A Random Priority based Scheduling Strategy for Wireless Sensor
Networks using Contiki
Sajid M. Sheikh, Riaan Wolhuter and Herman A. Engelbrecht
Department of Electrical and Electronic Engineering, University of Stellenbosch,
Private Bag X1, Matieland, 7602, South Africa
Keywords: Contiki, MAC, IEEE802.15.4, Priority Scheduling, Sensor Networks.
Abstract: In recent years, wireless sensor networks (WSNs) have experienced a number of implementations in various
implementations which include smart home networks, smart grids, smart medical monitoring, telemetry
networks and many more. The Contiki operating system for wireless sensor networks which utilises carrier
sense multiple access with collision avoidance (CSMA/CA) does not provide differentiated services to data
of different priorities and treats all data with equal priority. Many sensor nodes in a network are responsible
not only for sending their sensed data, but also forwarding data from other nodes to the destination. In this
paper we propose a novel priority data differentiation medium access control (MAC) strategy to provide
differentiated services called Random Weighted Scheduling (RWS). The strategy was implemented and
tested on the FIT IoT-lab testbed. The strategy shows a reduction in packet loss compared to the default
CSMA/CA scheduling strategy in IEEE802.15.4 WSNs when carrying data of different priority levels.
Wireless Sensor Networking (WSN) is one of the
main driving forces of the Internet of Things (IoT).
WSN have been deployed in a number of different
environments which include smart home networks,
smart health, smart transport, smart educations and
other IoT applications. All these networks carry
heterogeneous data with different levels of priority
(Glaropoulos et al., 2014). A WSN typically consists
of a large number of low cost and low power,
multifunctional sensor nodes. Sensor nodes are
normally equipment with different types of sensors
depending on the parameter they will be measuring,
different embedded microprocessors, different
operating systems, and different radio transceivers
(Jun Zheng, 2009).
WSN embedded operating systems include
among others TinyOS, Contiki, MANTIS, T-Kernal,
LiteOS and Nano-RK. The Contiki operating system
is one of the most popular operating systems for
embedded systems and IoT applications
(Glaropoulos et al., 2014). Contiki utilises the
CSMA/CA scheduling strategy. The standard
CSMA/CA mechanism does not provide any data
differentiation services to improve the quality of
service for time critical events such as alarms that
have a higher priority than normal data in a network
(Koubaa et al., 2006). CSMA/CA treats all data with
equal priority in a first in first out (FIFO) manner.
In this paper we propose a novel scheduling
strategy that has been developed under the Contiki
operating system and implemented and tested on the
FIT IoT-lab testbed. Our proposed scheme has
multiple queues for data of different priority and
smaller values of back-off exponent (BE) and
contention window (CW) are assigned to higher
priority data queues to gain access to the channel
faster than the lower priority queues. The data from
the different queues gain access to the channel by
randomly selecting a queue for transmission based
on weights assigned to the different queues.
The rest of this paper is organised as follows.
Section 2 presents the related work. Section 3
presents an overview of CSMA/CA in the
IEEE802.15.4 standard. In section 4, we present the
proposed RWS scheduling strategy. Section 5
presents an overview of the Contiki operating
system. Section 6 gives a brief overview of the
Testbed implementation. Section 7 presents the
results and in section 8 we conclude the paper.
Sheikh, S., Wolhuter, R. and Engelbrecht, H.
A Random Priority based Scheduling Strategy for Wireless Sensor Networks using Contiki.
DOI: 10.5220/0005949301210128
In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - Volume 6: WINSYS, pages 121-128
ISBN: 978-989-758-196-0
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
A large amount of work has been carried out to
optimize energy usage in WSN communications.
