Robust Real-time IEEE802.15.4 MAC Protocol in Multi-Hop ...
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International Journal of Engineering & Computer Science IJECS-IJENS Vol:13 No:04 1
130304-2626-IJECS-IJENS © August 2013 IJENS I J E N S
Robust Real-time IEEE802.15.4 MAC Protocol in
Multi-Hop Mesh Network for Distribution Smart
Grid-AMI Hikma Shabani, Musse Mohamud Ahmed, Sheroz Khan, Shihab Ahmed Hameed and Mohamed Hadi Habaebi
Department of Electrical and Computer Engineering, Kulliyyah of Engineering
International Islamic University Malaysia, P.O.BOX.10, Kuala Lumpur, 50728, Malaysia
E-mail: [email protected]
Abstract-- The success of Smart Grid will be based on grid-
integrated real-time communication between various grid
elements in generation, transmission, distribution and loads.
Merit to the ZigBee/IEEE802.15.4std low cost, low power, low
data rate, short range, simplicity and free licensed spectrum that
makes wireless sensor networks (WSNs) the most suitable
wireless technology for smart grid applications. This paper
focuses at the distribution layer from advanced metering
infrastructure (AMI) gateways at the consumer premises to the
distribution point where a multi-hop mesh network is built for
large coverage data exchange. While beacon-enabled mode is
adopted for energy efficient operations, IEEE802.15.4std does not
define any mechanisms to enable beacon mode in mesh network.
Therefore, in this paper, a modified Zigbee WSN MAC protocol
for real-time multi-hop mesh network topology is developed. The
protocol performance is evaluated using NS-2 simulation and the
preliminary results are encouraging.
Index Term-- Smart Grid, Mesh Network, AMI, WSN, ZigBee,
MAC sub-layer, Superframe, real-time.
I. INTRODUCTION
The current power grid is designed long time ago and the grid
components are near the end of their normal life span. Hence,
the capacity limitations of electricity distribution, lack of
automated analysis, and slow response time due to mechanical
switches, poor visibility, growing population and demand for
more energy, and equipment failures have contributed to the
blackouts happening over the past 40 years [1]. As a result,
the Energy Independence and Security Act of 2007 gives a
start for the smart grid implementation in the United States [2]
with many countries following suit.
The smart grid is a modern electric power grid
infrastructure using the innovative transmission and
distribution networks to deliver electricity to end-users more
efficiently to almost close to the maximum transmission
capacity of network. In smart grid, the electricity delivery
system monitors, protects and automatically optimizes the
operation of its interconnected devices from generation to
distribution systems via transmission grid [3]. The cornerstone
of a smart grid is the ability for various entities to interact via
bidirectional communication network. The distribution smart
grid AMI comprises of key components such as smart meters,
AMI gateways known as neighborhood area network (NAN)
gateways and repeaters [4]. Hence, wireless sensor networks
(WSNs) provide a feasible and cost-effective sensing and
communication solution for smart grid. To enable large scale
deployments, the sensing nodes must collaborate to efficiently
route data over long distances from source to destination [5].
Thus, multi-hop mesh networks offer flexibility and robustness
by enabling path formation from any source to any destination
mostly through intermediate nodes within the network. ZigBee
is one of the promising standards for WSNs due to its
simplicity, mobility, robustness, low bandwidth requirements,
low cost of deployment, easy network implementation. In
addition to that, its operation is within the range of 2.4GHz
unlicensed industrial, scientific and medical (ISM) radio
spectrum and as being a protocol based on the IEEE
802.15.4std [6].
The IEEE 802.15.4 MAC sub-layer permits two modes for
transmitting and receiving data: the asynchronous beaconless
and synchronous beacon-enabled mode. Mainly, the
asynchronous beaconless mode requires nodes to listen for
other nodes’ transmission at all the times, which can drain
battery power faster. Moreover, the beaconless mode does not
provide any guarantee for data delivery and all transmissions
are done after executing an un-slotted carrier-sense-multiple
access with collision avoidance (CSMA/CA). On the other
hand, the beacon-enabled mode supports the transmission of
beacon packets between transmitter and receiver providing
synchronization among nodes after performing the slotted
CSMA/CA algorithm. Hence, the synchronization allows
devices to sleep between coordinated transmission, which
results in energy efficiency and prolonged network lifetime
[7]. However, the current IEEE802.15.4std specification
restricts the beacon-enabled mode to star or cluster tree
topologies only. Whereas the first is one-hop limited, which
reduces network coverage, the second does not provide the
scalability and robustness enabled by mesh topologies [8].
