m time intervals
is the average of
for j = 1, 2… m.
3.3.3. Transmission Phase
After receiving a scheduling message by an active node, it
could send its data on assigned channel. If a time slot in a
channel is assigned to an active node, it could go to sleep
mode until its time slot for sending data approaches.
As mentioned before, some passive nodes may get active
at this phase, and send its REQ messages at channel-R. So,
if free channels or time slots in channels has been assigned
to such nodes, they could send its data at scheduled time.
Figure 4. Scheduling algorithm.
When cluster head receives packets from its cluster
members, it classifies traffics based on its priority then
schedules it for sending toward the sink. Such framework
illustrated in Figure 5.
Nodes receive acknowledgement messages (ACK) for
proper sent packets, if specified before at REQ message.
Nodes could request unused timeslots, if exist, in an inter-
val for lost packets.
As mentioned earlier the streaming video traffic is as-
signed the highest priority and the best effort traffic is as-
signed the lowest priority. We will now analyze the aver-
age delay incurred in each of this traffic class. The mean
waiting time of a type i customer is denoted by E(Wi) and
) is the number of type i customers waiting in the
queue. Further let’s assume the processing time of traffic
class i is i
μ, with mean E(i
μ) and residual processing
time (Ri), with mean E(Ri). Then the traffic intensity of the
system is given by:
i ii
[12]. Hence, for the
highest priority streaming video traffic it holds that
( )
1 11 1
( )()
= +
where r is the number of different traffic classes whose
service is in progress during the arrival of the highest prior-
ity traffic class.
And the mean waiting time for lower priority traffics [7]
could be estimated as
( )
( )()()
1( )
= =
=− ++
j jjj
:2 .∀ ≤≤i in
4. Performance Evaluation
In this section we show different simulation results demon-
strating the efficiency of this proposed MAC protocol. We
have developed a discrete-event object oriented pack-
et-level simulator in C++. In the simulations presented in
this section, the considered packet size is 25 bytes. We
assume 3 data channels at sink that each channel capacity
is 250 kbps. Experiments are repeated 10 times. The per-
formance of our algorithm is compared with COM-MAC
Figure 5. Traffic differentiation and priority queuing in
Scheduling algorithm of Proposed MAC Protocol
1. Sort “REQs based on Priority in descending order”
2. Sort “equal priority REQs based on amount of data in
descending order”
3. Repeat
4. Find “first minimum occupied channel time”
5. Assign “channel or timeslot in channel to
6. Until REQ (i) exists, i = 1, 2,…, k
7. New_T = Find “maximum occupied channel
Incoming Traffic
Outgoing Traffic
Copyright © 2010 SciRes. IJCNS
[8] and a baseline protocol, the multichannel TDMA
(M-TDMA) protocol. For M-TDMA, the cluster head first
evenly distribute the cluster members on the available
channels. Then, the cluster head generates a TDMA sche-
dule on each channel and allocates a fixed slot to each
cluster members.
Figure 6 compares the network throughput performance
of proposed MAC protocol with COM-MAC and M-
TDMA for different cluster sizes. Our protocol works well.
But, when the number of nodes in cluster increases, the
proposed protocol throughput decreases. To find the reason
we repeat simulation up to 100 nodes in a cluster, Figure 7,
I see that our protocol throughput reach to a steady state
and it has better throughput than other protocols. This is
because the channel tends to be saturated when more nodes
are trying to utilize the channel.
As expected, proposed MAC protocol outperforms other
two protocols. This is because our protocol is designed to
maximize the network throughput by adaptively changing
intervals and using unused channels and time slots for pas-
sive nodes that get active after request phase.
Figure 8 shows the delay performance compassion of
three protocols as cluster size increases. We see that our
proposed MAC incurs lower delay when compared to
It is because that in our protocol, when a passive node
gets active, it could send request from channel-R to use
unassigned space on channels for data transmission. So it
incurs lower delay than other two protocols. With COM-
MAC and M-TDMA such nodes should wait until next
intervals thus this increases packet delay. We also notice
that the delay performance increases as cluster size in-
creases. This is because that larger cluster size will lead to
heavier network load so that a packet has to wait longer to
be transmitted.
Figure 9 explains the throughput-dynamics for different
traffic classes. The novelty of our MAC protocol is that it
first classifies the traffic into different classes depending on
the type of service (ToS) then schedules it for data trans-
mission. The streaming video traffic is given the highest
priority, the NRT traffic is given the second priority and
Figure 6. Throughput performance for various cluster sizes
up to 45.
Figure 7. Throughput performance for various cluster sizes
up to 100.
Figure 8. Packet delay performance for various cluster.
Figure 9. Differentiated MAC-throughput.
the BE traffic attains a lowest throughput.
5. Conclusions
In this paper we have developed a cross layer multichannel
QoS-MAC protocol for wireless multimedia sensor net-
works which classifies the wireless traffic into different
class, and adaptively assigns channel to various traffics. In
our proposed protocol nodes get active only when the net-
work layer specifies that there are data for sending. We
Throughput (Mbps)
Copyright © 2010 SciRes. IJCNS
verify the advantages of our protocol through network si-
mulation, in terms of network delays, throughput and dif-
ferentiated throughput of different traffic classes. We see
that our proposed MAC protocol provides better ener-
gy-efficiency, high-throughput, and data reliability support
in WMSNs.
