CPS/IoT Ecosystem: Indoor Vertical FarmingSystem

Isakovic Haris∗, Alexander Fasching∗, Lukas Punzenberger †, Radu Grosu∗

Technische Universitat Wien, Vienna, Austria∗ name.surname@tuwien.ac.at

† name.surname@student.tuwien.ac.at

Abstract—Building large-scale IoT applications requires enor-mous infrastructural support. As infrastructure we understandhardware, software and communication channels necessary toensure interconnection between various heterogeneous compo-nents. Project CPS/IoT Ecosystems explores infrastructural re-quirements for large-scale applications. The applications are real-world scenarios such as smart parking, smart agriculture orsmart buildings. In this paper we explore infrastructural require-ments necessary to build modular indoor vertical farming system.The proposed prototype is a service-oriented platform distributedover three scopes of operation: cloud, fog, sensor/actuator.

Index Terms—IoT, Cyber-physical System, Vertical Farming,Smart Farming

I. INTRODUCTION

Cyber-Physical Systems (CPS) represent an integration be-tween computational (cyber) system and an physical environ-ment, where parts of the system can reside in either one or theother [1]. With the advancement of telecommunications andestablishment of Internet-of-Things (IoT) it became almost ef-fortless to collect data from a physical environment, aggregateand transform data into useful information, transfer the data tolarge storage facilities, perform analysis to optimize existingand discover emerging physical processes [2]. This practicecan increase efficiency of both complex industrial systems(e.g., industrial robots) and ordinary consumer products (e.g.,toothbrush).

The world is rapidly changing in many aspects and manyof them are positive, however some of these changes i.e.,climate change are potentially catastrophic. By large marginwe are still bound to technologies dating back to industrialrevolution. Creating novel and smarter approaches to improvethese legacy systems and make them more sustainable is animperative behind CPS/IoT revolution. The worlds populationgrowth is currently at the rate of about 1% per annum [3].The projections show this trend will continue with gradualdecrease. The world will reach about 9.1 billion people by2050, it is projected that the food production needs to increasefor about 70% in the projected period. At the same time naturalresources and the ability to expand agricultural land are gettinglower, and approximately 90% of food gain is expected tocome from a yield increase, and only 10% from enlargementof agricultural area [4].

The implementation of smart agricultural systems (e.g., cli-mate monitoring, automated greenhouses, cattle managementetc.) will be one of the pillars of sustainable food production

in the future. In this paper we will reflect on implementationof smart agricultural systems using CPS/IoT infrastructure andoffering Infrastructure-as-a-Service (IaaS) and Experiment-as-a-Service (EaaS) for smart farming. In particular we willdemonstrate a vertical farming technology or plant factory withartificial lighting (PFAL) implemented using CPS/IoT toolsand methods. The IoT infrastructure provides us an ability totailor the production in a vertical farming system, accordingto a plant culture, environmental factors, and other functionaland non-functional requirements.

Following chapter provides theoretical and practicaloverview of tools and methods used in this work. Further,we provide a brief crossection of related works. Further, inChapter IV we provide details on implementation, followedby discussion on results and future work.

II. BACKGROUND

A. CPS/IoT Ecosystem

The project CPS/IoT Ecosystems explores integration ca-pabilities between CPS and IoT both from the aspect ofinfrastructure and application. Development of smart systemsrequires hardware and software infrastructure that are in mostcases generic and reusable. CPS/IoT Ecosystem is universalplatform for development of CPS/IoT applications, it providesinfrastructure, the ability to prototype and experiment applica-tions as a service. It is a testbed that provides IoT infrastructurein three scopes of operation: cloud, fog, and sensor/actuator.An infrastructure in general terms is a basic physical andorganizational structures and facilities that allow operation ofa system or society [5]. Basic research on CPS/IoT, as well asapplication development are often hindered by infrastructuralproblems that increase overall efforts.

