Wireless Technologies for Agricultural Monitoring using Internet of ThingsDevices with Energy Harvesting Capabilities

Sebastian Sadowskia, Petros Spachosa,∗

aSchool of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada

Abstract

Technological advances in the Internet of Things (IoT) have lead the way for technology to be used in ways thatwere never possible before. Through the development of devices with low-power radios, Wireless Sensor Networks(WSN) can be configured for almost any type of application. Agricultural has been one example where IoT andWSN have been able to increase productivity, efficiency, and output yield. Systems that previously required manualoperation can be easily replaced with sensors and actuators to automate the process such as irrigation and diseasemanagement. Powering these devices is a concern as batteries are often required due to devices being located whereelectricity is not readily available. In this paper, a comparison is performed between three wireless technologies:IEEE 802.15.4 (Zigbee), Long Range Wireless Area Network (LoRaWAN), and IEEE 802.11g (WiFi 2.4 GHz) foragricultural monitoring with energy harvesting capabilities. According to experimental results, LoRaWAN is theoptimal technology to use in an agricultural monitoring system where power consumption and network lifetime area priority. The experimental results can be used for the selection of wireless technology for agricultural monitoringfollowing application requirements.

Keywords: Wireless Technologies, Internet of Things, Energy Harvesting.

1. Introduction

In the era of Internet of Things (IoT), everyday ob-jects are equipped with microcontrollers and communi-cation devices that work together to improve one’s qual-ity of life [1, 2]. While IoT has been of great valueto society in the automation of tasks, IoT devices oftenconsume a large amount of energy [3, 4]. Energy con-sumption comes from various processes such as sensingsystems, application operating systems, and the com-munication radio [5]. In order to improve energy effi-ciency, each of the individual processes needs to be op-timized [6]. When it comes to IoT devices, processessuch as the operating and sensing systems are oftenbased on the application requirements and difficult toreduce. The easiest function to modify and optimize thedevice power consumption is the communication radio.

IoT devices can be used in monitoring systems con-sisting of nodes that interact with the environment us-ing sensors to gather real-time information and trans-mit it to a destination. In every monitoring system [7],

∗Corresponding authorEmail address: petros@uoguelph.ca (Petros Spachos)

power consumption is often a top concern in order forthe system to function properly. If a sensor node ceasesto transmit, information at the node’s location would bemissing and the system would no longer behave accu-rately. In order to optimize power consumption, theapplication requirements are required. One applicationwhere IoT monitoring systems can be used is in agricul-tural. When IoT and Wireless Sensor Networks (WSN)are used in agricultural, advanced farming techniquescan be applied which is known as Precision Agricultural(PA) [8].

PA allows for a greater amount of control in the grow-ing of crops and the raising of livestock. By using tech-nology to monitor crops, the efficiency can be increasedand costs can be reduced since more precise remediescan be applied to crops [9]. In PA applications WSN of-ten consist of batteries that are used to power the sensornodes while outside. This is a major issue as after thebattery dies either the battery will need to be replacedor if possible, recharged. Due to the node being out-doors, rechargeable batteries and an energy harvestingdevice can be used. Since solar power is readily avail-able it can be easily harvested in-order to allow for the

Preprint submitted to Computers and Electronics in Agriculture May 7, 2020

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sensor node to function for a longer period of time. Oneof the most optimal methods of optimizing the batterylife of nodes in WSN is through the wireless technol-ogy communicating the node’s information. In agri-cultural applications the most commonly used wirelesstechnologies have been: IEEE 802.15.4 (Zigbee) [10],Long Range Wireless Area Network (LoRaWAN) [11],and IEEE 802.11 (WiFi) [12] [13]. By using a differenttechnology the lifetime of the nodes could be increased,in-turn extending the lifetime of the network.

Batteries have become a major source of energy usedin a variety of electrical components and devices. Dis-posable single-use batteries are often used in portabledevices. Once the energy in the battery has been de-pleted, it must be properly disposed and replaced in thedevice. In recent years, rechargeable batteries have be-come popular due to their ability to be recharged andreused multiple times, reducing the amount of wastecreated. Through the development of devices with ef-ficient energy harvesting capabilities, rechargeable bat-teries have become more efficient. Devices are now ableto be scattered in remote locations and function for longperiods of time through the use of batteries that canquickly collect energy and recharged.