Hence, in this paper, a robust real-time IEEE802.15.4 MAC
protocol for beacon-enabled mode based multi-hop mesh
network for large coverage, better power efficiency, improved
scalability, robust and collision free network to ensure the QoS
and real-time control of the distribution smart grid components
is designed.
The rest of the paper is organized as follows. In section II,
the IEEE 802.15.4 Slotted CSMA/CA protocol is highlighted.
International Journal of Engineering & Computer Science IJECS-IJENS Vol:13 No:04 2
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Section III presents the problem definition and the scope.
Section IV depicts the methodology whereas in Section V, the
CSMA/CA Analytical Model for Multi-hop Mesh Network is
presented. Finally, Section VI presents the simulation results
whilst section VII presents the conclusion and perspectives.
II. IEEE 802.15.4 SLOTTED CSMA/CA PROTOCOL
There are two types of node in an IEEE 802.15.4 network: Full
Function Devices (FFDs) and Reduced Function Devices
(RFDs). FFDs known as beacon-enabled devices can operate
either as Personal Area Network Coordinators (PANc), Cluster
Head (CH) or Routers whereas RFDs called beaconless
devices can only operate as end devices. A network includes at
least one FFD, operating as the PANc and the other devices
can either act as end devices forming a star topology around
the coordinator, as routers creating a mesh topology or as a
combination of CHs and end devices creating a cluster-tree
topology [9].
The network operation consists of a Contention Access
Period (CAP) in which a transmitting node competes with
other nodes using slotted CSMA/CA mechanism to access to
the channel and a Contention Free Period (CFP) which
contains Guaranteed Time Slot (GTS) that provides certain
guarantees on eventual time and packet delivery in the network
[10].
In the slotted CSMA/CA channel access mechanism,
each time a node wishes to transmit data during the CAP must
initially sense the radio medium to determine whether the
channel is available or not. If the channel is idle, the node
transmits; otherwise for a busy channel, a random number of
backoff slots should be waited. After the random delay, a two
slot clear channel assessment (CCA) is carried out [11].
There are three key parameters in IEEE 802.15.4
CSMA/CA: Number of Backoff (NB) that is the number of
times the CSMA/CA algorithm is required to delay while
attempting the current transmission. NB is initialized to 0
before every new transmission and it is set to certain
boundaries, beyond which the transmission would be aborted
to avoid too much overheads. Contention Window (CW) that
is a content counter length defining the number of slot periods
that need to be clear of activity before the transmission can
start. It is initialized to 2 before each transmission attempt and
reset to 2 each time the channel is assessed to be busy. Finally,
Backoff Exponent (BE) is related to how many slot periods a
device must wait before attempting to assess the channel [12].
Even though, the CSMA/CA is similar to the IEEE 802.11
CSMA/CA in using binary exponential backoff, 802.15.4
differs from 802.11 in that, a backoff counter value of the
device decreases regardless of the channel status, and the
device monitors the channel, i.e. performs clear channel
assessment (CCA) twice when the value reaches zero [13].
III. PROBLEM DEFINITION AND SCOPE
Wireless mesh networks are the most popular choice for
deploying advanced metering infrastructure (AMI) extensively
used all over the world where the Smart Grid initiative is
gaining momentum. The primary purpose of deploying these
networks is to allow utilities to facilitate automated meter
readings and acquire periodic data which is highly granular.
This data can be used to provide demand response programs.
Such systems require highly reliable communications between
head end systems and metering devices [14]. This paper
focuses at the distribution layer where data exchange occurs
between AMI gateways at the house hold in multi-hop mesh
topology to the distribution point or ZigBee Network
Coordinator (ZNC).
According to IEEE802.15.4std, all the communications
have to go through the neighbor nodes (FFDs) and the data has
to be stored in the neighbor nodes until being advertised in the
beacon of the next superframe [15]. Therefore, it has been
shown that with the usage of guaranteed time slot (GTS), the
real-time data is sent at the end of the superframe to the PAN
coordinator and the reception is done using slotted CSMA/CA
which may not guarantee the immediate access to the medium.
The other limitations are related to the current superframe
structure itself. Hence, even if the node has reserved a GTS, it
can contend for channel access in the CAP period which
decreases the performance of the other nodes. The scheduling
of the contention free period (CFP) at the end of the active
portion of the supeframe is another additional limitation of
superframe structure of IEEE802.15.4std. This scheme gives
the normal data a faster channel access than the real-time data,
since the real-time data may wait until the end of the CAP to
get a deterministic channel access [16].
Moreover, since in Wireless Sensor Networks (WSNs) it
is recommended to set a low duty cycle (high sleep time) for
power saving purpose, the data may be kept in the network
coordinator for at least a time equal to the inactive period
duration which is given by equation (1) [17]
(1)
Furthermore, if the Duty Cycle (DC) is not set properly
(e.g. too short) the transmission latency may increase; since
during sleep time, data may have to wait until the active
portion of the next superframe to start the transmission [18].
The DC is calculated as the ratio between the superframe
duration (SD) and the beacon interval (BI) that can be related
to Beacon Order (BO) and Superframe Order (SO) via the
following equation (2) [19]
( ) (2)
Where:
𝑂 𝑂
International Journal of Engineering & Computer Science IJECS-IJENS Vol:13 No:04 3
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IV. METHODOLOGY
This robust real-time IEEE802.15.4 MAC protocol is based on
the combination of the modified Enhanced superframe
structure developed in [7] integrating the beacon collision
avoidance technique as suggested in [20]. To the best of the
authors’ knowledge, there are no citations reporting to develop
WSN standards for smart grid distribution systems.
In mesh networks, each beacon enabled node requires the
list of its two hops neighbor nodes which is achieved by using
two commands, namely, MLME-NEIGHBOUR_SCAN to
obtain its neighbor’s beacons and MLME-NLIST_REQ to
request the neighbors’ neighbor list. A Reserved Broadcast
Duration Slot (RBDS) in the active superframe is used as time
reference to compute the beacon offset of beacon enabled
nodes [20]. The superframe of the proposed robust real-time
IEEE802.15.4 MAC protocol is shown in the following Fig. 1
RBDS
B CFP CAP B+1
A.
B.
time Active Inactive
Period (SD) Period
Beacon Interval (BI)
Fig. 1. IEEE 802.15.4 Superframe structure of RBDS model
The above proposed structure has similar periods as the model
developed in [7] with the following modifications:
1) A new time slot labeled reserved broadcast duration slot
(RBDS) has replaced the PRTPU of the model (Fig. 1)
developed in [7]
2) The RBDS is located before the beacon and at the
beginning of the SD. The RBDS is used by all nodes
(FFDs) to send critical real-time data while GTS is used
to send normal real-time data.
This new structure gives the following improvements:
1) Nodes with critical real-time data can access the channel
faster than those having normal data, since they don’t need to
wait for the end of the CAP to send their data.
2) The normal real-time nodes do not need to contend for
the channel access in the CAP, since they send all their data in
the CFP period that is placed at the beginning of the
superframe.
3) The third improvement is very important since it is
related to the energy-delay tradeoff. In this technique, the real-
time data is sent and received in the same superframe. The
node (FFD) uses the following ALGORITHM I (given below
in a pseudo code) to create the neighbors and neighbor’s
neighbor list.
ALGORITHM I
- Send MLME-NEIGHBOR_SCAN at the beginning of
superframe to create a list of neighbors
If Find an empty slot (entry) in the neighbor table
Record the beacon transmission time of each neighbor
in the neighbor table. Send MLME-NLIST_REQ to request the neighbors’
neighbor list in the broadcast active superframe duration
If Find an empty slot (entry) in the neighbors’
neighbor list
Record the short address and slot offset in the
neighbor table
keep RBDS in the allocated slot offset
Else
Return “no mesh network”
Else
Start beacon as described in IEEE802.15.4std
The node (FFD) uses ALGORITHM II (given in a pseudo code)
to send sensed data. The node (FFD) will wait until data is
sensed while different backoff exponent “BE” are used to
differentiate between the sensed data so that:
. If there is no real-time
data all nodes will use the standard backoff. Since all nodes
(FFDs) will receive the Beacon and RBDS and will know the
types of sensed data (Critical real-time data packets are sent in
the RBDS, normal real-time data list is sent in the GTS, and no-
real time data is sent in the beacon).
ALGORITHM II
- If node has real-time data
If “Critical data” sent in RBDS with
Else
“Normal data” sent in GTS with
- Else
Start the CAP as described in IEEE802.15.4std
10 1 0
2 3 4 5 6 7 8 9 12 13 14 11 15
GTS1 GTS2
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V. CSMA/CA ANALYTICAL MODEL FOR MULTI-HOP MESH
NETWORK
In this section, we attempt to introduce a queuing theory model
for the new MAC protocol at hand. Let N be the maximum
number of identical nodes or routers (FFDs) operating on a
transmission range of device with range since nodes further
than two hops do not cause collision in the mesh network [20].
For the sake of energy efficiency no acknowledgement is
implemented whilst it is supposed that the packet arrival process
has no after effect. This analytical model is based on the
modification of the Markov chain models for slotted CSMA/CA
developed in [21] and [22].
Let ( ) be the stochastic process representing the backoff
stages if ( ( ) ∈ * , … ,𝑀+) with 𝑀 the maximum number of
backoff (𝑁 ) or the transmission stages if ( ( ) ), and
( ) be the stochastic process representing the length of backoff
or transmission duration counters of a device at time . The
MAC sub-layer delays for a random number of complete
backoff periods in the range of , , - where
backoff exponent ( 𝑀 ). Hence, when the length of backoff counter is decremented
to zero, the states * ( ), ( ) + and * ( ), ( ) + correspond to the first and second channel assessment ( 𝐴 ) and ( 𝐴 ), respectively. If two consecutive clear channel
assessments ( 𝐴) are idle, the node starts the transmission of
packet. And if 𝐴 fails due to busy channel, the value of both
𝑁 and is incremented by one up to
𝑀 𝑥 𝑀𝐴 𝑘 and 𝑀 𝑥 , respectively. The
transmission fails if 𝑁 > 𝑀 𝑥 𝑀𝐴 𝑘
The idle state * ( ) , ( ) + represents the sleep
period when the node has no packet to send. However, in this
analysis, it is assumed that a node always has a packet available
for transmission. Finally, the state * ( ) , ( ) , . . . , 𝐿 + represents the transmission state where 𝐿 is the
packet transmission duration measured in slots. The randomly
picked backoff window size at stage can take either value in
the set * ,𝑊 + where the value zero indicates immediate
sensing and the delay window (𝑊 ) is initially defined as
𝑊 and doubled at any stage until 𝑊 𝑊 with ( 𝑀 𝑥 𝑀 ) 𝑁 [23]
Let 𝛼 be the probability of assessing the channel busy
during the 𝐴 and 𝛽 be the probability of assessing it busy
during 𝐴 , assuming that it was idle in 𝐴 . Fig. 2
illustrates the proposed 2-D-Markov chain model for robust real-
time IEEE802.15.4 MAC protocol for multi-hop mesh network
with states represented by * ( ), ( )+ at a given time .
Fig. 2. 2-D Markov model for CSMA/CA mechanism of IEEE 802.15.4 robust real-time multi-hop mesh
,
, ,
, ,
𝑀, 𝑀,
,𝐿
𝛼
𝛼
𝛼
𝛽
𝛽
𝛽
𝛼
𝛼
𝛼
𝛽
𝛽
𝛽
𝛾
𝛾
,𝑊
,𝑊
𝑀,𝑊𝑀
𝛾
𝛾
𝛾
𝛾
𝑃𝑓
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From Fig.2, the following transition probabilities are obtained.
1. 𝒑*𝒊, 𝒌|𝒊, 𝒌 + 𝟏+ (𝟏 𝜸), 𝒊 ∈ (𝟎,𝑴), 𝒌 𝑾𝒊 𝟏 (3)
Where 𝛾 .
/ is the probability that there is not an available
empty slot offset [20].
2. 𝒑*𝟎, 𝒌|𝒊, 𝟎+ (𝟏 )(𝟏 )(𝟏 𝜸)
𝟐, 𝒊𝝐(𝟎,𝑴), 𝒌 𝑾𝟎
( )
3. 𝒑*𝒊, 𝒌|𝒊 𝟏, 𝟎+ *, (𝟏 ) -(𝟏 𝜸) 𝜸+
𝟐, 𝒊 ∈ (𝟏,𝑴), 𝒌 𝑾𝒊
(5)
4. 𝒑*𝟎, 𝒌|𝑴, 𝟎+ (𝟏 )(𝟏 )(𝟏 𝜸)
𝟐+
𝟐, 𝒌𝝐(𝟎,𝑾𝑴)
(6)
Where 𝑃 *,𝛼 + ( 𝛼)𝛽-( 𝛾) + 𝛾+ is the probability
for transmission failure
Let the stationary probability of being in the state * , 𝑘+ be
, 𝑃{( ( ), ( )) ( , 𝑘)}, ∈ ( ,𝑀), 𝑘 ∈ ( ,𝑊 ).
Specifically, , 𝑃{( ( ), ( )) ( , )} and
, 𝑃{( ( ), ( )) ( , 𝑘)}
Therefore,
, ( 𝛼) , (7)
, 𝛼 , + 𝛽 , (8)
, ( 𝛼) , (9)
Then substituting equation (9) into (8), , leads to
, 𝛼 , + 𝛽 ( 𝛼) , (𝛼 + 𝛽 𝛼𝛽) , (10)
The general state probability formula can be obtained as
{ , ,
, ( 𝛼) ,
Where 𝛼 + 𝛽 𝛼𝛽
Due to the Markov chain regularities, the following relations
are obtained.
, ( )
{( 𝛼)( 𝛽)( 𝛾)∑ , + 𝑃
}, (12)
, ( )
, , ∈ ( ,𝑀), 𝑘 ∈ ( ,𝑊 ) (13)
Let be the probability that a node performs 𝐴 in a
random chosen time slot when the backoff counter reaches
zero, that is regardless of backoff stage and independent across
nodes. Since the probabilities must sum to 1, the equation (14)
is obtained as follow:
∑ ∑ , + ∑ ,
+ ∑ ,
+∑ ,
∑ , {𝑊
+ ( 𝛾), + ( 𝛼)( + ( 𝛽)𝐿)-}
(14)
By taking into account interaction between node according to
[23], the expressions for 𝛼, 𝛽 and are obtained as follow:
𝛼 𝐿 0 ( ( 𝛾)) ( )
1 ( 𝛼)( 𝛽) (15)
𝛽 [
( ( ))
] 0 ( ( 𝛾)) ( )
1 (16)
∑ , [ ( ) ]( )
,
(17)
Hence, the network operating point parameters , 𝛼 and 𝛽 are
obtained by solving the three non-linear equations (15), (16)
and (17).
VI. SIMULATION RESULTS AND ANALYSIS
This model is validated by using NS-2 simulator (version 2.34)
[22]. In this section, the analytical model is used to study the
energy consumption and throughput behavior of IEEE
802.15.4 multi-hop mesh network. TABLE I shows different
parameters used for this model.
TABLE I
General Simulation Settings
Radio Parameters
𝐴
5
𝑊
𝑊
𝑊
. 𝑊
. 6 𝑊
Variable and fix parameters
𝑀 𝑥 𝑀𝐴 𝑘
𝑀
𝑀 𝑥
𝑊
𝑘 𝑃
5,
,
5
.
