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Reliability and Delay Analysis of Slotted Anycast Multi-Hop Wireless Networks Targeting Dense Traffic IoT Applications

TL;DR: Performance analysis shows that the proposed 3D Markov model captures the behaviour of relay nodes most accurately, considering reliability and delay as key performance metrics.
Abstract: Studies on IEEE 802.15.4 MAC in the current literature for anycast multi-hop networks do not capture a node's behaviour accurately. Due to the inaccurate modeling of state-wise behaviour of a node, the optimization of network parameters has not been efficient so far. In this work, we include the state-wise behaviour of a relay node into a 3D Markov model to more accurately investigate the protocol performance. Performance analysis of the proposed analytical model is evaluated for different variants of active state length, packet length and wake up rates considering reliability and delay as key performance metrics. Performance analysis shows that the model captures the behaviour of relay nodes most accurately.

Summary (2 min read)

I. INTRODUCTION

  • The analytical study proposed in [3] incorporates joint sleep and contention control guaranteeing throughput and SINR requirements for extending network life time.
  • The authors analysis concludes that node wake up rates and active state time periods have a significant impact on the performance of the network.

II. SYSTEM MODEL

  • The authors consider a generic Cyber Physical Systems (CPS) wireless network scenario with four clusters as shown in Fig. 1(a) .
  • After successfully receiving a packet in Idle-Listen state relay nodes jump to Active-Tx state and wait for a beacon from their next cluster (cluster-2 is the next cluster to cluster-3).
  • The relay nodes that are successful in receiving a beacon within a maximum of L a slots of the Active-Tx state follow CSMA/CA flow depicted using a 3D Markov chain shown in Fig. 2 (a) with backoff stages (m), backoff counter (k) and collision retries (n) as the three dimensions.
  • Idle-Listen and Active-Tx state probabilities along with the CSMA/CA model.
  • With basic understanding of busy probabilities in [4] , [5] and [6] , one can drive the mathematical model in the following section.

III. MATHEMATICAL MODEL

  • The authors formulate the proposed model in two stages by deriving the transition probabilities for all states in Fig. 1 (b) in the first stage and then follows the formulation of CSMA/CA model in second stage.
  • An are the probabilities to enter into the first slot of Sleep state when the received packet is discarded in CSMA/CA after exceeding maximum m and n respectively.
  • P SL s |CSM Asucc is the probability of the node to enter the first slot of Sleep state after the node successfully forwards the packet.
  • Reliability and delay of the proposed model are derived and analyzed in the following section.

A. Reliability model

  • The reliability of a relay node can be determined by deriving the failure probabilities.
  • Failure can occur due to exceeding m, n and active timeout.
  • Y indicates the probability of a node transitioning to next retry after successfully sensing the channel from any of the m stages shown in Fig. 2(a) .

IV. ANALYTICAL RESULTS

  • The proposed anycast clustered multi-hop analytical model's accuracy is validated by emulating a scenario similar to that shown in Fig. 1(a ) which has 4 clusters with 10 nodes each.
  • The proposed emulation model has the following assumptions: Congestion due to ACK and interference from other 2.45 GHz users is negligible.
  • Each relay node switches among 3 different channels for Tx, Rx and beacon modes to reduce interference between nearby nodes of different clusters.
  • From the figures one can infer the importance of λ in the performance of the network.
  • Increase in µ w reduces average waiting time in Active-Tx state and the chances for packet being dropped because of active timeout are less.

V. CONCLUSION

  • A slotted anycast model for clustered multi-hop networks with the state-wise behaviour injected into 3D Markov chain is developed and analyzed.
  • Reliability and delay performance metrics are analyzed with variation in parameters such as CSMA/CA retries, number of nodes, wake up rate and active time for different packet arrival values, and are validated using both analytical and emulation results with less than 0.5% error.

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1
Reliability and Delay Analysis of Slotted Anycast
Multi-hop Wireless Networks Targeting Dense
Traffic IoT Applications
Author1, Author2, Author3
Abstract—Studies on IEEE 802.15.4 MAC in the current
literature for anycast multi-hop networks do not capture
a node’s behaviour accurately. Due to the inaccurate
modeling of state-wise behaviour of a node, the optimiza-
tion of network parameters has not been efficient so far.
In this work, we include the state-wise behaviour of a
relay node into a 3D Markov model to more accurately
investigate the protocol performance. Performance analysis
of the proposed analytical model is evaluated for different
variants of active state length, packet length and wake up
rates considering reliability and delay as key performance
metrics. Performance analysis shows that the model cap-
tures the behaviour of relay nodes most accurately.
Keywords-Anycast clustered multi-hop network, 3D
Markov chain, Analytical model of IEEE 802.15.4 MAC.
I. INTRODUCTION
Analytical models for IEEE 802.15.4 MAC proposed
in the literature [1], [2] & [3] do not accurately capture
the state-wise behaviour of a relay node with generic
routing strategy for large dense networks. In [1], a two-
dimensional Markov model for IEEE 802.15.4 MAC
with anycast routing is proposed and in [2], a three-
dimensional Markov model for IEEE 802.15.4 multi-
hop scenario with reduced sensing and non-homogenous
traffic is analyzed. The analytical study proposed in [3]
incorporates joint sleep and contention control guaran-
teeing throughput and SINR requirements for extending
network life time. The effect of adaptive MAC param-
eters on single hop and multi-hop wireless sensor net-
works are well studied using three-dimensional Markov
models in [4] and [5] respectively. This work primarily
investigates the accuracy of a three-dimensional Markov
chain model for slotted IEEE 802.15.4 CSMA/CA MAC
with slot-wise state modelling for Sleep, Idle-Listen and
Active-Tx states of a node. Our analysis concludes that
node wake up rates and active state time periods have
a significant impact on the performance of the network.
The rest of this letter is organized as follows. Section
Author1, Author2 and Atuthor3 are with Department of Electrical
Engineering, Affiliation1
cluster-3
cluster-1
Relay
Relay Relay
cluster
Leaf
Relay
Sensor
Sink
cluster-2
1
(a)
n
m
succ
1-P
S |I
S
L
s
S
1
S
L
s
1
I
1
I
L
i
I
L
i
1
P
x
P
x
1-P
x
A
L
a
1
A
L
a
A
1
Sleep states
CSMA/CA states
1-P
a
1-P
a
1-P
a
Active-Tx
states
P
S |I
P
S |A
P
a
P
a
1
1-P
x
1-P
x
Idle-Listen
states
I
1
1-P
S |A
1
(b)
Fig. 1: a) Network scenario b) Relay node state model
2 discusses the system model based on a generic 3D
Markov chain and Section 3 provides the mathematical
formulation of the proposed model. The performance
analysis from the emulations and the analytical model is
discussed in Section 4 and finally, Section 5 concludes
the letter.
II. SYSTEM MODEL
We consider a generic Cyber Physical Systems (CPS)
wireless network scenario with four clusters as shown in
Fig. 1(a). Sensor data collected at the leaf nodes have to
be routed to a network sink via intermittent random relay
nodes known as anycast routing. The functional model of
relay nodes is captured accurately in four different states
namely, Sleep (S
i
), Idle-Listen (I
i
), Active-Tx (A
i
) and
CSMA/CA as shown in Fig. 1(b). In Sleep state relay
nodes sleep until assigned a wake up time. In Idle-Listen
state, relay nodes broadcast a beacon to the predecessor
cluster (cluster-3 is the predecessor to cluster-2) and
wait for a packet from it. After successfully receiving
a packet in Idle-Listen state relay nodes jump to Active-
Tx state and wait for a beacon from their next cluster
(cluster-2 is the next cluster to cluster-3). Time intervals
between beacons observed by a relay in Active-Tx are
Poisson distributed which determine the waiting time in
Active-Tx state described later in the delay model of this

2
letter. The relay nodes that are successful in receiving a
beacon within a maximum of L
a
slots of the Active-
Tx state follow CSMA/CA flow depicted using a 3D
Markov chain shown in Fig. 2(a) with backoff stages
(m), backoff counter (k) and collision retries (n) as the
three dimensions.
Our primary investigation focuses on the effect of
CSMA/CA retries in multi-hop scenarios with accurate
state-wise behaviour of relay nodes. In this letter busy
channel probabilities α and β in Clear Channel As-
sessment (CCA1 and CCA2) states and channel sensing
probability τ along with collision probability P
c
shown
in Fig. 2(a) are derived considering the effect of Sleep,
Idle-Listen and Active-Tx state probabilities along with
the CSMA/CA model. With basic understanding of busy
probabilities in [4], [5] and [6], one can drive the
mathematical model in the following section.
III. MATHEMATICAL MODEL
We formulate the proposed model in two stages by
deriving the transition probabilities for all states in Fig.
1(b) in the first stage and then follows the formulation
of CSMA/CA model in second stage. In the rest of our
discussion µ
w
indicates average wake up rate per node
in a cluster consisting of N nodes, L
a
and L
i
are length
of active and idle slots respectively.
In Eq. (1), P
x
is the transition probability of a relay
node to move into the next Idle-Listen slot from the
current one, when there is no packet arrivals at a given
slot with an average of λ Poisson arrivals and P
a
indicates the transition probability of a relay node to
move into the next Active-Tx slot from the current, when
there is no beacon arrival at a given slot with an average
of λ
a
Poisson arrivals shown in Eq. (4). Using Eq. (1),
the probability of a node transitioning to Sleep state
from the last slot of Idle-Listen (P
S|I
) and Active-Tx
(P
S|A
) states can be obtained as shown in Eq. (2) and
(3) respectively. Finally P
S|CSM A
in Eq. (5) indicates the
transition probability of a node from CSMA/CA state to
the first slot of Sleep state which should always equal to
one.
P
x
= exp (λ) , P
a
= exp (λ
a
) (1)
P
S|I
= P
S
L
s
|I
1
= P
L
i
x
(2)
P
S|A
= P
S
L
s
|A
1
= P
L
a
a
(3)
λ
a
= µ
w
N (4)
P
S|CSM A
= P
S
L
s
|CSM A
m
+ P
S
L
s
|CSM A
n
+P
S
L
s
|CSM A
succ
= 1
(5)
P
S
0
= P
S
0
P
L
i
x
+P
S
0
(1P
L
i
x
)P
L
a
a
+P
S|CSM A
b
0,0,0
(6)
In Eq. (5), P
S
L
s
|CSM A
m
and P
S
L
s
|CSM A
n
are the
probabilities to enter into the first slot of Sleep state
when the received packet is discarded in CSMA/CA
after exceeding maximum m and n respectively.
P
S
L
s
|CSM A
succ
is the probability of the node to enter
the first slot of Sleep state after the node successfully
forwards the packet. Eq. (6) can be simplified to arrive
at probability P
S
0
of a node to stay in the first sleep slot
at any given time slot in terms of b
0,0,0
, where b
0,0,0
is
the probability of a node to stay in the first CCA1 slot
of the CSMA/CA model.
P
CSM A
=
m
X
i=0
W
i
-1
X
k=0
n
X
j=0
b
i,k,j
+
n
X
j=0
m
X
i=0
b
i,-1,j
+
n
X
j=0
(
L
s
-1
X
k=0
b
-1,k,j
+
L
c
-1
X
k=0
b
-2,k,j
) = Z
1
b
0,0,0
(7)
P
S
+ P
I
+ P
A
+ P
CSM A
= 1 (8)
P
S
0
h
L
s
+
L
i
X
i=1
P
i1
x
+ (1 P
L
i
x
)
L
a
X
i=1
P
i1
a
i
+Z
1
b
0,0,0
= 1
(9)
The probability of a node to reside in CSMA/CA
state (P
CSM A
) at a randomly given time slot is the
sum of backoff, CCA2, success and failure state prob-
abilities respectively given in Eq. (7). Z
1
indicates the
proportionality factor after expressing all probabilities
in Eq. (7) in terms of b
0,0,0
. In Eq. (8) P
S
, P
I
, P
A
and P
CSM A
indicate the probabilities for a node to
reside in Sleep, Idle-Listen, Active-Tx and CSMA/CA
states respectively in any given random time slot. All
probabilities P
S
, P
I
, P
A
and P
CSM A
of Fig. 1(b) can
be written in terms of P
S
0
and b
0,0,0
as shown in Eq. 9.
Now using normalization Eq. (8), b
0,0,0
can be derived
as in Eq. (10). After getting the relation for b
0,0,0
, we
derive α, β and τ expressions utilizing b
0,0,0
in a similar
methodology shown in [6]. Numerical methods are used
to solve highly nonlinear α, β, τ and b
0,0,0
to arrive at a
closed form solution. Both node and cluster probabilities
(α, β, τ and P
c
) are in close agreement with less than 5%
deviation due to equal number of nodes in each cluster
and random selection of relay nodes. Reliability and
delay of the proposed model are derived and analyzed
in the following section.
A. Reliability model
The reliability of a relay node can be determined by
deriving the failure probabilities. Failure can occur due
to exceeding m, n and active timeout. Considering these
cases, the reliability of node k (R
k
) is given in Eq. (11).

3
b
0,0,0
=
(1 P
x
)(1 P
a
)(1 P
L
i
x
)(1 P
L
a
a
)
(1 P
a
)
h
L
s
(1 P
x
) + L
i
(1 P
L
i
x
)
i
+ (1 P
L
i
x
)(1 P
L
a
a
)(1 P
x
) [L
a
+ Z
1
(1 P
a
)]
(10)
a)
-2,L
c
-1,n
-2,0,n
-2,L
c
-1,0
-2,0,0
-1,0,1
Success
1
st
retry
n
th
retry
-1,0,n
P
c
1-P
c
α
α
1/W
0
I
1
0,-1,0 0,0,0
m,0,0
1-β
-1,L
s
-1,0
-1,0,0
P
c
β
β
m,-1,0
stages
Retry
m>m
max
m=-2 packet collision
m=-1 packet success
succ
m
n
Entry into
CSMA/CA
1-β
1-α
1-α
m>m
max
m>m
max
1-P
c
Success
1/W
m
0,W
0
-1,0
m,W
m
-1,0
n>n
max
-1,L
s
-1,1
-1,L
s
-1,n
1
5 10 15 20
0
0.2
0.4
0.6
0.8
1
Wake up rate (per node)
Reliability
b) R
E
vs µ
w
n=[0 3]
Sim−n=0
Analytical−n=0
Sim−retry−n=1
Analytical−n=1
Sim−retry−n=2
Analytical−n=2
Sim−n=3
Analytical−n=3
5 10 15 20
0
10
20
30
40
50
60
70
80
Wake up rate (per node)
Delay in ms
c) D
total
vs µ
w
n=[0 3]
Sim−n=0
Analytical−n=0
Sim−retry−n=1
Analytical−n=1
Sim−retry−n=2
Analytical−n=2
Sim−n=3
Analytical−n=3
Fig. 2: 2a) Three-dimensional Markov model of IEEE 802.15.4 CSMA/CA; Fig. 2b and 2c: Simulation parameters
for 2a) and 2b) are m
0
= 3, m = 4, λ = 0.01, n=[0 3], µ
w
=[2 20], L
a
= 100, L
p
= 10, L
i
=100, L
s
= 100.
R
E
in Eq. (12) depicts the end-to-end reliability calcu-
lated over h independent links/clusters/hops. ey indicates
the probability of a node transitioning to next retry after
successfully sensing the channel from any of the m stages
shown in Fig. 2(a).
R
k
= 1
x
m+1
(1 + ey) ey
n+1
1 P
L
a
a
P
L
a
a
(11)
R
E
=
h
Y
k=1
R
k
(12)
B. Delay Model
Total delay incurred by an individual node for
forwarding a packet successfully is contributed by
CSMA/CA delay and active state delay. Delay due to
CSMA/CA is given in Eq. (13).
D
csma
= T
s
+ D
avg
+ (T
s
+ D
avg
)
h
y
1 y
((n + 1) (y
(n+1)
))
1 y
n+1
i
(13)
D
avg
= 2S
b
h
1 + 0.25{
1 b
l
1 b
m+1
l
h
2W
0
1 2b
m+1
l
1 2b
l
3(m+1)b
m+1
l
1 b
l
]
i
+
3b
l
1 b
l
(W
0
+ 1)}
i
(14)
P
(delay=i)|success
=
P
i1
a
(1 P
a
)
(1 P
L
a
a
)
(15)
D
active
=
L
a
X
i=1
(i)
P
i1
a
(1 P
a
)
(1 P
L
a
a
)
(16)
D
total
= (D
csma
+ D
active
S
b
) h (17)
In Eq. (13), D
avg
indicates the backoff delay and is
derived in Eq. (14) by taking expectation over m stages
of CSMA/CA, where D
csma
of a link can be obtained
from computing average probability of success after j
retries. T
s
and S
b
are packet transmission time and
unit backoff time, b
l
is max{α,(1-α)β}, W
0
indicates
minimum backoff window. L
p
and L
c
are length of time
slots required for successful packet transmission and
collision respectively and T
c
indicates packet collision
time. In Eq. (16), the average number of active slots
(D
active
) that a node waits before a beacon arrives is
obtained. Finally total delay which is the sum of delays
incurred by CSMA/CA and Active states computed over
h independent links is given by Eq. (17).
IV. ANALYTICAL RESULTS
The proposed anycast clustered multi-hop analytical
model’s accuracy is validated by emulating a scenario
similar to that shown in Fig. 1(a) which has 4 clusters
with 10 nodes each. The proposed emulation model has
the following assumptions: Congestion due to ACK and
interference from other 2.45 GHz users is negligible.
Each relay node switches among 3 different channels for
Tx, Rx and beacon modes to reduce interference between
nearby nodes of different clusters.
We first analyze the effect of CSMA/CA retries (n) on
R
E
and D
total
. Fig. 2(b) and Fig. 2(c) plots R
E
and D
total
versus µ
w
respectively for 4 different values of n. R
E
and D
total
are observed to be increasing and decreasing
respectively with increase in µ
w
as the average waiting
time to receive a beacon in active state decreases and
failures in active state due to active timeout are reduced.
R
E
is observed to increase by 15% and D
total
is increased

4
4 6 8 10 12 14
0
0.2
0.4
0.6
0.8
1
Number of nodes
Reliability
a) R
E
vs N λ=[0.01 0.04]
Sim−λ=0.01
Analytical−λ=0.01
Sim−λ=0.02
Analytical−λ=0.02
Sim−λ=0.03
Analytical−λ=0.03
Sim−λ=0.04
Analytical−λ=0.04
4 6 8 10 12 14
0
10
20
30
40
50
60
70
80
Number of nodes
Delay in ms
b) D
total
vs N λ=[0.01 0.04]
Sim−λ=0.01
Analytical−λ=0.01
Sim−λ=0.02
Analytical−λ=0.02
Sim−λ=0.03
Analytical−λ=0.03
Sim−λ=0.04
Analytical−λ=0.04
5 10 15 20
0
0.2
0.4
0.6
0.8
1
Wake up rate (per node)
Reliability
c) R
E
vs µ
w
λ=[0.01 0.04]
Sim−λ=0.01
Analytical−λ=0.01
Sim−λ=0.02
Analytical−λ=0.02
Sim−λ=0.03
Analytical−λ=0.03
Sim−λ=0.04
Analytical−λ=0.04
5 10 15 20
0
10
20
30
40
50
60
70
80
Wake up rate (per node)
Delay in ms
d) D
total
vs µ
w
λ=[0.01 0.04]
Sim−λ=0.01
Analytical−λ=0.01
Sim−λ=0.02
Analytical−λ=0.02
Sim−λ=0.03
Analytical−λ=0.03
Sim−λ=0.04
Analytical−λ=0.04
40 60 80 100 120
0
0.2
0.4
0.6
0.8
1
Length of active slots
Reliability
e) R
E
vs L
a
slots λ=[0.01 0.04]
Sim−λ=0.01
Analytical−λ=0.01
Sim−λ=0.02
Analytical−λ=0.02
Sim−λ=0.03
Analytical−λ=0.03
Sim−λ=0.04
Analytical−λ=0.04
Fig. 3: Parameters for Fig. 3a and 3b: m
0
= 3, m = 4, µ
w
= 10, n = 1, L
a
= 100, N=[3 15], λ =[0.01 0.04]; Fig.
3c and 3d: m
0
= 3, m = 4, n=1, L
a
= 100, µ
w
= [2 20], λ = [0.01 0.04]; Fig. 3e and 3f: m
0
= 3, m = 4, n=1, L
a
= [25 125], λ = [0.01 0.04], µ
w
= 10; parameters fixed for all 6 scenarios: L
p
= 10, L
c
= 10, L
s
=100 and L
i
=100
by 6 slots with single retry after collision compared to
the “no-retries” scenario. Improvement in R
E
and D
total
with higher retries (n=2 & 3) compared to n=1 is merely
visible, as the probability to have successive collisions
for a node is minimal. Degradation in R
E
and D
total
is observed when analysis is performed by incrementing
number of nodes (N) for 4 different λ as shown in Fig.
3(a) and 3(b). Degradation in R
E
is valid since increase
in channel congestion values (α and β) results in more
packet drops and collisions due to exceeding maximum
m and n. More delay (D
total
) with increase in λ is
primarily due to degradation in D
csma
.
Fig. 3(c) and Fig. 3(d) plots R
E
and D
total
versus
µ
w
for four different λ. From the figures one can infer
the importance of λ in the performance of the network.
Increase in µ
w
reduces average waiting time in Active-
Tx state and the chances for packet being dropped
because of active timeout are less. The increase in λ
results in channel congestion, leading to more packet
failures due to active timeout and more delay due to
backoff stages.
Fig. 3(e) and Fig. 3(f) plots R
E
and D
total
versus L
a
for four different λ. R
E
was enhanced and D
total
was
growing higher with increase in L
a
as the chances of
beacon reception before active timeout increases signif-
icantly.
V. CONCLUSION
In this letter, a slotted anycast model for clustered
multi-hop networks with the state-wise behaviour in-
jected into 3D Markov chain is developed and analyzed.
Reliability and delay performance metrics are analyzed
with variation in parameters such as CSMA/CA retries,
number of nodes, wake up rate and active time for
different packet arrival values, and are validated using
both analytical and emulation results with less than 0.5%
error. The proposed integrated model and the analysis
can greatly aid in driving the future research in modelling
of dense traffic wireless multi-hop sensor networks.
Optimization of model parameters is a focus of future
research.
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Citations
More filters
Journal ArticleDOI
TL;DR: A novel Markov-chain-based analytical model for unslotted CSMA/CA and PCA for industrial applications offers satisfactory performance with less than 5% error when validated using Monte Carlo simulations and is verified using real-time testbed.
Abstract: Recently proposed IEEE 802.15.4-2015 MAC introduced a new prioritized contention access (PCA) for transfer of time-critical packets with lower channel access latency compared to carrier sense multiple access—collision avoidance (CSMA/CA). In this paper, we first propose a novel Markov-chain-based analytical model for unslotted CSMA/CA and PCA for industrial applications. The unslotted model is further extended to derive the analytical model for slotted CSMA/CA and PCA. Primary emphasis is laid on understanding the performance of PCA compared to CSMA/CA for different traffic classes in industrial applications. The performance analysis shows that the slotted PCA achieves a reduction of 63.3% and 97% in delay and power consumption respectively compared to slotted CSMA/CA, whereas unslotted PCA achieves a delay reduction of 53.3% and reduction of power consumption by 96% compared to unslotted CSMA/CA without any significant loss of reliability. The proposed analytical models for both slotted and unslotted IEEE 802.15.4-2015 MAC offer satisfactory performance with less than 5% error when validated using Monte Carlo simulations. Also, the performance is verified using real-time testbed.

26 citations

Journal ArticleDOI
TL;DR: The effect of the number of sectors, number of stations, arbitration inter-frame space, packet length, and minimum contention window size on the throughput of the four different access or service categories present in the EDCA under saturated traffic conditions, is investigated.
Abstract: The recently developed IEEE 802.11ad (WiGig) standard offers multi-Gbps connectivity for consumer wireless local area networks and operates in the millimeter wave frequency band (around 60 GHz). It supports two different contention-based channel access mechanisms, namely distributed coordination function, which offers fair service for every station, and the enhanced distributed channel Access (EDCA) for offering service differentiation among the stations. In this paper, we study the throughput performance of the IEEE 802.11ad EDCA mechanism under saturated traffic conditions using both analytical and simulation models. A novel five-dimensional Markov chain developed is used for analytically modeling the throughput behavior of the IEEE 802.11ad EDCA in the presence of virtual antenna sectors, high-gain directional beamforming, and presence of contention and contention-free access periods. Using this developed analytical model, the effect of the number of sectors, number of stations, arbitration inter-frame space, packet length, and minimum contention window size on the throughput of the four different access or service categories present in the EDCA under saturated traffic conditions, is investigated. When compared with the simulation outcomes, the proposed analytical model accurately analyzes the network performance by achieving less than 4% error.

16 citations


Cites background from "Reliability and Delay Analysis of S..."

  • ...11 DCF inspired many studies to analyze and optimize the performance of contention-based MAC protocols for different wireless technologies using Markov chains in the literature [14], [18]–[20]....

    [...]

Proceedings ArticleDOI
01 Jun 2017
TL;DR: An energy efficient distributed relay selection (EDRS) technique is proposed for multi-hop WSNs and the energy consumption is reduced by selecting a stable relay and power allocation for collaboration and the network lifetime is increased.
Abstract: The growing Internet of Things (IoT) is increasing wireless sensor networks (WSNs) for different applications. Energy efficiency and reliability are key factors for multi-hop path source to sink. A relay node forwards other node's data to sink, therefore, it consumes more energy. When relay runs out of battery it may cause network failure in WSNs of IoT. A relay selection in multi-hop transmission can play an important role to increase the network lifetime. A stable, WSN is the fundamental requirement for IoT data gathering in multi-hop networking from source to sink. In this paper, an energy efficient distributed relay selection (EDRS) technique is proposed for multi-hop WSNs. The energy consumption is reduced by selecting a stable relay and power allocation for collaboration. In addition, the proposed EDRS selects a relay node with optimal power levels to prolong the network lifetime. The simulation results demonstrate, the EDRS reduces energy consumption and increases network lifetime.