One application that can greatly benefit from the useof rechargeable batteries and energy harvesting is agri-culture. New technologies and IoT devices have revolu-tionized the way farmers are able to interact and moni-tor their growth. By combining traditional approaches,such as energy harvesting techniques, with IoT devices,PA can be performed. A promising solution towardachieving PA is the use of IoT system with energy har-vesting capabilities. The IoT uses small, low-power em-bedded electronics that transmit data across a network.Often, when sensor nodes are configured and placedoutdoors in a field, a power source is required. However,in a field, electricity is not readily available and batter-ies must be used. Due to the need to replace batteriesonce depleted, rechargeable-batteries are often used.

In this paper, through extensive experimentation, acomparison between the power consumption of threewireless technologies: Zigbee, LoRaWAN, and IEEE802.11g (WiFi 2.4 GHz) is performed. The technologieswere selected based on the prevalence and popularity inagricultural applications. They are compared throughthe use of an agricultural monitoring system using IoTdevices with solar energy harvesting capabilities. Threeidentical systems were created each performing identi-cal tasks functioning using different wireless technolo-gies.

The main contributions of this work are as follows:

• A prototype was designed using IoT componentsfor agricultural monitoring applications with solarenergy harvesting capabilities.

• Extensive experimentation was performed demon-strating the benefits of using IoT devices with en-ergy harvesting for agricultural applications. In to-tal, three experiments were conducted throughouta year and in different seasons.

• The experimental results can be used as an indi-cator of the power requirements of the wirelesstechnologies when used for agricultural monitor-ing with IoT devices.

• According to experimental results, LoRaWAN isthe optimal wireless technology to use for agri-cultural monitoring if power consumption and net-work lifetime are a concern.

The rest of this paper is organized as follows: Sec-tion 2 reviews the related work on wireless technologiesin agricultural applications. In Section 3, a frameworkof the designed system is presented, followed by Sec-tion 4, with a description of the experimental procedure.The experimental results and a discussion are presentedin Section 5. Finally, Section 6 concludes this work.

2. Related Work

Over the years, applications using IoT devices andWSN in PA have become more commonplace in to-day’s society. By using battery powered sensor nodescombined with traditional farming practices an increasein output efficiency and a reduction in costs can beachieved. While some researchers have focused onimplementing energy harvesting to extend sensor nodelifetime, others have modified the sensor nodes touse less energy through its standard operating proce-dure [14].

In [15], a survey was performed studying the lifetimeof WSN and the energy saved with different types ofnetwork topologies. Based on determined results, thereare a lot of problems and issues when selecting a topol-ogy for extending the network lifetime. One issue is thetrade-offs that need to occur in the network. In order forthe network lifetime to be extended trade-offs must oc-cur and other parameters are required to be sacrificed. Itwas suggested that energy efficient articles be developedto optimize the energy supply.

In the works [16] and [17], systems were proposedfor agricultural monitoring. In [16], the designed sys-tem used WiFi-based IoT devices to monitor nitrate con-

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centrations in ground water. In the design of the sys-tem, WiFi was selected for communicating the informa-tion due to its low cost, high throughput, and ease ofintegrating with web-based services. In [17], a WSNfor irrigation control was developed using Zigbee forwireless communication. Zigbee was selected due toits low cost and off-the-shelf components to reduce thehardware complexity. In order to reduce the powerconsumption the transmit power was configured to be0 dBm. In both of the systems presented, the powerconsumption was not a major concern, with a greaterfocus being placed on the total cost and ease of inte-grating with the system. If the network lifetime was agreater concern, more emphasis could be placed on re-ducing the power consumption.

In [18], a study was performed on sensor nodes withsolar energy harvesting capabilities. An energy man-agement policy is used in order to produce the optimalthroughput and minimizes the mean delay in the net-work. Another method of optimizing power consump-tion can be seen in [19]. Where a circuit was created forwireless sensor nodes where energy from a solar panelcould be transferred to a rechargeable battery even if inpoor weather conditions.

In other works, it was determined that the samplingrate of the nodes can greatly affect the energy consumedand power supplied. In [20], a method is presented todecide the sampling rate of sensor nodes to manage itsenergy more efficiently. Simulations demonstrated theefficiency of the proposed algorithm compared to otheralgorithms.

The system presented in [21] used a WSN in a cottonfield to monitor soil moisture with automatic drip irri-gation. Sensor nodes were developed to function usingbattery power while relay nodes functioned using solarpower. A routing protocol was used in order to routedata and increase power savings. Experimental resultswere conducted over a six month period and demon-strated that the system could function for a long periodof time while collecting sensor information.

