Feasibility and Benefits of Passive RFID Wake-up
Radios for Wireless Sensor Networks
He Ba, Ilker Demirkol, and Wendi Heinzelman
Department of Electrical and Computer Engineering
University of Rochester, Rochester, NY, USA
e-mail:{ba,demirkol,wheinzel}@ece.rochester.edu
Abstract—Energy efficiency is one of the crucial design criteria for
wireless sensor networks. Idle listening constitutes a major part of
energy waste, and thus solutions such as duty cycling and the use of
wake-up radios have been proposed to reduce idle listening and
save energy. Compared to duty cycling, wake-up radios save more
energy by reducing unnecessary wake-ups and collisions. In this
paper, we investigate the feasibility and potential benefits of using
passive RFID as a wake-up radio. We first introduce a physical
implementation of sensor nodes with passive RFID wake-up radios
and measure their energy cost and wake-up probability. Then, we
compare the performance of our RFID wake-up sensor nodes with
duty cycling in a Data MULE scenario through simulations with
realistic application parameters. The results show that using a
passive RFID wake-up radio offers significant energy efficiency
benefits at the expense of delay and the additional low-cost RFID
hardware, making RFID wake-up radios beneficial for many delay-
tolerant sensor network applications.
Keywords-Wireless sensor networks; wake-up receivers; passive
RFID wake-up; data MULE
I.
I
NTRODUCTION
Wireless sensor networks (WSNs) consist of a number of
sensor nodes that have the ability to sense the environment,
process the sensed data, and disseminate the processed data to
one or more sinks. WSNs have been proposed for several
applications including disaster monitoring, surveillance, target
tracking, and health monitoring. Since the sensor nodes are
usually powered by batteries, WSNs are highly energy
constrained, creating the need for innovative solutions to
reduce energy dissipation.
Idle listening, when a sensor node is active and waiting to
receive data, is a large source of energy drain in WSNs.
Generally there are two approaches to reduce the energy
consumption due to idle listening: duty cycling the node and
using a wake-up radio. Since sensor nodes do not have data to
send all the time, it is common to use duty cycling, where the
nodes are periodically set into the sleep mode. Duty cycling
saves a significant amount of energy at the expense of latency
in data delivery. However, one problem in utilizing duty
cycling is that the nodes wake up periodically regardless of
whether or not any other nodes have data to transmit to them.
In this situation, the nodes waste significant energy due to
unnecessary wake-ups. Furthermore, duty cycling relies on
tight time synchronization among the nodes to achieve good
performance. On the other hand, when utilizing wake-up radios,
the nodes are awakened by neighboring nodes only when they
need to receive packets. Such an on-demand mechanism has
the potential to save significant energy waste due to idle
listening, unnecessary wake-ups, overhead in control traffic,
and collisions.
Wake-up radios can be classified into two categories as
active and passive wake-up radios. Active wake-up radios
consume power, but they have longer wake-up ranges than
passive wake-up radios. Passive wake-up radios use the energy
harvested from the wake-up radio and thus operate over short
ranges. One possibility is to use passive RFID as the wake-up
technology, as there are off-the-shelf passive RFID tags and
readers readily available. A major drawback of using passive
RFID tags for the wake-up functionality is that multi-hop
communications cannot be supported due to the large size and
large power consumption of the RFID reader. It is not yet
practical to equip all sensor nodes with RFID readers.
Additionally, it is not known how well passive RFID would
perform as a wake-up radio, in terms of wake-up distance,
wake-up probability, and energy consumption for the sensor
node to be woken up. Hence, determining the feasibility of
using passive RFID for a wake-up radio and the potential
benefits of such a wake-up radio in real scenarios require a
separate study, which is the aim of this paper.
In this paper, we describe a physical implementation of a
passive RFID wake-up device using existing hardware. By
combing WISP (Wireless Identification and Sensing Platform)
passive RFID tags developed by Intel Research [1] with Tmote
Sky motes [2], we created a passive RFID wake-up device,
which is referred to as a WISP-Mote in this paper. We
characterize the performance of the WISP-Motes by measuring
the power consumption in different operation stages, including
sleeping, wake-up, transmitting and receiving, and by testing
the wake-up probability for different ranges. To show the
benefits of WISP-Motes, and hence the benefits of passive
RFID-based wake-up radios, we compare the use of WISP-
Motes with a standard mote architecture that utilizes duty
cycling for a single-hop Data MULE [3] data collection
scenario.
The remainder of this paper is organized as follows. In
Section II, we discuss the related work on wake-up radio
architectures and the use of data MULEs in WSNs. Section III
presents the implementation and characterization of our passive
RFID wake-up mote, the WISP-Mote. A comparison of the
performance of the WISP-Mote and the duty-cycle
architectures for single-hop data MULE scenarios are provided
in Section IV. Section V concludes the paper.
II. R
ELATED
W
ORK
Wake-up radios can be categorized as active or passive. A
passive radio wake-up circuit does not consume any energy
from the batteries, while an active one requires a power supply.
Different low-power radio wake-up receivers have been
designed, such as those described in [4-6]. Gu et al. proposed a
passive radio wake-up circuit that theoretically could operate at
a range of 10 feet with 5 ms latency based on SPICE
simulation results [7]. If a comparator and an amplifier, which
respectively consume negligible currents of 350 nA and
880 nA, are added to the wake-up circuit, it could theoretically
reach up to 100 feet with 55 ms latency. However, there are no
existing physical implementations of passive RFID wake-up
radios described in the literature.
To the best of our knowledge, the only performance study
on the passive RFID wake-up technique is by Jurdak et al. [8],
[9]. In their work, an RFID wake-up mechanism is proposed,
namely RFIDImpulse, for which analytical models of energy
consumption are presented. The performance of the proposed
mechanism was investigated through MATLAB simulations
and compared with BMAC and the IEEE 802.15.4 standard.
Their results show that RFIDImpulse performs better than both
of the other methods for low and medium traffic scenarios.
However, an important assumption is that all nodes have the
capability to wake up their neighbors, which is not feasible in
real scenarios, due to the considerable amount of energy
consumed by the RFID reader as well as its large size. In
addition, the energy consumption analysis does not include the
energy consumed by the nodes to wake up. In reality, the wake-
up energy consumption includes the energy used for MCU
boot-up and for radio initiation, which can be comparable to
the energy consumed for radio transmission. In our work, the
energy consumed during wake-up is also considered and is
based on the actual measurements for our WISP-Mote device.
To have wide network coverage, WISP-Motes are used
with mobile sinks in this paper due to their short wake-up
ranges. The mobile sink wakes up the WISP-Motes when it
gets within their wake-up range to collect their data. This is
similar to the three-tier layered architecture described in [10],
where a simple analytical model for a data MULE network is
presented based on random walks on a two-dimensional grid.
The grid is used to simplify the communication by assuming
the transmission is successfully completed when the MULE
and the sensor are in the same grid point. In our scenarios, we
investigate random walk along with two other MULE mobility
models and present their performance comparison.
In [11], a source-to-sink delay analysis in a single-hop
mobile sink scenario is presented. The nodes are placed in a
rectangular area with a mobile sink moving along the central
axis of a rectangular area. The authors assume a negligible
transmitting and queuing delay. The simulation results show
that the average delay decreased as the transmission radius
increased and as the mobile sink velocity increased. A MULE
discovery protocol is presented in [12] where the data MULE
periodically advertises its presence by sending special
messages called beacons. The static nodes periodically wake up,
and once a node receives a complete beacon, it switches to the
always-on mode and sends its data. After transferring all
available data, the node goes back to its periodic wake up
scheme. Simulation results show that this protocol achieves
energy efficiency with a low duty cycle, while achieving a
throughput sufficient for common environmental monitoring.
We employ a similar beacon-based wake-up mechanism in our
simulations for duty cycling scenarios.
III. T
HE
WISP-M
OTE
P
LATFORM
The lifetime of a wireless sensor network node is limited by
the sensor node’s battery supply. To extend a node’s lifetime,
duty cycling can be utilized. To reduce the node’s energy
consumption, the duty cycle must be set to a relatively low
value (e.g., 10% duty cycle, which means the node is “on” for
10% of the time). However, this will increase the average data
transmission latency, as packets that arrive at a node during the
sleep period must be buffered until the next active period.
Although there are protocols designed to reduce large delays
caused by sleeping, such as DMAC [13], these approaches
require additional overhead and global routing management.
When a node has no information about its environment, idle
listening is inevitable with the duty cycling approach. Besides
idle listening, control packet overhead and synchronization
overhead are also sources of energy waste observed with duty
cycle approaches. All of the above issues motivate us to utilize
radio wake-up techniques in wireless sensor networks to further
improve energy efficiency. This section introduces the
implementation of a combined passive RFID-based wake up
radio and a sensor mote, which we call a WISP-Mote, and
provides measurement results of the wake-up probability and
the energy consumption of the WISP-Motes.
A. Radio Wake-up Basics
Most sensor nodes use a microcontroller (MCU) to provide
computation and data processing, control the radio and sensors,
and manage memory and power. An internal clock, called the
watchdog timer, is used to wake up the system when a timer
fires. By setting this timer, a node can wake up periodically to
perform its functionalities. On the other hand, nodes lose their
functionalities while sleeping. The only other way to wake up a
node from the sleep state is to send an external interrupt signal
through the pins of the MCU. Such an external interrupt signal
is generated by the radio wake-up circuitry.
B. Implementation
A WISP is a passive RFID tag with simple sensing and
computing capabilities, developed by Intel Research for
research purposes. A WISP can be powered and read by an off-
the-shelf UHF RFID reader. Tmote Sky motes are widely used
sensor nodes. Both devices’ specifications are presented in
Table I.
TABLE I. D
EVICE SPECIFICATIONS
WISP 4.1DL [14] Tmote Sky [15]
TI MSP430 F2132 (512B
RAM, 8K+256B Flash)
TI MSP430 F1611 (10k RAM, 48k Flash)
250kbps 2.4GHz IEEE 802.15.4 Chipcon
Wireless Transceiver
Accelerometer, Temp. Humidity, Temperature, Light
Fast wakeup from sleep (<6µs, typically 292ns)
We combine a WISP and a Tmote Sky (mote) to create a
passive wake-up mote, a WISP-Mote, as shown in Fig. 1. We
wire an output pin of the WISP to one of the mote’s GPIO pins
to send an interrupt signal. The voltage required to trigger an
external interrupt is 0.92V. The output signal from the WISP’s
MCU is 1.8V, which is enough to wake up the mote. To make
our WISP-Mote stable, we also need to wire the GNDs of the
two devices together.
Figure 1. A WISP-Mote.
There were a few challenges faced in creating a robust
WISP-Mote. These include:
• The ubiquitous noise in and between the WISP and the
mote is large enough to influence the wake-up. To
avoid this, we bridge the two GNDs with a large
inductor to filter high frequency noise.
• The communication range of the WISP is limited, less
than 3 meters, if it runs the UHF RFID standard C1G2
protocol. In order to extend the wake-up range, we
disable the WISP-to-reader communication and
eliminate all other computation burdens in the WISP
MCU. The only function of the WISP is to keep
harvesting power from the reader’s radio and output an
impulse once it can. Using this approach, the wake-up
range is extended to approximately 5 meters as shown
in this paper.
C. Wake-up Probability
The energy a WISP is able to harvest decreases with
increasing reader-to-WISP distance due to path loss. Thus, it is
important to measure the wake-up probability as a function of
distance. We performed field tests of the WISP-Motes in a
large hall, which is similar to an outdoor environment. We
raised both the WISP and the reader’s antenna off the ground to
reduce multipath fading. We enabled the interrupt of the WISP-
Mote periodically, and we counted the number of times the
WISP-Mote can be successfully woken up as a function of
distance. The test results, which determine the wake-up
probability, are shown in Fig. 2.
0 1 2 3 4 5 6
0
10
20
30
40
50
60
70
80
90
100
Distance between WISP-Mote and MULE / meter
Wake-up Probability
WISP-Mote 1
WISP-Mote 2
Figure 2. Wake-up probability of the WISP-Motes.
As seen in Fig. 2, the wake-up probability starts to decrease
after 4 m and sharply drops down to 0 beyond 5 m. In our
simulations, we use a conservative value of 4 m.
D. Energy Consumption Measurements
The major advantage of passive RFID wake-up is to reduce
the energy waste of a sensor node and enhance its energy
efficiency. The Tmote Sky datasheet [15] provides the current
consumptions in typical operating conditions. We measured
current consumption in booting and radio initiation, which is
essential for the energy consumption analysis of RFID wake-up.
The results are shown in Table II. Our measurements are
consistent with those from the Tmote Sky datasheet. We can
see that besides radio transmission and reception, node wake-
up also consumes energy that cannot be ignored. This would
support the need for an accurate energy analysis for the radio
wake-up mechanism when characterizing a wake-up mote.
TABLE II. P
OWER
C
ONSUMPTION
M
EASUREMENTS OF A
T-
MOTE
S
KY
N
ODE
Operation Average current
consumption
Time
Wake-up and Radio Initiation 10.4 mA 5 ms
Transmit 12 byte packet 18.2 mA 30 ms
Receive and idle listening 20.2 mA
Sleep 0.2 mA
IV. U
SE OF
WISP-M
OTES IN A
D
ATA
M
ULE
S
CENARIO
The main advantage of the WISP-Motes is high energy
efficiency through on-demand wake-up. However, a short
wake-up range is achieved compared to the communication
range. To evaluate the benefits of WISP-Motes, we consider a
sparse delay-tolerant network of WISP-Motes with data
MULEs that collect the sensor data. The MULE architecture
provides connectivity for a sparse sensor network using single-
hop communications. In this scenario, one or multiple mobile
MULEs move throughout the network field collecting data
from the sensor nodes. The MULEs are equipped with RFID
readers and can wake up the WISP-Motes. Once a MULE is
within wake-up range of a sensor node (within 4 m range for
our simulations), the node is awakened and senses the channel
if it has buffered data. If the channel is busy, the WISP-Mote
will remain active and sense again in the next slot. Once the
channel is free, the WISP-Mote will start transmitting its
buffered data. If the WISP-Mote is not awakened by a MULE,
the node remains asleep. In real scenarios, any moving agent,
such as a person, an animal, or a vehicle, could act as a data
MULE.
We compare the performance of the WISP-Mote network
with a network of conventional sensor nodes that utilize duty
cycling to save energy. In the latter case, the MULEs
periodically send advertisement packets to declare their
presence. Nodes periodically wake up, and if the node has
buffered data, it will sense the channel. If a node receives an
advertisement packet from a MULE, it responds by sending its
buffered data. If the MULE is in communication with another
node, the sensor node will stay active and sense the channel
again in the next time slot. If there are no MULEs within the
range of the sensor node, the sensor node returns to sleep until
the next duty cycle wake-up period.
A. Simulation Setup
To simplify the simulations, we make the following
assumptions:
• Propagation delay is ignored.
• We consider a simplified MAC layer and assume there
are no collisions and no link failures. Once a node
senses a free channel, it will send a packet with
guaranteed arrival at the MULE.
• A MULE’s appearance in one time slot is enough for a
node to detect it and finish one packet transmission.
• MULEs have the ability to communicate directly to the
data sink. Therefore, packet delay is counted from the
time a packet is generated until the time it is delivered
to a MULE.
• Whenever nodes receive MULE advertisement packets
or sense the channel, they dissipate power (receive
power) during the entire slot time.
• We ignore the energy cost for sensing activities as
these will not impact the performance evaluation.
• The radio wake-up range is set to 4 m. The mote’s
communication range is set to 40 m based on
experiments described in [16] as well as our own field
experiments.
In our network simulations, nodes are uniformly randomly
deployed in a 200m x 200m square region with a density of
0.001 nodes/m
2
. MULEs begin with uniformly random
locations, and they move at each time slot according to a
Random Direction mobility model. Each MULE randomly
selects a speed from [5 m/s, 15 m/s] and a direction from [0, 2π]
and moves according to this speed and direction until it reaches
the network boundary. Each node generates a packet every 10
minutes, i.e., with 0.1 packets/min. We compare the average
packet delay and the energy consumed in 2 hours of operation
for the WISP-Mote scenario and for the duty cycling scenario.
1 2 3 4 5
0
100
200
300
400
500
600
MULE Quantity
Average packet delay (s)
1 2 3 4 5
0
500
1000
1500
2000
2500
3000
MULE Quantity
Energy consumption per node (mJ)
10% Duty Cycling
2% Duty Cycling
0.25% Duty Cycling
0.1% Duty Cycling
WISP-Mote
10% Duty Cycling
2% Duty Cycling
0.25% Duty Cycling
0.1% Duty Cycling
WISP-Mote
Figure 3. Packet delay and energy consumption comparisons as a function of
the number of MULEs.
B. Performance Results
Fig. 3 shows the results of delay and energy consumption
for 0.1%, 0.25%, 2% and 10% duty cycling and for the WISP-
Mote. Compared to duty cycling, the WISP-Mote has to buffer
data for a longer time until a MULE is within its wake-up range,
which results in a high packet latency. On the other hand, in the
duty cycling scenario, the lower the duty cycle value, the
higher the probability of missing a MULE, since the nodes are
in sleep mode longer. The resulting delay becomes large for
very low duty cycle values (e.g., 0.1%). Therefore, the delay
performance of the WISP-Mote is worse than 10%, 2% and
0.25% duty cycling, but it achieves better delay than 0.1% duty
cycling. The energy consumption values, provided in Fig. 3,
show that the WISP-Mote uses much less energy than 0.1%,
0.25%, 2% and 10% duty cycling, since the WISP-Mote does
not waste energy in unnecessary wake-ups and idle listening.
Another important factor in the delay results is the number
of MULEs in the area. More MULEs provide larger coverage
per time unit and therefore decrease the packet latency. We can
see from Fig. 3 that when the number of MULEs is increased
from 1 to 5, the average packet delay decreases by about 75%
for both the WISP-Mote and the duty cycling scenarios. This
provides a solution for applications with specific packet latency
requirements at the cost of increasing the number of MULEs.
0.50.250.1670.1250.1
0
100
200
300
400
500
600
700
800
900
1000
Packet generation rate (packets/min)
Average Packet Delay (s)
0.50.250.1670.1250.1
0
200
400
600
800
1000
1200
Packet generation rate (packets/min)
Energy Consumption per node (mJ)
0.1% Duty Cycling
1% Duty Cycling
WISP-Mote
0.1% Duty Cycling
1% Duty Cycling
WISP-Mote
Figure 4. Packet delay and energy consumption comparisons as a function of
packet generation rate.
Fig. 4 shows the performance under various traffic loads for
3 MULEs. We assume only one node is allowed to transmit
data to a certain MULE in one time slot and will consume
energy in sensing again if the channel is busy. Therefore,
increasing the traffic load leads to an increase in delay and
energy consumption due to re-sensing the channel. The packet
delay caused by re-sensing is not significant compared to the
delay due to buffered data. We observe that when the packet
generation rate increases from 0.1 packets/min to 0.125
packets/min, the average packet delay of all three scenarios
only increased slightly. However, when the packet generation
rate increases further, the packet delays of 0.1% duty cycling
and the WISP-Mote increase exponentially, due to accumulated
data in the buffers. In the 1% duty cycling scenario, when
packet generation rate is 0.5 packets/min, nodes are still able to
deliver packets before new packets are generated. Therefore,
the delay is still increased linearly. On the other hand, the
energy consumptions in the duty cycling scenarios are
dominated by re-sensing the channel when the packet
generation rate is increased. The WISP-Mote scenario has less
chance of re-sensing due to its limited wake-up range, which
results in less energy consumption compared to the duty
cycling scenarios.
1 2 3 4 5
0
100
200
300
400
500
600
700
MULE Quantity
Average packet delay (s)
1 2 3 4 5
40
60
80
100
120
140
160
180
200
MULE Quantity
Energy consumption per node (mJ)
Snake
Random Direction
Random Walk
Snake
Random Direction
Random Walk
Figure 5. Mobility model comparisons.
The MULEs’ mobility model also has an impact on the
performance of the networks. We compare three different
mobility models: Snake, Random Walk, and Random Direction.
In the Snake algorithm, each MULE sweeps over the entire
field by following a snake-shaped route with a constant
velocity of 10 m/s. In the Random Walk algorithm, each
MULE randomly selects a speed from [5 m/s, 15 m/s] and a
direction from [0, 2π] and moves according to this speed and
direction for a random duration of between 1 and 100 time slots.
The Random Direction mobility model is described in Section
IV.A. The average velocities of the three models are the same.
From Fig. 5, we can see that the Snake algorithm has the best
performance in terms of packet delay while the energy
consumption is virtually the same for all three mobility models.
To reduce average packet delay, the MULEs could follow a
scheduled route and go directly to the sensor nodes one by one.
V. C
ONCLUSION
In this paper, we present and characterize a physical
implementation of a passive RFID wake-up device using
existing hardware. In the Data MULE scenario, the benefit of
our device in terms of reducing energy consumption is shown
through simulation results. By trading off the extra hardware
cost and increased packet latency, the lifetime of the entire
network can be greatly extended. For a similar packet delay
performance, a network utilizing WISP-Motes can save up to
89% of the energy consumption compared with 0.1% duty
cycling for one MULE. To reduce the packet delay and
improve the network robustness, multiple data MULEs can be
deployed.
A
CKNOWLEDGEMENT
The WISPs used in this research were donated by Intel as
part of the WISP Challenge.
R
EFERENCES
[1] A. P. Sample, D. J. Yeager, P. S. Powledge, and J. R. Smith, “Design of
an RFID-based battery-free programmable sensing platform,” IEEE
Transactions on Instrumentation and Measurement, Vol. 57, No. 11, Nov.
2008, pp. 2608-2615.
[2] J. Polastre, R. Szewczyk, and D. Culler, “Telos: Enabling Ultra-Low
Power Wireless Research”, The Fourth International Conference on
Information Processing in Sensor Networks: Special track on Platform
Tools and Design Methods for Network Embedded Sensors IPSN 2005:
364-369, April 25-27, 2005.
[3] R. C. Shah, S. Roy, S. Jain, and W. Brunette, “Data MULEs: modeling a
three-tier architecture for sparse sensor networks”, Proceedings of the
First IEEE. 2003 IEEE International Workshop on (2003), pp. 30-41.
[4] N. Pletcher, S. Gambini, and J. Rabaey, “A 65
μ
W, 1.9 GHz RF to
Digital Baseband Wakeup Receiver for Wireless Sensor Nodes,” IEEE
CICC 2007, San Jose, 16-19 Sept. 2007.
[5] M. J. Miller, and N. H. Vaidya, “Minimizing energy consumption in
sensor networks using a wakeup radio”, IEEE WCNC, 2004.
[6] P. Le-Huy and S. Roy, “Low-Power Wake-Up Radio for Wireless
Sensor Networks,” IEEE International Conference on Wireless and
Mobile Computing, Networking and Communications, 2008.
[7] L. Gu and J. Stankovic, “Radio-Triggered Wake-up for Wireless Sensor
Networks,” Real-Time Systems archive. Volume 29, Issue 2-3, March
2005.
[8] A. G. Ruzzelli, R Jurdak, and G. M. P. O’Hare, “On the RFID wake-up
impulse for multi-hop sensor network”, SenseID Workshop at ACM
SenSys 2007, Sydney, Australia. November, 2007.
[9] R. Jurdak, A. G. Ruzzelli, and G. M. P. O’Hare, “Multi-hop RFID
Wake-up Radio: Design, Evaluation and Energy Tradeoffs”, Computer
Communications and Networks, 2008. ICCCN '08.
[10] R. C. Shah, S. Roy, S. Jain, and W. Brunette, “Data MULEs: modeling a
three-tier architecture for sparse sensor networks”, Proceedings of the
First IEEE. 2003 IEEE International Workshop on (2003), pp. 30-41.
[11] F. Zhang, B. Huang, Z. Xu, and L. Tu, “Delay Analysis of Single-hop
Communication in Wireless Sensor Networks with Mobile Sink”,
Second International Multi-Symposiums on Computer and
Computational Sciences, 2007. IMSCCS 2007.
[12] G. Anastasi, M. Conti, and M. Di Francesco, “Data collection in sensor
networks with data mules: An integrated simulation analysis”, IEEE
Symposium on Computers and Communications, 2008. ISCC 2008.
[13] G. Lu, B. Krishnamachari, and C. S. Raghavendra, “An adaptive energy-
efficient and low-latency MAC for tree-based data gathering in sensor
networks”, Wirel. Commun. Mob. Comput. 2007, 7:863–875.
[14] http://wisp.wikispaces.com/
[15] Tmote Sky, http://sentilla.com/files/pdf/eol/tmote-sky-datasheet.pdf.
[16] M. Holland, R. Aures and W. Heinzelman, "Experimental Investigation
of Radio Performance in Wireless Sensor Networks," IEEE SECON
2006 Poster Session, Sept. 2006.
