SNUPI: sensor nodes utilizing powerline infrastructure.
ABSTRACT A persistent concern of wireless sensors is the power consumption required for communication, which presents a significant adoption hurdle for practical ubiquitous computing applications. This work explores the use of the home powerline as a large distributed antenna capable of receiving signals from ultra-low-power wireless sensor nodes and thus allowing nodes to be detected at ranges that are otherwise impractical with traditional over-the-air reception. We present the design and implementation of small ultra-low-power 27 MHz sensor nodes that transmit their data by coupling over the powerline to a single receiver attached to the powerline in the home. We demonstrate the ability of our general purpose wireless sensor nodes to provide whole-home coverage while consuming less than 1 mW of power when transmitting (65 ¼W consumed in our custom CMOS transmitter). This is the lowest power transmitter to date compared to those found in traditional whole-home wireless systems.
Conference Paper: BOSS: building operating system services[Show abstract] [Hide abstract]
ABSTRACT: Commercial buildings are attractive targets for introducing innovative cyber-physical control systems, because they are already highly instrumented distributed systems which consume large quantities of energy. However, they are not currently programmable in a meaningful sense because each building is constructed with vertically integrated, closed subsystems and without uniform abstractions to write applications against. We develop a set of operating system services called BOSS, which supports multiple portable, fault-tolerant applications on top of the distributed physical resources present in large commercial buildings. We evaluate our system based on lessons learned from deployments of many novel applications in our test building, a four-year-old, 140,000sf building with modern digital controls, as well as partial deployments at other sites.Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation; 04/2013
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ABSTRACT: Providing effective feedback on resource consumption in the home is a key challenge of environmental conservation efforts. One promising approach for providing feedback about residential energy consumption is the use of ambient and artistic visualizations. Pervasive computing technologies enable the integration of such feedback into the home in the form of distributed point-of-consumption feedback devices to support decision-making in everyday activities. However, introducing these devices into the home requires sensitivity to the domestic context. In this paper we describe three abstract visualizations and suggest four design requirements that this type of device must meet to be effective: pragmatic, aesthetic, ambient, and ecological. We report on the findings from a mixed methods user study that explores the viability of using ambient and artistic feedback in the home based on these requirements. Our findings suggest that this approach is a viable way to provide resource use feedback and that both the aesthetics of the representation and the context of use are important elements that must be considered in this design space.IEEE transactions on visualization and computer graphics. 12/2011; 17(12):2489-97.
Conference Paper: Your noise is my command: sensing gestures using the body as an antenna.[Show abstract] [Hide abstract]
ABSTRACT: Touch sensing and computer vision have made human-computer interaction possible in environments where keyboards, mice, or other handheld implements are not available or desirable. However, the high cost of instrumenting environments limits the ubiquity of these technologies, particularly in home scenarios where cost constraints dominate installation decisions. Fortunately, home environments frequently offer a signal that is unique to locations and objects within the home: electromagnetic noise. In this work, we use the body as a receiving antenna and leverage this noise for gestural interaction. We demonstrate that it is possible to robustly recognize touched locations on an uninstrumented home wall using no specialized sensors. We conduct a series of experiments to explore the capabilities that this new sensing modality may offer. Specifically, we show robust classification of gestures such as the position of discrete touches around light switches, the particular light switch being touched, which appliances are touched, differentiation between hands, as well as continuous proximity of hand to the switch, among others. We close by discussing opportunities, limitations, and future work.Proceedings of the International Conference on Human Factors in Computing Systems, CHI 2011, Vancouver, BC, Canada, May 7-12, 2011; 01/2011
SNUPI: Sensor Nodes Utilizing Powerline Infrastructure
Gabe Cohn1, Erich Stuntebeck4, Jagdish Pandey3,
Brian Otis3, Gregory D. Abowd4, Shwetak N. Patel1,2
2Computer Science & Engineering
UbiComp Lab, DUB Group
University of Washington
Seattle, WA 98195 USA
A persistent concern of wireless sensors is the power
consumption required for communication, which presents a
significant adoption hurdle for practical ubiquitous
computing applications. This work explores the use of the
home powerline as a large distributed antenna capable of
receiving signals from ultra-low-power wireless sensor
nodes and thus allowing nodes to be detected at ranges that
are otherwise impractical with traditional over-the-air
reception. We present the design and implementation of
small ultra-low-power 27 MHz sensor nodes that transmit
their data by coupling over the powerline to a single
receiver attached to the powerline in the home. We
demonstrate the ability of our general purpose wireless
sensor nodes to provide whole-home coverage while
consuming less than 1 mW of power when transmitting
(65 μW consumed in our custom CMOS transmitter). This
is the lowest power transmitter to date compared to those
found in traditional whole-home wireless systems.
Wireless Sensing Lab,
University of Washington
Seattle, WA 98195 USA
4School of Interactive
Computing & GVU Center
Georgia Institute of Technology
Atlanta, GA 30332 USA
Wireless sensing, ultra-low-power radio, powerline
ACM Classification Keywords
C.2.1. Network Architecture and Design: Wireless
Communication; B.0. Hardware: General
Design, Experimentation, Measurement
INTRODUCTION AND MOTIVATION
The success of many domestic ubiquitous computing
(ubicomp) applications, especially as we transition from
instrumented living laboratories and into real homes, will
be contingent on practical and long-lived sensing solutions.
These solutions must be easy to deploy and maintain .
While battery-powered wireless sensor nodes are typically
easy to deploy, to date they have not been easy to maintain
due to the need to frequently change the batteries.
