Practical Precision Agriculture with LoRa based Wireless Sensor

Networks

Jonathan Gresl

, Scott Fazackerley

2 a

and Ramon Lawrence

1 b

Department of Computer Science, University of British Columbia, Kelowna, Canada

Department of Electronic Engineering Technology, Okanagan College, Kelowna, Canada

Keywords:

LoRa, Wireless Sensor Network, Precision Agriculture, Orchard, Vineyard.

Abstract:

Precision agriculture is enabled by using real-time data to manage environmental variations, measure perfor-

mance to improve upon previous seasons, and perform predictive analysis to make better growing decisions,

resulting in higher production yields with lower production costs. Although beneﬁcial, collecting and ana-

lyzing the environmental data is an expensive and complicated endeavor. Numerous existing wireless sensor

network (WSN) solutions rely on protocols such as 802.15.4 b (LR-WPANs) and 802.11x (WLAN) but provide

limited transmission range, complex communication stacks and data management, and high power consump-

tion. Additionally, many existing services introduce challenges with data ownership and residency. These

factors present a high barrier to entry for growers. The resources required to implement and maintain sensor

networks are too high to justify the investment. This work presents an approach that uses inexpensive and

effective hardware that is easily setup and maintained. Costs to implement the network are reduced through

the use of open-source hardware. Transmission ranges and power consumption are improved by using long

range (LoRa) radio transceivers. By addressing these limitations, growers will be better enabled to adopt new

technologies, ultimately improving sustainability, viability, quality and proﬁt margins in agriculture.

1 INTRODUCTION

Precision agriculture is an approach to farming that

uses information to ensure crops can grow in optimal

conditions. Environmental variations can result from

climatic conditions, soil composition, cropping prac-

tices, weeds, or diseases. Monitoring key point indi-

cators allow growers to track their crop’s status, which

can help them to determine if crops are suffering from

water stress, nitrogen stress, or if diseases are devel-

oping. Environmental variations result in loss of crops

and inconsistent growth. When conditions vary, ad-

justments can be made to regular farming activities

to control or stimulate growth in plants. The collected

data is also useful for measuring performance and im-

proving upon previous seasons. Using precision agri-

culture reduces production costs by mitigating waste.

Ultimately, precise growers can produce higher qual-

ity products with fewer resources and increased proﬁt

margins.

Sensor networks have been extensively investi-

gated for use in agricultural settings (Ojha et al., 2015;

https://orcid.org/0000-0002-1323-272X

https://orcid.org/0000-0002-6779-4461

Jawad et al., 2017) to collect real-time environmen-

tal data. Early investigators experienced challenges

with complexity of deployment, network connectiv-

ity and battery life (Beckwith et al., 2004). While

systems can be deployed with a wire-based system

that provides power and facilitates data transfer over

a stable wired connection, they are inconvenient be-

cause large amounts of wire are needed to connect

the sensor nodes. The wire must also be concealed

in a manner that does not limit accessibility and is

protected from machinery and environmental degra-

dation. Damaged wires will disable some or all of the

sensor network and will require user intervention to

troubleshoot and restore functionality.

Wireless sensor networks were introduced to im-

prove practicality. The wireless components are pow-

ered by batteries and connected through a wireless

medium. Figure 1 depicts a typical wireless network

deployed in an agricultural setting. The sensor nodes

(blue pins) are deployed strategically across a ﬁeld

within range of the central gateway (orange pin) and

depending on the choice of transceiver, modulation

technique and allowable transmission power, nodes

may communicate directly with the gateway or re-

quire intermediate nodes to act as cluster-heads and

Gresl, J., Fazackerley, S. and Lawrence, R.

Practical Precision Agriculture with LoRa based Wireless Sensor Networks.

