Abstract —As future M2M (Machine-to-Machine) communica-tions aim at supporting wireless networks which feature longrange, long operating duration and large device number, theIEEE 802.11ah Task Group is going to specify a global WLANstandard that utilizes carrier frequencies below 1GHz. To powerthe M2M devices, harvesting energy from ambient environmenthas attracted attentions from researchers. Although applying theIEEE 802.11 PSM (Power-Saving Mode) scheme reduces energyconsumption, devices go to sleep only when the traffic bufferis empty, staying awake unnecessarily, which wastes energy inoverhearing the uplink traffic transmitted to the AP. In this paper, we propose DeepSleep, a novel energy-aware scheme, whichgrants higher channel access priority for low energy level devicesdynamically. Moreover, applying DeepSleep scheme alleviates the
channel congestion by randomly deferring the wake up timeof the devices, thereby achieving higher energy-efficiency, whichsaves nearly 75 % of energy per delivered packet. Additionally, the overall performance improvement when DeepSleep devicesco-exist with 802.11 devices is also verified.
I. INTRODUCTION
M2M communications are typically characterized by con-
necting autonomous devices to other devices. These devices
communicate with a central controller or with each other
without human intervention. M2M applications are low power
consuming, such as lighting control, security sensing, medical
devices and entertainment systems, and thus there is large
market potential for M2M services.
Recently the IEEE 802 Working Group 802.11 is approveda project under the Task Group 802.11ah to amend the 802.11
standard to include Sub 1 GHz operation. With improved
wireless propagation characteristic and larger coverage range,
using carrier frequencies below 1 GHz is highly beneficial
for outdoor rural area communications. The IEEE 802.11ah
Task Group is going to specify the Sub 1 GHz global WLAN
standard. M2M services such as Smart Grid, Surveillance, and
Smart Farming are the potential use cases that can be realized
by using the Sub 1 GHz band.
Although M2M services are often low power consuming,
the limited energy source still poses great challenge to the
wireless communication between devices, which is especially
critical for a large range and large device number network. In
situations where the devices are powered by batteries, 802.11
PSM can be applied to trade delay performance for longer
battery life. In an M2M infrastructure WLAN with one Access
Point (AP) and a number of devices serving as environment
monitors, the data communications are mostly uplink only.
Normally when a device has some data packets to send to the
AP, it wakes up and transmit the packets through the process of
IEEE 802.11 DCF (Distributed Coordination Function), after
which it enters sleep mode again. But if the device depletes
all the energy, the operation suspends until the battery is
replaced. Most of the research deals with the issue of power
management by purposely delaying the data transmission orlowering the duty cycle to extend the inactive period, and
thereby saving more power. Yet the energy saving comes at a
cost of larger transmission delay.
Energy harvesting is an emerging technology by which
M2M devices can be powered from external sources rather
than batteries. Such technology is cost-effective and energy-
efficient, as devices can independently harvest and supply
for their power use. The ambient energy come from external
sources like solar, mechanical, heat or wind, which are renew-
able energy harvested to power devices for a long period of
time. The property meets the demands of M2M application
such as structural health monitoring, whose devices are hard
to access after deployed. Therefore, the recent advance inenergy harvesting technology has shown great potential to
power wireless devices operating in industries [1].
In a wireless network of mostly uplink traffic, the capability
of IEEE 802.11 PSM to save energy is limited. When the
number of devices increases, there will be much more energy
wastage from overhearing and idle listening during backoff
procedure. Lowering the contention window to shorten the
backoff procedure is an option but it can lead to a higher
collision probability. In view of that, we came up with a
solution considering the fact that energy-harvesting devices are
characterized by their various energy levels which also vary in
time. In particular, we can provide a better energy expenditure
Algorithm 1 IEEE 802.11 Baseline Scheme: Beginning of
Beacon Period1: if in outage state and E ≥ E th then
2: leave outage state
3: end if 4: if not in outage state then
5: wake up and receive beacon frame
6: if packet buffer is empty then
7: go to sleep
8: else
9: transmit packets
10: go to sleep when buffer is empty
11: end if
12: end if
Algorithm 2 IEEE 802.11 Baseline Scheme: Short of Energy
1: if E < E 0 then
2: go to sleep3: enter outage state
4: end if
As a result, the increase of devices contending for the channel
causes more energy wastage in a backoff procedure.
TABLE I
DEFINITION OF RI (RADIO I NTERFACE) STATE
State Definition
T ransmitting The RI is on and transmitting packetsReceiving The RI is on and receiving packets destined to itself Overhearing The RI is on and receiving packets destined to others
Idle Listening The RI is on and the channel is idleSleeping The RI is off
Different from the battery-powered devices, which minimize
energy consumption to extend battery lifetime, for energy-
harvesting devices, we should optimize the energy expenditure
to improve the overall network performance. As the energy-
harvesting rate differs between devices and varies with time,
there will be high energy level devices and low energy level
devices, and their roles may change after a period of time.
Additionally, the device currently on the edge of outage state
can switch to a more power-efficient and high-prioritized
strategy, and the devices with high energy level will have little
concern on energy wastage. Taking advantage of this property,the energy expenditure can be better designed to improve the
overall performance including the reduced outage probability,
which is the indication of network reliability. We come up with
an idea that using smaller contention window value, the low
energy devices can have higher priority in channel access and
save more energy. This is in the cost of energy wastage and
lower priority of other devices, which care little about energy
shortage.
IV. DEE PSLEEP SCHEME
The DeepSleep scheme is designed to support the net-
work in which energy-harvesting devices are widely deployed.
The objective of DeepSleep is to reduce the overall outage
probability, packet loss rate, delay time and amend energy-
efficiency. We mainly focus on reducing the energy wasted in
overhearing the transmission to other devices, and also allevi-
ating the channel contention level caused by large number of devices. The DeepSleep scheme is developed based on 802.11
baseline scheme.
A. High Priority Energy-Aware Sleeping
The first part of DeepSleep scheme consists of Energy-
Aware Sleeping algorithm and High Priority algorithm. First
we focus on the value of CW min in backoff procedure of IEEE
802.11, as it can affect the overall collision probability and
duration of overhearing and idle listening. If we intentionally
allow some devices to use lower CW min value to transmit
packets, their priority will be higher than others, which reduces
overhearing and idle listening probability to save energy. But if
too many devices are allowed to use lower CW min value at thesame time, the contention level will become too high, leading
to much more retransmission, degrading energy-efficiency.
Based on the concept, the devices short of energy are granted
higher channel accessing priority after sleeping for a while to
drop out channel access.
In Energy-Aware Sleeping algorithm, the device constantly
checks its battery level E . If E is decremented below
E DeepSleep , a threshold higher than the usable energy level E 0,
then the device goes to sleep, marking bool HighP riority
true, and skipping nBP beacon frames. As for High Priority
algorithm, when the device wakes up to transmit packets, it
will check the value of bool HighP riority, if it’s true, the
device will set its CW min a smaller value CW DeepSleep andset bool HighP riority false. Otherwise, the device will set
its CW min back to the default value CW minOriginal . The
pseudo-code is given in algorithm 3 and algorithm 4.
Fig. 6. Energy consumption of DeepSleep and 802.11.
20 30 40 50 60 70 80 90 1000.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
Device Number
E n e r g y p e r P a c k e t D e l i v e r e d
DeepSleep devices in co−
existence caseBaseline devices in co−existence case
Baseline devices in all using baseline case
Fig. 7. Energy consumption of co-existing case and 802.11 case.
802.11 PSM, the devices wake up to transmit uplink datatraffic and go to sleep when the traffic buffer is empty.
During the backoff process, there is a large energy wastage
generated in overhearing and idle listening. Taking advantage
of the property from energy harvesting, DeepSleep manages
to reduce such energy wastage and channel contention level in
order to achieve higher overall energy-efficiency. DeepSleep
is an energy-aware scheme that favors the low energy devices
by allowing them to have higher priority for channel access.
At the same time, these favored devices are forced to sleep
for a longer period of time. Moreover, DeepSleep randomly
20 30 40 50 60 70 80 90 1000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Device Number
O u t a g e P r o b a b i l i t y
DeepSleep devices in co−existence case
Baseline devices in co−existence case
Baseline devices in all using baseline case
Fig. 8. Outage probability of co-existing case and 802.11 case.
defers part of the active devices so the channel congestion is
significantly alleviated, reducing overhearing and idle listening
time to provide higher network reliability. Our simulation
results show that DeepSleep is also capable of improving the
performance of the co-existing 802.11 baseline devices.
VII. ACKNOWLEDGEMENTS
This work was also supported by National Science Council,
National Taiwan University and Intel Corporation under Grants
NSC 100-2911-I-002-001, and 10R70501.
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