8 citations


Cites background from "Reliability and Delay Analysis of S..."

  • ...Multi-hop transmission is an import part of IoT WSNs, how to collect data and transmit to sink [5, 6]....

    [...]

Proceedings ArticleDOI
01 Nov 2016
TL;DR: A 3D Markov chain based model is developed for analysing the performance of proposed MAC framework under different configurations of network and shows that the model captures behaviour of the sensor node most accurately with 95% confidence level.
Abstract: In this paper, we propose and model an enhanced IEEE 802.15.4 MAC for sensor nodes operating in multi-hop scenarios. Existing IEEE 802.15.4 MAC, when adapted to multi-hop networks may not perform efficiently due to lack of knowledge about the instantaneous state of local gateway. We therefore ameliorate the channel access mechanism by introducing a new Active-Tx state, which aids in identifying the state of local gateway along with traditionally existing Sleep and CSMA/CA states. A 3D Markov chain based model is developed for analysing the performance of proposed MAC framework under different configurations of network. Upon considering reliability, energy and delay as the key performance metrics the analysis shows that the model captures behaviour of the sensor node most accurately with 95% confidence level. In addition performance is also analysed using real time deployment and found to be in good accordance with analytical and simulation outcomes.

4 citations


Cites methods from "Reliability and Delay Analysis of S..."

  • ...For the purpose of modelling, the overhead associated with beacon handshaking process between sensor node and local gateway is negligible and was considered as a realistic assumption in most popular of earlier studies such as [5], [15], [16]....

    [...]

Dissertation
01 May 2018
TL;DR: A new uplink multiple access scheme for the IoT in order to support ubiquitous massive uplink connectivity for devices with sporadic traffic pattern and short packet size and an algorithm that is robust to time-frequency transmission impairments.
Abstract: Parameter estimation and signal identification play an important role in modern wireless communication systems. In this thesis, we address different parameter estimation and signal identification problems in conjunction with the Internet of Things (IoT), cognitive radio systems, and high speed mobile communications. The focus of Chapter 2 of this thesis is to develop a new uplink multiple access (MA) scheme for the IoT in order to support ubiquitous massive uplink connectivity for devices with sporadic traffic pattern and short packet size. The proposed uplink MA scheme removes the Media Access Control (MAC) address through the signal identification algorithms which are employed at the gateway. The focus of Chapter 3 of this thesis is to develop different maximum Doppler spread (MDS) estimators in multiple-input multiple-output (MIMO) frequency-selective fading channel. The main idea behind the proposed estimators is to reduce the computational complexity while increasing system capacity. The focus of Chapter 4 and Chapter 5 of this thesis is to develop different antenna enumeration algorithms and signal-to-noise ratio (SNR) estimators in MIMO timevarying fading channels, respectively. The main idea is to develop low-complexity algorithms and estimators which are robust to channel impairments. The focus of Chapter 6 of this thesis is to develop a low-complexity space-time block codes (STBC)s identification algorithms for cognitive radio systems. The goal is to design an algorithm that is robust to time-frequency transmission impairments.

3 citations

References
More filters
Journal ArticleDOI
TL;DR: Whether this MAC scheme meets the design constraints of low-power and low-cost sensor networks is analyzed, and a detailed analytical evaluation of its performance in a star topology network, for uplink and acknowledged uplink traffic is provided.
Abstract: Advances in low-power and low-cost sensor networks have led to solutions mature enough for use in a broad range of applications varying from health monitoring to building surveillance. The development of those applications has been stimulated by the finalization of the IEEE 802.15.4 standard, which defines the medium access control (MAC) and physical layer for sensor networks. One of the MAC schemes proposed is slotted carrier sense multiple access with collision avoidance (CSMA/CA), and this paper analyzes whether this scheme meets the design constraints of those low-power and low-cost sensor networks. The paper provides a detailed analytical evaluation of its performance in a star topology network, for uplink and acknowledged uplink traffic. Both saturated and unsaturated periodic traffic scenarios are considered. The form of the analysis is similar to that of Bianchi for IEEE 802.11 DCF only in the use of a per user Markov model to capture the state of each user at each moment in time. The key assumptions to enable this important simplification and the coupling of the per user Markov models are however different, as a result of the very different designs of the 802.15.4 and 802.11 carrier sensing mechanisms. The performance predicted by the analytical model is very close to that obtained by simulation. Throughput and energy consumption analysis is then performed by using the model for a range of scenarios. Some design guidelines are derived to set the 802.15.4 parameters as function of the network requirements.

461 citations


"Reliability and Delay Analysis of S..." refers background or methods in this paper

  • ...With basic understanding of busy probabilities in [4]–[6], one can drive the mathematical model in the following section....

    [...]

  • ...After getting the relation for b0,0,0, we derive α, β and τ expressions utilizing b0,0,0 in a similar methodology shown in [6]....