a) Cloud: Infrastructure-as-a-Service (IaaS) originated inscope of cloud servers where users could lease computer re-sources for a specific time and perform application deploymentand experiments without hardware constraints. Cloud serversare build so they adapt can dynamically adapt to applicationrequirements. In context of CPS/IoT cloud servers are usedto store large amount of data, or to perform computationallycomplex and challenging tasks.

b) Fog/Edge: Fog or sometimes referred as edge devicesreside on the edge of the internet and are responsible of main-taining connection between low level devices (e.g., sensors,actuators) and large scale servers in local network or over

2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT)

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internet. They provide ability to transform raw data into usefulinformation, compress the data to reduce communication over-head, or to perform data aggregation and basic analysis. Fognodes are located closer to the physical environment. Thus,they are capable of performing real-time control of physicalprocesses.

c) Sensor/Actuator: Sensors and actuators are simplecomputational devices that are either equipped with sensingdevices to collect information from the physical environment(e.g., thermometer) or actuating devices that are capable ofmanipulating its physical environment (e.g., heater).

B. Arrowhead IoT Framework

Internet-of-Things (IoT) represent systems with highly het-erogeneous and distributed structure. They are an excellentexample of Cyber-Physical System-of-Systems (CPSoS) [2].They combine multiple organizations, applications and ser-vices that need to provide optimal functionality with respect tothe balance between dependability, security, safety and sustain-ability. Arrowhead [6] is a middle-ware software platform forIoT systems that conforms exceptionally to these requirements.It is a service-oriented architecture (SoA) designed for applica-tions with firm safety and security constraints. It supports IaaSconcept as proposed by the CPS/IoT Ecosystem (see SectionII-A), and the multi-cloud/fog structure this project is basedon.

C. Indoor Vertical Farming System

Plant factory with artificial lighting (PFAL) is a processof growing food in the artificial environment [7]. Commonagricultural processes for growing vegetables and fruit arehighly dependent on natural environmental factors such asgeographical location, weather or surrounding fauna. For ex-ample, growing vegetables gets more difficult the farther thelocation is away from the equator due to the reduced number ofsunny days and cold weather. Plants are using photosynthesisto generate energy used for growth of the plants. The photo-synthesis combines carbon-dioxide CO2 and water (H2O) thatreact under sun light and generate sugar and oxygen (O2). Inaddition different plants need different mineral supplementsdepending on their function and origin. To grow plants inan artificial environment it is necessary to ensure sun-lightor adequate replacement, source of water, CO2 from the airand mineral nutrients. Vertical farming systems profit fromthe artificial environment that can be controlled so each plantgets most optimal conditions for growth. This allows plants aconstant fast and healthy development. Further advantages arereduction of pesticide usage, reduction of water consumptionand all year weather independent production. Major downsideof indoor vertical farming systems is energy consumption.Artificial lighting, water flow systems, ventilation and climatecontrol are all systems that consume electrical energy invertical farming production. However, advancement in low en-ergy lighting, passive climate control and automation systemsreduce these costs drastically and make indoor food productionviable. There are four basic types of vertical farming systems:

• Hydroponic: Roots of the plants are submerged in water.• Aeroponics: Plants grow suspended in the air and the

roots are sprayed with water.• Aquaponics: Water is used both for plant growth and to

breed fish or other water cultures (e.g., shrimp).• Soil: Plants are seeded in soil.

Depending on the water supply most common hydroponicsystems are deep flow technique (DTF) systems and nutrientfilm technique (NTF) systems [7]. In this paper we present atype of DTF hydroponic system with artificial LED light (seeFigure 1). This experimental setup was developed as a smallurban farming system for a house or a restaurant with lowwater usage and optimized energy consumption.