Recently in literature, there have been many worksthat have used renewable energy sources for agriculturalapplications to extend network lifetime. In [22], dueto the unpredictability associated with weather condi-tions, solar energy harvesting was combined with wire-less charging in order to allow for nodes to function forlonger periods of time. By combining the advantagesof both solar energy harvesting and wireless chargingit was found that based on experiments performed asignificant increase in network performance could beachieved.

In this work, we expand on the papers described

above and design a system for agricultural monitoringwith energy harvesting capabilities. Based on the pa-pers above there is a lack of research performed on us-ing different types of wireless technologies for agricul-tural applications. In search of a system design, a com-parison is performed between three wireless technolo-gies: Zigbee, LoRaWAN, and WiFi to determine whichtechnology consumes the lowest amount of power andprovides the longest network lifetime. Three identicalsystems each using one of the wireless technologies tocommunicate are developed and tested.

3. System Framework

The proposed system has a number of hardware com-ponents that were selected for measuring the power con-sumption of the different wireless technologies for agri-cultural applications. Each of the systems contained anArduino Uno, a power converter, one rechargeable bat-tery, a solar panel, a soil moisture sensor, and a commu-nication unit. Each node forwards the sensor data to thebase station using a different wireless technology. ForZigbee, a Series 2 2mW Wire Antenna XBee was used,for LoRaWAN, a Dragino LoRa Shield, and for WiFi, aCC3000 WiFi Shield. The system framework is shownin Fig. 1.

3.1. Components

• Solar Panel: To provide energy harvesting ca-pabilities to the system, a Star Solar D165X165monocrystalline solar panel was used [23]. Beingonly 170 x 170 x 2 mm, the solar panel is capa-ble of providing a 6.0 V output at a peak of 3.65Wwhen full sunlight is present. The small size makesit suitable for placement in a field where it wouldhave minimal interference to any of the growingplants surrounding it while still providing a signif-icant energy output. The solar panel is shown inFig. 2a.

• Grand-Pro Lithium Polymer Battery: The batteryused was a Grand-Pro 3.7 V 6600 mAh LithiumPolymer (LiPo) [24]. When connected to a batterythe power converter was designed to provide a con-stant 5 V power output while the charge on the bat-tery was above 3.4 V. If the charge dropped below3.4 V the power converter would cease to functionand would wait until the battery was sufficientlycharged before supplying power again. This safetyfeature allowed the battery to maintain a voltage

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Figure 1: System framework used for experimentation.

level preventing it from over-discharging and dam-aging the battery cells. The battery is shown inFig. 2b.

• Power Converter: A developed power converterwas used to supply power to the sensor node sys-tems [25]. In addition to supplying power, the con-verter was also capable of interfacing with othertypes of components such as an energy harvestingdevice and a recharge a Lithium Polymer (LiPo)battery. The energy harvesting device could thenbe used to both supply power to the node andrecharge the battery. If it was no longer providingpower the battery could then supply power to thesystem. The power converter is shown in Fig. 2c.

• Arduino Uno: In order to connect all the hard-ware components together, an Arduino Uno Rev3microcontroller was chosen [26]. The ArduinoUno was selected based on its low power con-sumption and ease of development in configuringall the components together. It is based on theATmega328P, which contains six analog-to-digitalconverts that can be used to easily connect and readdata from analog sensors. The Arduino Uno isshown in Fig. 2d.

• Grove Soil Moisture Sensor: To measure the mois-ture levels in the soil a Grove Soil Moisture Sensorwas used [27]. This sensor was selected as it drawsa significant amount of current of reducing the de-vice lifetime when environmental conditions arebeing measured. Soil moisture is also a commonlymeasured parameter in agricultural monitoring al-lowing for a system design similar to what wouldbe used in a real-life application. The soil moisturesensor is shown in Fig. 2e.

• Series 2 XBee with 2mW Wire Antenna: To cre-ate a Zigbee network between the devices a pairof Series 2 XBees with a 2mW Wire Antennaswere used [28]. The Series 2 XBees are low powerradios which communicate on the Zigbee meshnetwork. These devices are capable of creatingpoint-to-point or multi-point networks connectingtogether hundreds of nodes. Devices using Zig-bee have a transmission range of 120 m in Line-of-Sight (LoS), which can provide many benefitsfor use in an agricultural monitoring system suchas reducing the system costs and allowing for easyconfiguration of the devices. The Series 2 XBee isshown in Fig. 2f.

