Effective & Efﬁcient Access Control in Smart Farms: Opportunities,

Challenges & Potential Approaches

Ghadeer I. Yassin and Lakshmish Ramaswamy

Department of Computer Science, University of Georgia, Athens, GA 30602, U.S.A.

Keywords:

Access Controls, Smart Farming, Precision Agriculture, IoT.

Abstract:

The Internet of Things technologies has revolutionized the sector of farming and agriculture. It also helped

it to face the current environmental and societal challenges. IoT technologies are able to assist the farming

sector in many different applications including reducing wasted resources, real time monitoring of crops,

monitoring environment conditions, precision agriculture, farm data analytics and improving crops quality,

while decreasing the number of workers needed to complete farm related tasks. However, the nature of the

Smart farms are diverse in terms of the number, type and location of the installed smart devices, the variety

of the collected data, the number and type of workers who help in the farm and have access to farm related

data and equipment. At the same time, Farmers are very protective of their own data and sometimes refrain

from incorporating smart farming technologies to insure the safety of their data. Therefore, in this paper we

outline the security challenges in smart framing settings and explain the need for multi-user, multi- device

aware access controls in smart farms. We highlight different possible security scenarios that challenge the

adoption of IoT solutions in smart farms and discuss possible solutions.

1 INTRODUCTION

The world population is expected to increase to 11.2

billion by the end of the century according to the

United Nations (Roser, 2013). With the rapid growth

in world population, the food consumption worldwide

grows rapidly as well, it is estimated that food pro-

duction must increase by 70 % to feed this popula-

tion (Bruinsma et al., 2009). As a consequence farm-

ers are facing a growing demand to produce more

food regardless of the struggle they endure because

of the climate change, the scarcity of water, the ex-

treme weather conditions and more (aer, 2021). In

order to increase the yields of their farms to meet that

demand, farmers are turning into smart farming.

“Smart farming” combines traditional agricultural

methodologies with actuators, sensors, Internet of

Things (IoT), Information and communication tech-

nology (ICT) and drones to provide the farmers with

precise and accurate information that help in optimiz-

ing farming tasks with less labor force, increasing

crop yields, improving crops’ quality, decreasing the

waste in resources such as water, fertilizers and herbi-

cides while increasing farm proﬁtability. Smart farm-

ing follows a cycle that includes observing data from

the surrounding environment then ensuring it follows

a set of predeﬁned rules, identiﬁes any deﬁciencies

and informs the users about them to take the appro-

priate action if needed.

Smart farms are considered a multi-device envi-

ronment in the sense that multiple smart devices can

be found scattered across the farm such as Drones,

Animal’s wearable devices, Soil moisture sensors and

more. It is also considered a multi-users environment

with a signiﬁcant number of human workers cooper-

ating to monitor the environment data and make in-

formed decisions. In addition to that, There is hetero-

geneous data related to farms originating from multi-

ple sources such as sensor data, satellite imagery and

farm historical data. Each data type has a validity pe-

riod and a unique value associated to it based on the

information derived from this data and how this in-

formation can be used. This derived information can

have great impact on the livestock health and wellbe-

ing, the farm value and the owners themselves. There-

fore, effective access control mechanisms are needed

in smart farms to ensure the security of its data con-

sidering its diverse nature.

In this paper, we highlight the importance of effec-

tive access control mechanisms in smart farms. We

compare and contrast smart farm nature with other

smart domains as well as the security challenges that

Yassin, G. and Ramaswamy, L.

Effective Efﬁcient Access Control in Smart Farms: Opportunities, Challenges Potential Approaches.

DOI: 10.5220/0010873000003120

In Proceedings of the 8th International Conference on Information Systems Security and Privacy (ICISSP 2022), pages 445-452

ISBN: 978-989-758-553-1; ISSN: 2184-4356

445

face smart farms. We discuss which access con-

trol mechanisms could be suitable for the smart farm

system and discuss some potential solutions and ap-

proaches.

2 BACKGROUND INFORMATION

2.1 Smart Farm

A Smart Farm in its simplest form is a combination of

computing devices, machine learning (ML), artiﬁcial

intelligence (AI) and humans integrated into an infor-

mation driven system that is capable, knowledgeable

and a real time-decision maker, therefore,” a Smart

System”. Smart farm’s sensors and equipment have

a predeﬁned function related to the farm that can be

as simple as collecting temperature data or as com-

plicated as harvesting ripened crops and leaving un-

ripened ones behind.