III. RELATED WORK

The ultimate goal of each production system is to in-crease production and reduce losses. Agriculture is a long-established production activity that dates back to the dawnof human civilization. Applying state-of-the-art computer andcommunications technologies to build ”smarter” agriculturalsystems is the latest attempts to extend food productioncapabilities [8]. With help of global positioning systems (GPS)[9], wireless communication networks, computer automation,semiconductors and Internet, farmers are capable of gain-ing extraordinary new insights into their production systems.It ranges from smart irrigation systems where farmers usecomputer automation systems and environmental sensors tooptimize water or energy consumption [10] [11] [12]. Further,continuous monitoring of crop and livestock in relation toenvironmental factors is becoming an essential feature of everyfarming system. A large variety of sensing devices are beingintroduced to agriculture, from imaging devices, to environ-mental sensors and livestock sensors [13]. These devices canbe stationary, mobile or attached directly on livestock. Useof unmanned areal vehicles (UAV) is a most common mobilesensing approach used for everything from precision farmingto livestock tracking [14] [15]. The efficiency and powerconsumption of semiconductor products i.e., LEDs provides uswith the ability to build artificial farming systems and producefood in closed and regulated environment [7]. Internet-of-things (IoT) provides a new dimension for interconnectionand interoperability between heterogeneous disciplines andtechnologies. A combination of these technologies createssustainable fully automated indoor plant production systemswith ability to scale [16] [17] [18].

IV. IMPLEMENTATION

In this section we propose an IoT indoor vertical farmingsystem with three levels operation (see Figure 1). The systemis built using service-oriented architecture. This approachprovides an ability to expand and adapt the system if necessaryand to enable development and deployment of each functionindependently.

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Fig. 1. High-level architecture for vertical farming stack

A. Hardware Setup

The proposed indoor vertical farming system consists ofmultiple physically decoupled entities. As mentioned abovethe proposed system is implemented on three levels of oper-ation. The cloud level is realized as a virtual machine and itis deployed on a remote server. Depending on the size of theindoor vertical farming system, the performance properties ofthe server platform can vary. Fog node depicted in Figure 1is a Raspberry Pi Model 3 B [19] single board computer. Itserves as a local control device for the actuators and sensors,and it can operate in isolation if disconnected from the uppercloud level.

1) Sensor Nodes: There two types of sensor nodes: theenvironmental sensor node and soil measurement node. Theyare based on Simblee RFD77101 [20] system-on-a-chip (SoC)integrated with a set of environmental and soil measurementsensors. Simblee SoC hosts a 32bit ARM CORTEX M0 pro-cessor and Bluetooth Low Energy (BLE) transceiver with anintegrated antenna [21]. The communication between sensorboards and the Fog node is done via BLE.

Each Vertical Farming stack consists of one environmentalnode, responsible for measuring environmental parameters: airtemperature, pressure, humidity, CO2 and emission rate of totalvolatile organic compound (eTVOC).

Each level has one dedicated soil board, measuring soil tem-perature and humidity. The proposed indoor vertical farmingsystem is implemented as a hydroponic system so the soilsensors are used to measure water level and water temperature.

2) Actuators: All actuators on the indoor vertical farmingsystem are controlled trough a relay IO board. It is controlled

via Ethernet interface. Each level on the indoor vertical farm-ing system has one LED light source and two valves for watersupply or drainage mounted. A central pump is providingwater supply for all plant pots.

B. Software Infrastructure

The application consists of two local automation clouds,each running the Arrowhead core system. The individual ser-vices are depicted in Figure 2. The controller cloud is respon-sible for controlling the actuators and reading of sensor values.It interacts with a management cloud using Arrowhead’s inter-cloud orchestration system. The management cloud provides auser interface to set watering and light schedules and receivessensor data from the controller cloud. The connection betweenthe two clouds is established using Arrowhead’s Gateway andGatekeeper services. The Gatekeepers negotiate connectiondetails and the Gateways tunnel the HTTP traffic througha RabbitMQ [22] message broker using the AMQP [23]protocol.