    [...]

Journal ArticleDOI
TL;DR: A Markov chain is proposed to model these relations by simple expressions without giving up the accuracy and derive a distributed adaptive algorithm for minimizing the power consumption while guaranteeing a given successful packet reception probability and delay constraints in the packet transmission.
Abstract: Distributed processing through ad hoc and sensor networks is having a major impact on scale and applications of computing. The creation of new cyber-physical services based on wireless sensor devices relies heavily on how well communication protocols can be adapted and optimized to meet quality constraints under limited energy resources. The IEEE 802.15.4 medium access control protocol for wireless sensor networks can support energy efficient, reliable, and timely packet transmission by a parallel and distributed tuning of the medium access control parameters. Such a tuning is difficult, because simple and accurate models of the influence of these parameters on the probability of successful packet transmission, packet delay, and energy consumption are not available. Moreover, it is not clear how to adapt the parameters to the changes of the network and traffic regimes by algorithms that can run on resource-constrained devices. In this paper, a Markov chain is proposed to model these relations by simple expressions without giving up the accuracy. In contrast to previous work, the presence of limited number of retransmissions, acknowledgments, unsaturated traffic, packet size, and packet copying delay due to hardware limitations is accounted for. The model is then used to derive a distributed adaptive algorithm for minimizing the power consumption while guaranteeing a given successful packet reception probability and delay constraints in the packet transmission. The algorithm does not require any modification of the IEEE 802.15.4 medium access control and can be easily implemented on network devices. The algorithm has been experimentally implemented and evaluated on a testbed with off-the-shelf wireless sensor devices. Experimental results show that the analysis is accurate, that the proposed algorithm satisfies reliability and delay constraints, and that the approach reduces the energy consumption of the network under both stationary and transient conditions. Specifically, even if the number of devices and traffic configuration change sharply, the proposed parallel and distributed algorithm allows the system to operate close to its optimal state by estimating the busy channel and channel access probabilities. Furthermore, results indicate that the protocol reacts promptly to errors in the estimation of the number of devices and in the traffic load that can appear due to device mobility. It is also shown that the effect of imperfect channel and carrier sensing on system performance heavily depends on the traffic load and limited range of the protocol parameters.

136 citations


"Reliability and Delay Analysis of S..." refers background or methods in this paper

  • ...The effect of adaptive MAC parameters on single hop and multi-hop wireless sensor networks are well studied using three-dimensional Markov models in [4] and [5] respectively....

    [...]

  • ...With basic understanding of busy probabilities in [4], [5] and [6], one can drive the mathematical model in the following section....

    [...]

Journal ArticleDOI
TL;DR: Analytical and experimental results show that Breath is tunable and meets reliability and delay requirements, thus ensuring a long lifetime of the network and is a good candidate for efficient, reliable, and timely data gathering for control applications.
Abstract: An energy-efficient, reliable and timely data transmission is essential for Wireless Sensor Networks (WSNs) employed in scenarios where plant information must be available for control applications. To reach a maximum efficiency, cross-layer interaction is a major design paradigm to exploit the complex interaction among the layers of the protocol stack. This is challenging because latency, reliability, and energy are at odds, and resource-constrained nodes support only simple algorithms. In this paper, the novel protocol Breath is proposed for control applications. Breath is designed for WSNs where nodes attached to plants must transmit information via multihop routing to a sink. Breath ensures a desired packet delivery and delay probabilities while minimizing the energy consumption of the network. The protocol is based on randomized routing, medium access control, and duty-cycling jointly optimized for energy efficiency. The design approach relies on a constrained optimization problem, whereby the objective function is the energy consumption and the constraints are the packet reliability and delay. The challenging part is the modeling of the interactions among the layers by simple expressions of adequate accuracy, which are then used for the optimization by in-network processing. The optimal working point of the protocol is achieved by a simple algorithm, which adapts to traffic variations and channel conditions with negligible overhead. The protocol has been implemented and experimentally evaluated on a testbed with off-the-shelf wireless sensor nodes, and it has been compared with a standard IEEE 802.15.4 solution. Analytical and experimental results show that Breath is tunable and meets reliability and delay requirements. Breath exhibits a good distribution of the working load, thus ensuring a long lifetime of the network. Therefore, Breath is a good candidate for efficient, reliable, and timely data gathering for control applications.

107 citations


"Reliability and Delay Analysis of S..." refers background in this paper

  • ...4 MAC proposed in the literature [1], [2] & [3] do not accurately capture the state-wise behaviour of a relay node with generic routing strategy for large dense networks....

    [...]

  • ...In [1], a twodimensional Markov model for IEEE 802....

    [...]