Limited work has been done to develop priority
based scheduling strategies in WSNs. A priority
scheduling scheme is proposed in (Sun and Xu,
2010) which is based on queue management and
MAC layer back off. When a packet arrives at the
node, it gets placed at the end of the queue in a FIFO
queue that does not differentiate the priorities of
packets. DRAG (Sun and Xu, 2010) is a priority-
based queue management policy to provide priority
guarantee. The packet gets placed in the queue in an
appropriate place relative to the previously sorted
packets instead of placing the packet at the end of
the queue. Furthermore, the strategy selects a high
priority packet to send.
Other MAC layer priority based scheduling
strategies have also been proposed in literature such
as Q-MAC and PRIMA (Barua et al., 2014). With
Q-MAC, a queuing model with multiple queues for
different priority levels packets is proposed. The
strategy tries to minimize energy consumption and
provides QoS for intra-node and inter-node
scheduling. The intra-node scheduling places data
into the different priority queues, while the inter-
node scheduling provides channel access to reduce
energy consumption by reducing collisions. Five bits
of information are added to the existing packets of
which 2 are for identification of the packet type and
the other 3 are for sensing data. Packets that have
travelled more hops have a higher priority. In Q-
MAC, the queue architecture consists of five queues
with one specified as an instant queue. PRIMA is
also an energy efficient MAC protocol which
minimizes the idle listening periods by making
nodes that have no data to send to go to a sleeping
state. PRIMA also employs multiple queues where
data is classified and placed in the respective queues.
Queues with higher priority are given first access to
the channel compared to the low priority data.
To provide service differentiation to rate
sensitive applications, (Na, 2011) proposes a Multi-
rate Service Differentiation (MSD) model. This
model is implemented with two components namely
a Virtual Medium Access Control (VMAC) and the
Adaptive Back-off Window Control (ABWC). The
VMAC is the priority queue mechanism that adapts
its back-off based on the conditions of the network.
With VMAC, it is possible that more than one data
packet collided with each other if they finish the
back-off period at the same time. A Virtual Collision
Avoidance Control (VCAC) is designed to address
this situation which adjusts the back-off times of
data frames. In (Koubaa et al., 2006), a mechanism
that tunes the existing parameters of CSMA/CA for
data of different priority is proposed. These include
BE and CW values. This strategy differentiates
between data traffic and command traffic in a
network. Command traffic are given higher priority
by assigning smaller BE and CW values.
The above priority strategies are implemented in
sensor networks working on the IEEE802.15.4
standard. In the IEEE 802.11e standard, a contention
based strategy called enhanced distributed channel
access (EDCA) is used to provide differentiated
services for data of different priority levels. With the
default EDCA strategy, an unfairness problem is
known to exist between higher and lower priority
data classes as higher priority data can starve lower
priority data (Choi et al., 2008; Kuppa and Prakash,
2004; Tseng et al., 2007). EDCA introduces the
concept of access categories (AC) for data types and
consists of four ACs. Each AC has specific
parameters associated to its priority class such that
the higher probabilities ACs have a higher
probability of medium access (Poonguzhali, 2014).
Q-MAC is a complex strategy that introduces
extra overhead in the network by the introduction of
5 extra bits added to every message designed for
energy conservations. There are many applications
such as smart home networks, smart grids, smart
medical monitoring and telemetry networks where
some nodes can be designed to act as backbone
nodes and relay information from these clusters to
the destination. In our proposed scheme, we use the
same concept as Q-MAC by having multiple queues
for data of different priority and we assign smaller
values of BE and CW for higher priority data.
However, the design does is not concerned with
energy conservation and scheduling to reduce
energy wastage and therefore, we do not compare its
performance to Q-MAC.