6. Acknowledgements
This work was supported in part by grants from Faculty of
Engineering Ferdowsi University of Mashhad under the
contracts 18069.
7. References
[1] I. F. Akyildiz, T. Melodia and K. R. Chowdury, “A Survey
on Wireless Multimedia Sensor Networks,” Computer
Networks (Elsevier), Vol. 51, No. 4, 2007, pp. 921-960.
[2] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E.
Cayirci, “A Survey on Sensor Networks,IEEE Commu-
nications Magazine, Vol. 40, No. 8, 2002, pp. 102-114.
[3] W. Heinzelman, J. Kulik and H. Balakrishnan, “Adaptive
Protocols for Information Dissemination in Wireless
Sensor Networks,Proceedings of the 5th Annual ACM/
IEEE International Conference on Mobile Computing
and Networking, Seattle, WA, August 1999, pp. 174-185.
[4] Y. Yao and J. Gehrke, “The COUGAR Approach to
In-Network Query Processing in Sensor Networks,” ACM
SIGMOD Record, Vol. 31, No. 3, 2002, pp. 9-18.
[5] R. A. F. Mini, M. do V. Machado, A. A. F. Loureiro and B.
Nath, “Prediction-Based Energy Map for Wireless Sensor
Networks,Ad Hoc Networks, Vol. 3, No. 2, 2005, pp.
[6] I. F. Akyildiz, T. Melodia and K. R. Chowdhury, “A
Survey on Wireless Multimedia Sensor Networks,The
International Journal of Computer and Telecommunica-
tions Networking, Vol. 51, No. 4, 2007, pp. 921-960.
[7] N. Saxena, A. Roy and J. Shin, “Dynamic Duty Cycle and
Adaptive Contention Window Based QoS-MAC Protocol
for Wireless Multimedia Sensor Networks,Computer
Networks (Elsevie r), Vol. 52, No. 13, 2008, pp. 2532-
[8] C. Li, P. Wang, H.-H. Chen and M. Guizani, “A Cluster
Based On-demand Multichannel MAC Protocol for
Wireless Multimedia Sensor Networks,IEEE Interna-
tional Conference on Communications, Beijing, May
19-23, 2008, pp. 2371-2376.
[9] I. Demirkol, C. Ersoy and F. Alagoz, “MAC Protocols for
Wireless Sensor Networks: A Survey,IE EE Communi-
cations Magazine, Vol. 44, No. 4, 2006, pp. 115-121.
[10] T. Kuhn, “A QoS MAC Layer for Ambient Intelligence
Systems,Proceedings of the 4th International Confe-
rence on Pervasive Computing, Dublin, 2006, pp. 69-72.
[11] T. V. Dam and K. Langendoen, “An Adaptive Ener-
gy-Efficient MAC Protocol for Wireless Sensor Networks,
Proceedings of the 1st International Conference on Em-
bedded Networked Sensor Systems, Los Angeles, 2003,
pp. 171-180.
[12] L. Kleinrock, “Queueing Systems, Theory,” John Wiley &
Sons, New York, 1975.
[13] T. Hea, J. A. Stankovica, C. Lub and T. Abdelzahera,
“SPEED: A Stateless Protocol for Real-Time Communi-
cation in Sensor Networks,” 23rd IEEE International
Conference on Distributed Computing Systems, Rhode
Island, USA, May 2003, pp. 1-10.
[14] S. S. Tang and W. Li, “QoS Supporting and Optimal
Energy Allocation for a Cluster Based Wireless Sensor
Network,” Computer Communications, Vol. 29, No.
13-14, 2006, pp. 2569-2577.
[15] Q. Gao, K. J. Blow, D. J. Holding, I. Marshall and X. H.
Peng, “Radio Range Adjustment for Energy Efficient
Wireless Sensor Networks,Ad-Hoc Networks, Vol. 4, No.
1, 2006, pp. 75-82.
[16] Y. Liu, I. Elhanany and H. Qi, “An Energy-Efficient
QoS-Aware Media Access Control Protocol for Wireless
Sensor Networks,IEEE International Conference on
Mobile Adhoc and Sensor Systems, Washington, DC,
November 7, 2005, pp. 191-193.
[17] K. Paek, J. Kim, U. Song and C. Hwang, “Priority-Based
Medium Access Control Protocol for Providing QoS in
Wireless Sensor Networks,IEICE Transaction Letters on
Information Systems, Vol. E90-D, No. 9, 2007, pp. 1448-
[18] Z. Liu and I. Elhanany, “RL-MAC: A QoS-Aware Rein-
forcement Learning Based Mac Protocol for Wireless
Sensor Networks,IEEE International Conference on
Networking, Sensing and Control, Lauderdale, 2006, pp.
[19] N. Sabena, A. Roy and J. Shin, “Dynamic Duty Cycle and
Adaptive Contention Window Based QoS-MAC Protocol
for Wireless Multimedia Sensor Networks,Computer
Netwo rks, Vol. 52, No. 13, 2008, pp. 2532-2542.