Figure 1 Shows the different entities in smart farms

and the interaction that takes place among them. Dif-

ferent sensors collect huge amounts of farm-related

data and are connected to gateway devices that per-

form preprocessing steps such as ﬁltering and analyz-

ing the collected data and transfer only the necessary

data to the cloud services where it can be further pro-

cessed and stored then presented to the user through

suitable interfaces.

2.2 Smart Systems

IoT technology and its applications are currently used

in multiple smart systems such as smart homes that

contain multiple IoT devices connected together and

to the internet through a communication protocol and

are controlled, accessed and monitored remotely us-

ing a mobile application or voice commands issued to

a smart assistant. In addition, smart health care refers

to completely autonomous and connected healthcare

solutions provided for patients along with feedback

from doctors. Smart Cities, on the other hand, refers

to any city that utilizes modern technologies to con-

vert conventional city’s entities into an autonomous

entity and consists of a collection of smart services

such as city lighting, emergency services, trafﬁc man-

agement, water management etc.

2.3 Data Sources & Analytics

Smart farms are majorly driven by data and its ana-

lytics. Sensors detect changes and collect data about

their environment. While actuators introduce change

Figure 1: Different Smart Farm Entities’ Interaction.

to its environment based on the sensor’s collected

data. This is a real time process that is mostly auto-

mated. There are major domains in smart farms that

use the heterogeneous data collected by sensors and

apply them in different applications that are capable

of providing the farmers with the most accurate an-

alytics to help them improve farm productivity and

decrease human interventions as shown in Figure 2.

Precision Farming: Achieved through multiple

monitoring and controlling applications including

continuous weather monitoring, Crop’s health mon-

itoring and Soil analysis which is one of the most im-

portant applications of precision farming that helps to

easily decide the best type of crops to plant in the soil,

the best timing to start sowing etc.

Precision Livestock: Achieved by providing live-

stock with different monitoring sensors and veterinary

wearable devices to continuously detect, analyze and

transmit data related to the animal’s health and loca-

tion.

Smart Greenhouse: Enables planting different types

of plants anytime of the year regardless of the weather

conditions by precisely monitoring and controlling its

climate.

2.4 Access Control Policies

Access control refers to processing every request

to the system’s data and resources, then deciding

whether it should be denied or granted. Access con-

trols identify users through verifying their login cre-

dentials and then grant them the appropriate access

level that is associated with their authenticated cre-

dentials.

There are different access control policies that can

be used in smart systems based on its requirements

including the “RBAC” model that manages the access

to the different resources based on hierarchical rights

and permissions assigned to various roles. “ABAC”

model, on the other hand, shifts from the list of roles

to a list of attributes and contextual factors by eval-

uating requests against the requester’s attributes, the

required action, and the context. “MAC” model lim-

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446

Figure 2: Main Smart Farms’ Domains, Applications, Data & Data sources.

its access to system resources based on its sensitivity

and the user’s authorization to access a resource with

that speciﬁed sensitivity. “TBAC” enables placing re-

strictions on the access to resources and its data on the

basis of particular time of the day or particular days

of a week. Finally, “LBAC” restricts the access to re-

sources based on the location of the user so that the

users can access the resource only if they are physi-

cally present in the predeﬁned location.

3 MOTIVATION

Many farming systems use insufﬁcient access con-

trols and new smart farming related projects do not

consider using them. (De Araujo Zanella et al., 2020).

On the other hand, smart farms have a unique nature

that requires specialized access control solutions.

3.1 Comparing Smart Farms with

Other Smart Systems

There is a wide range of differences and similari-

ties between Smart farms and other Smart systems

as shown in Table 1. These differences indicate

that security-related countermeasures applied to other

Smart systems are not suitable for Smart Farms.

Stakeholders & Security Awareness: In smart

farms, The stakeholders range is very wide and the

system is very dynamic as the owners usually hire

different workers based on the farming season or the

scalability of their farm. The stakeholders have dif-

ferent levels of security awareness. Owners are more

aware about security since their main goal is to pro-

tect their data as it directly affects their proﬁtability

if it were to be stolen or leaked to a rival. However,

other stakeholders usually have much less awareness

about security and might not be concerned about it

since it does not directly affect their proﬁt.

