Content uploaded by Daniel Dauda Wisdom
Author content
All content in this area was uploaded by Daniel Dauda Wisdom on Nov 30, 2020
Content may be subject to copyright.
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
432
An Improved Battery-Life Power Saving Scheme (IBPSS) in IEEE
802.16e Networks
Daniel Dauda Wisdom,
Department of Mathmatics
Usmanu Danfodiyo University
Sokoto, Nigera
danieldaudawisdom1@gmil.com
Nasir Faruk
Department of Telecom-Science
University of Ilorin, Kwara State, Nigeria
Faruk.n@unilorin.edu.ng
Samson Isaac
Department of Computer
Science
Kaduna State University
Kaduna, Nigeria
samson.isaac@kasu.edu.ng
Sani Magami
Department of Mathematics
Usmanu Danfodiyo University,
Sokoto, Nigeria
smagamee@yahoo.ca
Muhammad Aminu Ahmad
Department of Computer Science, Kaduna
State Uni-
Kaduna, Nigeria
muhdaminu@kasu.edu.ng
Yusuf Elijah
Department of Electrical Engineering,
Ahmadu Bello University Zaria (ABU)
ndahi123@gmail.com
.
Abstract
IEEE 802.16e features made Battery Life of Mobile Station (MS) challenging, since MS are battery powered with
limited resources. Numerous power saving scheme have been proposed to extend MS lifetime. However, the
existing scheme ignores real time traffics which will improve Power savings accordingly. Hence, we have
proposed a new scheme that unified power saving classes of type I (PSC I) and II called an Improved Battery-
Life Power Saving Scheme (IBPSS). The propose scheme analytically modified the sleep parameters based on
traffic load and remaining battery power. It employed Hyper-Erlang distribution to capture the actual variant
traffic characteristics. In addition, the proposed scheme used an improved algorithm to improve performance.
And the simulation results publicized that, the propose scheme achieved a better performance compared to
existing scheme in terms of average response delay and power savings efficiency.
Keywords: Battery-Life IEEE, PSC I and II
1. Introduction
Worldwide interoperability for microwave access (WiMAX) is developed with supports for Mobility
characteristics for Mobile Stations (MS) Known as mobile devices; at high data rate, less cost of wireless
network deployment, higher bandwidth as well as Promising economic and societal gain for our next
generation wireless broadband systems.
Thus, vital features were added to the existing standard but these features made the battery life of mobile
devices an important problem. Since MS are battery powered with a limited superimpose rechargeable life;
Formally the IEEE 802.16 is designed for fixed subscriber stations (SS) (IEEE, 2004), while the subsequent
version IEEE 802.16e is an extension of the former standard with mobility so that MS can be mobile during
Services (IEEE, 2005)(IEEE, 2009). Hence, efficiency of MS is an important challenge for battery-powered
devices. In order to prolong the lifetime of a mobile device, power saving classes (PSCs) of type I, II, and III
were designed. Type I is designed for Best Effort (BE) and other non-real-time variable rate (NRT-VR)
traffics, Type II for unsolicited grant service (UGS) and real time variable rate (RT-VR) traffics, Type III is
designed for managing operations and multicast connections. The three PSC differ by their parameter sets,
methods of activation/deactivation, and the policies of MS availability for data transmission (Wisdom et al.
2019a) (Draft Amendment 2010). The IEEE used three parameters to enhance saving of power, which are
namely: idle threshold, initial sleep window and final sleep window (Xiao, 2005) ( Wisdom et al. 2019b). The
idle threshold is a period of inactivity, when the MS at this time has no messages to transmit/receive before
switching to sleep mode. Usually, MS before switching to sleep mode negotiates with it current BS for
approval to switch to sleep mode. The BS then allocates the sleep parameters which are namely: initial sleep
window (Tsmin), final sleep window (Tsmax) and listening window (L) to a MS. The MS then switches to a
sleep session when it has received these parameters Wisdom et al. 2019. The Tsmin is the length of the first-
sleep interval (T) that a MS will go to sleep. It wakes up after the first T to listen to possible traffic indication
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
433
message from the BS for the duration of listening window (L). When the message is negative, The MS
continues to sleep mode after the listening window intervals. Else, the message indicates positive, and then
the MS returns to wake mode. The T alongside with its L is the sleep cycle. When the MS remain in the
sleeping mode, the next sleep cycle starts, and the T is doubled. These processes are continuously repeated
until Tsmax is reached which is the maximum length of sleep period (Threshold value).
several schemes have been proposed to improve efficiency of MS in WiMAX Network Xiao (2005); Saidu et
al. (2015) Xiao et al. (2006); Jenhui et al. (2014); Feng (2015) and Wisdom et al.(2019a,b). However, the
schemes in Eunju et al. (2007); Xiao et al. (2006); Chou et al. (2013). Wastes energy due to their excessive
listening operations and or frequent switching frequency from sleep/wake mode. While Saidu et al. (2015)
reduces the excessive listening operations of mobile station (MS), with a longer sleep interval. More so, the
scheme also minimized the average energy consumption under PSC of type I, but ignores PSC of Type II, as
such the existing scheme is limited only to best effort (BE) and other non-real time Variable rate (NR-VR)
traffics in which packet delay and/or packet loss are still experienced which is still an interesting challenge
pose in WiMAX Networks. PSC of type II is designed to address these important problem, since, PSC of type
II is an assured means of communication, with a dedicated bandwidth; most especially live streaming or
unsolicited grant services (UGS) Voice over internet traffics (VOIP), where stringent requirements on delay is
required. PSC of type II uses a dedicated bandwidth and type II is design to conveniently handle these
challenges mentioned, which became our motivation for this research paper.