• Dragino LoRa Shield: To communicate devicesusing LoRaWAN a pair of LoRa Shields for Ar-duino developed by Dragino were used [29]. Lo-RaWAN is known for being a long range tech-nology communicating at a low frequency of915 MHz, signals produced have larger wave-lengths hence can travel further distances. InLoS LoRaWAN is capable of transmitting up to15000 m. Due to this, LoRaWAN is consideredone of the best technologies to use for agricul-tural monitoring. Its large transmission range cangreatly reduce the number of nodes required and itslow power consumption can keep nodes function-ing for a longer period of time compared to morecommonly used technologies. The LoRa Shield isshown in Fig. 2g.

• CC3000 WiFi Shield: To connect the Arduino us-ing WiFi a Sparkfun CC3000 WiFi Shield wasused [30]. WiFi is one of the most commonlyused wireless technologies, available in most de-vices, used to connect to a Wireless Local Area

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(a) Solar Panel. (b) Battery. (c) Power Converter. (d) Arduino Uno.

(e) Soil moisture sensor. (f) Series 2 XBee. (g) Dragino Lora. (h) WiFi (2.4Ghz).

Figure 2: Hardware components used for experimentation.

Network (WLAN) and the Internet. For agricul-tural purposes, WiFi is rarely used in the transmit-ting of information. WiFi has a short transmissionrange in LoS only capable of reaching up to 50 mdistance. In addition, WiFi has a very large powerconsumption which often makes it a poor choiceto use in wireless devices outdoors that require apower supply. The CC3000 communicates usingthe IEEE 802.11g standard. The WiFi shield isshown in Fig. 2h.

3.2. System Parameters

When comparing different types of wireless technolo-gies, parameters such as transmission range and currentconsumption are important in determining the optimaltechnology for agricultural monitoring. While the cur-rent consumption of a wireless technology is importantin determining the longevity of a node’s power supply,often other parameters should be considered first in theselection of a communication technology. Transmissionrange is one parameter that is often compared. By usingdevices that transmit further, a fewer number of relaynodes are required in order for a transmission to reachthe intended destination. Another common parameter

is the throughput. If a higher throughput is used, alarger amount of data can be transmitted in a periodof time. Other parameters often used are the cost andease of implementation, having a lower cost per-devicecan allow for a greater number of nodes to be imple-mented in the network. While having a simpler ease-of-implementation can allow for the network to have alower set up time and make it easier to debug if a prob-lem occurs.

A summary of the parameters of the wireless tech-nologies being compared in this paper can be seen inTable 1. In terms of throughput, WiFi can transmitthe most amount of data reaching speeds of 54 Mbit/s.Zigbee is the next highest with 250 kbit/s, followedby LoRaWAN with 50 kbit/s. For transmission range,LoRaWAN is the optimal capable of reaching up to15000 m in LoS. Zigbee was the second furthest in LoSwith 120 m, while WiFi has the lowest transmissionrange only capable of reaching 50 m in LoS.

Other parameters such as the current consumption,the sampling frequency, transmission interval, andtransmission power are important in the measuring ofthe power consumption of a device. Table 2, summa-rizes the current supplied by the battery, maximum cur-rent utilized by the various components, and the other

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Table 1: Summary of wireless technologies.

Technology Throughput TransmissionRange

Power Con-sumption Advantages Disadvantages

Zigbee 250 kbit/s 120 m Low Easy to set up Require extra hardware

LoRaWAN 50 kbit/s 15000 mExtremelyLow Wide range Requires extra hardware

WiFi (2.4Ghz) 54 Mbit/s 50 m Moderate Wide availability High energy consumption

Table 2: System parameters corresponding to componentsused in sensor nodes.

Parameter ValueBattery Current Supply 6600 mAh

Arduino Uno (Max Current Consumption) 45 mA

Grove Soil Moisture Sensor (Max CurrentConsumption)

35 mA

Series 2 XBee (Max Current Consumption) 40 mA

Dragino LoRa Shield (Max Current Con-sumption)

10 mA

CC3000 WiFi Shield (Max Current Con-sumption)

190 mA

Sampling Frequency 1 Hz

Tranmission Interval 1 s

Transmission Power -10 dBm

Table 3: Estimated monitoring node max current consumptionand min lifetime for each wireless technology.

