Living IoT: A Flying Wireless Platform on Live Insects
Vikram Iyer*, Rajalakshmi Nandakumar*
Anran Wang*, Sawyer B. Fuller, Shyamnath Gollakota*Co-primary Student Authors{vsiyer,rajaln,anranw,minster,gshyam}@uw.edu
University of Washington

ABSTRACT
Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time
is limited by the energy density of current battery technology.
We explore an alternative, biology-based solution: integratesensing, computing and communication functionalities onto
live flying insects to create a mobile IoT platform.
Such an approach takes advantage of these tiny, highly
efficient biological insects which are ubiquitous in many
outdoor ecosystems, to essentially provide mobility for free.
Doing so however requires addressing key technical challenges of power, size, weight and self-localization in orderfor the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees
which includes backscatter communication, low-power selflocalization hardware, sensors, and a power source. We showthat our platform is capable of sensing, backscattering data
at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, allwithin a total weight budget of 102 mg.

ACM Reference Format:
Vikram Iyer*, Rajalakshmi Nandakumar*, Anran Wang*, Sawyer B.
Fuller, Shyamnath Gollakota. 2019. Living IoT: A Flying WirelessPlatform on Live Insects. In The 25th Annual International Conference on Mobile Computing and Networking (MobiCom ’19), October
21–25, 2019, Los Cabos, Mexico. ACM, New York, NY, USA, 15 pages.
https://doi.org/10.1145/3300061.3300136

CCS CONCEPTS

1• Information systems → Sensor networks; Global positioning systems; • Hardware → Networking hardware;
Wireless devices.

Mobility in sensor networks has the potential to transform
agriculture by enabling smart farming applications including precision irrigation [4] and environmental sensing [40].
Sensor mobility significantly reduces the overhead of manual sensor deployment and upkeep, which remains a major
barrier to adoption of smart farms [46]. Drones, which havethus far been the platform of choice for enabling mobility, areseverely power constrained and last for only 5–30 minutes
on a single charge due to the energy density limits of existing
battery technologies [74]. This is mainly because the motors
drones use for mechanical propulsion and control are power
consuming and unlike digital electronics, cannot scale with
Moore’s law. As a result, the majority of a drone’s power
consumption is in its propulsion and control systems [21].
This paper explores the idea of creating mobile wirelesssensors by placing them on live insects. Using live insects
such as bees is attractive for two key reasons.

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https://doi.org/10.1145/3300061.3300136

Figure 1: Living IoT platform on a bumblebee. (Left) Bee
carrying our platform; (Right) Bee flying with our platform.

KEYWORDS
Bio-robotics; IoT; Smart farming; Backscatter communication; Wireless localization; Miniature programmable systems

INTRODUCTION• Flight Time. Unlike drones, flying insects use chemicalenergy stored in fats and carbohydrates, which have a muchhigher energy mass-density than batteries. This allows formuch longer flight times: flies have been shown to fly forhours without food [17], while worker bees spend most daylight hours foraging for nectar and pollen [20, 79] and can
fly while carrying payloads of over 100 mg [27]. Further,
these animals have evolved to have aerodynamic and musculoskeletal systems that minimize power usage [18, 19].
• Ubiquity. Insects are nearly ubiquitous across the planet
and adapted to live in diverse ecosystems, making it easy
to find a species well suited for a particular environment
or application. Moreover, while some are regarded as pests,
others are essential to human activities. For example bees
are needed to pollinate many commercial crops, and are inmany case intentionally introduced for that purpose [53].
We piggyback on these insects to enable mobility for sensor networking applications including smart farms. Using
live insects like bees however introduces two key challenges:
1) they are physically small and can only carry small payloads which severely limits options for power, computationand communication, and 2) we cannot easily control theflight of small insects like bees (see §5).
We present Living IoT, a novel general-purpose wirelesssensing platform that is low-power, low-weight and can support computing and communication operations on flying
insects like bees. In order to meet their stringent size andweight requirements, we present a backscatter based communication system that can be achieved with commerciallyavailable microcontrollers and has a small and light-weight
form factor compatible with insects. Our design includes thecapability to compute as well as sample onboard sensors. Additionally backscatter offloads many power expensive components in radios to an access-point (AP) set up near the
hive, enabling low power operations.
To address the lack of flight control, our insects localize
themselves in the 2D space using RF signals transmitted
by access points in the environment. This self-localizationarchitecture, similar in spirit to GPS, is attractive because it
does not require the AP to estimate the locations and send
them back to each insect. Thus, it can scale well with thenumber of insects, works at high speeds and does not require
the insects to transmit signals. More importantly, this enables
location-based mobile sensors where the insect can associate
location information with its sensing data as well as performsensing operation only when they pass over the desired setof locations. The sensor data can then be uploaded to the AP
when it returns within range of the hive.
Achieving self-localization on our living IoT platform ishowever challenging for three key reasons:• It should be low-power in nature as we cannot run power
hungry GPS radios on the tiny batteries that small insects
such as bees can carry.
• Accurate wireless tracking relies on phase information [25,39, 76] which requires a radio receiver at the insect. This ischallenging because radios are power consuming. Further,
we are unaware of small low-power off-the-shelf radios thatprovide I/Q samples of the raw RF signals or CSI data.
• Existing localization algorithms are designed for Wi-Fi
chips [25, 76] or software radios that are not constrained bytheir computational capabilities [52, 67, 78]. In contrast, selflocalization at the insect requires algorithms that can operatewith a tiny antenna and a low-power microcontroller.
Our design instead eliminates power-hungry radio receiver components (e.g., high frequency oscillators) by using
passive operations to perform envelope detection and extract just the signal amplitude. While this design enables
the insect to receive at a low-power, it discards the phase
information, which is essential for RF localization.
We present a novel technique that extracts the angle to the
AP from the amplitude information output by the envelope
detector. Our intuition is as follows: say the AP broadcasts
signals to the insects from two of its antennas. By changing
the relative phase between the two transmit antennas at theAP, we can create amplitude changes at the insect’s envelope
detector over time. These amplitude changes effectively give
us multiple equations that allow us to solve for the angle toeach AP. Combining the angle information from two APs
allows our platform to localize itself on a farm using a passive
envelope detector. In §3.2, we build on the above intuitionand present a low-complexity algorithm that works in thepresence of multipath as well as at speeds of up to 9.1 m/s.
Summary of results. Fig. 1 shows our hardware including the antenna, backscatter communication, wireless receiver for self-localization and the circuit board that connects
the microcontroller and sensors in a lightweight form factor
using micro-fabrication techniques (see §3.1.2). We attach
our 102 mg platform, including a 70 mg battery, to threecommon species of bumblebees (Bombus impatiens, Bombus
vosnesenski, and Bombus sitkensis).
Our results show the following.
• Communication. Our microcontroller-based backscatter
design, placed on the bee, can transmit bits using ON-OFF
keying at 1 kbps, when the bee returns to the hive.
• Self-localization. Across deployments in a soccer field andfarm, our envelope detector design on the bees achieves a
median angular resolutions of 4.6◦ at ranges up to 80 m.
Ground-truth benchmarking with drones show that similar
angular resolution can be achieved even at speeds of 9.1 m/s.