Hence, a new scheme called an Improved Battery-life power saving Scheme (IBPSS) for mobile broadband
network service is proposed. The scheme adaptively adjusts three sleep parameters based on the traffic arrival
pattern and remaining battery power. It uses an improved sleep mode control algorithm and a Hyper-Erlang
distribution to determine the actual variant traffic characteristics and unified both power saving classes of type
I and II as well as resolved the challenges of stringent requirements on delay in order to resolve issues of
congestion, buffer overrun and packet loss. Thus, the propose Scheme is applicable for all traffic class such as
BE, UGS like Voice over Internet Protocol (VOIP), RT-VR, and other live streaming services unlike the
Exiting Scheme, the proposed scheme simulation results outperformed the existing Scheme in terms of both
average response delay and efficiency of MS.
1.1 Power Saving Class of Type I (PSC I)
Power saving class (PSC) of type I has a sleep interval with an exponential growth, and do not
receive/transmit packet at the listening intervals. An example of PCS of Type-I is shown in Figure 1. Type is
designed for connections of best effort (BE) service and other non-real time variable rate (NRT- VR) service.
In addition, in PSC of type 1, the MS is designed to be awake during the listening windows to check the value
of the send by the BS, if it is negative (-) then the next sleep cycle begins with a sleep
window doubling the preceding sleep window, i.e. ( ). This behavior will continue until the
maximum sleep window ( ) is reached, that is ) On the other hand, if the
is positive (+) the MS quit the sleep mode and becomes active Wisdom et al (2019b).
1.2 Power Saving Class of Type II (PSC I)
The amazing characteristic features of PSC of Type II in IEEE 802.16e is the allowance of packet delivery at
the listening windows. Regular termination for Type II shares the same definition with Type I, however, the
causes that induce this termination are considered different. Regular termination for Type II in the nth cycle
can be attributed to the composite effects. PSC of Type-II Figure 2, Is designed for connections of unsolicited
grant service (UGS) and Real-Time Variable Rate (RT-VR) traffic, such as VoIP. Here the behavior of the MS
is similar to that of PSC-I with only two differences. First, the sleep window remains constant throughout the
sleep mode with a dedicated bandwidth. Secondly, traffics are serviced during the listening window, thus the
MS only quits the sleep mode if the available traffics are beyond the capacity of the listening window.
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
434
Figure 1: PSC I sleep mode operation in the IEEE 802.16e
Figure 2. PSC II sleep mode operation in the IEEE 802.16e
Figure 3. PSC III sleep mode operation in the IEEE 802.16e
The rest of this paper is ordered as follows: Section 2: presents Related Literatures, Section 3: The Propose
Scheme, Section 4: Procedure of Parameters Adjustments, Section 5: Performance Evaluation and section 6:
Concludes this search paper.
Related Literatures
This section presents a review of related works on power saving schemes. The schemes are reviewed by
highlighting their individual operational procedure, strength and weakness as follows:
A Mechanism with periodic traffic indications was proposed in Eunju et al. (2007); to minimize delay of MS.
The scheme used traffic indication (TRF-IND) messages to initiate transmission at every constant time. The
TRF-IND messages consist of a listening interval, wake interval and a sleep interval. During the listening
interval a MS synchronizes with the serving BS and decides whether to switch to wake-mode or remain in a
sleep. If there are data traffics in the buffer for the tagged MS, the BS sends a positive TRF-IND message and
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
435
the MS switch to wake-mode. The BS sends data during the wake-mode and the wake-mode terminates if no
traffic arrives during a time-out fixed time of a constant length (T). If any data traffic arrives during inactive
time (T), the MS changes to wake-mode and transmits the data otherwise, the MS goes to a sleep without
exchanging MOB-SLP-REQ/RSP messages. It reduces the average delay because of its frequent changes from
sleep/wake state, at the cost of an increase in energy-consumption due to frequent switching.