Traditionally, the dominant factor in determining battery
life for these nodes has been the power cost of wireless
communications, not the sensing or computation tasks. This
paper presents a new technique for wireless communication
from sensor nodes in the home that reduces communication
costs so significantly that battery life is now dominated by
the cost of the sensing and computation, a complete reversal
of the status quo.
Although low-power radios are available , their range is
so limited they are not viable for whole-home sensor
networks with a single base station. To reduce power, a
common approach is to implement a multi-hop mesh
network where each node only needs to communicate with
nearby nodes, rather than communicating the full distance
to the base station. This approach reduces the power
consumption of the transmitter but requires significant
power to continuously run a receiver on every node.
We have developed an approach for wireless sensor nodes,
which dramatically reduces the power consumption of each
node while continuing to offer whole-home range. SNUPI
(Sensor Nodes Utilizing Powerline Infrastructure) nodes
contain an ultra-low-power transmitter that extends its
range by coupling its wirelessly transmitted signal to the
existing powerlines in order to obtain whole-home range.
Several commercial networking and home convenience
products currently use the home powerline infrastructure as
a medium for communicating high frequency signals;
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UbiComp ‘10, September 26–29, 2010, Copenhagen, Denmark.
Copyright 2010 ACM 978-1-60558-843-8/10/09...$10.00.
Figure 1. Both sides of operational SNUPI sensor nodes
however, in all of these systems both the sender and
receiver are directly connected by wire to the powerline
[13, 14, 29]. In the SNUPI system, only the base station
receiver is wired directly to the powerline (i.e, plugged into
an outlet). Each node in the sensor network transmits
wireless signals that couple to nearby powerlines, creating
signals that travel through the infrastructure to the base
station receiver. In this way, the sensor nodes can transmit
at much lower power because signals do not need to
propagate over-the-air for the entire path to the receiver,
they only need to propagate to the nearest powerline.
SNUPI is a fully-programmable wireless sensing platform
that features an ultra-low-power 16-bit microcontroller, a
16-bit ADC, and a custom 27 MHz, 9.6 kbps frequency-
shift-keying (FSK) wireless transmitter, which is capable of
providing coverage within an entire home while consuming
less than 1 mW. Our custom transmit-only radio accounts
for just 65 µW of the 1 mW, rendering its power
consumption as negligible when compared to the ultra-low-
power microcontroller. Our initial prototype of the SNUPI
sensor node measures 3.8 cm by 3.8 cm by 1.4 cm and
weighs only 17 grams including the battery and antenna
(see Figure 1). The battery life of a SNUPI node with a
simple light sensor beaconing once per minute will
dramatically outlive the 10 year shelf-life of its small
225 mAh coin-cell battery.
The use of the powerline as a receiving antenna offers a
number of advantages because its length makes it an ideal
receiving antenna at low frequencies (10–40 MHz). First,
this allows us to greatly reduce the power output of the
radio, while still achieving a practical communication
range. Second, the efficiency of the powerline antenna,
allows us to reduce the size of the antenna on the sensor
node itself (see Figure 1), despite it having to operate at
lower frequencies where ideal antennas are prohibitively
large. Finally, only a single powerline-connected base
station receiver is needed for whole-home coverage,
thereby reducing the installation time by an end-user.
In this paper, we discuss the design and implementation of
SNUPI, examine a series of experiments that characterize
the performance of SNUPI as a viable ultra-low-power
whole-home wireless sensing solution, and compare our
approach to traditional sensor systems found commercially
and in the research literature.
BACKGROUND AND RELATED WORK
Recent work in ubiquitous computing has examined
repurposing the existing infrastructure of a home as a
means for extracting relevant activity information, thus
reducing the need for complex distributed sensing systems
[6, 10, 11, 18, 19, 20, 21, 25]. Although this infrastructure-
mediated sensing approach helps address many practical
obstacles to deployment of home activity sensing, it is
inherently limited by what information can be practically
and reliably extracted from a home’s infrastructure. Thus, a
need still exists for distributing sensor nodes where
obtaining certain data in the home is impossible using just
the infrastructure. Our work attempts to strike a balance
between minimizing the need for additional infrastructure
requirements (i.e., reducing the receiver down to a single
base station), but still allowing the flexibility of deploying
long-lived wireless sensors throughout the home.
Although designed as a transmission line for low-frequency
AC electrical power at 50–60 Hz, the in-wall residential
powerline is capable of carrying higher frequency signals.
This phenomenon has been successfully leveraged for a
variety of in-home communications applications. X10 and
Insteon are two well-established low-data-rate powerline
communication technologies that enable home automation
and control [14, 29]. The HomePlug Powerline Alliance
maintains a standard for high-data-rate home networking
over the powerline on which numerous commercial
consumer devices are based . These devices typically
act as a bridge between wired Ethernet networks and the
powerline network, and can currently provide data rates up
to 200 Mbps. In all of these systems, both the transmitter
and receiver are wired directly to the powerline.
Powerline for Sensing Applications
There are several systems that leverage the existing
powerline infrastructure for sensing and location tracking
[19, 20, 25]. The PowerLine Positioning (PLP) system [21,
25], which tracks the location of small active tags
throughout a home, makes use of the home powerline as a
transmitting antenna for signals between 447 kHz and
20 MHz. Other work has looked at passively listening to the
powerline in order to infer electrical activity from the noise
generated by the switching of electrical devices [11, 19].