• Power. With the on-board 70 mg rechargeable battery, oursystem could last up to 7 hours while sampling its location
once every 4 seconds. We also show the feasibility of fullyrecharging the battery back at the hive within 6 hours, using
RF power.
• Sensing. We build prototypes including humidity, temperature, and light intensity sensors. These sensors fit within
our 102 mg prototype, enabling mobile sensing using bees.
Contributions. While we are not the first to place electronics on insects — prior work in biology uses electronics
on bees to understand their foraging behavior [16, 29, 60] —we show for the first time that insects such as bees can beused to carry general purpose sensors, localize themselves
and perform location-based sensing operations.
To summarize, we make the following conceptual, technical and systems contributions. First, we explore the idea that
insects can be used in lieu of drones to enable mobility forsensor networks. Second, we present a novel general-purpose
platform that is low-weight and can support computing, communication and sensing operations on flying insects. Third,
we introduce the first self-localization technique for small
insects like bees using a novel algorithm that computes 2D
location using only the output of a passive envelope detector.

2

APPLICATION SCENARIOS

Smart farming techniques utilize a variety of sensors to measure plant health. For example, moisture and humidity sensors measure the availability of water, light sensors measure
the availability and intensity of sunlight, and temperature
sensors can help determine whether conditions are optimal
for particular crops. Live insects like bees present an attractive option for use as mobile sensing platforms for agriculture.
Bees are among nature’s best pollinators and are regularlypurchased for use on farms [53], and many fruit crops dependon bees for pollination. These insects fly for hours foraging
for food, and also fly up to individual plants, which is difficult
to do with drones. Further, tracking bees alone could giveimportant insights about pollination which is not available
from commercial sensors. This includes pollination patterns
that can help maintain genetic diversity [7, 9].
We outline two deployment scenarios: 1) the sensors periodically wake up from a low power sleep mode to sample
sensor data and location, store the data in memory, and thenreturn to sleep mode. As the insect goes about its business
foraging for food, we get samples of data along its trajectory.
This is ideal for low power sensors such as temperature, humidity, etc. that simply require storing a single value, and 2)
we can imagine other applications where we wish to acquire
more detailed information at a particular location that is programmed when the bees are the hive. In this case, the system

Figure 2: Insect-borne sensor packages can self-locate andcollect location-dependent data using on-board sensors. The
data are uploaded to AP when the bee is back at the hive.

could periodically check its location, and opportunisticallysample the sensor if it is close to the destination.

3

LIVING IOT DESIGN

The idea of using flying insects as a mobile sensing platform raises a number of wireless networking and sensing
challenges. At a high level, this requires an ultra-miniaturesensing and computation package, memory to store data, a
power source, and wireless connectivity for data transfer. Additionally, it requires a wireless localization method capableof connecting the sensor measurements to specific locations.
Our solution consists of two components: a lightweightbattery powered electronics package including a sensor, RF
switch used for backscatter communication and microcontroller that can be mounted on a flying bee, and multi-antennaaccess points with dedicated power sources that broadcast
the RF signals needed for backscatter and localization.Since
large bumblebee hives cover foraging areas of roughly 8, 000
m2 [14], we target an operating range of 50–100 m for localization. The range requirements for communication however
are much smaller as sensor data can be stored in onboard
memory and uploaded when the bee returns to the hive.
To achieve this, our bee-mounted electronics package requires the following two components:• Low-power self-localization. The first requirement is a localization algorithm that runs on the insect mounted platform
and computes its location based on wireless transmissions
from APs at known locations. By adopting this broadcast architecture similar to GPS, any number of insects can concurrently compute their location. We present a novel algorithm
that uses a passive envelope detector to receive the RF signal
and compute the location on a microcontroller.
• Communication. The second requirement is a form factor compatible wireless communication link to download

PCB

Our design

Voltage (mV)

500
400
300
200
100
0
-20 -25 -30 -35 -40 -45 -50
Power (dBm)

Figure 3: Complete electronics package including an antenna, envelope detector, backscatter transmitter, and sensor shown on a US penny for scale (left). Custom miniatureantenna (right).

sensor data. Our backscatter approach has the advantage ofrequiring only an antenna connected to RF switches and a microcontroller, all of which can be achieved using off-the-shelfcomponents.
In the rest of this section, we first describe our insect form
factor compatible platform. We then present algorithms toestimate the location from the output of the envelope detector. Finally, we describe our backscatter design for uploading
data back to the AP when the bee returns to the hive.

3.1

Form-Factor Living IoT Platform

3.1.1 Understanding form-factor requirements. We choose
bumblebees as it has been well documented that they can fly
while carrying payloads of their own body weight or more.
We purchased a commercially available Bombus impatiens
colony [53]. We progressively added weight to the insects
and observe that healthy workers measuring roughly 13 mm
in length are capable of controlled flight while carrying loads
of approximately 105 mg. When adding weight beyond thislimit, they are unable to perform controlled hovering andhave difficulty taking off. We perform similar experimentswith wild bees specifically Bombus vosnesenski (Yellow faced
Bumble bee) and Bombus sitkensis (Sitka Bumble bee). We
note that these insects are slightly larger, 14 and 16 mm
respectively and can carry slightly more weight. With theweights noted above, the insects were active and exhibited
normal behavior. Thus, we target 105 mg or less for our platform. Of this, 70 mg is consumed by our 3V 1 mAh rechargable lithium ion battery [31]. This allows only 35 mg forcommunication, computing, sensing and self-localization.
3.1.2 Fabrication Method. Our platform consists of four different elements: a microcontroller, RF switches, an envelope
detector, and sensor. For each of these we leverage commercially available components available in ultra-miniature
packages. The core of our design is the Kinetis KL03z ARM
Cortex M0+ microcontroller [1] which is available in an 2 x
1.61 mm package and weighs only 4.1 mg. We use this microcontroller to sample the output of the envelope detector

Figure 4: Envelope detector performance, comparing a standard thickness PCB to our light-weight design (left) showing
it does not compromise performance.