Energy Mechanism was proposed by Xiao (2005) to improve performance of the battery-life of MS. it
considers the MS to be in sleep-mode during listening period but it’s in active. The mechanism increases the
sleep interval exponentially when there is no arrival of loads. It significantly reduces the frame response time
with excessive listening operations, which may lead to waste of energy. Hence, An Efficient Battery Lifetime
Aware Power Saving Scheme (EBLAPS) was proposed in Saidu et al. (2015), to reduce the energy
consumption of MS. The scheme adaptively adjust the three parameters: based on traffic arrival pattern. It
used a sleep mode algorithm to consider Non Real Time Services (PSC I) in the DL operation of the 802.16e
in order to reduce the frequent transition to listening mode under low traffic arrival rate. The scheme reduces
the average energy consumption. However, it ignores type II unsolicited grant services (UGS), Real-Time
variable rate (RT-VR) Services due to their stringent requirements on response delay. More so, before now,
about 75% of mobile users only pay attention to web browsing, email messaging and other non-real-time
variable rate (NRT-VR) services, but with the emerging advancement in technology, live streaming services
such as UGS like voice over internet protocol (VOIP), RT-VR, video streaming, instant messaging, video
conferencing is gradually gaining a wider acceptance in the global community, PSC of type II requires fixed
byte for communication which is of interest especially, with the emerging social network platforms like face
book, what sap, Skype and the likes. Thus, an enhanced energy scheme was proposed in Xiao et al. (2006), to
reduce the excessive listening operations of MS in the existing scheme. The proposed scheme used half of the
last sleep interval to adjust the Tmin when it exits from the previous sleep-mode operation as the initiate sleep
interval in the next sleep-session. When the initiate sleep interval is less than Tmin, then the initiate sleep
interval is set to Tmin. The BS is not notified of the initiate sleep interval through the sleep request message
sent by the MS. When the traffic is low the inter-Service Data Unit (SDU) arrival is large enabling the
mechanism to effectively decrease the number of listening intervals in one sleep mode operation. The
mechanism improved energy conservation by extending the MS lifetime; however, it has higher response
delay due to the longer sleep period.
A Battery Lifetime-Aware Power Saving Scheme was proposed in Chou et al.(2012), to minimize energy
consumption and average delay of MS. The scheme dynamically adjusts three operating parameters, idle
threshold, Tsmin and Tsmax according to the residual energy and the traffic load. However, the scheme
extended the battery life with an increase in response delay, more so the scheme frequently goes to listening
mode when the traffic arrival is low thereby causing an increase in the consumption rate. A Delay-Aware Auto
Sleep Mode Operation was proposed in Shangqing et al. (2007), to minimize delay and conserve energy of
MS. The algorithm dynamically tunes the initial sleep window based on the traffic load and the delay
requirements, after serving all buffered packets; the MS reverts to the initial-sleep window which depends on
the number of packets served. It successfully bound the delay to a certain range. However, it suffers an
increase in power-consumption.
Remaining Energy-Aware Power Management Mechanism (REAPM) was proposed by Min-Gon et al.
(2008), to improve the battery-life of MS and minimize the response delay. The REAPM updates the sleep
parameters dynamically taking into consideration the remaining energy and the inter-arrival of each frame.
The Tmax is updated using smoothing technique with current inter-arrival time of a MAC SDU at each frame
arrival and adjust Tmin by considering the remaining energy and the Tmax. More so, after the mechanism
initialize parameters such as, the Tmin and Tmax and the current inter-arrival time of MAC SDU. It continues
to normal operation mode and terminates from this mode when it receives request message to enter sleep-
mode. This mechanism can achieve low response delay if there is sufficient energy and prolong the battery-
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
436
life. However, there is little increase in the energy consumption due to the constant Listening interval (L) that
is between the sleep intervals.
A Dynamic Traffic Load Aware Sleep Mode Operation Algorithm was proposed in Jianbin et al. 2008; to
enhance the performance of the battery powered mobile devices. The algorithm employs a dynamic scheme to
tune the idle check time of a MS which is the waiting period after the packet arrival in the wake-mode before
entering the next sleep mode. The idle check time is adjusted dynamically after the entire buffered packets are
served based on the number of packets served and the previous sleep window. The waiting time is then set to
zero when the last sleep window is bigger than the Tmin which makes MS to go to sleep immediately but
when the last sleep window is the same as Tmin it is set to a certain value, which makes MS to wait for some
time before transiting to sleep mode, the algorithm improves power with a slight increase in its algorithm
complexity. Therefore, DAPSS Base on load in Traffic was Proposed by Wisdom et al. (2019) to reduce the
longer sleep intervals the scheme successfully reduced the longer sleep interval, but with an increase in the
consumption rate. and, an efficient sleep window based power saving scheme (ESPSS) was proposed to
reduce the consumption rate of the DAPSS in Wisdom et al. (2019). The scheme takes an average sleep
window and dynamically adjusts the sleep parameters appropriately so as to improve power savings. The
scheme successfully improved MS performance but ignores capturing the actual arrival of traffics accordingly
to further improve QoS. Hence, a partially observable Markov decision process (POMDP)-based sleeping
windows determination (PSWD) strategy was proposed in Kai-Ten et al. (2015), to enhance power savings for
mobile devices. The PSWD algorithms adaptively adjust the sleep window at every fixed interval with a
consideration for quality of service (QoS). The algorithm appropriately captures the traffic status on each
adjustment of the sleeping window as well as control the sleep cycles, each control cycles Ci (i = 1) is
overlapped with the adjacent control cycles C i—I and C i+1. The first control cycles C 1 starts at the last
frame of an advanced mobile stations (AMS) idle session in the normal mode. The remainder control cycles
are individually started at the end of every listening window within the previous control cycles. The POMDP
algorithm saves energy better than the standard IEEE 802.16e/m and satisfies the delay requirements but has
issues of algorithm complexity.