Researchers of sensor networks have also exploited the
60 Hz AC line radiation for synchronizing distributed
sensor nodes . In contrast to this work, we attempt to
use the powerline as a wireless receiving antenna for
communication back to a base station receiver.
Powerline as a Receiving Antenna
The PowerLine Positioning (PLP) system [21, 25]
demonstrated the power infrastructure could be used as a
transmission antenna. The electromagnetic principle of
reciprocity therefore dictates that the powerline should
work equally well as a receiving antenna. The powerline’s
ability to receive wireless signals is a well-known
phenomenon, but only recently has it been exploited for in-
home communication. This was first shown with the PL-
Tags system, in which tags were briefly excited to create a
low frequency (few hundred kHz) burst, which was coupled
to the powerline at very short range . However, the
ability for the powerlines to receive and propagate RF
signals is strongly dependent on frequency, and therefore it
is important to characterize the powerline over frequency.
Carrier Frequency Selection
Since wireless communication is subject to local and
international regulations regarding the use of the radio
spectrum, we only considered frequency bands which are
designated by the International Telecommunications Union
(ITU) as unlicensed for wireless communications: 6.78,
13.56, 27.12, 40.68, 433.92, 915.00 MHz, and 2.45 GHz
. These spectra are used by a variety of wireless devices
including consumer electronic devices. Most countries
adhere to this standard when allocating the unlicensed radio
bands, although notably 433.92 and 915.00 MHz are not
globally available, and are heavily used in regions where
they are available. We therefore eliminated these two
frequencies from consideration.
The HomePlug standard, mentioned earlier, uses three of
the unlicensed bands: 6.78, 13.56, and 27.12 MHz. We
tested for interference at these frequencies due to
HomePlug by measuring the power level on the powerlines
at several locations in a home using a spectrum analyzer.
We found that HomePlug devices generate significant
interference at 6.78 and 13.56 MHz, yet no interference is
seen at 27.12 MHz. Due to the growing popularity of
HomePlug , we eliminated 6.78 and 13.56 MHz as
options for the SNUPI sensor nodes.
For the remaining frequency bands, we conducted
experiments in which we injected signals into the powerline
at 27.12, 40.68 MHz, and 2.45 GHz using a signal
generator connected to a custom powerline interface, and
measured the signal levels at various locations of a 3 floor,
371 square meter home using a spectrum analyzer. We
concluded that 27.12 and 40.68 MHz signals travel through
the powerline with little attenuation in comparison to
2.45 GHz signals. To further test both 27.12 and
40.68 MHz, a small 4 cm x 4 cm loop antenna was excited
with a signal generator, and placed at every position in a 1
meter grid (250 locations) of a 2 floor 280 square meter
home. A spectrum analyzer was used to monitor the
received signal at a single wall outlet. The spectrum
analyzer was connected to the ground and neutral lines of
the powerline, and a simple high-pass filter was used to
block the 60 Hz signal from the powerline. A similar
experiment, using sparser grid, was also conducted on a
75 square meter apartment. From these experiments, we
found that both 27.12 and 40.68 MHz perform very well;
however, 27.12 MHz was chosen due to the availability of
crystal resonators and power consumption can be reduced
by operating at lower frequencies.
These whole-home experiments showed that we were able
to couple a 27.12 MHz signal to the powerline from
anywhere in the home, and detect the signal at a single
central location on the powerline with a signal to noise ratio
(SNR) of at least 3 dB, when using an antenna output power
of only -45 dBm, assuming a 50 Ω load. This extremely
low-power result motivated us to design a full sensor node
that could operate while consuming less than 1 mW of total
power while transmitting.
Low-Power Sensor Nodes
A number of wireless sensing platforms exist both in the
research community and commercially. MITes is a low-
power sensor kit that was designed for long-term
deployments to sense human activity . A number of
other platforms have also been available to the ubicomp
community, such as Smart-Its , uParts , Berkeley /
Crossbow Mica-family motes , iMotes , and
BTNodes . Commercially available platforms include
sensor nodes available through EnOcean , various
ZigBee platforms , Crossbow Motes , and
SunSPOTs , among others. As we discuss later, these
solutions are often very power hungry when used for long-
range in-home applications, even at low bitrates of 10 kbps.
Work in the wireless sensing community has led to a
variety of ultra-low-power wireless solutions that typically
are not as programmable and include only an ADC and a
radio in a single chip. These solutions are limited in range
to a few meters line-of-sight, but operate under 1 mW of
power . We show how our custom transmitter
consumes even less power than these short range radios.
Other work has demonstrated wirelessly powering RFID-
based sensors using long-range RFID readers. For example,
the Wireless Identification and Sensing Platform (WISP) is
a passive UHF RFID tag that uses an ultra-low-power
microcontroller for sensing and communication .
Although promising, these solutions are limited in range,
and require considerable RFID infrastructure, which may
not be commonly found within the home environment.
HARDWARE AND SOFTWARE DESCRIPTION
The SNUPI sensor node is a generic fully-programmable
platform for whole-home, low throughput, ubiquitous
computing applications. In many of these low throughput
applications, battery life is the most important design
criteria. For this reason, SNUPI was designed specifically
to maximize battery life by minimizing power consumption.
The initial prototype of the SNUPI sensor node measures
3.8 cm by 3.8 cm by 1.4 cm, weighs only 17 grams
including the battery, and has whole-home range while
consuming less than 1 mW of power when transmitting (the
radio itself only consumes 65 µW). In order to minimize
power consumption over long periods of time, each part of
the node is duty cycled to only consume power when
needed. Between transmissions the node is in an ultra-low-
power sleep state, and a timer on the microcontroller is used
to periodically wake up the node in order to sample and
transmit a packet of data. Figure 2 shows a high-level block
diagram of the SNUPI node, and Figure 1 shows the
completed node. The following sections describe the design
of each sub-system of the SNUPI sensor node.