The popularity and features of machine-to-machine
communications and the Internet of things (IoT),
have resulted in a wide areas of research leading to
development a low-power, low-rate, and low-cost
wireless system. The IEEE 802.15.4 standard has
become a standard for low rate wireless personal
area network (LR-WPAN) communications (Hwang
and Nam, 2014). The IEEE 802.15.4 standard which
operates at the link and physical layers is designed
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems
for simple, low data rate, low-power and low-cost
wireless communication with wireless personal area
networks (WPANs). It is implemented in wireless
sensor networks. The unlicensed Industrial,
Scientific and Medical (ISM) band that operates
worldwide with this technology is the 2.4 GHz ISM
band (Petrova et al., 2006). In this band of 2.4 GHz,
the ISM offers 16 channels with a data rate of 250
kbps (Collotta et al., 2013). Wireless data exchange
is done through the direct sequence spread spectrum
(DSSS) modulation scheme (Petrova et al., 2006). In
our study we use a radio model that also uses the 2.4
GHz ISM band.
According to this standard, a node can optionally
operate in beacon-enabled mode or non beacon
enabled mode (Collotta et al., 2013). In this section,
we present a brief overview of the beacon enabled
mode which is based on the slotted mode; and the
non-beacon enabled mode CSMA/CA mechanism of
the IEEE 802.15.4 standard which is based on the
un-slotted mode. Our work is based on the un-
slotted, non-beacon enabled mode CSMA/CA
mechanism to access the channel and transmit data.
3.1 Beacon Enabled Mode
For the slotted mode, the slots are aligned with the
beacon frames sent periodically by the Personal
Area Network (PAN) coordinator. With the un-
slotted mode, there are no beacon frames (Kim et al.,
2007). The principle of operation of this standard
depends on beacon messages in the form of
superframes regularly sent from a PAN coordinator.
The MAC superframe structure is shown in fig 1.
The time between the beacons is split into 16 slots.
The superframe consists of an active period with the
Contention Access Period (CAP) and CFP
(Contention Free Period), and an inactive period.
The CFP consists of Guaranteed Time Slots (GTS)
which are allocated to support QoS such as real-time
applications (Youn et al., 2007). In the CFP region,
nodes can obtain access to the medium without
collisions. In the inactive period the radio interface
can be put in a low energy consumption status in
order to improve energy savings (Collotta et al.,
2013). GTS are provided by the PAN coordinator for
nodes that need to transmit data within a certain
time. Nodes access the CAP using CSMA/CA. In
this mechanism, a node that wants to send data first
senses the medium after a random number of back-
off periods. If the medium is free the data is
transmitted, otherwise a back-off is performed.
There are seven GTS slots that can be
accommodated in one frame. There are limitations
Figure 1: The MAC super frame structure in the IEEE
802.15.4 standard.
Figure 2: The slotted CSMA/CA mechanism in IEEE
Figure 3: Flow chart for the non-beacon enabled unslottted
mode for the CSMA/CA mechanism with ACK in the
IEEE 802.15.4 standard.
with GTS as it can only support a limited number of
nodes and does not provide any method to support
QoS in the CSMA/CA mode (Youn et al., 2007).
A Random Priority based Scheduling Strategy for Wireless Sensor Networks using Contiki
An overview of CSMA/CA in slotted mode is
presented in fig 2. NB denotes the number of times
that the algorithm is required to back-off due to the
medium being busy during channel assessment. CW
is the contention window which is the number of
back-off periods that need to be clear of channel
activity before the packet transmission can be
started. BE is the back-off exponent which is the
number of back-off periods that a device should wait
before attempting to assess the channel.
When a packet arrives, NB, BE and CW are
initialized (ie. NB = 0, CW = CWinit = 2, BE = 2 or
BE = min(2,macMinBE) where macMinBE is the
default minimum BE value). After initializing of the
variables, the back-off period is started which is
chosen by a random number generated in the range
of [0, 2
1]. When this back-off has expired, the
algorithm then performs one Clear Channel
Assessment (CCA) to verify if the channel is busy or
free. If the channel is found to be busy, the CW is
again initialized to CWinit = 2, the NB and BE
variables are incremented by one. If the channel is
found to be free (idle), the CW is decremented by
one. The CCA process is than repeated until the CW
value becomes 0. After this the data is transmitted.