DOI: 10.5220/0010394401310140

In Proceedings of the 10th International Conference on Sensor Networks (SENSORNETS 2021), pages 131-140

ISBN: 978-989-758-489-3

131

Figure 1: A potential node deployment in a grape vineyard.

facilitate routing to the gateway (Heinzelman et al.,

2002; Fazackerley et al., 2009). The gateway is con-

nected to the Internet to allow remote users to moni-

tor the different growing parameters that can be used

to improve decision making in an agricultural setting.

A database stores the sensor data and is accessible

through the network for conﬁguration and sensor data

analysis (Ojha et al., 2015).

Although wireless sensor networks are more prac-

tical than wired networks, they also come with their

own set of challenges and limitations. The hardware

required to setup a network to cover large rural areas

is expensive. There is also a signiﬁcant amount of ef-

fort required to setup or modify the network. WiFi

or Bluetooth protocols can be used for the data trans-

fer, but are complicated protocols that provide lim-

ited transmission range with high power consump-

tion. While mesh networks can be constructed to in-

crease transmission range, it increases the complexity

of the solution, leading to higher power consumption

and increased network trafﬁc. Both WiFi and Blue-

tooth protocols have high overhead that are unneces-

sary for applications in agriculture.

For agriculture sensing networks, barriers to entry

are high costs, complexity, and power consumption.

High costs discourage growers from wanting to in-

vest in the technology without a clearly identiﬁed re-

turn. Deployments need to be accessible to users and

straight forward to implement and expand. Finally,

devices must be power efﬁcient reducing the need for

continual service.

In this work we propose a sensor network data ex-

change and control protocol targeted for agricultural

applications. The system is based on LoRa (Semtech,

2020) physical layer transceivers which are com-

monly available in low cost formats. The contribu-

tions of this paper are:

• A self-determining distributed time slot transmis-

sion algorithm and novel collision protocol

• An architecture for node management and control

• A localized data management strategy

• A deployment of a prototype system, directly ap-

plicable to real-world agricultural challenges.

The paper outline is as follows. Section 2 presents

an overview of previous approaches and discusses the

LoRa protocol. The proposed architecture, commu-

nication and control and time slot algorithm are pre-

sented in Section 3. Discussion of the practical de-

ployment is in Section 4. The paper closes with future

work and conclusions.

2 BACKGROUND

The Okanagan Valley is one of Canada’s principal

fruit growing regions. The semi-arid region is an agri-

culturally intense area stretching 200 km north from

the US border and is approximately 20 km wide. The

region presents exceptional growing conditions with

hot summers and relatively mild winters. Extensive

information on soil conditions is available with ar-

eas of high productivity stretching along a series of

long, deep narrow lakes which help to regulate grow-

ing conditions for different tree-fruit and grape va-

rieties. In terms of agriculture in British Columbia,

the majority of the province’s tree fruits are produced

in this region, including extensive high-value cherry

production for the Asian market as well as grapes for

wine production. The Okanagan Valley is one of the

largest fruit and wine producing areas in Canada.

Figure 2: Scab infected apples showing the development of

lesions on the foliage and fruits

Due to the geography of the Okanagan valley,

growing regions are small with many compact grow-

ing operations. The margin on fruit crops is low due

to increasing operational costs and climate variability.

Shuhrataxmedov, CC BY-SA 3.0 https:

//creativecommons.org/licenses/by-sa/3.0 via Wikime-

dia Commons

WSN4PA 2021 - Special Session on Wireless Sensor Networks for Precise Agriculture

132

Figure 3: Grapes infected with powdery mildew

Interest has focused on how precision agriculture can

improve crop quality and increase proﬁt margins.

In the fruit growing industry, growers are often

faced with making decisions based on limited infor-

mation for the well-being of their crop. Interest has

grown in the area of disease modelling to better pre-

dict and manage the crop. With a more in-depth un-

derstanding of crop growing conditions, predictions

can be made to help manage disease outbreaks with

minimal cost leading to higher overall crop quality.