and the sensor, and to toggle the RF switches for backscatter
communication. We use two Skyworks 13314-374LF single
pole dual throw switches weighing 3.3 mg each, the first to
select between the envelope detector and backscatter, and thelatter to toggle between the two backscatter impedances. We
construct the envelope detector out of small diodes and capacitors consuming a total area of 7.26 mm2 . We test different
sensors including a TI HDC2010 humidity and temperature
sensor as well as an ALS PT19 photodiode to measure lightintensity. In total our whole platform weighs 102 mg andmeasures 6.1 × 6.4 mm 2 .
We must also consider the weight of the substrate such as
the printed circuit board (PCB) used to create the circuit thatconnects these components. The weight of a 8×6×3.175 mm
PCB made of copper clad FR4 with standard thickness anddensity of 2.6 g/cm3 is greater than 390 mg. We instead fabricate our own light-weight PCBs by laser micromachining
0.5 oz copper coated sheet of 127 um thick FR4 and the result
is illustrated in Fig. 3. We begin by cleaning the copper withisopropanol and placing it on a low tack adhesive [58]. We
then raster out the desired pattern of copper traces using ourlaser micromachining system [13] followed by a low power
cleaning raster over the resulting copper traces to remove
dust and cut away the excess material. The resulting circuit
is approximately 100 µm thick. We then populate the board
with components and solder them.
3.1.3 Envelope detector. To design our envelope detector
circuit, we use the 3 stage rectifier design shown in Fig 5 thatis presented in [56]. In order to minimize size and weight,
we instead use small SMS7630-061 zero bias silicon Schottky
detector diodes which are available in a 0.3x0.6 mm package
and 0201 capacitors each weighing less than 0.5 mg [70] andtune the circuit for operation in the 915 MHz ISM band. To
understand whether our miniaturized circuit compromises
performance, we compare its performance to the one in [56]made with larger components on standard thickness PCBs.
We use a USRP to transmit a tone at 915 MHz and measure therectifier output voltage with a digital multimeter. Fig. 4 shows
the rectifier output voltage versus input power for the two

RF In L t
From
Ant

RectifierCt

Tuning

C1

C3

D2

D3 D4

C2

C4

C5
To MCU

D1

D5

D6

DCOut

C6

Figure 5: Envelope detector circuit diagram.
designs, demonstrating we achieve comparable performance.
Our receiver sensitivity of -40 dBm is similar to the values
achieved in [71].
3.1.4 Antenna design. For simple antenna geometries, thefrequency of operation directly affects the size of the required
antenna. Typical antennas are a half or quarter of the wavelength in size. At 915 MHz, the wavelength is approximately
33 cm, making these antennas significantly longer than abumblebee (8.5-16 mm) [42]. We explore the design space toachieve a high performance small and light-weight antenna.
First we consider chip antennas that attempt to miniaturize antennas by using materials such as high dielectric
constant ceramics. While a 900 MHz chip antenna may only
be 3.2 × 10 mm, they often require a large ground plane toperform efficiently. For example, the recommended groundplane for the above chip antenna is 50 × 120 mm, and theantenna requires clearance to other components [6]. Additionally, the dense ceramic materials increase the weightmaking the antenna 288 mg. An alternative is a wire antenna
which is a significant fraction of a wavelength. Althoughthese may be longer than the insect [27], the wires could
hang off the sides or back. A 15 cm long straight wire hanging off the sides however could introduce other practicalissues in the context of insect flight — thin wires can beeasily tangled and caught in the insect’s wings, legs, or evenplants since they don’t retain a rigid shape.
To address these concerns we instead design a base loadedwhip antenna constructed using the 43 AWG wire. This antenna design is a common solution used in cars and handheld
transmitters with electrically short antennas. Rather thanhaving just a straight wire whip which with shortened length
presents capacitive reactance, adding inductance at the basecancels this and creates a resonant antenna. We fabricate
this antenna using 7 turns of 43 AWG wire with a diameterof 2 mm followed by a straight 10 mm wire. We then apply
cyanoacrylate glue (Loctite 416) to the antenna to stiffen it
and prevent it from losing its structure. The resulting structure is shown in Fig. 3, has a total weight of 4.6 mg, andlength of 10 mm which is smaller than the bee and avoids
the above issues of entanglement. We compare this to theperformance of a 900 MHz monopole antenna. We place eachantenna 3 m from a transmitter and find that our lightweight
design receives roughly 6-8 dB lower power.

3.1.5 Attachment to Live Insects. We begin by capturing individual bees in plastic jars. For our B. impatiens colony weopen the door to the hive and place a small cotton swab
dipped in sugar syrup near the hive exit to lure bees out
one at a time and trap each insect in a jar. For the otherspecies, we capture live bees from a small farm, again bytrapping them in a plastic jar while they forage on flowers.
After isolating a single insect, we place the plastic jar in afreezer at approximately 0◦ C for 4-5 min to cold anesthetize
the insect [36]. At this point the bees typically stop moving
allowing for 2–5 min of working time to attach the electronics. We then grip the insect by the sides of the thorax witha pair of tweezers and adhere our electronics package byapplying a drop of cyanoacrylate glue and quickly applying
a small amount of an accelerator compound (Loctite SF712)
to immediately dry the glue and hold it in place. We experiment with attaching the electronics in two locations: the topof the thorax behind the head and on the upper abdomen.
We find that the bees are able to fly when the weight is attached in either position, however we prefer the abdomen
as it naturally droops down away from the wings when thebee is held off the ground and minimizes the risk of excess
glue flowing onto the wing joints on the thorax.

3.2

Self-localization of insects

Living IoT’s localization system employs APs which transmit
RF signals in the 900 MHz ISM band. At the insect-mounted
receiver, due to the power and size constraints, living IoT uses
a passive envelope detector connected to a small antenna toreceive only the amplitude of the RF signals broadcast by theAPs. Unlike an active radio that gives both amplitude andphase, the envelope detector only provides the amplitudeof the signal but can do so with small, zero-power passive
hardware components [37, 64]. The phase of the signal ishowever essential to achieve wireless localization.
To address this, we first extract the phase difference of thesignals from each of the transmit antennas of an AP using theamplitude output by the envelope detector. We then use thisphase to compute the angle of the insect with respect to that
AP. By using the angles from two APs we can identify the
2D location. Typically bees only fly a few meters above theground and hence 3D localization is not essential. However,
the technique presented in this paper can be generalized toachieve 3D localization by adding an additional AP. Next,
we describe each of these techniques in further detail.
3.2.1 Phase from Envelope Detector. Consider a setup inwhich an AP transmits RF signals using two of its antennas
as shown in Fig. 6. The two transmissions will travel different
distances to the receiver and therefore combine at the insectwith a phase difference corresponding to this distance difference. The small antenna at the insect receives the combinedAngular error (degree)2