The application itself is implemented as a collection ofArrowhead compliant services. Each service provider regis-ters itself in the Service Registry and announces authorizedconsumers to the Authorization System. Consumers lookup aservice through the Orchestrator and interact directly with eachother using a REST API. Measurements and control decisionsare published as events to the Event Handler and distributedto subscribers. Communication between services by events areindicated as dotted lines in Figure 2.

1) Sensor Node Firmware: Sensor nodes are programmedusing generic firmware that allows BLE communication withupper layers. Further each service is extended in a sensor

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Raspberry Pi

Docker EnginePython

BLEService

Flask

MariaDB

BLE Driver

Java

Gateway

Java

Gatekeeper

Java

Orchestrator

Java

ServiceRegistry

Java

AuthorizationService

Java

EventhandlerPython

ControllerService

Flask

Python

ManagementInterface

Flask

Python

SensorService

Flask

Python

ActuatorService

Flask

Server

Docker Engine

Python

ManagementService

Flask

Python

UserInterface

Flask MariaDB

Java

Gateway

Java

Gatekeeper

Java

Orchestrator

Java

ServiceRegistry

Java

AuthorizationService

RabbitMQ

Controller CloudManagement Cloud

AMQP

AMQP

HTTP

HTTP

MariaDB

HTTP

Fig. 2. Software Stack

collection functions. Each sensor node has a unique BLEaddress. Only single sensor can be connected to the controllerat the same time. The sensor nodes are programmed usingArduino IDE [24].

2) Controller Cloud: The controller cloud is local cloudlocated in fog. It designed for low level control of the indoorvertical farming system, with a direct connection to the sensornodes and actuator control board. It hosts Arrowhead coresystem and set of application services: Controller Service,Management Interface, Sensor Service, Actuator Service andBLE Communication Service. The Controller Cloud is shownon the left side of Figure 2.

Management Interface is the only service that communicateswith Management Cloud. It serves as an application gatewaybetween cloud and fog layer of operation. It communicateswith Management Cloud via Arrowhead Gateways Service.

Sensor Service is a communication and aggregation servicefor sensor data. The Sensor Service periodically requests rawsensor data from the BLE Service, which interacts directlywith the operating system’s BLE driver. The BLE service andthe sensor nodes communicate using the GATT protocol [25].The Raspberry Pi acts as a GATT client, the sensor boards asa GATT server. The raw measurement packets are returned tothe Sensor Service and the measurements are extracted. Theyare then annotated with their type and published to the EventHandler. The different types of events are shown in Table I.

Controller Service receives schedules from the ManagementInterface and subscribes to waterlevel events. It period-ically compares the current targets to an estimated state ofthe indoor vertical farming system and sends the appropriatecommands to the Actuator Service to reach the target state.These commands are also published as events for logging.

Actuator Service receives commands from the ControllerService, it communicates with the Relay board over TCP/IPto control valves, LEDs and the pump. It logs all eventsin the controller cloud and sends them to the management

TABLE IEVENT TYPES

Event Description

temperature air temperaturehumidity air humidity

CO2 CO2 concentration in the airTVOC total volatile organic compounds in the air

air_pressure atmospheric air pressuresoil_temperature temperature in the flower box

waterlevel water level in the flower box

cloud either on request or periodically. It also receives newschedules from the management cloud and forwards them tothe Controller Service.

3) Management Cloud: Management Cloud is located ona remote server. It hosts Arrowhead core system, and twoapplication services: Management Service and User InterfaceService. The software stack for Management Cloud is depictedon the right side of Figure 2.

Management Service is the main component of the Manage-ment Cloud. It is only service capable of interacting with otherArrowhead compliant services. It serves as a gateway betweenuser interface service and the rest of the system. It is accessedby the UI application when new schedules are pushed to thecontroller cloud. Its public Arrowhead interface is discoveredby the Management Interface through inter-cloud orchestrationand used to push event logs to the management cloud, wherethey are written to a database and read by the UI application.