This mechanism ensures that at least two CCA
operations are performed to prevent potential
collisions (Youn et al., 2007). If the channel is busy,
both values of NB and BE are increased by one. BE
cannot exceed the set macMaxBE having the default
value 5 and CW is reset to 2. If NB becomes greater
than the set maximum back-offs allowed, the
algorithm terminates with a channel access failure
status. This failure will be reported to the higher
protocol layers, which decide whether or not to
attempt the transmission as a new packet again (Kim
et al., 2007).
3.2 Non-Beacon Enabled Mode
Just like in the beacon enable mode, when a packet
arrives, the number of back-offs (NB) and the back-
off exponent (BE) are initialized. After this
initialization of the variables, the back-off period is
started which is chosen by a random number
generated in the range of [0, 2
1]. Initially, BE is
initialized to BEmin which is 3 by default. BEmax is
5 by default. When this back-off has expired, the
algorithm then performs one CCA to verify if the
channel is busy or free. If the channel is found to be
busy, the NB and BE variables are incremented by
one. The procedure is repeated until NB is less than
the set maximum allowed transmissions. After the
maximum Transmissions allowed set + 1
unsuccessful attempts to access the channel, the
packet is dropped. If the channel is found to be free
(idle), a transmission takes place.
In the IEEE802.15.4 standard, the
acknowledgement (ACK) mode to transmitted
packets is optional unlike in the IEEE 802.11
standard. It is an optional feature as it can increase
network overhead and have an effect on the
achievable throughput of the network. If ACK mode
is enabled, for any transmission that does not receive
an acknowledgment, the NB and BE values are
increased. If NB becomes greater than the set Max
Transmissions allowed, the algorithm terminates
with a channel access failure status (Kim et al.,
2007). Fig 3 presents the flowchart for the operation
of CSMA/CA in non-beaconed un-slotted mode.
This section presents an overview of the proposed
strategy. In each node three queues are created.
These are for high, medium and low priority data. In
the packet, a data field is created of 2 bits which
carries information on the priority of the packet.
Using this information, data is placed in either one
of the 3 queues depending on the priority set in the
packet header. The priority field in the packet is
shown in fig 4. When a node has data to transmit
and more than one queue has data, a selection
process is followed. If only one queue has data, then
the packet from that queue is selected for
transmission without the need to follow a selection
process. However, the BE, CW and NB processes
are carried out after the selection process as is in the
un-slotted non-beaconed based CSMA/CA. With our
strategy we assign smaller values of BE and CW for
higher priority data to allow the higher priority data
to gain access to the channel faster than the lower
priority data. The queue selection followed when
two or more queues have data is as follows:
1. Probability weights are initially assigned to each
data priority queue.
2. The strategy determines the size of the individual
queues. If all of the queues have data, the
original assigned weights in stage 1 are used. If
all the queues do not have data, then the weight
of the queues with data are added and then the
weights of the queues with data are normalised
and assigned new weights. The queues with no
data are assigned a weight of zero i.e. 0.
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems
Figure 4: Integration of priority information in the packet.
Figure 5: Queue Selection process in RWS.
Figure 6: Proposed RWS strategy mechanisms.
3. The range values for each of the priority data
classes are assigned over a scale with the high
priority data being first on the scale, then
medium priority data and lastly low priority data.
4. A random number is then generated in the range
of 0 to the maximum scale value. A packet is
chosen for transmission from a queue from
which the number generated falls in its range as
shown in fig 5.
5. After the packet is selected, the BE, CW and NB
processes are followed as stated earlier. The
complete scheduling strategy is shown in fig 6.
The scheduling scheme proposed in this paper was
implemented in Contiki as Contiki does not have a
priority based scheduling mechanism for data of
different priority. In this section we give a brief
overview of this Contiki operating system which is
an open source operating system.
Contiki is implemented in the C language
developed at the Swedish Institute of Computer
Science (SICS) (Dunkels, 2004); (Networks, 2011).