Two key crops where sensor-based precision agricul-

ture can signiﬁcantly impact growing decisions lead-

ing to improved yield and proﬁt margins are apples

and grapes. Apple scab (Figure 2) is a common and

ongoing disease caused by the Ventruia inaequalis

fungus in the wetter interior growing regions. In the

Okanagan Valley it is especially common in years

with above average rainfall (AgriService BC, 2018).

With wine grapes, similar challenges exist with

the development and management of the fungus Unc-

inula necator. It causes grape powdery mildew which

attacks grape plants and limited related species. For

popular wine grape varieties in the interior of British

Columbia, it is the most common and widespread

disease of grapevines (British Columbia Ministry of

Agriculture, 2015).

While both diseases signiﬁcantly impact local

fruit crops, disease modelling and forecasting for

improved management can be done with tempera-

ture and leaf wetness sensors (Garofalo and Cooley,

2020). In many cases, modelling is completed with

a limited number of data input points. With early

model tests in the Okanagan valley, accuracy was re-

duced due to model variability based on local con-

ditions (British Columbia Ministry of Agriculture,

2015); more temporal and spatial data is needed for

accurate modelling. While commercial systems are

available, they are cost prohibitive to many growers as

they require not only hardware costs but ongoing fees

Maccheek, CC BY-SA 3.0, https://commons.

wikimedia.org/w/index.php?curid=971184

for data access. This presents additional barriers for

growers as they do not have direct control over their

data. While larger data sets over a wide area help to

improve modelling and prediction allowing growers

to better visualize and predict risks during a growing

season, current solutions present barriers to adoption

due to cost and complexity of implementations.

2.1 Sensor Networks

In the design of low-power wireless sensor networks,

energy consumption is a key factor. Transmission

of data costs more in terms of energy than local

storage and processing (Pottie and Kaiser, 2000).

Sensors typically have been a small 8-bit processor

with limited local memory, a series of sensors and

a transceiver for communications (Akyildiz et al.,

2002). Processors are often chosen based on cost,

memory and energy efﬁciency. Energy efﬁciency is

a key consideration as it impacts the usable life of a

sensor in the ﬁeld before having to be serviced or re-

placed. A device or sensor node is required to last

for an extended period of time without service. Pre-

vious research has led to the development of numer-

ous sensor platforms such as the MicaZ and TelosB

nodes. These have been replaced by a number of low-

cost, open source platforms such as the Feather M0

that include numerous transceiver options. These

new platforms offer signiﬁcantly increased processing

speeds and memory capacity, due to the introduction

of low-cost and high-speed 32-bit ARM based cores.

This creates a further opportunity for local processing

and increased energy efﬁciency.

Sensor networks have been an ongoing area of re-

search that has been accelerated by the Internet of

Things. Sensor networks have been extensively inves-

tigated in areas such as industrial and factory automa-

tion, environmental monitoring, agriculture and mili-

tary applications (Akyildiz et al., 2002; Culler et al.,

2004; Romer and Mattern, 2004). Sensor networks

have been further enabled by being able to connect

to cloud data storage, visualization, and analysis plat-

forms to leverage the volume of data that is collected.

It is an emerging paradigm that is fusing existing data

collection systems with smart systems, frameworks

and other devices to offer potential growth in eco-

nomics and industry (Kumar et al., 2019).

With the explosive growth of IoT devices in ar-

eas such as healthcare, smart homes, trafﬁc manage-

ment, industry 4.0, security and surveillance and agri-

culture (Kumar et al., 2019), it is important to un-

derstand that each of these domains present different

challenges and opportunities for the IoT.

https://www.adafruit.com/category/830

Practical Precision Agriculture with LoRa based Wireless Sensor Networks

133

Precision agriculture for high value crops presents

unique challenges that are different from other IoT ap-

plication domains. While many IoT applications fo-

cus on extremely large numbers of data generation de-

vices that are potentially in motion while connected to

the internet, many agricultural applications focus on

data collection from a low number of static devices.

Agricultural industries beneﬁt from using wireless

sensor networks, but growers are hesitant to invest in

the technology if it is not practical for their applica-

tion. An ideal agricultural product is one similar to

smart home technologies with light bulbs. Growers

should be able to purchase a central gateway that sen-

sor nodes can communicate with. New sensor nodes

should easily synchronize with the central gateway

and be conﬁgurable through it. The gateway should

also support the option to be connected to the Inter-

net to provide remote conﬁguration and data analysis.

Above all, growers should rarely need to replace sen-

sor nodes or their batteries.

For wireless sensor networks to be sustainable in

agriculture, they must be affordable, require little user

intervention, stay powered for long periods of time,

stay protected from the elements of nature, be easy

to implement and modify, and transmit at long ranges

while handling radio interference. This is a long list

of requirements, but they can all be achieved inex-

pensively with the emergence of open-source hard-

ware, open-source software, and 3D printing tech-

nology. In general, improving transmission distances

and reducing power requirements will make wireless

sensor networks more practical and affordable for the

grower.

Previous works have considered using protocols

such as 802.15.4 b (LR-WPANs, including Blue-

tooth and Zigbee) and 802.11x (WLAN) but provided

limited transmission range, complex communication

stacks and data management, and high power con-

sumption (Vieira et al., 2003; Buratti et al., 2009;

Fazackerley and Lawrence, 2010). For applications

that have focused on using these technologies in agri-

cultural applications, challenges exist with the power

and limited transmission distance. This means that

for areas of more that 25-50 meters, devices are re-

quired to use multi-hop route or mesh networking.

This introduces additional complexities in terms of

device synchronization, energy consumption and life-

time (Cagnetti. et al., 2020).

Recently, interest has grown in the area of Low

Power Wide Area Networks (LPWAN) as a way to

address power and link distance challenges presented

by previous technologies (Lavric and Petrariu, 2018).

Numerous vendors are active in this space and are fo-

cusing on improving the performance of wireless sen-

sors (Georgiou and Raza, 2017). These devices pro-

vide coverage areas where gaps exist in the current

short-range wireless space and address many of the

requirements (Raza et al., 2017). One of the most

promising technologies in this space is LoRa which

allows for ﬂexible, long range communications at a

low price point and power budget. LoRa is a layer

1 protocol that allows higher levels and network ar-

chitecture to be built upon it. The LoRa Alliance has

deﬁned a cloud-based medium access control (MAC)

layer protocol called LoRaWAN (Sornin, 2017) al-

lowing for the development of large scale systems.

Numerous applications have been proposed utilizing

LoRa and LoRaWAN technologies.

2.2 LoRa and LoRaWAN

The LoRa, (Long Range) protocol is a RF modulation

technology developed by Cycleo in 2009, which was

later acquired by Semtech in 2012. The technology

uses a proprietary chirp spread spectrum modulation

technique which enables data communication over

long ranges (>15 km line of sight), while using little

power, making it a ﬂexible solution for rural use cases

in smart agriculture (Ojha et al., 2015). LoRa operates

in the unlicensed ISM bands worldwide. Although

there are multiple license-free bands, most long range

protocols operate in the sub-gigahertz license-free

bands, the most prominent of which are a large con-

tiguous band from 902-928 MHz and narrower bands

at 864-870 MHz, and 433 MHz depending on the re-

gion of the world a device is operating in.

With LoRa, key parameters need to be agreed

upon that control the channel bandwidth (BW), the

spreading factor (SF) and the coding rate (CR). The

spreading factor controls the duration of the chirp

with larger SF’s being able to transmit further but with

a slower data rate for a given bandwidth. LoRa also

includes the option of forward error correction as cod-

ing rate that will encode 4-bit data with redundancies

into 5, 6, 7 or 8-bits. For LoRa devices to communi-

cate, two devices must be operating in the same band,

and share the same channel bandwidth, spreading fac-

tor and coding rate. Unlike other wireless technolo-

gies, LoRa data transmission rates are in the order of

kilobits per second.

As the data rate is low, this makes LoRa most

suitable for implementations that do not require large

amounts of data transferred over short periods of time.

LoRa is ideally suited for low volume, and periodic

transmission of sensor data.

LoRaWAN is a MAC layer protocol utilizing

LoRa, that focuses on medium access and network

congestion. While LoRa allows physical point-to-

WSN4PA 2021 - Special Session on Wireless Sensor Networks for Precise Agriculture

134

point communications, LoRaWAN offers a complete

network topology, focused at scalability towards hun-

dreds of thousands of devices connecting to the Inter-

net. With LoRaWAN, the topology requires a gateway

that encapsulates network dataframes as well as pro-

viding cloud-based network services for storage and

analysis (Ert

urk et al., 2019). LoRaWAN as a com-

munication protocol has been used for agricultural ap-

plications (Davcev et al., 2018; Kokten et al., 2020).

While a suitable technology, numerous limitations ex-

ist as LoRaWAN gateways and end nodes are costly

compared to LoRa. Additionally, with LoRaWAN a

cloud-based network service is required as data resi-

dency is no longer on site. LoRaWAN is targeted for

large scale networks and is prohibitive for cost sensi-

tive applications.

In contrast, LoRA allows developers to utilize

low-cost, open-source solutions knowingly sacriﬁc-

ing high scalability. Speciﬁcally for cost sensitive,

agricultural applications this is suitable as an instal-

lation may only require hundreds of nodes. Unlike

with LoRaWAN, developers can implement low-cost,

local data storage and visualization tools, allowing a

grower to maintain control of data privacy and own-

ership. Finally, as many agricultural locations lack

internet connectivity, LoRa devices can be run decou-

pled from the internet, offering maximum ﬂexibility.

3 SENSOR ARCHITECTURE

USING LoRa

A sensor network architecture for agricultural moni-

toring captures low speed and a low volume of data,

which offers ﬂexibility in the design of the architec-

ture. LoRa is an ideal candidate to satisfy power and

link budgets and forms the communication backbone

of the proposed architecture. For small scale agricul-

tural deployments, LoRa eliminates the need for com-

plex and energy intensive routing and synchronization

protocols, allowing for sensor nodes to communicate

directly with the data collection point.

For the development of apple and grape disease

models, parameters such as air temperature, leaf-

wetness, and humidity are measured. These are rel-

atively slow moving parameters with respect to time.

This allows nodes to sample at a low interval on the

order of minutes between readings. Additionally, as

data is used in a predictive fashion for forecasting,

the real-time delivery requirements for data can be re-

laxed. This allows latency tolerant delivery where a

node is not required to deliver data immediately upon

sampling. Another key consideration is the size of the

data being transmitted. With LoRa and the time on

air constraints, focus must be given to minimize the

amount of data being transmitted against the needs of

the application.

The following protocol deﬁnes interactions and

exchange of data between nodes and a gateway us-

ing the LoRa physical layer and is called the LoRa

eXchange protocol (LoRa-X). With LoRa-X, two key

parameters are:

• Sample Frequency which deﬁnes the expected

sample frequency or rate needed for a given sen-

sor in a device’s sensor suite.

• Transfer Frequency which deﬁnes how often a

node will attempt to transmit data to the sink.

These parameters are used to determine expected

node behaviour with the gateway.

The following sections discuss the node and gate-

way software architecture and behaviours, as well as

node-gateway interactions and transmission time slot

determination.

3.1 Node Architecture

Figure 4: The three phase LoRa eXchange protocol system.

Nodes operate in one of three stages during their life-

time (Figure 4).

1. Idle - During the idle phase, devices operate in a

predetermined low energy phase.

2. Sample - During the sample phase, devices com-

plete the required sampling and local processing

of data as required by the sensor suite which can

include storing data in a local persistence layer.

The transceiver is left in a low energy state during

this phase and the mechanics of sampling is spec-

iﬁed by the the application. Any data or messages

required to be sent to the sink are constructed and

transferred to the transmission queue which will

be serviced during the communication phase.

3. Communication - During the communication

phase, the node will switch to a transmission

Practical Precision Agriculture with LoRa based Wireless Sensor Networks

135

phase. During this phase, the node will attempt

to transmit all data messages in the transmission

queue.

In provisioning the system, consideration must be

given to the the amount of data that is being transmit-

ted along with the SF, channel bandwidth and coding

rate for the LoRa transceiver. In some regions devices

are limited to a strict time on air or channel idle times

when operating in the ISM band. The overarching

goal is to have the device on the air as little as possible

and minimize the amount of data being transmitted.

3.1.1 Transmission Queue

Each node maintains a queue to manage the transmis-

sion of data to the sink. When data is generated during

the sampling phase, each sample set is added to the

queue in order. Data sets will be transmitted based on

the order of sampling. When the node enters into the

communication phase, it will attempt to transmit the

queued messages to the sink. Each sample is trans-

mitted to the gateway utilizing a reliable datagram. If

the gateway acknowledges receipt of a sample, it is

removed from the queue and the node will attempt to

transmit the next element in the queue.

If the node does not receive a response within a

given timeout period, a re-transmission will be at-

tempted. The sensor node will attempt to transmit

the sample in the front of the queue until it receives

acknowledgment or until it has attempted an amount

of transmissions equal to the current size of the queue

(number of elements currently held). If the gateway is

not available at a given time slot, the samples will re-

main in the queue and are transmitted during the next

scheduled transmission. Conﬁguration of the sample

and transfer frequencies need to consider the size of

the data being transmitted in addition to the number of

devices in the network (the number of available time

slots) to ensures proper queue management, such that

it does not ﬁll up quickly.

3.2 Gateway Architecture

The gateway is constructed from a single channel

LoRa transceiver coupled with a Raspberry Pi sin-

gle board computer. This reduces costs signiﬁcantly

while supporting the ability to customize packets. As

a result of the long transmission distance of the LoRa

physical layer, the system utilizes a single gateway.

The majority of target installations are smaller in size

than the transmission distance for LoRa eliminating

the need for complex routing and device-to-device

synchronization.

The gateway is continuously powered and pro-

vides access services for the nodes and users. It

provides a local database that maintains information

about the gateway transceiver, sensor node conﬁgura-

tions, geo-location information for nodes and the data

generated by each node. It also controls synchroniza-

tion of sampling and communication parameters be-

tween nodes and the gateway. Additionally, it main-

tains a local web server allowing users to interact with

the gateway and nodes, and provides an MQTT hook

so sensor data can be published to an external broker.

3.2.1 LoRa-X Interaction Models

When a new node is initially powered in the network,

it will send its unique serial number to the gateway

until it receives a response. The serial number is

based on the unique 128-bit serial number assigned to

the microprocessor at manufacturing. With LoRa-X,

as devices only need to be uniquely identiﬁed within

the local cluster, this address is only used to uniquely

identify the node during initialization. For regular

communications, the sensor node will utilize a logical

ID assigned by the gateway for local communications.

The size of the logical ID can be adjusted depending

on the number of logical devices in the network. The

logical ID is used for addressing to reduce the number

of bytes required during transmissions.

When the gateway receives the serial number from

the sensor node, it will query its local database to de-

termine if the node has already been synchronized

and assigned a logical ID. If the unique 128-bit se-

rial number does not exist in the system, the gateway

will generate and assign a logical ID. If the node is

already registered with the gateway, the current con-

ﬁguration information and logical ID will be queried.

The gateway will return to the node the assigned logi-

cal ID, the current gateway date and time, and the de-

sired sample frequency and transmission interval fre-

quency. Once a node has been assigned a logical ID

and received its sample frequency information, it is

considered to be synchronized.

The gateway maintains a local web service that

allows a user to connect to the gateway and add ad-

ditional node conﬁguration information, descriptions,

and coordinates. A user can change conﬁguration

information regarding sampling and transmitting fre-

quencies. Changing either of sampling or transfer fre-

quency will update the sync status to Required, and

will ﬂag the gateway to initiate a re-synchronization

with the sensor node during the target node’s next

time slot. This allows for the parameters of the net-

work devices to be modiﬁed without having to restart

the network.

During normal operations, a node will transmit

data to the gateway during its calculated time slot.

When data is received from a node, the gateway

WSN4PA 2021 - Special Session on Wireless Sensor Networks for Precise Agriculture

136

Figure 5: Autonomous greedy time slot determination.

parses sensor sample data, inserting the payload into

the gateway database. The gateway acknowledges the

successful receipt of data from a node.

3.3 Autonomous Greedy Time Slot

Determination

The goal is to allow the network to converge to a state

where nodes are not transmitting at the same time in-

terval to reduce the energy expenditure. Unlike other

LoRaWAN techniques that use pure Aloha (Hax-

hibeqiri et al., 2018), slotted-Aloha (Polonelli et al.,

2019) or autonomous slot assignment (Zorbas et al.,

2020), sensor nodes utilize a transmission frequency

interval and channel inactivity to determine an avail-

able unstructured time slot. The goal of the time slot

algorithm is for a node to determine an available time

window and attempt to utilize the same interval for

future communications.

For a node to determine its time slot, it ﬁrst listens

for channel activity. If the channel becomes free, a

node will attempt to transmit messages currently be-

ing held in the queue. If the message transmission

is successful, the node will receive an addressed ac-

knowledgement from the gateway. The next time slot

for transmission is calculated as:

time

slot

t+1

= t

window end

+ t

(1)

where time slot

t+1

is the start time of the next avail-

able window for the local device, t

window end

is the end

time of the last successful transmission window and

is the requested interval between communication

events. The assumption is that t

 t

window

. Each

node uses the local real time clock to determine the

end of the last transmission that was acknowledged

successfully and calculates the start of the next win-

dow by adding the required communications interval

time.

Neither Aloha, where nodes attempt to transmit

when they have data and use an exponential back-off

(windowless), or slotted Aloha, which uses discrete

time-slots for transmission, use the status of the last

transmission windows as a basis for determining the

next likely available transmission window. With the

proposed protocol, as each node listens before trans-

mission, no two nodes will have the same window end

time for any transmission. In the event that two or

more devices attempt to transmit at the same time, the

gateway will only acknowledge one device, forcing

the other to re-enter the transmission cycle.

Consider Figure 5 which demonstrates the time-

line for three nodes autonomously determining the

next available time slot. For node 1, as the channel

is clear, it is able to transmit and receiving an ACK

without contention, thus determining the next time

slot during which it should attempt communications.

For nodes 2 and 3, they both determine that the

channel is busy and wait for the channel to go idle. In

the example, both devices detect a clear channel and

attempt to start their transmission window. While the

transmission window represents a probabilistic time

period where there are no other devices transmitting,

each individual packet transmission is handled in a

pure-Aloha fashion to resolve single over-the-air col-

lisions. In this example, node 2 is acknowledged forc-

ing node 3 to wait an additional time period before

attempting to re-transmit again.

To reduce the chances of collision, nodes use a

randomized back-off factor during the initial startup

to reduce the probability of multiple devices attempt-

Practical Precision Agriculture with LoRa based Wireless Sensor Networks

137

ing startup at the same time interval. While the chance

exists that a large number of devices will transmit at

the same time during startup, the practical implemen-

tation of this is low due to randomized offset, and

clock imprecision and drift in each node.