Similar to Smart Farms, The stakeholders range in

Smart Cities and Smart Healthcare is wide. How-

ever, they are usually provided with technical security

training on how to protect their data. Those Systems

are less dynamic than smart farms as the stakeholders

are hired by the government and their change is much

less frequent when compared to smart farms.

Data Security: Smart Farming security systems are

in their infancy. Very little efforts are being taken to

ensure the security of farm data. In addition, farms are

mostly self-ﬁnanced by their owners who do not have

enough ﬁnance to implement enough security mea-

sures. Smart Homes are also self-ﬁnanced systems

but their security measures might not be as costly as

Smart Farms and therefore its owner can easily sup-

port it. Other systems such as smart healthcare and

smart cities are supported by many organizations and

a lot of investment is being offered to them.

3.2 Data Validity

Farm data originates from various sources such as

sensor’s data, satellite data or historical data. Each

type of the data has its own validity period; when-

ever passed, the data is deemed useless. The validity

periods range from long validity such as the yearly

planted crops in the ﬁeld to seasonal validity such

as season’s weather data to short term validity such

as soil moisture data that are acted upon immedi-

ately. Therefore, the data should be stored and pro-

tected during its validity period and erased otherwise.

For example, whenever a livestock animal is sold or

sent to a slaughterhouse, its data becomes unneces-

sary to store and therefore should be deleted immedi-

Effective Efﬁcient Access Control in Smart Farms: Opportunities, Challenges Potential Approaches

447

ately while soil data should be deleted once it is ana-

lyzed and provided to the farmer etc.

3.3 Farm Ownership

Farms can be owned, rented and operated by differ-

ent types of owners (Bigelow et al., 2016), (Bigelow,

2019) such as families, non-operator landlords, oper-

ator landlords, cooperatives etc.

Family Farms: In family farms, the decision mak-

ers are typically the owners of the equity and the as-

sets who usually assign predeﬁned tasks to the other

family members. However, a lot of conﬂicts happen

between the family members. Therefore, the family

holds a broad meeting to discuss members’ roles and

responsibilities and negotiate their demands. There-

fore, it would be beneﬁcial to propose access control

mechanisms that support the negotiations of roles and

demands and ensure they are enforced.

Rental Farms: The operator landlords of rented

farms usually have conﬂicts with the tenants due to

their involvement in farm decisions as both of them

combine their own resources to produce the required

farm commodities. Typically, they negotiate their

terms and create a farm lease contract describing their

agreement that covers the amount of fertilizers that

will be used, the time when a speciﬁc equipment will

be used, the time when the ﬁeld will be harvested etc.

However, the conﬂicts happen due to the unawareness

of the landlord about certain circumstances that affect

the execution of the agreement. Therefore, granting

access of the farm data to landlords based on a prede-

ﬁned role and agreement will keep them more aware

of circumstances and thus eliminate such conﬂicts.

Partnership Farms: Partners and cooperatives have

conﬂicts over the share of each partner; some farm

assets such as livestock or crops might belong to

one partner only. Those assets and their related data

shouldn’t be accessible by the other partners as they

are out of the scope of the partnership. Therefore, ac-

cess controls should be ﬂexible enough to suit assets’

data accessibility based on such cases.

3.4 Participants’ Proﬁles

Figure 3 assumes the different in-farm and outside-

farm access attempts to different smart devices and

sensors. The ﬁgure shows 8 different individuals in-

teracting with the smart farm based on their assigned

tasks. Both workers A and B are responsible for ﬁeld

sowing and irrigation. Therefore, they have access to

plant data and soil data. Worker C is the only worker

permitted to monitor the autonomous tractor. There-

fore, its accessibility is granted only to him and he is

restricted from accessing any other equipment or data

in the farm system. On the other hand, Worker D is

granted accessibility to the drone when he is present

inside the ﬁeld and is denied accessibility otherwise.

Other individuals such as the veterinarian and worker

G are involved in occasional animal health monitoring

and therefore do not need continuous access to the an-

imal data.