A Real Time Heuristic Algorithm was proposed in Gary et al. (2009), to minimize the switching frequency of
MS. The algorithm operates based on three criteria, the probability of buffer overflow, expected delay
violation, and battery lifetime expiry. It uses the probability of the finite buffer overflow to ensures that when
the packet arrives during the next sleeping window the expected delay may not exceed the delay violation
tolerable bound, and checks the battery lifetime in order to ensure that the power is sufficient to extend the
sleeping mode by at least a period, and that the MS still has enough resources to perform the transmission so
as to obtain all the packets coming to the buffer during the period of time. The algorithm minimizes energy
consumption with an increase in the average waiting time. Yuguang (2001), presents a hyper-Erlang
distribution model as well as the application of wireless network and mobile computing systems. The scheme
demonstrates that the hyper-Erlang model provides a general model for users’ mobility and may provide a
realistic approximation to fat-tailed distribution which leads to the self-similar traffic. The main difference
from the traditional approach in the self-similarity study is an approximation model which preserves the
Markovian property of the resulting queueing systems. More-so this study revealed that the hyper-Erlang
distribution is a natural model for the characterization of the systems with mixed types of traffics. Therrar
(2015) considers the sum of independent Hyper-Erlang distributions and showed that the probability density
function of this distribution is related to probability density function of the sum of independent Erlang
distributions- the Hypoexponential distribution. As a consequence, found an exact closed expression for the
probability density function of both distributions.
In Saidu et al.(2017), a Hyper-Erlang Battery-Life Energy Scheme (HBLES) was proposed in order to
improve efficiency as well as minimize the average response delay. The scheme analytically modified three
sleep parameters namely: idle threshold, initial sleep window and final sleep window according to the
remaining battery power and the traffic arrival pattern. It also uses a Hyper-Erlang distribution to determine
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
437
the real traffic characteristics. The simulation results proved that the HBLES scheme out performs the existing
scheme in terms of energy savings and average delay.
Therrar (2017) presents, On the distribution of the Ratio of Two Hyper-Erlang Random Variables was
proposed the study revealed the exact distribution of the ratio of two independent HyperErlang distributions
by derivation. However, closed expressions of the probability density, cumulative distribution function,
reliability function, hazard function, moment generating function and the rth moment are found for this ratio
distribution and proved to be a linear combination of the Generalized-F distribution. Moreover, the particular
case, the ratio of two independent Hyper-Exponential distributions was examined.
The 802.16e supports QoS for real time traffics which include voice over internet protocol and video
streaming with different QoS requirements Tang et al. 2010. The five classes defined by the IEEE Standard
are shown in table 1.
Table 1: Summary of IEEE QoS Service Classes
Services
Description
Example
QoS
Real Time
Variable Rate
Real time data streaming for variable-sized
data
MPEG Video
High Quality
UGS
Real time data streaming for fixed-sized
data
VOIP
High Quality
Extended RT-VR
IEEE 802.16e Added support real-time
applications with variable data rates
VOIP With
Silence
Suppression
High Quality
NRT-VR
Delay Tolerant data streams for variable-
sized data
FTP
High Quality
Best Effort
No minimum Service Level is required
Web Browsing
E-mail
Low Quality
Unsolicited grant Services ( UGS), Non Real Time Variable Rate (NRT-VR), File Transfer Protocol (FTP)
Voice over internet Protocol (VOIP).
3. Proposed Improved Battery-Life Power Saving Scheme (IBPSS) for MBB Network Services
An Improved Battery-Life Power-Saving Scheme (IBPSS) which is an improvement of the existing EBLAPS
Scheme is proposed. The existing scheme is first presented with its strengths and weakness highlighted as
follows:
The EBLAPS scheme minimized the frequent transition of MS to Listening Mode under PSC of type I, as
well as increase energy savings. It however ignores real time services of PSC of type II such as unsolicited
grant services (UGS), Voice over internet protocol (VOIP), real time variable rate (RT-VR), due to their
stringent requirements on response delay, as such the existing scheme is limited only to best effort (BE)
traffic, which still result to excessive packet delay and packet loss which may have effects on the overall
quality of service (QoS), user dissatisfaction especially for services that may require stringent requirements on
response delay, for example unsolicited grant services (UGS) like Voice Over Internet Protocol (VOIP), Video
streaming, Real time Variable Rate (RT-VR) Services. In live streaming services, response delay may degrade
the user experience of an interactive websites having a significant effect on the overall performance. Thus,
improving on these will be an edge in the WiMAX Networks, and PSC of type II is designed to address these
challenges mentioned above. Therefore, the existing scheme is not applicable to all traffic classes like UGS
(VoIP) and other live streaming services that produce packets of fixed bytes at fixed intervals, and demands
stringent requirements on response delay. More so, the continued increasing number of multimedia
applications such as Skype, What sap, Facebook and the likes that has gain acceptance globally with a gradual
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
438
majority engaging in live/active network services daily, that may require a dedicated bandwidth for an
excellent communication is an open area of research study which is the motivation for this research work.