Figure 6: Self-localization using two antennas at AP.
RF signals and the envelope detector outputs its amplitude.
The key insight here is that the amplitude depends upon thephase difference at which the two transmitted signals combine at the receiver. For example, if the transmitted signals
are perfectly in-phase they will add constructively giving
the maximum amplitude; in contrast a phase difference of π
will cause the two signals to cancel each other completely.
Our key insight is that by intentionally introducing an
additional phase difference between the two AP antennas,
we can create amplitude changes at the receiver. We cananalyze these changes to estimate the phase corresponding
to the angle of the insect from the AP.
To explain this in more detail, consider x 1 (t) and x 2 (t) tobe the signals transmitted from the two antennas. Let us set
both these signals to x(t) = Ae jωt . Assuming no multipath
which we will discuss later, the signal at the receiver envelope
detector can now be written as:py(t) = |ax 1 (t) + ae jϕ x 2 (t)| = aA( 2 + 2 cos(ϕ))
Here a is the signal attenuation, and ϕ is the phase difference between the two paths. Note that the amplitude attenuation difference between the two signals is negligible since
the separation between the two antennas is small comparedto the distance between the AP and bee.
Now if the AP intentionally introduces a phase differenceof θ on the second transmit antenna, i.e., x 2 (t) = x(t)e −jθ ,the signal at the receiver can be written as,py(t) = |ax(t) + ae j(ϕ−θ )x(t)| = aA( 2 + 2 cos(ϕ − θ ))The maximum value for the above equation happens whenϕ − θ = 0 mod 2π . Hence, to get an estimation ϕˆ of ϕ, we
can let the AP sweep θ from −π to π at a constant rate.
At the receiver side, the envelope detector simply samples
the amplitudes corresponding to each of the θ s. It then getsthe sample with the maximum amplitude and infers θmaxfrom the time of that sample. Now, the ϕˆ we are interested
in is simply θmax . The angle Θ̂ of arrival of the receivercorresponding to the two transmit antennas at the AP can bederived by ϕˆ = d sin(Θ̂) where d is the distance between the
two transmit antennas in radians. In our design, this distance
between transmit antennas is set to half a wavelength.

3

4

5

60
50
40
30
20
10
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Multipath ratio R

Figure7: Simulation results for different multipath ratios,ÍR=M
i =2 a j
a1, using 2–5 transmit antennas.

A key consideration in the above design is that as the distance increases, noise affects the signal quality. We mitigate
the effect of this noise by sampling for a longer time, i.e., theAP dwells on each phase difference θ for a longer duration.
The upper bound on this duration however is determinedby the motion of the bee. Our empirical results found that aduration of 50 ms per sweep across all the angles, is a goodtrade-off between the noise level and motion tolerance.
3.2.2 Addressing Multi-path. The above discussion assumes
that the phase difference ϕˆ estimated at the insect can betranslated into angle of arrival using amplitude variations.
We however need to address the potential amplitude variations due to multipath. Unlike systems like Wi-Fi which
operate indoors with mostly no LOS path, our system is designed for outdoor farm use in the natural habitat of insects.
For example, in deployment scenarios such as open fields andfarms there is a direct strong line of sight. However, we stillneed to account for the amplitude variations that result fromthe other paths constructively and destructively interferingwith the dominant direct line of sight path. To this end, we
utilize more than two antennas per AP to reduce the error
in the angle caused by multipath.
Formally, suppose we use N antennas separated by half awavelength each. Similar to the above two-antenna scenario,
we introduce a phase offset of θ i for the ith antenna. Suppose
M independent multipaths exist and the first one is the direct
path, the received signal can be represented as, yenv (t) =ÍMÍN −1|A k=1
ak i=0x(t)e j(i∗ϕk −θ i ) |. Here ak is the attenuation
for the kth path. Let us set θ i = θ ∗ i, and sweep θ from −πÍÍN −1 ji(ϕk −θ )to π . We now get, yenv (t) = A| kM=1 ak i=0e|.
We solve for θmax using the same procedure as in the 2antenna case and estimate ϕˆ = θmax , where the amplitude
output by the envelope detector has the maximum value.
ÍThe key insight is that, when M
j=2 a j < a 1 , which is truein most line-of-sight scenarios like the farm, the error in ourestimate of the angle with respect to the AP, which is |ϕˆ −ϕ 1 |,is bounded. Moreover, this error decreases linearly as thenumber of antennas increases. To verify this intuition, wefor APi in {AP1 , AP2 } do
APi transmit preamblei
for Θ = −π /2 to π /2 by δ dofor j = 2 to 4 do
set the phase shift of the jth phase shifter to(j − 1) ∗ π ∗ sin(Θ)
endsleep for (T − Tpr eambl e )/π ∗ δ
end
end
Algorithm 1: Pseudo-code at the AP.

perform a simulation where we compute this error by changing the number of antennas. We repeat this for increasing
multipath ratios R, ratio of the sum of the amplitudes of all
NLOS paths with respect to the amplitude of the LOS path.
Assuming that the angles of indirect paths are uniformly distributed, Fig. 7 shows the mean error as a function of thesetwo parameters. The plot shows that the error is less than10◦ when using four antennas even if the total amplitude of
all NLOS path is 60% of the amplitude of LOS path. With fiveantennas, we can get a similar error even when this ratio is
close to 0.95. This shows that by increasing the number ofantennas at the AP, we can reduce the error due to NLOS
paths and achieve an accurate angle estimation.

while True do
receive and store amplitude samples during the last
3T time into memory as S
if preamble 1 detected in S 1..2T located at i thenp1 = arдmax j ∈(Tpr e ambl e ,T )Si+janдle 1 = (p1 −Tpr eambl e )/(T −Tpr eambl e )∗π −π /2
locate preamble 2 in Si+T ..3T at i ′p2 = arдmax j ∈(Tpr e ambl e ,T )Si ′ +janдle 2 = (p2 −Tpr eambl e )/(T −Tpr eambl e )∗π −π /2(If Euclidean position is needed) get x and yfrom anдle 1 and anдle 2 using look-up table
output (anдle 1 , anдle 2 ) or (x, y)
end
end
Algorithm 2: Pseudo-code of Living IoT platform.