The UI service hosts a website for entering schedules andviewing basic stack-based data. Figure 3 depicts the UI for onelevel and exemplary data. On top of the screen, the differentenvironmental sensor values are shown. On the bottom left, thesensor values related to the corresponding level are shown. Aschedule is defined as a list of mappings from timestamps towater level and led state, i.e.: ti → (wi, li). This is drawn on

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Fig. 3. User Interface with exemplary values for one level

the bottom right of Figure 3.4) Integration and Deployment: All services are executed

in a Docker environment, where each service runs in a separatecontainer [26]. The containers are connected using Docker’soverlay network feature, which enables the distribution ofcontainers across multiple devices without any change to theinternal configuration of the services. All Docker images areprebuilt and hosted in a private Docker Registry.

Arrowhead core services use OpenJDK 8 [27] preinstalledon Alpine Linux [28] as base and contain solely their des-ignated Java archives. The user services are based on aDebian [29] Python 2.7 image, where the needed Pythonpackages are loaded at startup. Debian was chosen over AlpineLinux, as it is uses musl-libc library and most Python packageswithin the Python Package Index are dynamically linked toglibc, which is part of Debian.

The application services are implemented in Python [30]with the Flask [31] framework. For this project we neededto implemented Arrowhead consumer and provider clients inPython, to ensure compatibility with the rest of Arrowheadservices.

We use Docker compose [32] to launch all services neededwithin one cloud depicted in Figure 2. Each local cloud hasits own Docker configuration file to ensure correct start-upsequence.

C. Discussion

The presented platform is a modular hydroponics verticalfarming system. The application is designed in three levels: a)first part resides in cloud and it is responsible for high level

Fig. 4. Experimental indoor vertical farming setup.

control and user interaction, b) second part is implemented infog layer with a purpose of controlling the physical productionprocess, c) intermediate control and data acquisition is per-formed through sensor nodes and relay actuator control unit.The software stack for the platform is implemented using thedistributed service oriented framework Arrowhead that ensuresthe ability to extend or adapt the system as required. It pro-vides secure infrastructure for individual services on multipledevices to communicate and exchange data. Modular approachallows us to operate multiple pots, floors or complete platformsat the same time, with the ability to host different plants anddifferent growing schedules. The safety measures ensure thesystem to operate in isolation in case of network interference,or to reduce risk of water damages in case of system failure.The software stack is based on software container deployment.This allows us to deploy components on various platformsindependent of the software and hardware infrastructure. Incombination with Arrowhead we can add or remove servicesduring run time without affecting basic control system. The

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system is designed to be used in industrial and householdenvironments. Maximum daily energy consumption is around2.4 kWh with all components active and injecting water intothe system. This can be optimized as not all components areactive continuously and different plants have different wateringschedules.

V. FUTURE WORK

The main idea behind this work is to achieve modular plug-and-play platform for private or small business indoor farm-ing systems. The prototype shows room for improvement interms of reliability, usability, energy consumption and sensormeasurements. To increase automation of the system we willdevelop a service for automated growth tracking. Further, weplan to build a database of plants with corresponding growthplans, depending on the age, type of the plant. Finally weaim to explore dynamic plan building based on environmentalfactors and plant type.

VI. CONCLUSION

In this paper we proposed a modular indoor vertical farm-ing system based on infrastructure provided by CPS/IoTEcosystem and Arrowhead IoT framework. Vertical farmingis sustainable technology of the future. It reduces use ofharmful chemicals and unnecessary water consumption. Itensures healthy and fresh food independent of climate orenvironmental factors. With help of IoT these systems canbe integrated in a Production-as-a-service (PaaS) ecosystemto optimize food production and reduce waist and energyconsumption. Software and hardware infrastructure for IoT isare essential components to achieve this goal.

ACKNOWLEDGMENT

This work has been conducted within a project that hasreceived funding from the Austrian Government throughthe Federal Ministry Of Education, Science And Re-search (BMWFW) in the funding program Hochschulraum-Strukturmittel 2016 (HRSM).

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