4 PROTOTYPE

IMPLEMENTATION

An experimental network deployment was developed

to validate the LoRa eXchange protocol connecting a

series of nodes to a gateway (Figure 6). The central

gateway is a Raspberry Pi 3 B+, with a 1.4 GHz pro-

cessor, 1 GB of SDRAM memory, WiFi and microSD

memory card support. The gateway uses the same

RF95 LoRa radio transceiver as the sensor nodes at-

tached to a Dragino LoRa Pi hat.

Nodes are based on the Feather M0 + LoRa open-

source platform which uses a Microchip SAMD21

ARM based processor running at 48 MHz and an

RF95 LoRa transceiver. Each node contains a ther-

mistor for temperature sampling as well as the ability

to report its own battery voltage level. Devices also

contained a digital BME280 sensor

which measures

relative humidity, temperature and barometric pres-

sure. Control of transceivers was done with the open-

source RadioHead packet radio library

that allows

for direct control of the LoRa transceiver as well as

providing reliable and addressable communications

between transceivers.

Figure 6: The LoRaX gateway and node architecture.

Local to the Raspberry Pi, a MySQL database is

used to store information about the central gateway,

https://www.bosch-sensortec.com/products/environmental-

sensors/humidity-sensors-bme280/

https://www.airspayce.com/mikem/arduino/RadioHead/

Figure 7: The LoRaX gateway and node conﬁguration.

Figure 8: The LoRaX gateway dashboard showing list of

samples with ﬁlter options.

sensor nodes, and sensor samples, which are visible

through a web portal (Figure 7). The Gateway has

attributes for a description and GPS coordinates. A

gateway can be named and pinned on a satellite net-

work map within the user interface.

A description and GPS coordinates are also cap-

tured for each node as well as information used by the

gateway while synchronizing with sensor nodes. The

device serial number is used to identify sensor nodes

before synchronization. Timestamp attributes are in-

cluded to track a sensor node’s most recent transmis-

sion and synchronization with the gateway. Lastly,

there is data storing the number of minutes a sensor

node should wait before sampling and transmitting

samples. The gateway contains an MQTT hook al-

lowing it to publish sensor data to an external MQTT

broker for use by other applications.

A test deployment was installed to evaluate net-

work performance. Node and gateway placement can

be viewed on the network map using the Google Maps

API and allows for the conﬁguration of the connected

components.

On power up, the sensor nodes synchronize with

the gateway and start sampling based on the sampling

and communication intervals provided by the gate-

way. Data that has been received at the local gate-

way can be viewed by logical node ID, and sensor

WSN4PA 2021 - Special Session on Wireless Sensor Networks for Precise Agriculture

138

Figure 9: The LoRaX gateway for longitudinal visualization

of temperature data.

type (Figure 8). The samples list can be ﬁltered to

only display sensor data for a speciﬁc node and sensor

type. Longitudinal data analysis can also be viewed

for multiple nodes by sensor type (Figure 9).

5 CONCLUSIONS AND FUTURE

WORK

Wireless sensor networks can increase sustainability

in agriculture by providing growers with usable data.

Through the use of open-source software and hard-

ware designs, coupled with LoRa transceivers, inex-

pensive and power efﬁcient sensing solutions can be

developed for agriculture allowing for long transmis-

sion distances and wide coverage from a single gate-

way installation.

The wireless sensor network developed in this

project uses LoRa transceivers and an efﬁcient chan-

nel management mechanism to reduce energy usage

for data transmission with a novel collision handling

protocol. The architecture is easily deployed and

maintained.

Future work will investigate detailed performance

characteristics for the LoRa eXchange protocol. Ad-

ditionally, a large scale network will be deployed in

an agricultural setting to demonstrate the beneﬁts of

precision agriculture and ease of use of the approach.

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