WirelessTechnology

Estimated MaxCurrent

Consumption (mA)

EstimatedMin

Lifetime (h)Zigbee-based node 120 55

LoRaWAN-based node 90 73WiFi-based node 270 24

parameters configured in the experiment that affect thepower consumption, such as the sampling frequency, thetransmission interval, and the transmission power.

In addition to the current draw of the components,while greatly affecting the power consumption, thetransmission ranges of the wireless technologies are im-portant when designing a system for agricultural moni-toring. If devices are used that have a further transmis-sion range, then a fewer number of nodes can be usedin the monitoring of a field. A monitoring node wouldrequire an Arduino Uno, a sensor, a battery, and a com-munication unit. Table 3 shows the expected lifetime ofeach monitoring node when each wireless technology isused with maximum current consumption.

Table 4: Estimated relay node current consumption and life-time for each wireless technology.

WirelessTechnology

Estimated CurrentConsumption (mA)

EstimatedLifetime (h)

Zigbee-based node 40 165LoRaWAN-based node 10 660

WiFi-based node 190 35

At the same time, if the communication units are usedas relay nodes to forward the data, the larger the trans-mission range the smaller the number of the requiredunits to reach the destination. To achieve the maximumtransmission range, the maximum current consumptionis required. Table 4 shows the estimated lifetime, wheneach of the communication units is used as a repeateralone, to forward the sensor data to the destination, us-ing the battery.

4. Experimental Procedure

In order to evaluate the proposed systems, a testbedwas created to evaluate which wireless technologywould be optimal for agricultural monitoring with en-ergy harvesting. For each of the systems, identicalnodes were configured except for their wireless commu-nication method. The experiments were conducted at anoutdoor environment where the solar panels for each ofthe nodes would obtain a similar amount of solar energythroughout the day. The testing area was a roof researchlab at the University of Guelph Engineering Building,shown in Fig. 3. To measure the charge left on the bat-tery, probes from the power converter were connectedand measured on the Arduino and transmitted to a com-puter that was functioning as the destination.

4.1. Experimental Setup

For testing purposes, nodes were configured to sam-ple the charge left on the battery every 1 Hz andtransmit the information every 1 s. Note that thesetimes were used in order for the systems to consume

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Figure 3: Green roof lab at University of Guelph.

a larger amount of power and therefore cease function-ing sooner. If the systems were to be placed in an actualenvironment for agricultural monitoring the times couldbe greatly reduced since actual conditions do not rapidlyvary in a short period of time. Before starting the exper-iments, the batteries that were connected to the nodeswere fully charged.

4.2. Outdoor Experiments

Four experiments were performed, the first withoutenergy harvesting capabilities and the following withenergy harvesting capabilities. Each experiment lasteduntil all the nodes power supplies were drained and eachof the nodes ceased to function:

1. Experiment 1 - No energy harvesting capabilities.In this experiment, the solar power was not con-nected to each node, to examine and characterizethe performance of the battery alone, without thesolar panel.

2. Experiment 2 - With energy harvesting. This ex-periment took during August 2018.

3. Experiment 3 - With energy harvesting. This ex-periment took during December 2018.

4. Experiment 4 - With energy harvesting. This ex-periment took during May 2019.

Due to uncontrollable weather conditions performingthree experiments would guarantee results that demon-strate the system performing with varying amounts ofsunlight.

The current consumed by the nodes was also mea-sured. In order to measure the current consumptionof the devices, the Monsoon Power Monitor was used.Monsoon is a monitoring tool that is capable of supply-ing an input voltage, measuring the current drawn by the

device, and can display the average measurements. Oneuseful function of the Monsoon is its ability to select abattery size and estimate the lifetime of the device basedon that battery. To measure the current consumption ofthe devices, nodes were first powered and warmed upuntil the system was fully operational. The current wasthen measured for two minutes and the values recorded.

5. Results and Discussion

In this section, the experimental results are presentedfollowed by a discussion on the acquired results.

5.1. Results

According to the experimental results obtained, eachof the proposed systems functioned as required, capa-ble of transmitting information until the battery in thesensor node was depleted. Due to the large amount ofdata that was gathered throughout the experiments, onlya fraction was used and is displayed.