3.2.3 Leveraging insect motion. We also leverage insect motion to reduce the effects of multipath and reduce the probability of small scale fading. This is specifically useful when
the bee is in motion. Since the typical speeds when the bee
is in motion are less than 10 m/s, the bee does not moveby more than a meter between consecutive 50 ms durations
where our AP cycles across the phase values. Despite introducing slight errors because of Doppler effects, our algorithm utilizes motion to improve the accuracy by leveraging
spatial diversity: the multipath combination can be significantly different for even a small displacement. Hence, we use
exponential smoothing to temporally average consecutive
measurements which yields a more accurate result. Formally,
the final angle of arrival Θt of each bee is calculated as:
Θt = ηΘt −1 + (1 − η) sin−1 (ϕˆt /π ), where η is the smoothing
constant which we set to 0.8. Note that the computationally expensive sin−1 operation can be offloaded to the accesspoint, as shown in Algorithm 1.

location of using the intersection of the two separate angles
of arrival. This intersection procedure can be implemented
using a look-up table to minimize the required computation.
Specifically, the two APs intermittently transmit their
sweep signal one after another. They are coarsely synchronized using TDMA so that no two sweeps will interfere witheach other. We transmit two predefined orthogonal preambles using ON-OFF keying, [1,0,1,0,1,0,1,0] and [1,1,0,0,1,1,0,0]
to identify each AP. The preambles are transmitted before
every sweep of each AP respectively so that the bee can findthe start of each sweep efficiently. The receiver first detects
the preamble using a simple state machine, then runs thealgorithm twice to get the two angles. Our pseudo-code ispresented in Algorithm 2. We note that the computation forour receiver algorithm scales linearly with the sampling rate
(which is in the order of kHz), and only simple arithmetic
operations are involved. This makes it efficient enough to
run on our microcontroller platform.
A key consideration while increasing the number of APs
is that the transmissions from each of them have to be timemultiplexed. This increases the delay required to computethe location value which can be challenging especially when
the bee is highly mobile. Given that each AP sweeps acrossvarious phase values in 50 ms segments, the delay to compute the 2D location is around 100 ms. Assuming a speed of
3 m/s, this translates to a motion of around 30 cm which iswithin the error of our location estimates and hence does
not significantly affect our accuracies.

3.2.4 2D localization of insects. To estimate the 2D locationof the insect, we employ two APs with four antennas each.
Separating the two APs and placing them perpendicular toeach other gives the best 2D accuracy. Given the known locations of the two APs, the two angles computed with respect
to each AP, uniquely identifies the 2D location. The bees caneither store these two angles or can also calculate their 2D

3.2.5 Localization power consumption. In order to minimize
power consumption, we duty cycle our localization and sensor by periodically performing a measurement and then returning to a low power mode until the next measurement.
Sampling the envelope detector and performing computation
for localization requires a peak current of 1.6 mA followed byreturn to a low power sleep mode with a current of 100 µA.

Vctrl2

TX/RX
switch

Z1

Z2

MCU
ADC

Envelope
Detector

Figure 8: Backscatter hardware including a block diagram(left) and light-weight hardware implementation (right).

If we check our 2D location every 4s, the average power
consumption is 138 µA. For our battery capacity of 1 mAh,
this results in potential battery life of up to 7 hrs on ourrechargeable battery.

3.3

Backscatter Communication Design

Backscatter requires three components: an antenna, a switch
to modulate the reflected signal, and a control signal forthe switch with the desired bits [32, 44], two RF switches,
and the microcontroller as shown in Fig. 8. We connect theantenna to the input port of the first switch which selects
between the envelope detector and the second switch used
for backscatter. We use the second switch to select between
two impedance states to create the backscatter signal. In
order to minimize extra components we simply use the open
and short impedance states which we implement by leaving
one port disconnected and connecting a 51 pF capacitor to
ground on the other port.
At a high level, the AP transmits a continuous wave signal from one of its antennas, which the living IoT platformbackscatters with the sensor data when the bee is back near
the hive. We use ON-OFF keying modulation to encode bits
using backscatter. Backscatter however presents the challenge of needing to receive the weak reflected signal in thepresence of the high power RF signal from the AP. To address
this using a technique called subcarrier modulation [32, 38]:
we use the microcontroller to generate a square wave at a
rate of 2 MHz which shifts the frequency of our backscattersignal 2 MHz away from the high power signal from theAP. By doing this, the AP can then filter out the transmitted
signal thereby significantly reducing the receiver noise floor
and improving our communication range. We then modulate
this signal by toggling it ON and OFF to produce an ON-OFF
keying pattern to encode bits.
To implement this with minimal power consumption, wedisable all unnecessary peripherals on the device and reducethe clock frequency to 8 MHz. We then generate a 2 MHz
square wave using a timer module in PWM mode at a fixed
50% duty cycle for sub-carrier modulation. We find that enabling and disabling the timer module in software incurs

Angular error (degree)

Vctrl14 antennas
2 antennas
3 antennas
25
20
15
10
5
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10 20 30 40 50 60 70 80
Range (m)Figure 9: Stationary self-localization angular accuracy.

delays, so we instead take advantage of our first switch: weuse a lower rate control signal from a GPIO pin on the microcontroller to toggle the first switch to the envelope detector
and disconnect the backscatter signal. Using this method we
can send data at a rate of 1 kbps which is sufficient for ourapplication where the sensor readings and angle values weneed to transmit are only 1-2 bytes.
We note the following about our communication design.
• Backscatter range. In our design the insects store thesensing data and upload it to the AP via backscatter when theinsect is back at the hive. This only requires a range of a few
meters. One can, in future designs, consider increasing thebackscatter range using coding to hundreds of meters [71].
• Downlink and MAC protocol. We reuse the envelope
detector for downlink communication from the AP to thebees. Our design uses a simple MAC protocol where, oncethe bees are back at the hive in the night, the AP queries
each of the insects one after the other, using the downlink.
The insect then uses backscatter to respond with the sensor
data and corresponding location information.
• Effect of interference. A concern with using an envelope detector is that it is not frequency selective and cantherefore see signals across a broad range of frequencies
at 900 MHz. This however does not significantly affect our
performance for two key reasons. First, our application isdesigned for smart farms where 900 MHz transmitters including cordless phones, LoRa and RFID readers are currently
uncommon. Second, most 900 MHz wireless deployments
including LoRa and SIGFOX are designed for sensitivities ofless than -118 dBm. These lower power signals do not registerat our envelope detector which has a -40 dBm sensitivity.
• Power requirements. Our micro-controller based backscatter requires a peak current of 1.8 mA when transmitting at
1 kbps. Unlike localization which is performed periodically,
data is only uploaded once upon returning to the hive anddoes not otherwise consume power. Offloading 10 sensor
measurements and the corresponding angle data would only
require running our backscatter transmitter for 32 ms and istherefore not a concern from a power perspective.

3.3 m/s

9.1 m/s

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Range (m)Range (m)(a) AP1(b) AP2

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Localization error (m)Figure 11: 2D accuracy with deployment in farm.

Figure 10: Mobile 2D localization angular accuracy.

4

EVALUATION

We evaluate various aspects of our living IoT platform.