Contiki has an event-driven kernel and follows a
linear programming style which was used for the
programming in this work. The Contiki protocol
stack is designed for resource-constrained devices
with constraints on memory and processing power
Figure 7: Contiki Layer Model.
(Colitti et al., 2009). It supports IPv6, RPL routing
protocol for low-power and lossy networks, Rime
and the Constrained Application Protocol (CoAP),
making it suitable to develop IoT applications
(Glaropoulos et al., 2014). Compared to many other
closed source firmware operating systems
implemented in hardware, Contiki is open source as
stated earlier. We therefore, used Contiki in our test
bed implementation as it allows us to use, modify
and make additions to this operation system.
The Contiki OS provides two communication
stacks namely uIP and Rime. uIP is a TCP/IP stack
that makes it possible for Contiki to communicate
over the Internet. Rime is a lightweight
communication stack designed for low-power radio
communication. Rime is a custom lightweight
networking stack. It provides primitives for single-
hop and multi-hop (mesh) communication
(Networks, 2011). In our study, the Rime
communication stack for multi-hop communication
was used as the other layers are less detailed. Fig 7
presents the communication protocol stack used in
our study. Our scheduling strategy is implemented at
the MAC layer as an enhancement to CSMA/CA.
The FIT IoT-Lab testbed was used to implement and
test the RWS scheduling strategy. The FIT IoT-Lab
is a very large scale open WSN test bed at INRIA,
France. This test bed allows for testing of scalable
protocols and applications on this large scale test
bed (Rosiers et al., 2011); (Inria, 2016).The
implementation and testing of the scheduling
strategy was done on nodes in Lille and Grenoble.
The strategy was implemented on M3 nodes which
has a 32-bit ARM cortex micro-controller, high
performance and uses a 2.4GHz radio interface.
A Random Priority based Scheduling Strategy for Wireless Sensor Networks using Contiki
Table 1: RWS parameters.
Traffic Type BE CW
High Priority Data 4 7 15
Medium Priority Data 5 15 31
Low Priority Data 10 31 1023
Table 2: Data Transmission Test Cases.
High Priority Data
Medium Priority
Low Priority
Case 1 60 packets/sec 60 packets/sec 60 packets/sec
Case 2 60 packets/sec 120 packets/sec 120 packets/sec
Case 3 120 packets/sec 60 packets/sec 120 packets/sec
The scheduling strategy is mainly developed for
backhaul nodes that will carry data of different
priority in a multi-hop fashion until it reaches its
destination. The M3 nodes were chosen over other
available test bed nodes such as the WSN430 and
A8 nodes as they have much higher processing
power, which is needed in backbone WMN nodes.
The nodes that were chosen as one hop away were
spaced 4.8m apart. The mesh network was setup so
that communication with the receiver takes place in
multiple hop fashion by limiting the transmission
range of the nodes. There are two ways of limiting
the transmission range. These are being either
decreasing the transmission power, or by setting a
minimal energy level for the packet reception. The
range of the nodes was limited so that it can only
communicate with its 1 hop neighbour.
The default CSMA/CA scheduling strategy in
Contiki works in a FIFO fashion and does not
differentiated packets of different priority. Our RWS
strategy was developed on top of this by introducing
3 queues in the nodes, one for each priority level. An
application was written that generates packets of
100byte of different priority levels at the
transmission rates of the different test cases. Packets
with the fields as shown in fig 5 were created. The
application at each node records the number of
packets sent and number of packets received. Before
implementing the scheduling strategy and
application to generate packets of different priority
level, the codes written in C were tested in the Cooja
simulator on Tmote Sky nodes. It was compiled for
the actual test bed nodes and then implemented on
the test bed.