Temporary Labor: It is common to hire temporary

labor at the time of harvesting or sowing. The main

problem that faces the farm owner with temporary la-

bor are the trust issues. Those workers are mostly

coming to work in the farm for the ﬁrst time and are

not trusted as other permanent workers and they might

abuse farm related data by uncovering them to a ri-

val or by disrupting the system by modifying sensor’s

parameters causing it to perform unnecessary actions

that leads to proﬁt losses. The accessibility granted to

temporary workers should be limited and end by the

time their hiring period ends to ensure they will not

be able to abuse the system in any form once they are

no longer hired.

Labor Working on Multiple Farms: Some workers

work in two or more different farms simultaneously.

Through those workers, a farm rivalry or a competi-

tor might gain access or insight to the farm data and

could use it to develop competing crops to increase

their proﬁtability. The data about the owners’ farm

practices can also be used against them for regula-

tory enforcement purposes. Therefore, farm owners

should decide which and when their data is accessed

by workers. For example, owners may choose to auto-

matically notify the worker to turn on/off an irrigation

system without receiving soil moisture data from soil

sensors. They also may want to limit workers from

accessing data outside the daily working hours or to

prevent them from receiving full farm’s history data.

Other Participants: The range of farm participants

is not limited only to farm workers and owners but it

also includes government ofﬁcials, technicians, aca-

demic researchers, veterinarians etc. Therefore, The

farm owners should be able to control accessibility to

their own resources based on what they believe is pro-

tecting its security.

3.5 Livestock

Security of livestock’s data is often neglected as it is

not classiﬁed as personal data and hence very little at-

tention is paid to its security. However, Livestock data

is a very precious part of farm data.

Livestock Location Data: Livestock location data is

continuously collected by GPS-powered wearable de-

vices and drones. This data is used to track the ani-

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448

Table 1: Differences and Similarities between Different Smart Systems.

Smart Farms Smart Cities Smart Healthcare Smart Homes

Stakeholders’ Range Wide Wide Wide Small

Security Awareness Lacked High High Medium

System Dynamism High Low Low Non

Devices Power Low High High High

Device location Outdoor Hybrid Indoor Indoor

Maintenance Frequency Low High High Low

Security Measures Weak Strong Strong Medium

Connectivity Unstable Stable Stable Stable

Financed By Self Organizations Organizations Self

mals when it is left to feed in open areas. However,

If adversaries or some untrustworthy workers were in-

correctly permitted access to only one animal location

data, they can plan to steal the animal and compro-

mise the safety of the whole herd.

Livestock Health Data: It is the most crucial part of

the livestock collected data and greatly inﬂuences its

market prices. Therefore, purchasers can be granted

access to the animal’s data in order to make an in-

formed decision. General live-stock’s data such as

previous health conditions might be used to negoti-

ate the offered prices with the owner. In addition, ac-

cessibility to the full herd data can lead to inferring

information about herd size and value. Therefore, the

accessibility should be very limited and only tempo-

rary during the purchasing process.

Veterinarians & Assistants: Some veterinarians

work with different farms in the same area and can

easily anticipate health issues breaking out in that area

through monitoring and combining livestock health

data from different farms. They can take advantage of

such knowledge to propagate exaggerated news about

infectious spread and inﬂuence the livestock market

price and consequently make proﬁt themselves by in-

vesting in livestock. Beside that, the assistants and

nurses working in the veterinarian’s ofﬁce access live-

stock health data to prepare health summaries or to as-

sign periodic timing for vaccination. In this case, they

should be granted a lower level of accessibility to the

data or to be granted accessibility only upon request.

Critical Data: When treating livestock with med-

ications, it is very important to use the appropri-

ate dosage which is generally determined in rela-

tion to the animal’s weight. Overdosing can cause

death while underdosing may lead to drug resistance.

Therefore, such data should be secured to prevent ad-

versaries from harming the livestock .

Livestock Environment: The environment where the

animals are kept is monitored with different types of

sensors to prevent animal sickness and death. If a ri-

valry gained accessibility to these sensors, they could

Figure 3: Farm Operations & Accessibility.

make adverse conditions inside the barns to affect the

yield of the animals or make them sick.

3.6 Smart Equipment

Smart farming depends heavily on a large number of

smart equipment with various security related aspects

that need controlled access control policies.