4.1

Self-Localization Accuracy

We first evaluate our low-power localization algorithm using
a static deployment of living IoT in different environments.
Since it is difficult to get the ground truth location with afree flying bee, we use a drone to evaluate the 3D accuracy of
our technique at different speeds. Then, we place the living
IoT platform on a live, wild caught bee that can fly freely
within a plastic enclosure and run our localization algorithm
at various points across a farm. Finally, to benchmark theeffect of different wing speeds, we run experiments with arobotic wing of similar dimensions.
4.1.1 Stationary experiments. For our experiments, each AP
consists of one USRP-N210 connected to a four way power
splitter followed by three phase shifters, each of which introduces a phase shift controlled by an NI myDAQ digitalanalog converter. Along with the original signal, the four
outputs are amplified to 28 dBm by a Qorvo RF5110G power
amplifier and then connected to four 2 dBi monopole antennas separated by 12 cm each. The cable lengths are carefully
calibrated so that no extra phase offset is introduced.
Soccer field deployment. We first conduct our range
evaluation outdoors on an open soccer field measuring approximately 100 × 100 m. We place an AP at one end of
the field and move our receiver along a straight line awayfrom the AP up to a maximum distance of 80 m. Fig. 9 plots
the angular error as a function of distance up to the point
at which the receiver had insufficient SNR to decode. The
plots show that our low-power envelope detector platform
can compute the angle of the AP up to 80 m from the APwith four antennas. This range could, in future designs, be
improved by increasing the sweep time at AP which in our
implementation is 50 ms. Also, the angular error improves
with the number of antennas at the AP. Further, with four antennas the range of the design increases because of antenna
diversity gains.

Farm deployment. We placed one 4-antenna AP at thecenter of two perpendicular edges of an 90 × 120 m farm.
We then place the living IoT platform at multiple locations
around the farm. We repeat this experiment multiple times
at each location and compute the x and y axis errors as wellas the 2D location by combining the angles from the twoAPs. Fig. 11 plots the ground truth locations and the CDF of
the error at all locations. The plots show that the median 2D
localization error is 1.9 m. For context, while prior AoA work
localizes devices at sub-meter resolutions [67], the range
at which the experiments were conducted is limited to lessthan 10 m. As the distance increases, as expected, the sameangular error results in a larger localization errors. This
meter-scale resolution however is sufficient for our smart
farm application.
4.1.2 High-speed experiments. Since we cannot control thebee motion it is difficult to run systematic accuracy experiments at different speeds on an actual bee since we do notknow the ground truth at long ranges in outdoor environments. Instead, we place our platform on a DJI Phantom 3drone. We place the first 4-antenna AP at one end of the soccer field and the second 4-antenna AP as shown in Fig. 9. We
run experiments at three different speeds using the Phantom
3’s wireless controller. We then use the drone’s flight record
which contains its GPS, altitude, accelerometer and compass
data as the ground truth. Fig. 10 plots the angular accuracy
at three different speeds. We plot the results for both theAPs. The average angular accuracy at different distances
are similar to the stationary angular accuracies observed in
the previous experiment. The fluctuations in error acrossall distances are due to the changing multipath over time,
however the exponential smoothing technique offsets thismaking some results better than the stationary scenario.
4.1.3 Wild bees observations. Over the course of our outdoor experiments we observe many wild bees in both thesoccer field and the farm. In total we observe 3 species including honeybees (Apis mellifera) as well as two speciesof bumblebees (Bombus vosnesenski and Bombus sitkensis).
In areas of the farm with flowers in bloom, we observe as
many as 20 or more in a single 1 m2 area. Even on the openxy

100 Hz150 Hz170 Hz0.3
Amplitude

CDF

2D
1
0.9
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field we observe many wild bees foraging in weeds withsmall flowers (e.g., dandelions). We note that while they typically fly at average speeds of 1-5 m/s when going longer
distances [41, 54], they settle in one region slowly hoveringor landing at individual flowers.
4.1.4 Experiments with bees. Next, we evaluate our self localization algorithm with our living IoT platform placed ona live bumblebee as shown in Fig. 12. We conduct this experiment with the bee in a plastic container with a volume of
30 × 20 × 20 cm, where the bee freely moved around and flew.
We run experiments again in the farm deployment where weplaced the two 4-antenna APs as shown in Fig. 11. We thenplace the plastic container with the mobile bee at different
locations in the farm. The location of the plastic container
was taken as the ground truth for computing the localization
error. Fig. 12 plots the CDF of the localization accuracy with
our living IoT platform on the bumblebee. This as expectedshows that the accuracies are similar to prior experiments
and demonstrates the feasibility of self-localization on a live
and mobile bee.
4.1.5 Effect of flapping wings. While a drone serves as an
excellent platform to perform systematic experiments withknown ground truth data, its flight mechanics are different
from that of an insect. So, we next evaluate the effect of flapping wings on localization accuracy. Unlike propeller driven
drones, insects use flapping wings to generate lift. Since theenvelope detector is mounted close to the wings of the insect
which move periodically and cause vibration in the body ofthe insect at the flapping frequency we next evaluate how
flapping wings affects the signal at the envelope detector.
In order to isolate the effect of flapping from flight motionof a real insect in a systematic manner, we instead attach ourenvelope detector and antenna to an insect scale robot design
with flapping wings that is inspired by [80] and shown in
Fig. 13. The robot has a wingspan of 35 mm which is similar
to a bumble bee (32 mm) and are designed with hinges tomimic the wing kinematics of real flying insects.
The robot’s wings are driven by 2 piezoelectric bimorph
actuators which we control to flap the wings at different
frequencies to determine whether the wing motion itself

0.1
0
-0.1

0 2 4 6 8 10 12 14 16
Localization error (m)Figure 12: Localization of live bee in enclosure on the farm.

0.2

Figure 13: Effect of flapping wings on envelope detector.

negatively impacts our localization performance. We thenincremented the phase difference at the transmitter antennas
by discrete steps of thirty degrees and monitored the change
in the amplitude of the signal received at the envelope detector. Fig 13 plots the raw received signals at the output of
the envelope detector. The figure does not show a noticeable
degradation in SNR due to wing motion or vibration of the
body as a whole. Further, the different amplitudes createdby the phase changes at the AP, appear intact at the outputof the envelope detector and are largely independent of the
flapping frequency of the insect’s wings.

4.2

Backscatter Evaluation

We implement our access point using two USRP softwareradios. For the transmitter, we configure a USRP to transmit
a continuous tone at 915 MHz and connect its output to a
Qorvo RF5110G power amplifier which outputs to a 6 dBipatch antenna. We use the same model of patch antenna forour receiver and place it parallel to the transmitter antenna
separated by a distance of 0.5 m. We set the receiving USRP toa frequency 2 MHz away from the transmitter corresponding
to the subcarrier generated by backscatter. We then apply aband pass filter in software to remove unwanted noise.
We evaluate our setup in our application scenario of thebee uploading its data when it is near the hive. We evaluate
this first with a dead bee placed at fixed locations, and then ona live bee in a plastic enclosure. We ensure that our evaluation
board is light enough to enable the bee to fly and walk aroundthe enclosure; we glue the board to the top of the insect’s
abdomen. We place our AP approximately 1 m from a bee
hive separated from the AP by an enclosure consisting of 2
layers of insect netting. We configure the AP to continuouslytransmit 100 bits and measure the error rate at 20 locations
within 2 m of the hive. We place the hive on a 1 m hightable and include points on every side of the hive to simulatebees taking a variety of trajectories to approach the hive.
We plot the uncoded BER in Fig 14. The plot shows that thelive bee has a similar performance to that of the dead bee
demonstrating that our backscatter system can operate in
the presence of bee motion. We note that the uncoded BERs

0.1
0.08
0.06
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1

2

uncoded BER

3

4

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Range (m)Figure 14: Backscatter performance. The uncoded BER is

Current
Time
550
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wire

BER

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static bee
mobile bee
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Figure 15: Recharging batteries using RF harvesting.

low and comparable to prior backscatter designs [77] andthe bee can upload data when it is back at the hive.

are similar to prior backscatter works [77] and can be further
reduced by applying error correcting codes [44, 71].
Finally, to understand the effect of the antenna withoutcomplicating factors of a live insect and its motion, we evaluate in a stationary case, with both our lightweight wire
antenna as well as a standard monopole antenna. We place
our AP at a fixed location in an outdoor environment andconfigure our backscatter device to transmit a known sequence of 200 bits at a rate of 1 kbps. We then move it toincreasing distances away from the AP and record the received bits at each location. Fig. 14 plots the uncoded BER
versus distances and show that our light-weight antenna canachieve low BERs upto 5 meters. Beyond 5 m, the monopole
antenna performs better than the light-weight antenna. This
5 m range is however sufficient for our application where
the bees upload the data when they are back at the hive.

4.3

Power harvesting Feasibility

4.3.1 Recharging batteries using RF. Since certain insects
like bees return to a single colony or hive we can use this asa central charging point. To evaluate the feasibility of thisapproach, we first measure the attenuation of 900 MHz signals through a real bumble bee hive (Natupol, Koppert) [53].
We place a 900 MHz 6 dBi patch antenna AP in different
locations near the hive including above, below, and on eitherside. We then place our platform on the opposite side. We
find that placement of the transmit AP below the hive causes
significant attenuation as these commercial hives containa package of sugar water below the hive to provide a foodsource. We also find that placing the AP above the hive results in the lowest path loss of approximately 15 dB, while theside and back are 3 and 6 dB worse respectively. Fig. 15 plots
the measured harvested power versus transmitted RF powerfrom the top of the hive. The results show that a 20 dBm AP
could charge a 1 mAh battery in about 6 hours.
4.3.2 Solar harvesting for battery-free platform. Insects suchas bumblebees are most active during daylight hours. This
makes solar power harvesting a particularly attractive option

as these creatures naturally fly in outdoor environments with
abundant sunlight. We evaluate an 8 mg 3x3 mm photovoltaic
cell [50] shown in Fig. 17(a) by measuring the output of thecell under a microscope light [5] with controllable intensity.
We measure the output current and voltage of the cell andplot the results for illuminance values ranging from 100020,000 Lux in Fig. 17(b). At 1000 Lux which is representativeof an overcast day the PV cell can harvest 1 µW of power and
up to 50 µW on clear sunny days. This shows the potential
for a battery-free design that could replace the 70 mg battery
with a solar cell and small storage capacitor.

4.4

Sensor Peripherals

We integrate three sensors: temperature, humidity, and lightintensity. These sensors are commercially available in small
packages compatible with our weight budget and operate
at low power. For performing temperature and humidity
measurements we use the TI HDC2010 IC which weighs 3 mg
and includes the physical sensor as well as an integrated ADC
and digital interface as shown in fig. 16. This chip is capableof providing high accuracy measurements while consuming
as little as 0.55 µA of current for measuring both sensors
once per second. Additionally, its low power sleep mode
allows for greater power savings by leaving it inactive until
our algorithm detects we are in a location we wish to sense.
We also measure light intensity using the ALS PT19 ambient
light sensor. This chip weighs only 1.5 mg, but does notinclude integrated readout electronics. Instead, we use thebuilt in ADC on our microcontroller to periodically sample
the analog output.
During operation, living IoT must also be able to log its
sensor data until returning to the hive to upload it. The
humidity and temperature values require 11 bits each, while
the light intensity requires 12 bits. Our microcontroller has
up to 32 KB of onboard flash memory. Using 2 bytes foreach measurement, 1 byte for each angle, is a total of 4 bytes
for a sensor measurement and 2 angle values. This would
allow us to store measurements once every 5s for over 10 hrs.
This is sufficient to cover daylight hours during which bees
are active and foraging. Alternatively, we can leverage ourPower (uW)Overcast

0

Figure 16: Close up images of temperature and humidity
sensor (left) and ambient light sensor (right).

localization technique to selectively log data at higher rates
near specific target locations.

5

RELATED WORK

Tracking bees. Prior biological research has explored the
problem of tracking bees to understand their behavior andhelp explain the decline in their worldwide population. [73]attaches bees with laser light-activated microtransponders
which can be detected by laser readers at distances of up to 10
mm from the reader. To know when the bees enter and leave
the hive, a 10 by 10 mm plastic tube walkway was attachedto the hive entrance, with two laser readers at the top of the
tube. Intel and the Australian Commonwealth Scientific andIndustrial Research Organization (CSIRO) are exploring thepossibility of attaching RFID tags on bees to track them at a
range of less than 10 cm allowing them to notice when thebees enter and leave the hive [29, 60]. Backscatter has also
been used with large dragonflies [75]; it uses a custom silicon
design and also does not have RF localization capabilities.
Radar based systems [15, 62] use a pulse radar with a
25 KW transmit power at 9 GHz as the reader and use apassive analog diode on an insect that creates harmonics at
18 GHz which the reader uses to track the insect at much
longer distances [62]. The key challenges with this design
are three-fold: 1) the analog design with the diode always
creates a 18 GHz harmonic and hence cannot support morethan one bee, 2) these passive analog diodes do not support
general-purpose sensors on the bee, and 3) the bee cannot
localize itself and has no computing capabilities.
Finally, [27] uses a 200 mg active pulse radar transmitter at100-200 MHz powered by a small battery on a large species ofEuropean bumblebee. These transmitters send small pulses
which the reader uses to track the bee. This does not satisfy
our requirements for five reasons: 1) across our experiments
the bees common in our environment, were unable to lift
more than 105 mg, 2) the bees do not have the localization
information and so it does not support self-localization, 3) it
is unclear if the design will scale to large numbers of bees
since they all must transmit the corresponding pulses, requiring a scalable MAC protocol, 4) transmitting high power

Daylight

50
40
30
20
10
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10 15 20
Illuminance (kilolux)

25

Figure 17: Solar harvesting for battery-free platform.
pulses at the bee would limit other operations that can be
performed and 5) it does not carry general-purpose sensors.
In contrast to this prior work, we make the following
contributions: i) design the first low-power self-localization
technique for flying insects, ii) present a general purpose
platform that enables computing, communication and sensing on aerial insects and, iii) demonstrate for the first timethat insects such as bees can be used to carry general purpose
sensors and thus enable mobility, in lieu of drones.
Controlling insect flight. Researchers have shown how tocontrol the motion of larger insects such as beetles, dragonflies and Locusts [12, 48, 55, 65]. An interesting future
research direction would be to develop low power flight control for small insects like bees. This however is challenging
since while the nervous system of larger insects like beetles
is well understood, bees are at least an order of magnitude
smaller in size and it has not been shown that their flight
can be controlled. Achieving this for small insects like bees
is an open problem and, if solved, can augment our work.
Power harvesting from large insects. Prior work has tried
to harvest power from large insects such as moths and beetles [66]. [2, 3] utilizes a piezoelectric transducer which converts the vibratory motion created by the insect’s flight into
electrical power to harvest 7–60 uW from beetles and hawk
moths. [10] harvests 0.8 mW from moth vibrations using amagnetic induction generator. Finally, [33] harvests around
1 µW from the chemical energy stored within a moth’s
hemolymph. These systems however use moths and beetles that are more than 10 times larger than bees. Instead weshow RF harvesting to recharge the battery and the feasibilityof using solar cells to enable battery-free designs.
Bio-inspired aerial robots. The robotic community has
spent the last two decades in the design of insect-scale aerial
robots [22, 45, 59] that are similar in size to houseflies andmimic the wing propulsion of insects. Despite significant research [8, 24, 26, 47, 80], these robots are largely tethered toa wire to power and control them, since they consume hundreds of milliwatts of power for the mechanical propulsion
and cannot carry batteries given the weight requirements.
While recent work shows the feasibility of wireless powerusing lasers at the range of a meter [34], providing hundredsof milliwatts of power at 80–100 m is challenging and difficult to scale beyond a single aerial robot. Our idea instead isto leverage biological insects which can be thought of as efficient biological machines that provide flight and piggyback
communication, computing and sensing on top of them.

eventually live out their lifespan. There are three approaches
to addressing this problem: i) ensure that the electronics
are removed before the expected lifetime of the bees, ii) the
electronics can be localized for a while even after the bees
die which can be used for cleanup purposes and iii) use
biodegradable electronics [30, 72] in the design of living IoT.

Size-constrained sensor systems. [11, 23, 35] designed
die-stacked sensor platforms that have the key building
blocks of miniaturized sensor nodes, such as data transceivers,
energy harvesters, power management units, and digital
logic circuits. This approach requires custom ICs which limits
availability to researchers elsewhere as well as programmability. We take an alternate approach by designing a systemusing commercial off the shelf components based on a microcontroller that is programmable to create a more modular
platform. In addition, we also demonstrate a self-localization
technique using only an envelope detector.
[43, 57, 81] design localization systems for sensor networks with a focus on decreasing cost, power, or improving
accuracy; however these techniques all require active radios and cannot easily be scaled down to the size and power
requirements for use on insects like bees.
Our prior work [52] designs a sub-centimeter sized backscatter device based on a microcontroller and localizes it using
LoRa backscatter [28, 71] at a software radio using non-linear
optimization on I/Q samples. In addition to being focused ona novel mobile insect application, our design differs from [52]in four key ways: First, our localization algorithm is not basedon backscatter and does not occur at the software radio. Instead we design a localization algorithm that runs on the
low-power device using the output of an envelope detector,
without access to the I/Q samples. Second, our backscatter
design does not use LoRa transmissions, which significantly
simplifies our design. Third, a key limitation of the designin [52] is that it cannot currently scale to more than one totwo devices. In contrast, our self-localization algorithm issimilar in spirit to GPS and hence can scale to a large number of insects at the same time. Fourth, unlike non-linear
optimizations that cannot effectively run on our low-power
microcontroller, we design a low-complexity algorithm toestimate the 2D location at the Living IoT platform.

• Fabrication. Our current prototype requires manual fabrication and attachment to the insects. However we use commercially available parts which allows for easy scaling of theelectronics fabrication and our process for gluing the electronics to the insects is similar to the process for attaching
tracking markers to bees in commercial hives. Additionally,
researchers have also shown that insects can survive common microfabrication processes such as deposition of conductive material in a vacuum chamber [68, 69] and performing surgeries at different stages of an insect’s life cycle. Thissuggests potential approaches for mass attachment of electronics or fabricating devices on insects themselves. Further
work on implanting devices at different stages of an insect’s
life cycle also has potential to improve fabrication [61].

6

DISCUSSION AND CONCLUSION

This paper explores the use of insects in lieu of drones toenable mobility for sensor networks. Making this vision pervasive, however, requires addressing additional challenges.
• Weather dependency. Bees hibernate during winter incooler climates. This however correlates with plant growthand increased activity on farms in warmer seasons.
• E-waste. Piggybacking on insects could lead to electronic
waste being scattered around the farms when the insects• Camera sensing. A future research direction is to integrate cameras [51] with the Living IoT platform. This canbe useful for smart farm applications like canopy monitoring. Centeye image sensors and cameras such as the Himax
HM01B0 offer a potential path for achieving such a camerabased sensing system within our weight/power budget.
• IACUC requirements. Finally, we note that while working with insects and other invertebrates is not governed by
the Institutional Animal Care and Use Committee (IACUC)
policies of our institution and insect consciousness is poorly
understood [49], we do our best to follow the three Rs ofanimal research [63]. We minimize use of insects by benchmarking each aspect of our system using drones to simulate
flight and robots to simulate flapping wings. For our experiments we perform no surgical modification, we simply
attach weight to the exoskeleton and we observe no significant changes in behavior after the procedure. Additionally
we use only a small number of insects for our experiments
and remove our electronics package after completing experiments. We also release wild caught insects back in the area
they were captured after completing experiments.

7

ACKNOWLEDGMENTS

We thank Swarun Kumar, Tom Daniel, Callin Switzer, members of the Air Force center of excellence on Nature Inspired
Flight Technologies and Ideas (NIFTI), members of the UW
Networks and Mobile Systems group as well as the anonymous reviewers at MobiCom for their constructive feedbackon this work. The authors are funded in part by NSF awards
CNS-1812554, CNS-1452494, CNS-1823148, Google Faculty
Research Awards and a Sloan Fellowship.

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