Custom CMOS Transmitter
Traditionally, the RF radio is the most power intensive
component of any wireless sensor node. Therefore, our
main focus was to dramatically reduce the power
consumption of the radio. In typical transceivers, most
power is consumed during receiving, a task that must
always be active in order to prevent data loss. To reduce the
power consumption of the SNUPI node, we removed the
receiver. SNUPI therefore uses a unidirectional
communications channel, meaning that each node can only
send data. This significant reduction in power comes at the
cost of communications reliability. Without two-way
communication, there is no handshaking to ensure that data
sent from the node is actually received by the base station.
This limitation will be discussed later.
Although removing the node receiver reduces the overall
power of the radio, there are many optimizations that can be
made with the transmitter itself. Our goal was to design a
27 MHz transmitter using minimum power. The efficiency
of the home powerlines as an antenna at 27 MHz and their
proximity to the nodes allows for further power reduction.
We implemented a binary frequency shift keying (2-FSK)
transmitter using a Pierce oscillator with a 27.0 MHz crystal
resonator (schematics shown in Figure 2). To modulate the
transmitter, a small 4 pF on-chip load capacitance across
the crystal resonator is switched to cause a 10 kHz
frequency shift. The crystal oscillator has a relatively slow
startup time, between 1 and 4 ms, which varies as a
function of the oscillator bias current such that the startup
time is longer when the oscillator consumes less power.
A digital buffer chain isolates the oscillator from the low
impedance (~350 Ω) loop antenna. In order to save power,
the buffers use a very low supply voltage. By adjusting this
supply voltage, the output power of the antenna can be
varied by 18 dB. At the minimum output power, the radio
consumes only 35 µW (900 µW for the whole node), and at
the maximum output power, the radio’s power consumption
is 190 µW (1.5 mW for the whole node). This power range
is specified by the component values used in our prototype,
and therefore the power range could be shifted or extended
using different sized components.
This transmitter design can be made very low-power as
long as the stray capacitance is not too large. Using a
discrete transistor implementation on a prototyping board,
we demonstrated that we could achieve whole-home range
while keeping the power below several hundred µW. In
order to further reduce the power, we implemented the
oscillator on a single silicon die using a 0.13 µm CMOS
process, which resulted in only 65 µW of power for whole-
home range. Figure 2 shows a microscope image of the
transmitter die, which was wire-bonded to the custom
SNUPI printed circuit board (PCB).
A microcontroller is used to control the operation of the
SNUPI sensor node. The Texas Instruments MSP430F2013
16-bit ultra-low-power flash microcontroller is used due to
its ultra-low-power capabilities, including several clocking
options, 2 KBytes of Flash ROM, 128 bytes of RAM, and a
multi-channel 16-bit Sigma-Delta
converter (ADC). The microcontroller is used for timing
control and computation. It controls powering the sensor,
sampling data, and both powering and modulating the
transmitter. The RF transmitter is powered directly from a
digital output pin on the microcontroller so that the
transmitter can be completely powered down during the
sleep phase. In addition, the microcontroller can be used as
a general computation platform since SNUPI exposes the
MSP430 programming interface. A SNUPI node’s
firmware can be easily reprogrammed by connecting a
programmer to the Spy-Bi-Wire (2-wire JTAG) interface.
All ADC input pins are exposed on the SNUPI PCB so that
a variety of different sensor connections can be used.
The operating frequency of SNUPI is 27 MHz, which
approximately corresponds to an 11 m wavelength. Since
high efficiency antennas are typically on the order of one
half wavelength in size, it is therefore not feasible to use a
high efficiency antenna on a small wireless sensor node. In
order to keep the senor node as small as possible, we
constrained the size of the antenna to be the size of the
node’s printed circuit board (PCB), which is 3.8 cm x
3.8 cm. Although this yields a highly inefficient antenna, it
is sufficient because the SNUPI node only needs to transmit
far enough to couple to the nearest powerline, which in a
home in rarely more than a few meters away. In addition,
the size of the powerline infrastructure makes it a fairly
efficient receiving antenna at 27 MHz.
Figure 2. (left) High-level block diagram of SNUPI sensor node. (center) schematics of our custom FSK transmitter. (right) 20X
microscope image of CMOS silicon die containing our custom transmitter. The transmitter circuitry is within the blue square
Although the efficiency of the SNUPI antenna is very low,
we optimized the performance as much as possible. After
testing the impedance match and radiation efficiency of
over 20 different antenna designs, we chose to use a 350 Ω
loop antenna consisting of 6 turns of 22 AWG wire wound
along the perimeter of the PCB. Multiple turns are used to
increase the impedance and improve the radiation
efficiency by increasing the radiation resistance. Heavier
gauge wire is used to reduce the loss resistance of the
antenna, which also improves the radiation efficiency .
The SNUPI node was designed for a 3.0 V battery, although
it will continue to operate correctly down to 1.8 V, at which
point the MSP430 microcontroller will cease to operate.
Although high-capacity coin-cell lithium batteries can be
purchased with capacities as large as 1000 mAh, they are
often larger, heavier, and more costly than other coin-cells.
For our prototype, we wanted to demonstrate that we could
obtain very long battery life using a common, inexpensive
battery. We therefore chose to use the CR2032, a very
common 20 mm diameter, 3.0 V lithium coin-cell battery
with a capacity of 225 mAh.
SNUPI is a fully-programmable sensing platform, and
therefore many of the implementation details related to the
communications protocol can be tailored to a specific
application by changing and reprogramming the firmware
on the microcontroller. This section will therefore describe
the implementations used in our prototype; however, many
of the details can be changed to suit different applications.
In order to reduce power consumption, we focused on
implementing a very simple communications protocol. We
use a star topology in which there is a single base station
receiver and many transmitting nodes that communicate
directly with the receiver. The powerline infrastructure
serves as a very convenient channel for this type of
network, because the powerlines are dispersed to almost
every position in a home. Using this topology, our network
uses a unidirectional version of the ALOHA protocol, in
which many transmitters communicate in short bursts on
the same channel . Since each transmission is very short,
the probability of a collision is low. The delay between
each transmission is different among each transmitter to
ensure that two transmitters do not become synchronized.
Using this protocol we can implement a very simple
unidirectional single-hop network and minimize the power
spent in transmission. The limitations of this protocol will
be discussed later.
Our prototype uses a 25-bit packet, which consists of a
single start bit, a 7-bit node ID, a 16-bit payload, and a
single parity bit. While the transmitter is starting up before
the transmission and shutting down after sending the data, it
transmits the “zero” value. The packet structure is entirely
controlled by the firmware on microcontroller, and can
therefore be changed for multiple applications, adjusting for
the size of the node ID, payload, and error checking. The
data is modulated using NRZ (non-return-to-zero) 2-FSK
(binary frequency shift keying). The frequencies used to
encode “one” and “zero” are 26.999 and 27.009 MHz,
respectively. Because of the 10 kHz bandwidth, the current
SNUPI prototype transmits at a bitrate of 9.6 kbps, which
means that the entire 25-bit packet is transmitted in 2.6 ms.
It takes less than 4 ms for the crystal oscillator and
transmitter to power up, so the total on-time of each
transmission is 6.6 ms. We later discuss how we would
implement a version of the SNUPI node with a higher
bitrate and a lower startup time.
To analyze the range and received signal of the SNUPI
nodes, we have conducted an in-depth study in a 280 square
meter, fully-furnished, single family home to determine the
power requirements, range, and reliability of the SNUPI
node, and to explore the preferred location of the base
station receiver. The home used in the experiments was
built with wooden walls and flooring and unshielded wiring
in 1991, and was occupied by four residents during all
testing. This section describes the methods, results, and
discussion of these in-home experiments with fully
operational SNUPI nodes.
To explore the preferred location of the base station, we
used a spectrum analyzer connected to our custom interface
box, containing a high-pass filter to protect the equipment
from the large low frequency signals on the powerline. The
plug-in interface is attached to the powerline via the neutral
and ground wires. Four different receiver locations were
tested, marked with squares in Figure 3. For each receiver
location, the signal to noise ratio (SNR) measured on the
powerline was recorded while the SNUPI node was moved
throughout the home. During these tests, the node was
tested in the center of each room of the home (14 total
positions tested for each receiver location). For all tests, the
SNUPI node was set to a transmit power of 1 mW (65 µW
for only the radio). This power setting was used to ensure
that we could more accurately measure the SNR, even in
locations with poor signal quality.
Once the best base station receiver location was found
(shown as a triangle in Figure 3), a denser SNR mapping
was performed in which measurements were taken with the
SNUPI node in the four corners and the centers of every
room (60 total locations). The node was typically 5 m or
less from the nearest powelines. All nodes were placed at
different heights, either on the ground or on top of furniture.
The SNR results are shown in Figure 3.
This SNR mapping was conducted a second time using the
same receiver location and the same node positions, but
instead of connecting the spectrum analyzer to the
powerline, an over-the-air dipole TV antenna (~1 m long)
was used. This test was to confirm that the range of the
SNUPI node is in-fact extended significantly by coupling
over the powerline infrastructure rather than over-the-air.
Base Station Receiver Location
In order to locate the preferred location of the base station
receiver, we measured the received SNR using four
different receiver placements: physically centrally located,
electrically centrally located (breaker panel), in an upper-
corner of the home, and in a lower corner of the home,
which was also very near the position with the worst SNR,
due to limited electrical outlets and powerlines. From these
tests, we found that the preferred receiver location is the
one that is physically centrally located around the area in
which the sensor nodes will be located. Signals that are
coupled to the powerline farthest from the receiver are
attenuated significantly over the powerline, and for this
reason a centrally located receiver appears to be the best.
The receiver can be placed inside rooms in which there is a
very low SNR due to long distances from the node and the
powerline (i.e., placing the node at the center of a large
open room) in order to improve the SNR from these
locations. We also found that the receiver location on the
upper floor was very ineffective, and covered less than 25%
of the home, which tends to be at the periphery of the
Our tests showed that powerline coupling did in-fact
significantly increase the range and signal quality. Using
powerline coupling, 95% of the home was in range, while
over-the-air communication gave only 77% coverage;
however, it was determined that much of the success of the
over-the-air communication was actually due to coupling
between the TV antenna and the powerlines. This is based
on the observation that signal strength improved as the
distance to the powerline decreased, which is an expected
result for powerline coupling, but not for over-the-air
communications. Regardless, powerline coupling resulted
in a higher SNR at every location tested, with an average
improvement of 10 dB compared to over-the-air.
The SNR mapping in Figure 3 shows the relative signal
strength throughout the home. The maximum SNR was
56 dB, and there was one position in which signal could not
be detected; however, the average SNR was 22 dB. Figure 3
highlights the locations with SNR less than 15 dB, as these
are the areas in which error rates may be significant. It can
be seen that using a receiver capable of decoding a signal
with at least 3 dB SNR, over 95% of the home is in range of
a single base station receiver when the SNUPI transmit
power is set to 1 mW (65 µW for the radio). By reducing
the power consumption to 950 µW (50 µW for the radio),
over 90% of the home is still covered. We later discuss how
the range and power can be further improved.
From these experiments we gained insight regarding the
optimal locations to place the SNUPI nodes. Since SNUPI
couples to the powerline, as a general rule: the closer to the
powerline, the better the SNR. Therefore, it is best to place
the sensor along the wall of the room, where the wiring
typically runs. The worst locations are typically the center
of the room, or walls without any powerlines. However, for
multi-story homes, the center of rooms on upper floors
typically have electrical wiring in the floor, because of
lighting fixtures from the lower story. One can imagine a
consumer using the electrical outlets, light switches, and
light fixtures as a way to help with sensor placement. It is
also important to realize that SNUPI couples to anything
connected to the powerlines, not just the wires in the wall.
As an example, we found that the SNR can be increased
dramatically by placing the SNUPI node near power cords,
power strips, and transformers which are commonly used
with consumer electronic devices.
SNUPI is not restricted to indoor use, as the exterior walls
of the home are a good location due to the wiring in the
walls. In addition, outdoor areas with wiring for lighting
can cause the range of the network to be extended into the
yard. Since copper plumbing is required by code to be
grounded, this network also acts as an extension of the
powerline antenna, therefore extending the range to
bathrooms and other areas with few electrical wires. To test
this, two of the node locations used in the aforementioned
tests were placed inside bathtubs.
The SNR data presented in Figure 3 can be used to estimate
the theoretical bit-error-rate (BER) for each sensor node
location. The signal strength can be expressed in terms of
the bit energy-to-noise density ratio (Eb/N0), which is a
function of the SNR, bandwidth (BW), and bitrate (rb).
Using Eb/N0 defined below, the bit-error-rate can be
calculated for 2-FSK using the following equation :
For our prototype node, the bandwidth is 10 kHz, and the
bitrate is 9.6 kbps. Using these values, Figure 4 plots the
mapping from SNR to the theoretical bit-error-rate. For
many users of a sensor node, it is more useful to know the
rate of packet loss rather than the bit-error-rate. Assuming
that the bit-errors are evenly distributed, we can estimate
the packet loss using the fact that our prototype
Figure 3. Whole-home mapping of SNR measured at the
receiver location marked by the triangle for a SNUPI node
transmitting at 1 mW (65 µW for the radio) at each of the
mapped positions. In locations with more than 12 dB SNR, bit
errors are insignificant, so the coloring does not reflect SNR
values greater than 15 dB. The squares indicate the other
receiver locations tested
implementation uses 25-bit packets. Figure 4 also shows the
expected packet loss.
The simplicity of our communications protocol comes at
the expense of reduced reliability. For many wireless
systems, data loss is intolerable, and as a result,
complicated protocols are used to minimize the amount of
data loss. However, in the case of low throughput in-home
sensing, it is often the case that data loss is tolerable. For
example, in environmental monitoring situations in which a
data packet is beaconed once per minute, the loss of a
packet every once in a while does not cause a problem.
Since SNUPI was designed specifically for these
applications, the reduced reliability is justified.
In applications in which a higher degree of reliability is
required, the protocol can be changed to accommodate the
reliability requirements. For example, longer error checking
sequences such as cyclic redundancy checks (CRC), or
error correcting codes (ECC) can be used. In addition,
multiple retransmission schemes can be implemented to
dramatically reduce the probably of data loss due to
collisions, which pose a significant problem for large
networks of unsynchronized transmit-only nodes .
IN-HOME SENSOR NODE TESTING
demodulation of data sent from the SNUPI nodes to a plug-
in powerline receiver, a simple experiment was setup to
measure the light intensity in a home over a 3 hour period.
the successful transmission and
Base Station Receiver
We implemented a prototype base station receiver using a
software defined radio. The Ettus USRP (Universal
Software Radio Peripheral) was connected to the powerline
through our powerline interface box, and the GNU Radio
software platform was used to demodulate the signal. In
order to demodulate the signal, we shift the incoming signal
to baseband using a mixer and a low-pass filter, then use
quadrature demodulation followed by another low-pass
filter to generate the demodulated signal. The demodulated
signal is then parsed to find each packet, which is checked
for validity based on the ID bits and the parity bit. All
packets with valid ID bits (i.e., corresponding to ID values
known to be presently in the system), and without parity
errors are considered to be valid data.
Two SNUPI nodes were used simultaneously with the same
base station receiver to demonstrate the ability for multiple
nodes to share the same channel. Both nodes use an
attached photo-resistor to measure light intensity which is
sampled periodically once per minute, and then the data is
wirelessly transmitted using the powerlines to the base
station receiver. The nodes recorded the changes in ambient
light due to the sun setting.
Figure 5 shows a 25 min. segment of the 3 hour data
collection in which there is a very noticeable decrease in the
light intensity as the sun sets. After sunset, the room was
completely dark, as indicated by the data. This experiment
clearly demonstrates that SNUPI nodes can be used in a
sensor network in which multiple nodes transmit data to be
received by a powerline coupled base station receiver.
Although no packet collisions were experienced in this
short-term two node system, a longer deployment with
more nodes is sure to produce collisions. The current
protocol does not handle packet collisions, and the data is
simply lost; however, as discussed earlier a more
complicated protocol can be used to recover such data when
robust data transfer is required.
COMPARISON TO EXISTING SENSOR NODES
To evaluate the novelty of SNUPI, it is essential to compare
its power consumption to that of other similar sensor nodes.
Since the application of the sensor node determines the
required throughput of the data channel, we compare the
average power consumption of each sensor node as a
function of the data throughput. Our intended application
area is whole-home sensing, and therefore we only evaluate
nodes that work in this environment. In addition, this
comparison is done independently of the sensor and the
power supply (only the power consumption of the
microcontroller, ADC, and transmitter are considered), and
all nodes are configured to run in transmit-only mode.
Figure 4.Thoeretical bit-error-rate and packet loss as a
function of SNR for the SNUPI nodes
Figure 5. Light intensity data recorded simultaneously from
two SNUPI nodes during 10 minutes of sunset, after which it
became completely dark
Comparing sensor nodes based on their published data is
difficult and certainly not unbiased, and therefore we
directly measure the parameters of interest in a lab
environment from each of the sensor nodes. The parameters
of interest include the power consumption and time period
of each of the two phases of operation, the on-phase ( ,
), and the sleep phase ( ). During the on-phase, a
single packet of data is transmitted, and contains bits of
data, excluding the packet header and footer. Using these
measured parameters, we calculate the average power
consumption of each sensor node as a function of the data
throughput (R), expressed in bits per unit time:
When comparing the power consumption of several sensor
nodes, it is important to attempt to normalize the
performance of each node. For example, one node may
consume significantly more power, yet it achieves a much
greater range, and has a more reliable communications
protocol. In this comparison we attempt to equalize the
transmission range and the reliability of the nodes under
consideration. We compare the average power consumption
of SNUPI to several popular commercial wireless sensor
nodes for whole-home sensing, the ZigBee-based SunSPOT
 and the Crossbow Motes . All nodes were
configured to run in transmit-only mode in order to make a
fair comparison to SNUPI, which does not have a receiver.
Table 1 shows the parameters measured from each of the
sensor nodes, and Figure 6 plots the average power
consumption of each sensor node as a function of the
throughput using the equation above. In order to express
these results in a more tangible way, we assume that all of
the sensor nodes are powered from a 3 V, 225 mAh battery,
and then we calculate the battery life of each node (see
Figure 6) using a simplified model of battery life, where Q
is the battery capacity and V is the battery voltage:
Results and Analysis
It can be seen from the sensor node comparison shown in
Figure 6 that at high throughput rates, the transmit power
dominates, but for low throughput applications, it is the
sleep power that determines the battery life of the node.
From Figure 6, it is clear that the commercial nodes can
operate at higher throughput rates than SNUPI, but for the
entire range of throughput for which SNUPI operates, it
consumes significantly less power. We discuss later how
SNUPI’s bitrate can be increased to accommodate high
throughput applications. The sleep power of SNUPI is so
low that without a sensor attached, the calculated battery
life of the node is 50 years, which is far longer than the
shelf life of the battery. Of course, a sensor can
dramatically reduce the battery life of the node, but the
SNUPI node still has a battery life five times longer than
the next best sensor node.
In addition, we compare SNUPI to Bumblebee, a state-of-
the-art 433 MHz wireless sensor node in the research
community, which to our knowledge is the lowest power
wireless sensor node in existence . Bumblebee was
designed for a very short range (<15 m) neural tag, and is
therefore not suitable for whole-home applications. In
addition, Bumblebee does not incorporate a general purpose
microcontroller, but rather has a fixed ADC integrated onto
the radio chip. Bumblebee has a startup time very
comparable to SNUPI, but since it does not have a full
microcontroller, the total node transmission power is
significantly lower: 500 µW, compared to 1000 µW for
SNUPI. Since these nodes are very different, it makes more
sense to compare only the transmitter of each device.
Bumblebee has 433 MHz radio that can transmit 15 m and
consumes 400 µW, while SNUPI has a 27 MHz radio that
can transmit data to anywhere within a 280 square meter
home using only 65 µW of power.
Our initial results show that the SNUPI node has taken a
significant step in overcoming the power consumption and
battery life barrier that restricts the use of wireless sensor
networks for long-term
applications. Here we discuss optimizations that could be
applied to SNUPI to further increase performance, future
work in designing the receiver, and a new class of ubicomp
applications that can be enabled using SNUPI.
deployments in ubicomp
Sensor Node (Frequency)
SNUPI (27 MHz) 1.00 6.6 16
SunSPOT (2.4 GHz) 153 108.9 36 816
Crossbow Mica1 (916 MHz) 58.8 18900 16 232
Crossbow Mica2 (433 MHz) 36.3 42 10.6 232
Crossbow MicaZ (2.4 GHz) 65.1 7.2 41 928
Table 1. Measured power consumption parameters for each
sensor node under comparison
Figure 6. Average power consumption and battery life
over throughput for five sensor nodes
Node Design Optimizations
Using the existing SNUPI prototype, several strategies can
be used to increase the SNR and therefore improve the
range and reliability. Since the SNUPI nodes have an
adjustable power output, the easiest way to improve the
signal quality is to simply increase the power output of the
nodes in locations with poor SNR. This solution of course
requires more power and therefore reduced battery life. The
same change can be made in the opposite direction to
increase the battery life of a node in a location with a higher
SNR than needed. There are also ways of increasing the
range without increasing the power. Since SNUPI couples
its signals to the powerline, the distance between the node
and the powerline is often the most significant parameter
affecting signal strength. The powerline can also effectively
be moved closer to a node, by running an extension cord
plugged into the wall at one end near a SNUPI node placed
in a location with poor SNR. From our testing, the presence
of the extension cord (without a load attached) 1 m away
from the node increased the SNR by 3 dB, and bringing the
extension cord 0.5 m away increased the SNR by 11 dB.