Thus, in this paper, we have proposed a new Scheme called an improved battery-life power saving scheme
(IBPSS) for mobile broadband network services. The scheme adaptively adjusts three sleep parameters
namely: Iddle threshold (Tth), Initial Sleep Window (Tmin), and Final Sleep Window (Tmax) according to the
residual battery power and the actual traffic arrival Characteristics. The proposed scheme used an improved
sleep mode control algorithm and unified both PSC I and II; as well as resolved the challenges of stringent
requirements on response delay and/packet loss. The proposed scheme also employed a Hyper-Erlang
distribution in order to determine the actual characteristics of the traffics arriving and analytically modified
the sleep parameters in order to reduce the excessive response delay and enhance power savings. Thus, unlike
the existing scheme, our propose Scheme is applicable for all traffic class such as best effort (BE), UGS like
Voice Over Internet Protocol (VOIP), RT-VR, and other live streaming services respectively.
The main difference between the existing scheme and the Proposed Scheme is as follows; first the analytical
modeling of the sleep parameters, the way of adjustment of the sleep parameters (Figure 5), and the
unification of both PSC of type I and II as well as the selective choice of the sleep intervals for the sleep
window. Finally, the Proposed Scheme adjusts Iddle threshold (Tth), Initial Sleep Window (Tsmin), and Final
Sleep Window (Tsmax) as follows:
3.1 Analytical Model: adaptive adjustment of the iddle threshold (Tth)
The proposed scheme adaptively adjust idle threshold while considering the remaining battery life of MS.
When the power remaining is small. That is when Power is equal to zero (0), and MS is totally depleted
(Figure 5). The MS immediately switch to sleep mode. As the battery life decreases the idle threshold value is
adjusted to a smaller degree in order to timely react to the current battery power.
In this research paper, Minimum idle threshold is considered due to the fact that Power required to wake up
(waking-up) still consumes energy, when the MS transits from sleep to wake mode. Figure 1: shows the
variant battery life in every state. The power consume during state transition may be greater than the
remaining battery power at the idle mode (state), especially when the threshold is too small, thus we derived
the minimum idle threshold as follows:
Figure 4: variant idle threshold (battery power).
3.2 Iddle Threshold
The modified iddle threshold uses a downlink traffic arrival approach to determine the best time for the next
iddle threshold so as to minimize the response time as well as reduce the rate of power consumption
respectively. The scheme employs an average weight smoothing technique to determine the iddle threshold
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
439
value which enables a mobile device with the flexibility to adjust it iddle threshold base on the traffic arrival
pattern and the remaining battery power.
The iddle threshold (Tth) is systematically derived as follows:
min min max
max 1
th s th s th s
th s
TTT
th T Otherwise
T if
We derived the minimum iddle threshold Tth-smin as follows:
2
min w
th s
id
P
TE
Where Pw is the sleeping waking up power during the state transition and Eiddle is the power during the iddle
state.
The sleep interval is given as Tsn = Tsmin
Where Tsn is the length of the sleep interval at the nth sleep cycle, Tsmin is the minimum sleep window size
computed by pre-negotiated MOB-SLP-REQ and MOB-SLP-RSP massages.
Note that, sleep intervals are interleaved with a listening interval.
The sleep intervals are of fixed time duration, and data transmission is allowed within the listening interval, as
long as the total amount of packets does not exceed the capacity of the listening interval the MS continues
processing packets otherwise MS deactivate the sleep mode in order to continue packets transmission, this is
because in the case of the proposed scheme, a MS requires a dedicated bandwidth for packets transmission,
with stringent requirement on response delay. Thus unsolicited grant services are required for packets
transmission.
Then, we calculate the iddle threshold
th
T
as follows:
(3)
remaining
th th new load previous load
total
P
TT E
Hence, we have
, 0 1
1 (4)
new load current frame arrival previous load
Where
is proportionality constant,
new load previous load
and
are the proposed new frame arrival rate and the previous frame
arrival rate in that order.
current frame arrival
is the current frame arrival rate.