Since the strategy is intended to be used in
backhaul networks receiving a high number of
packets from the different network domains, the
parameters for BE, NB and CW were adjusted to
match those of the EDCA strategy in the
IEEE802.11 standard. The modifications therefore
made to the CSMA/CA in the IEEE802.15.4 were as
1. The acknowledgment mechanism was activated
to receive acknowledgment messages for any
successful transmission as in the IEEE 802.11
CSMA/CA; 2. The maximum transmission
allowed value was set to 7 as is the case with
IEEE802.11g; 3. The values of BEmin and
BEmax were changed such that the CW size will
be the same as in the EDCA based on the
CWmin and CWmax values. Table 1 presents the
default parameters used for high, medium and
low priority data to match those used in EDCA.
In the IEE 802.11 standard, the CW length is
basically the number of back-off countdown
periods that need to sense the channel idle before
a transmission attempt can be made while in the
un-slotted IEEE802.15.4, it is the duration that
the back-off waits before performing CCA.
Channel activity is only performed at the CCA
stage; 4. For Distributed Coordination Function
(DCF), BE is set to 10 as BE of 10 equals a CW
size of 1023. The retransmission limit was set to
The proposed RWS strategy was tested against
CSMA/CA in many test cases. Three test cases are
presented in this paper as shown in table 2. The
packets are transmitted at different rates of high and
low combinations for number of hops from 1 to 7
hops. Each intermediate hop forwards data as well as
transmits its own data generated at the rates stated in
table 2. The chain topology was used for the
The packet loss results for the different test cases
with the default CSMA/CA and the proposed
scheduling strategy are shown in figs 8 to 10. In the
graphs we call CSMA/CA as DCF as it operates like
the DCF in the IEEE802.11 standard, with all data of
any priority level treated in a single queue, FIFO
manner. The results are those obtained from the real
test bed implementation as such the conditions of the
channel can change over time depending on the
environment. The performance of CSMA/CA
depends also on the value chosen for the back-off
which is randomly selected. For any two test bed
tests carried out, the exact conditions might not be
the same as the number generated might be different
which has an effect when the packets are transmitted
to the next hop as well as the link conditions. The
proposed scheme also largely depends on a random
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems
Figure 8: Packet loss in test case 1.
Figure 9: Packet loss in test case 2.
Figure 10: Packet loss in test case 3.
number generated to choose which queue must
transmit its data. To keep the conditions of the
channel the same, both tests for each hop number
were run immediately one after the other for
CSMA/CA and RWS for the same hop number to
make the comparison approximately the same.
For all the three test cases, the tests were run for
five minutes each. Less packet loss can be observed
for RWS over CSMA/CA in nearly all the test cases
as the number of hops increases. The performance at
2 or 3 hops is approximately the same as for the
original CSMA/CA technique. The performance
improvement is mainly observed at hops more than
3. There is also more packet loss observed at higher
loads (case 3), compared to lower loads (case 1).
When the data loads are the same, a higher packet
loss for the lower priority data is observed. All these
test bed results presented in this paper conform to
the 95% confidence levels.
A novel scheduling strategy for networks carrying
data of different priority has been developed and
implemented in the Contiki operating system.
Contiki is an open source operating system and
therefore, we modified the existing codes to
implement our strategy. The Rime protocol
communication stack was used as the other layers
are light weight and this helps to ascertain the
performance of the proposed scheme.
The proposed strategy has shown a reduction in
packet loss as the number of hops is increased for
most of the test cases implemented over the FIT IoT-
lab test bed. The assessment of the performance of
the strategy by means of a live test bed is more
accurate, as opposed to testing by simulation only.
This is clearly beneficial in terms of confidence in
A Random Priority based Scheduling Strategy for Wireless Sensor Networks using Contiki
the success of any future implementations. This
work is important in view of the rapid IoT and smart
application implementations.
The authors will like to thank the reviewers for their
comments; INRIA (France) and Nathalie Mitton for
letting us use the FIT IoT testbed; and Viktor Toldov
for assisting with the use of the test bed. This
research was supported by the University of
Botswana and the South African National Research
Foundation (NRF) under the THRIP project
Barua, S., Afroz, F., Islam, S. S., Ahmed, A. U., Ghosal,
P., Sandrasegaran, K. (2014). Comparative study on
Priority Based QoS Aware MAC Protocols for WSN.