There are several ways to improve the design of the SNUPI
node itself. Increasing the bandwidth and bitrate would
allow many more applications to take advantage of the
ultra-low-power capabilities of SNUPI. Currently, the
bandwidth is limited by the capacitance that can be
switched in and out of the oscillator while allowing it to
continue to oscillate. A different oscillator design or
multiple oscillators can increase the bandwidth, because the
radio now consumes negligible power compared to the rest
of the node. The addition of an ultra-low-power receiver on
the node would result in more reliable communications, and
enable many additional applications. We plan to design an
ultra-low-power 27 MHz receiver and explore bidirectional
communications over the powerline.
Other design optimizations can further reduce the power
consumption. The startup time is a source of wasted power,
because both the microcontroller and the radio are on and
consuming power during this time. The current prototype
could benefit from a substantial decrease in startup time by
changing some of the component values; however, this
requires a complicated optimization of several circuit
parameters. To design a robust prototype quickly, which
could be duplicated many times, we chose a conservative
design approach in terms of startup time. As discussed
earlier, increased current will allow the transmitter to
startup faster, and thus we could use a more complicated
scheme to drive large currents initially to get the transmitter
started, and then turn the current down to save power.
Because the power consumption of the node is dominated
by the microcontroller rather than the radio, the most
obvious next step would be to reduce the microcontroller’s
power consumption. Although the MSP430 is highly
optimized for general purpose ultra-low-power applications,
a custom circuit for only the control and computation
required on the SNUPI node could result in significant
power savings. In addition, this digital controller could be
implemented on the same CMOS silicon die as the radio,
and therefore reduce the size of the node.
There are several additional optimizations that can reduce
the size of the node, which would enable several new
applications. Currently, the size of the node is dictated by
the antenna size. In this work, we show that the transmitting
antenna on the node can be highly inefficient because the
receiving antenna (i.e., the powerline infrastructure) is very
efficient at 27 MHz. It may be possible to further reduce the
size of the antenna without a serious performance penalty.
We envision integrating the antenna into the PCB by using
a single copper trace, which could allow the remainder of
the PCB to shrink down to the size of the battery.
Base Station Receiver Design
This work focuses on the design and implementation of the
sensor node, but the base station receiver is an equally
important element in a SNUPI-based sensor network. We
will design and implement a base station receiver optimized
for powerline coupling. This includes determining a method
for choosing the optimal receiver location, finding a way to
reduce losses by impedance matching the receiver to the
dynamically changing powerline network, and designing a
receiver to work well for signals with very low SNR.
Applications of SNUPI
Having truly long-lived (~10 years) and small wireless
sensor nodes enables a breadth of ubiquitous computing
applications in the home and other environments. These
wireless sensors can be sealed and embedded in rigid
devices with no requirement for wired connections to the
outside environment, installed behind walls or hard-to-
reach areas, such as crawl spaces and attics, or even inside
the wall cavities of piping and ductwork. Using 27 MHz
also addresses many challenges with signal absorption and
attenuation caused by people and other objects, thus
enabling a new class of long-lived wearable applications.
SNUPI is the lowest power radio to date that offers a
practical communication range in the home with a single
base station receiver. Despite this, it is not intended to
replace existing wireless sensor network technologies for
all applications. SNUPI has two limitations that preclude
certain applications. First, SNUPI nodes obviously require a
powerline infrastructure in close proximity. Second, nodes
currently can only transmit, and are not capable of receiving
data from the base station. We envision a wide array of
applications for which these are not problems. One example
is environmental sensing within a home, such as
temperature, light, air quality, and humidity. The vision of a
“smart” home typically includes at least some of these
sensors for adapting a home’s lighting and climate control.
Yet, up until now, post-deployment maintenance of the
power source has been a significant barrier to sensor
deployment and the realization of this vision. Other smart
home applications include easily deployable security
systems via SNUPI-based motion sensors, glass-break
detectors, or automatic leak detection via moisture sensors.
On-body sensors are also possible, opening up the health
application space. Health-related sensors such as pulse
oximetry, blood pressure sensing, stress detection via
galvanic skin response, or glucose monitoring could be
either worn on-body or even implanted in-body. Through
our experimentation, we have seen that the proximity of a
human body to the SNUPI node significantly increases the
received SNR. We plan to explore this further, as such a
property would make SNUPI an ideal node for any kind of
wearable or in-body sensing system.
We presented SNUPI, a new platform for in-home wireless
communication and sensing that extends both the range and
battery life of wireless sensor nodes in a home. The novelty
of the SNUPI node is both in the way it sends its data and in
the ultra-low-power design of its components, where the
radio transmitter is no longer the dominant power
consumer. SNUPI is unique in that it uses the existing
powerline infrastructure as its wireless communications
channel, thus enabling whole-home range with a single base
station receiver. In addition, it employs a custom designed
ultra-low-power CMOS transmitter which allows it to
operate for several years without replacing the battery.
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