Journal ArticleDOI
TL;DR: A new generalized analysis of the unslotted IEEE 802.15.4 medium access control protocol concludes that heterogeneous traffic and limited carrier-sensing range play an essential role on the performance and that routing should account for the presence of dominant nodes to balance the traffic distribution across the network.
Abstract: Many of existing analytical studies of the IEEE 802154 medium access control (MAC) protocol are not adequate because they are often based on assumptions such as homogeneous traffic and ideal carrier sensing, which are far from reality for multi-hop networks, particularly in the presence of mobility In this paper, a new generalized analysis of the unslotted IEEE 802154 MAC is presented The analysis considers the effects induced by heterogeneous traffic due to multi-hop routing and different traffic generation patterns among the nodes of the network and the hidden terminals due to reduced carrier-sensing capabilities The complex relation between MAC and routing protocols is modeled, and novel results on this interaction are derived For various network configurations, conditions under which routing decisions based on packet loss probability or delay lead to an unbalanced distribution of the traffic load across multi-hop paths are studied It is shown that these routing decisions tend to direct traffic toward nodes with high packet generation rates, with potential catastrophic effects for the node's energy consumption It is concluded that heterogeneous traffic and limited carrier-sensing range play an essential role on the performance and that routing should account for the presence of dominant nodes to balance the traffic distribution across the network

73 citations


"Reliability and Delay Analysis of S..." refers background in this paper

  • ...01, n = [0 3], μw = [2 20], La = 100, Lp = 10, Li = 100, Ls = 100....

    [...]

  • ...4 MAC with anycast routing is proposed and in [2], a three-dimensional Markov model for IEEE 802....

    [...]

  • ...04]; (c) and (d): m0 = 3, m = 4, n = 1, La = 100, μw = [2 20], λ = [0....

    [...]

Journal ArticleDOI
TL;DR: The proposed joint control scheme improves the lifetime of wireless sensor networks compared to the existing schemes and forms the network lifetime maximization problem to satisfy the throughput requirement and each link's signal to noise interference ratio (SINR) requirement.
Abstract: In this paper, we propose a joint control scheme to maximize network lifetime in wireless sensor networks with joint contention and sleep control. For the contention resolution of sensor nodes in the network, we consider the p-persistent contention protocol. Also, the sleep control is introduced with the probability of turning off the transceiver of a sensor node. Using the probabilities of contention and sleep, we formulate the network lifetime maximization problem to satisfy the throughput requirement and each link's signal to noise interference ratio (SINR) requirement. Finally, we demonstrate that the proposed scheme improves the lifetime of wireless sensor networks compared to the existing schemes.

18 citations


"Reliability and Delay Analysis of S..." refers background or methods in this paper

  • ...The analytical study proposed in [3] incorporates joint sleep and contention control guaranteeing throughput and SINR requirements for extending network life time....

    [...]

  • ...4 MAC proposed in the literature [1], [2] & [3] do not accurately capture the state-wise behaviour of a relay node with generic routing strategy for large dense networks....

    [...]

Frequently Asked Questions (15)
Q1. What have the authors contributed in "Reliability and delay analysis of slotted anycast multi-hop wireless networks targeting dense traffic iot applications" ?

In this work, the authors include the state-wise behaviour of a relay node into a 3D Markov model to more accurately investigate the protocol performance. 

The proposed integrated model and the analysis can greatly aid in driving the future research in modelling of dense traffic wireless multi-hop sensor networks. Optimization of model parameters is a focus of future research. 

The increase in λ results in channel congestion, leading to more packet failures due to active timeout and more delay due to backoff stages. 

Time intervals between beacons observed by a relay in Active-Tx are Poisson distributed which determine the waiting time in Active-Tx state described later in the delay model of this2 letter. 

Sensor data collected at the leaf nodes have to be routed to a network sink via intermittent random relay nodes known as anycast routing. 

Degradation in RE is valid since increase in channel congestion values (α and β) results in more packet drops and collisions due to exceeding maximum m and n. 

Reliability and delay performance metrics are analyzed with variation in parameters such as CSMA/CA retries, number of nodes, wake up rate and active time for different packet arrival values, and are validated using both analytical and emulation results with less than 0.5% error. 

Increase in µw reduces average waiting time in ActiveTx state and the chances for packet being dropped because of active timeout are less. 

In Idle-Listen state, relay nodes broadcast a beacon to the predecessor cluster (cluster-3 is the predecessor to cluster-2) and wait for a packet from it. 

Px = exp (−λ) , Pa = exp (−λa) (1)PS|I = PSLs |I1 = P Li x (2)PS|A = PSLs |A1 = P La a (3)λa = µw ∗N (4)PS|CSMA = PSLs |CSMAm + PSLs |CSMAn+PSLs |CSMAsucc = 1 (5)PS0 = PS0P Li x +PS0(1−P Li x )P 

Fig. 2(b) and Fig. 2(c) plots RE and Dtotal versus µw respectively for 4 different values of n. RE and Dtotal are observed to be increasing and decreasing respectively with increase in µw as the average waiting time to receive a beacon in active state decreases and failures in active state due to active timeout are reduced. 

Both node and cluster probabilities (α, β, τ and Pc) are in close agreement with less than 5% deviation due to equal number of nodes in each cluster and random selection of relay nodes. 

La a +PS|CSMAb0,0,0 (6)In Eq. (5), PSLs |CSMAm and PSLs |CSMAn are the probabilities to enter into the first slot of Sleep state when the received packet is discarded in CSMA/CA after exceeding maximum m and n respectively. 

The relay nodes that are successful in receiving a beacon within a maximum of La slots of the ActiveTx state follow CSMA/CA flow depicted using a 3D Markov chain shown in Fig. 2(a) with backoff stages (m), backoff counter (k) and collision retries (n) as the three dimensions. 

The proposed emulation model has the following assumptions: Congestion due to ACK and interference from other 2.45 GHz users is negligible.