International Journal of Wireless & Mobile Networks
(IJWMN), 6(5), 175–181.
Choi, J., Yoo, J., Kim, C. (2008). A Distributed Fair
Scheduling Scheme with a New Analysis Model in
IEEE 802.11 Wireless LANs. IEEE Transactions on
Vehicular Technology, 57(5), 3083–3093.
Colitti, W., Steenhaut, K., Lemmens, B., Borms, J. (2009).
Simulation Tool for Wireless Sensor Network
Constellations in Space.
Collotta, M., Scatà, G., Pau, G. (2013). A Priority-Based
CSMA/CA Mechanism to Support Deadline-Aware
Scheduling in Home Automation Applications Using
IEEE 802.15.4. International Journal of Distributed
Sensor Networks, 2013.
Dunkels, A. (2004). Contiki - a Lightweight and Flexible
Operating System for Tiny Networked Sensors. In
Proceedings of the First IEEE Workshop on
Embedded Networked Sensors, Tampa, Florida, USA.
Glaropoulos, I., Vukadinovic, V., Mangold, S. (2014).
Contiki80211: An IEEE 802.11 Radio Link Layer for
the Contiki OS. IEEE International Conference on
High Performance Computing and Communications
(HPCC), 621–624.
Hwang, K., Nam, S. (2014). Analysis and Enhancement of
IEEE 802.15.4e DSME Beacon Scheduling Model.
Journal of Applied Mathematics, 2014.
Inria. (2016). FIT IoT-Lab. Retrieved from https://
Jun Zheng, A. J. (2009). Wireless Sensor Networks, A
Networking Perspective. John Wiley & Sons.
Kim, E., Kim, M., Youm, S., Choi, S., Kang, C. (2007).
Priority-based service differentiation scheme for IEEE
802.15.4 sensor networks. International Journal of
AEU of Electronics and Communications, 61, 69–81.
Koubaa, A., Alves, M., Nefzi, B., Song, Y.-Q. (2006).
Improving the IEEE 802.15. 4 slotted CSMA/CA
MAC for time-critical events in wireless sensor
networks. Workshop on Real Time Networks RTN, 1–
Kuppa, S., Prakash, R. (2004). Service differentiation
mechanisms for IEEE 802.11 based wireless networks.
Wireless Communications and Networking
Conference, 4, 796–801.
Na, C. W. (2011). IEEE 802.15.4 Wireless Sensor
Networks: GTS Scheulding and Service
Networks, S. (2011). Comparison of Simulators for
Wireless Sensor Networks.
Petrova, M., Riihij, J., Petri, M. (2006). Performance
Study of IEEE 802. 15. 4 Using Measurements and
Simulations. IEEE Wireless Communications and
Networking Conference (WCNC), 1, 487–492.
Poonguzhali, A. (2014). Performance Evaluation of IEEE
802. 11e MAC Layer Using Cell Processor.
International Journal of Sceintific and Technology
Research, 3(1), 255–261.
Rosiers, C. B., Chelius, G., Ducrocq, T., Fleury, E., &
Vanda, J. (2011). SensLAB: a Very Large Scale Open
Wireless Sensor Network Testbed, 2011.
Sun, G., & Xu, B. (2010). Dynamic Routing Algorithm for
Priority Guarantee in Low Duty-Cycled Wireless.
Springer-Verlag Berlin Heidelber LNCS, 146–156.
Tseng, K.-N., Wang, K., Shih, H.-C. (2007). Enhanced
Fair Scheduling for IEEE 802.11e Wireless LANs.
Journal of Information Science and Engineering,
1721, 1707–1721.
Youn, M., Lee, J., Kim, Y. (2007). IEEE 802.15.4 based
QoS support Slotted CSMA / CA MAC protocol. In
International Conference on Sensor Technologies and
Applications (pp. 113–117).
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems