# On the Feasibility of Battery-Less LoRaWAN Communications using Energy
Harvesting

Carmen Delgado1, José María Sanz2, Jeroen Famaey1  1IDLab, University of
Antwerp – imec, Antwerp, Belgium  2Kintech Engineering, Zaragoza, Spain

###### Abstract

From the outset, batteries have been the main power source for the Internet of
Things (IoT). However, replacing and disposing of billions of dead batteries
per year is costly in terms of maintenance and ecologically irresponsible.
Since batteries are one of the greatest threats to a sustainable IoT, battery-
less devices are the solution to this problem. These devices run on long-lived
capacitors charged using various forms of energy harvesting, which results in
intermittent on-off device behaviour. In this work, we model this intermittent
battery-less behaviour for LoRaWAN devices. This model allows us to
characterize the performance with the aim to determine under which conditions
a LoRaWAN device can work without batteries, and how its parameters should be
configured. Results show that the reliability directly depends on device
configurations (i.e., capacitor size, turn-on voltage threshold), application
behaviour (i.e., transmission interval, packet size) and environmental
conditions (i.e., energy harvesting rate).

###### Index Terms:

Internet of Things, battery-less IoT devices, energy harvesting, LoRa, low-
power wide-area networks

##  I Introduction

In the Internet of Things (IoT) vision, billions of interconnected devices
cooperate to sense, actuate, locate and communicate with each other with the
aim of supporting and improving daily life. Recent advancements in ultra-low
power communications technologies are driving the transformation of everyday
objects into an information source connected to the Internet. Usually, these
devices are equipped with a battery, a radio chip, a microcontroller unit
(MCU) and one or more sensors and/or actuators.

Low-Power Wide-Area Networks (LPWANs) are a new set of radio technologies that
are designed to support the needs of IoT deployments by combining low energy
consumption with long range communications [1]. LoRaWAN [2] is an LPWAN
technology that uses sub-GHz unlicensed spectrum and enables long-range
transmissions (more than 10km in rural areas) at low power consumption [3].

However, from the beginning, battery limitations have been one of the main
problems to realise the vision of a global IoT. Sometimes, devices are located
in hard-to-reach areas, and battery replacement or maintenance is not only
costly, but also dangerous. In other situations, small devices are needed, but
batteries are too bulky. In general terms, it is known that batteries are
expensive, bulky and hazardous; they are sensitive to temperature, short-
lived, and therefore incompatible with a sustainable IoT [4].

Since the number of IoT devices is quite high and will continue to increase in
the coming years, it seems clear that the use of batteries should be
reconsidered. To alleviate the IoT’s battery problem, battery-less IoT devices
are a promising solution. Battery-less IoT devices are smaller, live longer,
are more environmentally friendly, and are cheaper to maintain. This makes
them especially suitable for applications in hard-to-reach locations (e.g.,
intra-body health monitoring, remote-area sensing) and large-scale deployments
(e.g., dense building automation networks, smart cities).

In contrast to their battery-powered counterparts, battery-less devices need
to harvest energy from their environment and store it in tiny capacitors. Such
capacitors easily last more than ten years [5] thanks to the fact that they
can handle a larger number of charge cycles. Moreover, they are cheaper to
produce and easy to recycle, thus better for the environment. However, this
new paradigm faces some difficulties: energy harvesting is inconsistent,
energy storage is scarce, power failures are inevitable, and execution is
intermittent [4]. This intermittency, illustrated in Figure 1, causes the
device to turn on and off frequently, as it swiftly depletes the energy stored
in the capacitor. This results in a power failure until the device harvests
enough energy to turn on again.

In this paper, we propose a system model for a battery-less LoRaWAN Class A
device. This allows us to characterize device performance in terms of
reliability for various device configurations (e.g., capacitor size,
transmission interval, packet size, turn-on voltage threshold) and
environmental conditions (e.g., energy harvesting rate). Based on this
analysis, we determine the feasibility of using LoRa without batteries.

Figure 1: Turn-off and turn-on threshold of battery-less intermittent
behaviour

##  II Related Work

One of the main problems of IoT devices is their energy. When the energy of a
device is depleted, it will no longer fulfill its role unless either the
source of energy is replaced or a harvesting mechanism is used. Since we posit
the vision of battery-less devices, energy harvesting is the only way to
overcome the energy gap. It is a mechanism that allows extracting energy from
external sources, such as solar irradiance, wind, thermoelectric,
piezoelectric, or vibration [6].

Sherazi et al. present a mathematical model to calculate the battery life
using energy harvesting in order to analyze the impact of energy harvesting
sources [7]. Although they also use LoRaWAN in their calculations, they do
assume the use of batteries. In contrast, we consider that the harvested
energy is saved in a capacitor, which has a significantly lower energy storage
capacity. The smaller its capacity, the faster it will charge and discharge
for a given harvester, which can lead to power failures up to several times
per second [8].

Most prior works on battery-less devices use passive RF-powered
communications, such as backscatter [9] [10]. Instead, we focus on active
communications. Only a few works have considered the combination of battery-
less devices, energy harvesting and active radios. For example, Fraternali et
al. designed a battery-less Bluetooth Low Power (BLE) sensor node which
leverages ambient light and a power management algorithm to maximize the
quality of service [11]. Jornet and Akyildiz present an energy model for
nanodevices using energy harvesting mechanisms [12]. In their model, they also
consider energy harvesting with a capacitor, and then they analyze the energy
consumption of the communications in the Teraherz band. However, the
communications and networking solutions considered at nanoscale are vastly
different than those in traditional IoT systems, hence leading to a
significantly different system model.

##  III Model for a battery-less LoRaWAN device

###  III-A Circuit Characterization

Battery-less IoT devices are equipped with a harvester mechanism, a capacitor,
an MCU, a radio unit and the needed sensors. In order to model the behaviour
of these devices, we have considered the simplified electrical circuit shown
in Figure 2, where the circuit has been divided into three main parts: the
harvester (source of the energy), the capacitor (storage of the energy) and
the load (consumer of the energy: MCU, radio, sensors). More details about
these 3 parts are explained below.

Figure 2: Electrical circuit model of a battery-less IoT device

#### Energy Harvesting System Model

Although in reality the harvester model should depend on the type of energy
source (e.g., radiation, vibration), we consider a generic and simplified
approach, where only the generated power is taken into account. As can be seen
in the left side of Figure 2, the harvester is modelled as a real voltage
source composed of an ideal voltage source and a series resistance (denoted by
$E$ and $r_{i}$, respectively). The value of $E$ (in Volts) is chosen
according to the operating voltage of the circuit elements, which in this case
will be determined by the load. The series resistance $r_{i}$ limits the power
of the harvester and its value is calculated using the following equation:

$r_{i}=\frac{E^{2}}{P_{harvester}}$ (1)

where $P_{harvester}$ is the power of the harvester source, which can vary
greatly depending on the type of energy harvesting considered (e.g., up to
$1mW/cm^{2}$ for indoor natural light, and up to $100mW/cm^{2}$ for outdoor
sun) [13].

#### Capacitor Model

The capacitor is the part of the circuit where the energy is stored. As shown
in Figure 1, the behaviour of the system is a succession of intervals, where
the capacitor is being charged or discharged. Each interval is characterized
by a specific state of the load components (e.g., MCU is active and radio is
transmitting). We characterize the voltage of the capacitor throughout each
interval using $(V_{C})_{0}$ and $v_{C}(t)$. $(V_{C})_{0}$ represents the
initial voltage of the capacitor at the beginning of the interval (i.e., time
$t_{0}$), and $v_{C}(t)$ is the temporal evolution of the said voltage at time
$t$ (relative to $t_{0}$). Both $(V_{C})_{0}$ and $v_{C}(t)$ are included in
the circuit as an ideal voltage source and the voltage over time of an ideal
capacitor respectively, as shown in Figure 2.

#### Load Model

The load of the model corresponds to the set of components that consume the
stored energy in the capacitor, such as the MCU, radio or sensors. Each of
these components is characterized by a specific power consumption in each of
its states (e.g., active, sleeping, off). Therefore, they can be modelled as a
load resistance denoted by $R_{L}$ (in $\Omega$), which can be calculated as
follows:

$R_{L}=\frac{E}{I_{load}}$ (2)

where $I_{load}$ is the sum of the supply currents of all components in their
current state. $R_{L}$ thus varies across intervals depending on the state of
each component during that interval.

To determine if the device has enough energy at a specific time to perform its
tasks (e.g., transmit data), it is needed to calculate the voltage across the
load of the model $v(t)$:

$v(t)=E\frac{R_{eq}}{r_{i}}(1-e^{(\frac{-t}{R_{eq}C})})+(V_{C})_{0}e^{(\frac{-t}{R_{eq}C})}$
(3)

where $C$ is the capacitance in Farads, $t$ is the time spent in the current
interval, and $R_{eq}$ is the equivalent resistance of the circuit (in
$\Omega$), computed as:

$R_{eq}=\frac{R_{L}r_{i}}{R_{L}+r_{i}}$ (4)

The value of $v(t)$ provides the voltage available in the load, which will be
used to determine if a specific action (e.g., transmit, listen) can be
performed during an interval, according to the needed time $t$ it will take,
the energy harvesting rate $P_{harvester}$, the specific load $I_{load}$, and
the capacitor voltage $(V_{C})_{0}$ at the start of the interval. Note that
$v(t)$ can be increasing or decreasing depending of the specific parameters.

###  III-B LoRAWAN Class A Device Model

We consider a LoRa device, using the LoRaWAN medium access control (MAC)
protocol. The LoRaWAN standard defines three classes of end-devices. In this
work, we focus on class A, which provides the lowest energy consumption [2].

Class A devices spend most of the time in deep sleep, and only wake up when
they need to transmit data to the network server. Since they use an ALOHA-
based MAC protocol, Class A LoRAWAN devices do not perform listen before talk.
It is also characteristic that devices of this class are only reachable for
downlink transmissions after they transmit. As can be seen in Figure 3,
LoRaWAN class A devices have two reception windows. After the transmission,
the device waits 1 second in idle state and then switches to listening mode
(RX1). If the device receives a downlink transmission in this first window, it
does not need to stay awake for the second reception window. However, if
nothing is received, it must switch again to idle mode. In that case, 2
seconds after the end of the transmission, the device will listen again (RX2).

Figure 3: LoRaWAN Class A end device window timings

One of the important parameters to be configured is the spreading factor (SF),
which represents the ratio between the chip rate and the baseband information
rate and can range from 7 to 12. As the SF increases, so does the coverage
range and decoding robustness. This comes at the cost of a decrease in data
rate, and thus increase in airtime. In fact, the SF determines the TX time as
well as the RX1 time. The time of RX2 is fixed, as it always uses the slowest
spreading factor SF12. The formulas for calculating the different times can be
derived from the LoRaWAN standard [2]. First, it is convenient to define the
symbol duration $T_{sym}$ (considering SF bits per symbol):

$T_{sym}=\frac{2^{SF}}{BW}$ (5)

where $BW$ is the bandwidth, which is typically 125kHz.

Figure 4 shows the LoRa frame format of the physical layer. The preamble
($n_{preamble}$) is the number of programmed preamble symbols, which is 8 in
LoRaWAN 1.0. Then, the low-level header can be explicitly enabled ($IH=0$) or
disabled ($IH=1$). It is used to indicate the coding rate and payload length,
and can be left out if both sides of the communication have these parameters
fixed. The time needed to transmit or receive the preamble sequence is given
by:

$T_{preamble}=(n_{preamble}+4.25)\cdot T_{sym}$ (6)

The number of symbols that make up the packet payload and header is given by
Equation 7, and the payload duration is given by Equation 8.

$S_{payload}=8+\\\
max(\Bigg{\lceil}\frac{8PL-4SF+28+16-20IH}{4(SF-2DE)}\Bigg{\rceil}(CR+4),0)$
(7) $T_{payload}=S_{payload}\cdot T_{sym}$ (8)

where $PL$ is the number of payload bytes (which can vary from 13 to 51), $CR$
is the coding rate where higher values mean more overhead, and the low data
rate optimization can be enabled with $DE=1$ and disabled with $DE=0$ (which
intends to correct the clock drift at SF11 and SF12). Finally, the time on air
(or packet duration) can be calculated as follows:

$T_{packet}=T_{preamble}+T_{payload}$ (9)

Figure 4: LoRaWAN packet formatting

###  III-C Complete System Model

The main problem of battery-less devices is how to deal with the intermittent
behaviour, and the energy and time needed to be awake to perform the different
actions (e.g., listen, transmit, receive). For simplicity of the analysis, we
assume a constant energy harvesting rate during a single experiment. As shown
in Figure 1, an intermittent device has two main states: ON and OFF. When the
device is in the ON state, it can be in one of the following sub-states:
Sleep, Idle, TX, Listen, or RX. In these cases, it will stay turned on until
it actively turns itself off, or when the capacitor voltage drops below the
turn-off threshold $V_{min}$. This is the lowest voltage at which its radio
and MCU can safely operate. When the device is in the OFF state, it will stay
turned off until its capacitor reaches the turn-on voltage threshold $V_{SL}$,
which is a configurable parameter. Depending on the $P_{harvester}$ and the
power consumption of the MCU (which determines $R_{L}$), the capacitor can be
charging or discharging during sleep mode (cf., Equation 3).

The complete state diagram of the system is shown in Figure 5. Whenever the
device has something to transmit, it will be checked in which state the system
is. If the system is in off mode, it will not be able to transmit and the
packet will be lost. However, if the system is in the sleep state, it will try
to send it. If the current voltage in the load is enough to transmit it, it
will do it and will switch to idle state for 1 second (as shown in Figure 3).
In order to know the needed voltage to transmit, Equation 3 is used, where the
time $t$ corresponds to the time on air (or time needed to send the packet)
that can be calculated with Equation 9. After being 1 second in the idle
state, the system will switch to listen, in the first reception window (RX1),
where the device checks whether a preamble has been received. If a preamble
has been detected, the device continues receiving the downstream transmission,
and after that it will switch to sleep mode. The maximum time spent in the
first listening window is determined by Equation 6, where the $SF$ is the
corresponding spreading factor used in the transmission. If a preamble has not
been detected during this time, it closes the receive window and transitions
to the second idle state. 2 seconds after the end of the transmission, the
system will switch to the second reception window (RX2), where the device will
check again if a preamble has been received. In this case, the maximum time
spent in this window is determined by Equation 6 but using a $SF$ value of 12.
The system will switch to the off state immediately whenever the voltage of
the capacitor reaches the $V_{min}$ value, and any ongoing transmission or
reception will fail.

Figure 5: State diagram of the complete system model

##  IV Analysis of the model

In this section, we evaluate the performance of a battery-less LoRaWAN Class A
device in terms of reliability for various device configurations and
environmental conditions. This evaluation provides an overview of the needed
parameters and elements that will allow a battery-less LoRaWAN device to work
for uplink transmissions. In this analysis, we do not consider downlink
transmissions (although we do consider empty RX1 and RX2 slots), the energy
consumed by sensors (which in many cases is negligible compared to energy
consumed by the radio), or collisions between devices. These aspects are left
for future work.

We first provide an analysis of the needed capacitor and the minimum needed
voltage. Then, we evaluate the impact of the turn-on voltage threshold on the
reliability in terms of packet delivery ratio (PDR) for different data
transmission rates.

###  IV-A Simulation Setup

The simulation parameters are based on the Semtech SX1272/73 LoRa radio. . As
such, $V_{min}$ and $E$ have been defined as 1.8V (minimum operating voltage
of the SX1272/73) and 3.3V (typical operating voltage), respectively. For all
the cases, we assume periodic uplink transmissions at a constant interval.
Table I summarizes the general parameters used in the simulations.

TABLE I: General simulation parameters Parameter | Symbol | Value
---|---|---
min Voltage | $V_{min}$ | 1.8V
max Voltage of the system | $E$ | 3.3V
Coding Rate | $CR$ | 4/5
Bandwidth | $BW$ | 125kHz
Preamble symbols | $n_{preamble}$ | 8
Data rate optimization enabled | $DE$ | 0
Head disabled | $IH$ | 1

As explained before, the values used for the load depend on the specific state
of the load components. We consider that the load is formed by the radio and
the MCU (i.e., an STM32L162xE chip. In this case, current consumption in Low-
power run mode (active) and in Low-power sleep mode (sleep) are considered.
Table II defines the considerations of the MCU and the radio for the different
states of the system and the corresponding values for the load, $R_{L}$, which
are determined using Equation 2.

TABLE II: States of the system System State | MCU State | Radio State | $R_{L}$
---|---|---|---
Off | Off | Off |  600 $k\Omega$
Sleep | Sleep | Sleep |  589.286 $k\Omega$
Idle | Sleep | Idle |  471.428 $k\Omega$
Tx (+13 dBm) | Active | TX |  117.811 $\Omega$
Listen | Active | Listen |  313.957 $\Omega$
Rx | Active | RX |  294.354 $\Omega$

###  IV-B Analysis of the Capacitance and Voltage Requirements

The Class A LoRaWAN standard defines the sequence to follow when performing a
transmission as mentioned in Section III-B. In order to characterize the
system, it would be useful to know what kind of capacitor is needed to support
it.

Figure 6 shows the minimum capacitance ($C$) needed to complete one
transmission cycle for different values of payload, SF, and energy harvesting.
Such a cycle includes transmitting a packet and staying awake for RX1 and RX2
(assuming nothing is received). For high data rates (i.e., small SF values),
the payload has little influence on required capacitance. However, when using
SF11, the impact of the packet size is considerable (e.g., varying from
$11000\mu{}F$ to $19500\mu{}F$ when the energy harvesting rate is set to
$1mW$). As expected, lower energy harvesting rates are not compatible with
larger spreading factors, as the required capacitance becomes too high, which
is not compatible with the low cost and small form factor requirements of an
IoT device.

Figure 6: Minimum capacitance needed when varying the payload

Let us consider a capacitor with $C=4700\mu{}F$, which is in line with the
capacitance of low-cost off-the-shelf Aluminum-Polymer capacitors. The figure
shows that such a capacitor could support SF7 at any packet size, and SF9 for
small packet sizes (i.e., 16 bytes or less), even at an energy harvesting rate
of 1mW.

In order to get a deep insight in the specific values, Table III shows the
minimum voltage needed to perform a complete a transmission cycle for such a
capacitor. The energy harvesting rate $P_{harvester}$ is represented as EH.
According to these results, we can see that SF is the parameter with the most
impact. This is due to the fact that it greatly affects the time needed to
transmit, so that, more (or less) energy is used. On the other hand, the
influence of the payload size is small for SF7, but increases with increasing
SF and decreasing EH. The reason higher energy harvesting rates reduce the
effect of the payload size is because the loss of energy when performing
device actions is mitigated while harvesting. Additionally, if the data rate
is high (and SF low), larger packet sizes have a negligible effect on the
time-on-air. While for some configurations it is possible to complete a cycle,
for others it is impossible (marked as -). These cases correspond to those
that show a higher minimum capacitance than $4700\mu{}F$ in Figure 6. For this
reason, it will be needed to choose the proper capacitor size according to the
specific environmental conditions (energy harvesting rate) and network
conditions (SF and payload size).

TABLE III: Voltage needed to complete a transmission cycle |  | Payload (bytes)
---|---|---
EH | SF | 16 | 24 | 32 | 40 | 48
| 7 | 2.5628 | 2.6105 | 2.6591 | 2.7086 | 2.7846
1mW | 9 | 3.228 | - | - | - | -
| 11 | - | - | - | - | -
| 7 | 1.9604 | 1.9943 | 2.0289 | 2.0643 | 2.1186
10mW | 9 | 2.4184 | 2.5975 | 2.7919 | 3.0028 | 3.115
| 11 | - | - | - | - | -
| 7 | 1.8183 | 1.8224 | 1.8266 | 1.8309 | 1.8378
100mW | 9 | 1.8732 | 1.8995 | 1.9302 | 1.9661 | 1.9862
| 11 | 2.4617 | 3.0976 | - | - | -

(a) SF7, Payload 16B, EH 1mW

(b) SF7, Payload 48B, EH 1mW

(c) SF9, Payload 16B, EH 1mW

Figure 7: Successful TX and TX/RX transmission when varying the wake-up
threshold

###  IV-C Analysis of the Turn-On Threshold

Now that we know how to properly select the capacitance, we evaluate how the
performance is affected by varying the turn-on voltage threshold parameter. In
the following, we consider this threshold as a percentage of the maximum
voltage of the system ($E$). We vary the turn on threshold from 55% (i.e.,
slightly higher than the 1.8V turn-off threshold) up to 98%.

Figure 7 shows the packet delivery ratio, averaged over 1000 transmissions, as
a function of the turn on threshold and for different transmission intervals.
These results consider a harvesting rate of 1mW (most realistic for indoor
applications), and a capacitance of $4700\mu{}F$. The graphs show both the
number of successful uplink transmissions (referred to as TX in the legends),
as well as the number of successfully completed cycles (referred to as TX/RX).
The latter requires the device to stay awake for the reception windows RX1 and
RX2, as it should according to the LoRaWAN standard. The figure shows that if
only considering TX, for SF7 the device can successfully transmit every 10 or
20 seconds for a 16 or 48 byte payload respectively. If also required to
listen to the two RX opportunities, this increases to 45 and 75 seconds
respectively. For SF9, efficiency is greatly reduced, and a 100% success rate
can only be achieved with a small payload size of 16 bytes and a transmission
interval of 30 seconds (TX only) or 250 seconds (RX/TX).

When increasing the energy harvesting power ($P_{harvester}$), better
performance is achieved. For example, for $P_{harvester}=$ 100 mW, no
differences are found when varying the payload size because the time needed to
wake up is really small (i.e., 0.40875s when using a wake up threshold of 55%
and 1.53481s when using 95%). In these scenarios, the device is ON most of the
time, and can harvest enough energy to transmit successfully whenever it’s
turned on, independent of the SF or payload size (as long as it is supported
by the capacitor).

This is illustrated in Figure 8, where the time needed to reach the wake up
threshold, starting from $V_{min}$, is shown. This figure shows that for a
threshold of 55%, the device can always wake up within 1 second of turning
off. However, when the harvesting rate is low (i.e., 1mW), the time needed to
reach the turn on threshold quickly goes above 10 seconds for 65%, and up to
120 seconds for 98%. Based on this, it is clear that a low turn-on threshold
allows the device to transmit more often, but it may try to transmit when it
does not have enough stored energy to do so. A high turn-on threshold, causes
much longer off intervals, causing the device to potentially miss transmission
opportunities, but ensuring it has enough energy to perform its actions once
it wakes up. This is also illustrated in Figure 7. In some cases (e.g., SF7,
16 byte payload, 1mW harvesting, TX only every 10 seconds) the PDR is
maximized for low turn on thresholds close to 55%. In other cases (e.g., SF7,
16 byte payload, 1mW harvesting, TX/RX every 40 seconds) the optimal turn on
threshold is higher, and both very low and very high thresholds lead to
reduced PDR. In general, operations that require little energy (relative to
the energy harvesting rate and capacitance) benefit from a lower turn on
threshold. As the relative energy required to complete the operation
increases, so does the optimal turn on threshold. This clearly shows the need
for intelligent turn on mechanisms that take into account the environment,
device and network characteristics.

Figure 8: Needed time for turning on for different wake up thresholds

##  V Conclusions

In this paper, a model to characterize the intermittent behaviour of battery-
less LoRaWAN devices has been presented. We have analysed its performance for
LoRaWAN Class A, in order to explore the limits that these devices can
support. We have shown that the packet delivery ratio depends on device
configurations (capacitor size, turn-on voltage threshold), application
specification (transmission interval, packet size) and environmental
conditions (energy harvesting rate). Our results showed that a realistic
capacitor of $4700\mu{}F$ can support SF7 and 9 at a low energy harvesting
rate of 1mW, even when transmitting more than once per minute. SF11 can only
be supported for small packet sizes and high harvesting rates of 100mW or
more. Finally, we showed that the device turn on threshold significantly
affects performance, and it is necessary to dynamically tune it, based on
environmental and application characteristics.

## Acknowledgment

Part of this research was funded by the Flemish FWO SBO S004017N IDEAL-IoT
(Intelligent DEnse and Long range IoT networks) project.

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