⁄
Packets
𝑀𝐴
𝐿
𝑀𝐴 +
(11) for 𝑖 ∈ ( ,𝑀)
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A. Throughput Analysis
The network throughput that is the number of occupied slots
for a successful frame transmission under the saturation
condition is given in the equation (18) in which the impact of
duty cycle on throughput is considered:
𝐿𝑁 ( 𝛾), ( 𝛾)- ( )( 𝛼)( 𝛽) ( ) (18)
After twice successive sensing channel idles, the node can
occupy the channel alone and transmit the pending frame
without the interference brought by any other node. For the
low Duty Cycle( .56 ), 𝑂 and 𝑂 are set to be
and respectively to ensure the energy conservation. Hence,
from Fig. 3 it can be seen that the analytical results of
throughput are compared with the simulation results.
Fig. 3. Throughput vs. Number of nodes (BO=10, SO = 4)
B. Power Consumption and Delay Analysis
In energy consumption analysis, factors such as the slots used
for periodical beacon transmission/reception( ), the CAPs
in active and inactive portions and finally, the sleep-to-idle
transition time ( ) which is .6 backoff slots [24]
Therefore, the average power consumption of each node
can be obtained as follow:
𝑃 +
,𝐿( 𝛼)( 𝛽)( 𝛾) +
( 𝛾)( 𝛼)𝑃 - (19)
Fig. 4. Power Consumption vs. Number of nodes
For end to end delay, this model is compared with the IEEE
802.15.4 MAC standard and the simulation results are shown
in Fig. 5, 6 and 7. No analytical model is developed for the
delay in this paper and it would be further investigated in
future works
The graph in Fig. 5 shows the influence of the duty cycle
on the end-to-end delay for a higher number of nodes.
Fig. 5. End-to-end delay vs. Duty Cycle (51nodes, SO = 4)
In Fig. 6, the impact of BO to data delivery delay is depicted.
Fig. 6. End-to-end delay vs. Beacon Order (51nodes, SO = 4)
0
0.05
0.1
0.15
0.2
0.25
5 10 15 20 25 30 35 40 45 50
Thro
ugh
pu
t
Number of nodes
Analytical Simulation
0
0.5
1
1.5
2
2.5
5 10 15 20 25 30 35 40 45 50
Po
we
r C
on
sum
pti
on
pe
r n
od
e
(mW
)
Number of nodes
Analytical Simulation
0
5000
10000
15000
20000
1 5 6 11 16 21 26 31 36 41 46 51
En
d-t
o-e
nd
dela
y (
ms)
Duty Cycle (%)
IEEE802.15.4std Smart model
0
500
1000
1500
2000
5 6 7 8 9 10 11 12 13 14
En
d-t
o-e
nd
dela
y (
ms)
Beacon Order (BO)
IEEE802.5.4std Smart model
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Finally, the Fig. 7 shows an end-to-end delay for different
traffic loads (different number of nodes) for a duty cycle (DC)
of 1.56% (BO = 10, SO = 4).
Fig. 7. End-to-end delay vs. Number of nodes
As shown in the Fig. 3&4, the simulation results validate the
theoretical analysis well. Hence, from Fig. 3, the network
throughput keeps growing with the increase of number of
nodes which demonstrates good scalability of the proposed
IEEE 802.15.4 MAC protocol model for multi-hop mesh
network networks. Furthermore, from Fig. 4, it is observed that
as the number of nodes increases, a successful transmission of
every bit of data requires less energy. Finally, it can be seen
that the developed model provides a better delay performance
than the original GTS mechanism of the IEEE 802.15.4
standard for different levels of duty cycle (Fig. 5), beacon
order (Fig. 6) and node number (Fig. 7).
VII. CONCLUSION AND PERSPECTIVES
In this paper, an analytical model for robust real-time IEEE
802.15.4MAC protocol for multi-hop mesh network is
developed. The validity of the analytical model is
demonstrated by the fact that its predictions closely match the
simulation results. The presented results are encouraging and
open many research perspectives. As it is crucial to test the
proposed model in real world environment, in the future
works, this model will be implemented on real-sensors using
the iLive platform [25] with the Atmel open MAC stack
protocol since it provides an open source implementation of
the IEEE 802.15.4 standard [26].
ACKNOWLEDGEMENT
The authors wish to thank the International Islamic University
Malaysia (IIUM) and the Renewable Energy Research Group
(RERG), Faculty of Engineering.
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