While
remaining
P
is the remaining energy and
total
E
is the total energy
3.3 Initial Sleeping window
The initial sleeping
mins
T
window is calculated base on the remaining battery power and the new traffic
load as follows:
min max ,1 (5)
remaining
s new load
total
P
TE
3.4 Optimal Sleeping Window
The optimal sleeping window
maxs
T
is calculated when the initial sleeping window
mins
T
and frame
response delay is known. We derived the optimal sleeping window from Zhang (2007)
as follows:
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
440
1
11
( ) (6)
!
i
Zt
ii
i
d
zz
Ki
hi
ii
Zt
a t e
z
Where
i
i and
are constants with
0, 0 1 int .
i i i
and Z are positive egers
When we integrate (6) from 0 <x ≤ t we obtain the derived CDF as follows:
0
( ) ( ) (7)
dd
t
ht
c t f x dx
Suppose that
,p
d
h
is the remaining packets inter arrival time derived from Zhang (2007)
And Wisdom et al. (2019) the summation (
n
S
) is computed using (6) and (7) as follows:
1
,
1
00 !
i
dp
k
zjii
i
h i i
i I j k
ii
z
a e z L
zk
and we integrate (8) from 0 <
,qp
h
a
≤ t, to achieve the CDF as:
00
k
th i k th Sleep Listening waking up
kL
A E T E P T E E E
Suppose
n
S
to be the Summation of the final derivative from the 1st, 2nd 3rd 4th…up to the nth derived as
follows:
1
, 1 (10)
n
nj
j
S T L n
Let Tn represent the length of the nth sleep window
1min
max
2
(11)
ns
n
s otherwise
T if n Y
T
T
First: we derived equation (11) from Zhang (2007)
Let the probability of a frame arrival at the iddle state be represented as:
( ) 1
n th
p N T
1
10
(12)
!
i
ii
k
zj
Kzt
ii
i
i j k o
ii
zte
zk
Secondly:
Let the probability of a frame arrival during the nth sleeping interval also be represented as follows:
()
n
p N n
11
11
10
. (13)
!
iii
i i n i i n
k
zj
IKk
zc
z L z c
ii
inn
i j k o
ii
ze S L e s L e
zk
if n Y
Replacing (10), (11) into (13) and we obtain.
(14)
i i i i
Kk
z s z w
n
a A S L e W L e
Where
1min min min min
2 ( ), (2 )
nn
s s s s and A
S T nL T L W T nL T
1
1
00 !
ik
zjii
iii
i I j k
ii
ze z L
zk
If n ≥Y
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
441
Likewise, replacing (10), (11) and (13) we obtain
( ) ( ) (15)
i i i i
z a z Q
KK
h
a A a L e Q L e
Where
1min min ) !
((2 ) ( !!
Ys s L and
n
a T YL T r n r
min
((2 ) ( min)
Ys
Q T YL Ts
Using (10), (11)(14), and (15)
The average response delay is expressed as follows:
max (16)
22 i i i i
kk
z a z Q
s
LT
A D R A a L e Q L e
And then finally we compute the optimal sleep window from (16), and we have
max 2 [ ] 2 [ ] (17)
KK
s i i i i
T A D L R A a L e z a Q L e z Q
The average energy consumption A(E) is also expressed as:
00
18
k
th i k th Sleep Listening waking up
kL
A E T E P T E E E
Where
th
T
is the iddle threshold
Sleep Listening waking up
E E E
Is the energy consumed during the listening Sleep waking up period. at the wake mode MS has packets to
transmit, Where T1, T2, to Tn, is the length of the sleep interval at the nth sleep cycle. The subsequent sleep
intervals doubles the previous in a case where there is no traffic arrival, since the proposed scheme is
adaptive; the sleep intervals are dynamically adjusted based on the traffic load and the remaining battery life.
The sleep intervals are of fixed time duration, and data transmission is allowed as long as the total amount of
packets does not exceed the capacity of the listening interval the MS continues processing packets otherwise
MS deactivates the sleep mode in order to continue packets transmission. This is because in the case of the
proposed scheme a MS requires a dedicated bandwidth for packets transmission, with stringent requirement
on response delay. When packet transmission begins it does not allow for interruption until the task is
completed, i.e (Real Time Variable rate such as VoIP). But when there are no packets to transmit the MS
continues in sleep mode with an exponential growth otherwise ends the process.
3.5 Procedure of parameters Adjustment
The MS begin in a wake mode. It waits for a predefined idle time, if no packets are received, then check if the
weighted variance of the traffic arrival is larger than zero; MS sends a request to the BS to enter the sleep mode.
The BS computes the initial and final sleep windows for the MS and sends them to the BS for it’s to enter sleep
mode. If the MS has data to transmit it transits to wake mode. And then finally continues to normal operation
when the weighted variance and the battery power is larger than zero. Otherwise, it ends the procedure. The
pseudo code that controls the sleep mode of each MS is presented in algorithm 1.
Algorithm 1: Proposed Power Saving Scheme
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
442
Figure 5: the Proposed Procedure of parameters adjustment
The above Figure Illustrates the Proposed procedure of the MS sleeps parameter adjustment. The MS begin in a
wake mode. It waits for a predefined idle time, if no packets are to be sent/received, then check if the weighted
variance of the traffic arrival is larger than zero; MS sends a request to the BS to enter the sleep mode. The BS
computes the initial and final sleep windows for the MS and sends them to the BS for it’s to enter sleep mode. If
the MS has data to transmit it transits to wake mode at the listening intervals. And then finally continues to
normal operation, when the weighted variance and the battery power is larger than zero. Otherwise, it ends the
procedure. The pseudo code that controls the sleep mode of each MS is presented in algorithm 1.
4. Performance Evaluation
To evaluate the performance of the propose Scheme and that of the existing scheme, Discrete Event
simulator was used. The evaluation was based on the average power savings and response delay. In
addition, at the simulation time performance analysis of sleep window Determination scheme
(PSWD) was compared with that of our proposed power saving scheme for broadband wireless
systems; since, the existing battery lifetime power scheme ignored real time services.
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
443
Figure 6: Simulation Topology
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
444
Figure 7: Illustrates the average Power Consumption vs the Mean Arrival Rates
We observed that from the beginning both scheme have similar performance from 0-11. However,
subsequently our proposed scheme has a superior performance due to the appropriate adjustment of
the sleep parameters.
Figure 8: Illustrates the average Response delay vs the mean arrival rate
The results of Figure 9 and 10 are compared with that of the existing Performances Analysis Sleep
Window Determination (PSWD) for video services, as follows:
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
445
Figure 9: Illustrates the average Power consumption vs The Mean arrival rate of traffic load for video
Services
Figure 10: Illustrates the average response delay vs The mean arrival rate of traffic load for video
Services. Based on the proposed analytical results obtained in section 3.1 and the proposed simulation
results depicted in Figures 8-10, it is observed that the proposed power saving Scheme significantly
performed better than the Benchmark Schemes in terms battery consumption and delay respectively.
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
446
5. Conclusion
We have proposed a new Scheme that have enhanced the variant parameters of the existing scheme
called an Improved Battery-Life Power Saving Scheme (IBPSS) in wireless broad band networks
services in the DL operation of the WiMAX Networks. The Proposed Scheme adjusts the sleep
parameters based on the traffic load and the remaining battery power. The scheme employed a Hyper-
Erlang distribution in order to capture the various traffic load characteristics more appropriately or
just in time. The scheme analytically modifies the sleep parameters to improve efficiency and in-
cooperated real time services by unifying PSC of type I and II. Hence, the proposed Scheme is
applicable for all traffic classes such as Best Effort VIOP, Video Streaming, UGS and other RT-VR
traffics. The Scheme further addressed the challenges of excessive response delay resulting to poor
Quality of service (QoS) by assigning a dedicated bandwidth in transmission. Finally, a Discrete
Event Simulator was used for extensive simulation studies, and the results depicted in Figure 7-10
showed that the proposed Scheme outperformed the existing Scheme significantly in terms of
response delay and battery efficiency.
Acknowledgements
The authors wish to thank anonymous reviewers for their constructive critic comments that have improved this
paper.
References
Chou L.D, Li D. C and Hong W.Y (2013). Improving energy efficient Communicationswith a battery
lifetime-aware mechanism in IEEE 802. 16eWireless networks,Concurrency and
Computation: Practice and Experience, (25), pp. 94–111.
DRAFT Amendment to IEEE Standard for Local and Metropolitan Area Networks - Part 16: Air
interference for Fixed Broadband Wireless Access Systems — Advanced Air Interface, IEEE
P802.16m/D7, July 2010.
Eunju H, Kyung J.K, Jung J. S and Bong D. C (2007). The Power Savingm Mechanism with Periodic
Traffic Indications: A New Sleep Mode Scheme in the IEEE 802.16e. Proceedings of the Third
Korea-Netherlands Conference on Queueing Theory and its Applications to Telecommunication
Systems. 2007, pp. 319-334.
Feng K-T, Wun-Ci S and Chun-Yu C (2015).Comprehensive Performance Analysis and Sleep
Window Determination for IEEE 802.16 Broadband Wireless Networks.IEEE Transactions on
Mobile Computing. Pp1536-1233.
Gary K.W, Zhang Q and Tsang D-H. K (2009) Switching Cost Minimization in the IEEE 802.16e
Mobile WiMAX Sleep Mode Operation. (9) June 21 - 24, 2009.
IEEE 802.16 WG (2004). Standard for Local and Metropolitan Area Networks Part 16: Air
Interfacefor Fixed Broadband Wireless Access Systems, IEEE 802.16Working Groupand
others, IEEE Std, 802.16.
IEEE 802.16e WG (2005). IEEE Standard for Information Technology-Telecommunications
andInformation Exchange between Systems - LAN/MAN Specific requirements, Part 16: Air
Interface for Fixed and Mobile Broadband Wireless Access Systems, IEEE Std802.16e.
IEEE Standard for Local and Metropolitan Area Networks - Part 16: Air Interference for Fixed
Broadband Wireless Access Systems, IEEE Standard 802.16-2009, May 2009.