The voltage charge remaining on the battery overtime for the first experiment, with no energy harvest-ing capabilities, can be seen in Fig. 4. and in Fig. 5,Fig. 6, and Fig. 7 the remaining battery levels over timefor experiments 2, 3, and 4 is shown, respectively. Anoverall summary of the results gathered and the calcu-lated measurements can be seen in Table 5.

Using the Monsoon Power Monitoring, the averagecurrent consumption’s of the different devices alongwith the estimated lifetime could be measured. In termsof current consumption, it was determined that WiFiconsumed the most requiring 171.17 mA to function. Insecond was Zigbee which required 69.36 mA and lastly,LoRaWAN was determined to use the lowest amount ofcurrent only consuming 29.33 mA on average. Usingthese values along with the battery size of 6600 mAh,the expected lifetime of the devices could be calculated.A Zigbee based system should function for 95.15 h, aLoRaWAN based system would be expected to run for225.00 h, while a WiFi based system has an expectedlifetime of 38.56 h. According to the experimental re-sults, as seen in Fig. 4, the Zigbee system functioned for80.28 h before failing, LoRaWAN lasted for 166.23 h,and WiFi stopped after 29.06 h.

During the second experiment, shown in Fig. 5, it wasdetermined that the device using LoRaWAN technologywas the most optimal capable of lasting 228.20 h on asingle battery charge. The Zigbee based device was thesecond to cease operating stopping after 95.15 h. WiFiwas determined to be the worst operating technology fortransmitting data, only functioning for 38.56 h.

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(a) Zigbee. (b) LoRaWAN. (c) WiFi.

Figure 4: Experiment 1 - No energy harvesting capabilities.

(a) Zigbee. (b) LoRaWAN. (c) WiFi.

Figure 5: Experiment 2 - August 2018 with solar energy harvesting.

(a) Zigbee. (b) LoRaWAN. (c) WiFi.

Figure 6: Experiment 3 - December 2018 with solar energy harvesting.

(a) Zigbee. (b) LoRaWAN. (c) WiFi.

Figure 7: Experiment 4 - May 2019 with solar energy harvesting.

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Table 5: Summary of results.

WirelessTechnology Zigbee LoRaWAN WiFi

Average CurrentConsumption (mA) 69.36 29.33 171.17

No Energy HarvestingExpected

Lifetime (h) 95.15 225.00 38.56

Experiment 1 -Node Lifetime (h) 80.28 166.23 29.06

Solar Energy HarvestingExperiment 2 -

Node Lifetime (h) 104.80 228.20 28.3

Experiment 3 -Node Lifetime (h) 92.19 174.64 30.67

Experiment 4 -Node Lifetime (h) 88.25 189.22 28.85

In the third experiment, shown in Fig. 6, the resultsare similar to the previous experiments with fewer sun-light hours, forcing the nodes to use their batteries forpower. This experiment took place during December2018. The Zigbee based system was able to last 92.19 h,the LoRaWAN based system saw a large reduction inlifetime only functioning for 174.64 h, while the WiFisystem experienced a similar runtime of 30.67 h.

Results from the fourth experiment can be seen inFig. 7. This experiment took place during May 2019and the solar panels manage to collect a greater amountof energy than the third experiment, but less than theamount gathered from the second experiment. In thiscase, the Zigbee system only functioned for 88.25 h.Using LoRaWAN the system was capable of runningfor 189.22 h, and the WiFi based system finished after28.85 h.

5.2. Discussion

Through the experiments performed, it can be seenthat LoRaWAN is the optimal technology for communi-cating information between nodes in a wireless network.By analysis the current consumption of the different de-vices, it could be determined that LoRaWAN consumedover a fifth the amount of current as WiFi and over halfthe current as Zigbee. Hence, this allows for LoRaWANto last a much longer duration using batteries with sim-ilar capacities. The benefits LoRaWAN provides can beeasily observed when a battery is selected to comparethe lifetimes of the nodes. With a 6600 mAh battery,LoRaWAN is expected to last approximately 225.00 h,

which is much greater than the 95.15 h Zigbee can pro-vide, or the 38.56 h obtained from WiFi.

When compared with the first experimental results, itcan be determined that the estimated time calculated isnot accurate. For instance, Zigbee was only capable ofachieving a runtime of 80.28 h, while LoRaWAN func-tioned for only 166.23 h, with WiFi running for 29.06 h.Based on the real-world results, a great difference canbe noticed between the two experiments. One reasonfor the difference can be attributed to the nonlinear dis-charge rate of the LiPo batteries. With all batteries notbeing ideal drops in the charge can occur reducing thelifetime of the device [31, 32, 33]. Another factor is thepower converter. The power converter used to supplypower to the device from the battery was designed toprevent the battery from over-discharging. Therefore,the power converter would stop the supplying poweronce the charge on the battery reached 3.4 V.