Jianbin X, Zhanting Y and QiuYu Z (2008). Traffic Load-Aware Power-saving Mechanism for IEEE
802.16e Sleep Mode, in College of Computer and Communication Lanzhou University of
Technology Lanzhou, China. 2008, pp. 735-745.
Jenhui
C,
Woei-Hwa T and Jiann-Der I (2014). A Downlink and Uplink Alignment Scheme for Power
Saving in IEEE 802.16 Protocol. Scientific World Journal Volume Article ID 217973,11pages.
Kim, L. M.-G. Kang, M. and Choi, J. Y. (2008) Remaining energy-aware power management
International Journal of Information Processing and Communication (IJIPC) Vol. 9 No. 1&2 [May, 2020], pp. 432-447
Special Issue on Computing and Communication Technologies
Online: ISSN 2645-2960; Print ISSN: 2141-3959
447
Mechanism in the 802.16e MAC. In the 5th IEEE consumer communications And Networking
conference,(CCNC) (pp. 222–226), 2008.
Liu J, Ren F and Lin C (2010). A dual-threshold power saving mechanism in WiMAX. In IEEE/ACIS
9th international conference on computer and information science (ICIS) pp. 465—470
Min-Gon K, Kang M and Choi J. Y (2008).Remaining Energy-aware power Management mechanism
in the 802.16e mac, in 5th IEEE Consumer Communications and Networking Conference
(CCNC'08), pp. 222–226.
Saidu I, Azmi S and Zukarnain Z (2015). An efficient battery lifetime aware power saving (EBLAPS)
mechanism in IEEE 802.16 e networks.Wireless Personal Communications (80), pp. 29-49.
Saidu, I. Musa, H. Lawal, M. A. & Kane I. L. (2017). Hyper- Erlang Battery-Life Energy Scheme
in IEEE 802.16e Networks, Department of Mathematics and Computer Science Umaru Musa
Yar’adua University (UMYU)Katsina, Nigeria, Covenant Journal of Informatics & Comm
Unication Technology, Vol. 5 No. 2. Dec, 2017.
Shengqing Z, Xiaoyu M and Lujian W (2007). A delay-aware auto sleeps mode operation for rower
saving WiMAX. In Proceedings of 16th international conference on computer communications
and networks, 2007 (ICCCN 2007) pp. 997-1001.
Tang, Y.-W. Wu, T.-Y. Ding, J.-W. Chen,J.-J (2010). Resource Sharing and bandwidth allocation
WiMAX Mesh Networks using centralized scheduling. Journal of Internet Technology,
11(2):251-259, 2010.
Therrar, K. Khaled, S. Convolutions of Hyper-Erlang and of Erlang Distributions, International
Journal of Pure and AppliedMathematics Volume 98 No. 1, pp 81- 98, 2015.
Therrar, K. Khaled, S. & Seifedine, K. On The Distribution of the Ratio of Two
Hyper-Erlang RandomVariables,An InternationalJournalof Applied Mathmatics
and Information Sciences, 1 Jan. 2017.
Vatsa O. J, Raj M, Ritesh K. K, Panigrahy D and Das D (2007). Adaptive power saving
algorithm for mobile subscriber station in 802.16e. In the 2ndIEEE International
conference on communication systems software and Middleware, 2007
(COMSWARE 2007), pp. 1–7.
Wisdom, D. D., Saidu I, Tambuwal, A. Y., Isaac, S Ahmed M.A and Farouk N (2019a). An
Efficient Sleep-Window-Based Power Saving Scheme (ESPSS) IN IEEE 802.16e Networks,
15thInternational Conference on Electronics Computer and Computation (ICECCO 2019).
Wisdom, D. D., Tambuwal, A. Y., Mohammed, A Audu, A., Soroyewun, M. B., and Isaac, S
(2019b).a Delay Aware Power Saving Scheme (DAPSS) in IEEE 802.16e WiMAX
Networks, Institute of Electrical and Electronic Engineers Conference (IEEE), Ahmadu Bello
University Zaria, Kaduna State, Nigeria, 2019.
Xiao Y (2005). Energy saving mechanism in the IEEE 802.16e wireless MAN,
IEEE Communications Letters, 9(7), pp. 595–597.
Xiao J, Ren S. Z. B and Cheng S (2006). An Enhanced Energy Saving Mechanism in IEEE 802.16e
in 2006 31st IEEE Conference on Local Computer Networks; 2006. PP 1-5.
Yuguang, F. (2001).Hyper-Erlang Distribution Model and its Application in Wireless Mobile
Networks,2001 Kluwer Academic Publishers Manufactured in The Netherlands Wireless
Networks 7, 211–219, 2001.
Zhang Y (2007). Performance modeling of energy management mechanism in IEEE
802.16e mobile WiMAX. In Wireless Communications and Networking Conference (WCNC
2007). IEEE, pp. 205-3209.