In order to improve the battery life of the devices, thenext set of experiments saw the addition of a solar panelto provide energy harvesting capabilities to the devices.Based on the results produced, adding energy harvestingto a system can greatly increase the lifetime of the nodesin the network. It was determined that the amount ofsunlight obtained will greatly affect the additional life-time that the device will be able to function for. Thesecond experiment saw a large amount of sunlight sup-plying energy to the devices, with the third experimentsupplying a very little amount of energy, and the fourthexperiment providing energy to be between the previoustwo experiments.

In the Zigbee device, solar harvesting was able togreatly increase the lifetime of the node during the ex-periment. The second experiment saw Zigbee run for104.80 h, 92.19 h during the third experiment, and88.25 h in the fourth experiment. When compared tothe first experiment, with energy harvesting a Zigbeenode could last for an addition 25 h with a large amountof sunlight, while for a low amount lasted for 88.25 h.This is a great improvement for the Zigbee system as be-ing able to function for a greater period of time wouldallow for more data to be gathered before the battery inthe node would be to be recharged or replaced.

The LoRaWAN based system, a larger amount ofvariance between the runtimes could be observed. Thesecond experiment saw the node run for 228.20 h, thethird for 174.64 h, and the fourth for 189.22 h. Due tothe long base runtime of the device, it can be noticedthat a larger amount of solar harvesting could occur fur-ther increasing the runtime of the device. At its peak,the second experiment saw the device last for an addi-tional 50 h before failing, while in the third experiment

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the solar panel provided very little benefits to the sys-tem.

Lastly, a WiFi based system using energy harvest hasvery little impact on the runtime of the device. Duringthe second experiment the node runs for 28.3 h, while inthe third experiment for 30.67 h, and for 28.85 h in thefourth experiment. Overall, the largest impact gainedfrom energy harvest was approximately 1 h. A systemusing WiFi consumed too much power draining the bat-tery charge that using energy harvesting no impact couldbe made on the system.

In the WiFi experiments, it can be seen that the so-lar panel provided little benefits. There was a very littleamount of battery charge recovered over the period thatthe node functioned. For the Zigbee and LoRaWANsystems, the solar panel provided much more energyand made a bigger difference in the system. It canclearly be seen the points when the solar panel was pro-viding energy, after the solar panel stopped providingenergy the charge on the battery was slightly increased.

A number of other parameters such as the sam-pling frequency, transmission interval, and transmissionpower exist which could also affect the estimated andactual lifetime of the systems. In order to determinehow much each of the parameters affect the power con-sumption additional experimentation would need to beperformed.

According to the results determined in Table 5, WiFiis ideal if a large amount of information is required tobe transmitted between short distances. However, at thecost of such a high speed, a greater amount of powerconsumed. On the other hand, LoRaWAN has a muchlower throughput, but is able to transmit far distanceswith a very minimal amount of power being consumed.In the middle there is Zigbee. Zigbee has a slightlyhigher throughput than LoRaWAN, but a greatly re-duced transmission range. The power consumed by Zig-bee is still low and a network can be easily set up withnodes capable of being easily configurable and meshedtogether in the network.

6. Conclusions

In this paper, we provide an experimental analysis be-tween three wireless technologies: Zigbee, LoRaWAN,and WiFi when they are used in an agricultural monitor-ing system with energy harvesting capabilities. Identi-cal systems were created each functioning with a wire-less technology. The systems were placed outdoors andthe batteries could be recharged. The systems werecompared on the lifetime of the nodes, where the nodethat functioned for the longest time would be the most

optimal for an agricultural application. Experimentalresults demonstrated that LoRaWAN would be ideal asit was capable of functioning for the longest period oftime before failing. Zigbee was the next ideal, followedby WiFi.

However, power consumption and device lifetimeare usually not the only parameters that are consideredwhen designing a system. While WiFi has a poor powerconsumption, the throughput is much higher allowingfor a larger amount of information that can be transmit-ted between devices. The results produced in the papercan be used as an indicator for the selection of a wirelesstechnology to be used in an agricultural monitor systemwith energy harvesting capabilities.

References

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