Leasing, Lending & Borrowing: Smart farm equip-

ment are very expensive. Therefore, farmers turn to

leasing them. However, leased equipment can col-

lect sensitive crop yield data, fertilization’s applica-

tion rates, ﬁeld conditions etc. This data can infer

the ﬁnancial position of the farm and also affect the

selling prices of crops. Therefore, leased equipment

should be subject to access control negotiations be-

tween the farmer and the leasing company. Some

farmers also tend to lend and borrow equipment from

neighboring farms. Therefore, both farm’s data can

be combined to infer information about crop health

in the area and animal disease outbreaks. Therefore

there should be speciﬁc access control mechanisms to

ensure that the lender and the borrower of the equip-

ment do not obtain data about each other’s farms. This

can be achieved through policies that ensure data col-

lection is restricted by the location of the equipment

as well as the time of the use.

Equipment Repairs: Some companies such as John

Deere had made it illegal to repair their farm tractor

(Horton and Kirchmeier, 2020). And in case of the

Effective Efﬁcient Access Control in Smart Farms: Opportunities, Challenges Potential Approaches

449

tractor malfunction, the farmer will be forced to re-

pair the tractor at the company which will possibly

need access to the tractor sensors and data to identify

the source of the malfunction. In this case, the farmer

should be dynamically notiﬁed of any attempted ac-

cess to his equipment’s data through access control

requests and to have power to accept or deny such re-

quests.

Specialized Equipment: Some equipment such as

drones can collect a huge amount of data about the

food production process, location of critical infras-

tructure, footage of workers and livestock etc. Those

drones might be incorporated from other traditional

IoT sectors such as the military sector. This sector’s

drones are capable of monitoring and identifying their

users and tracking their performance and can compro-

mise the security of the whole smart farm system.

Critical Data: The pH levels are very critical to the

health of the crops. only speciﬁc levels allow the nu-

trients to be more available and effectively absorbed

by the plants. Any tamper of these levels can lead to

deﬁciencies in plant nutrients or plant toxitization if

it was not corrected by farmers. It can be corrected

by adjusting the ammonium supply levels. If adver-

saries get insight to this critical type of data, they can

plan a physical attack that aims to temper the pH level

with required levels of ammonium that ensure that the

crops will be damaged.

4 RESEARCH QUESTIONS

There are multiple access control policies that can be

applied to smart settings in general as discussed in

section 2.4. However, Identifying which of these poli-

cies are relevant to smart farms and can be practically

applied to it might be tricky. Therefore, we discuss

some of the questions that are critical for determining

the best access control mechanism that suits farms.

1. Are the Policies based on Devices or Capabili-

ties?

Current access control policies implementations in

other smart systems are mostly device-based. That

means that the access is either granted or denied per

the device itself. However, this implementation is not

ﬂexible enough to capture the access control desires

that the farmers may think of to protect their data.

Therefore, the policies shouldn’t be simpliﬁed for de-

vice control only but rather it should be expanded to

involve its capability as well. This is driven by our

observation that the majority of smart farm devices

combine multiple capabilities with different sensitiv-

ities in a single device. For example, Drones are ca-

pable of tracking livestock and spraying ﬁelds. The

sensitivity of livestock tracking might be higher than

spraying ﬁelds from the farmers point of view. While

other capabilities such as erasing sensor’s data is very

critical and might not be granted to any of the stake-

holders. Therefore, a capability-centric model should

be designed where the capabilities that can be per-

formed over each smart device can be deﬁned.

2. Which Contextual Factors Affect the Policies?

Some contextual factors might be essential to con-

sider with access control policies such as:

Location of the Devices: Ensures that certain de-

vices’ capabilities are used with devices in certain lo-

cations.

Location of the Stakeholders: Ensure that stake-

holders are restricted from using certain capabilities

unless they are connected to the farm network.

Time of Use: Is important especially for tasks that

should be timed in advance.

Presence of Others: Is important for some capabili-

ties that require supervision from others.

3. Should Policies Vary based on the Roles?

Since there are many roles assigned to different indi-

viduals inside the farm, those roles must play an im-

portant factor when deciding the suitable access con-

trol policies. For instance, the owner’s role differs

from a regular worker’s role and therefore the individ-

ual with the owner role should be able to express ac-

cess controls over the individual with a regular worker

role. Also, a returning temporary worker might be

more trusted than a new temporary worker and there-

fore can be assigned a role with more ﬂexible access

controls in the farm system.

4. What happens when Access Controls Fail?

There are consequences for system malfunctions re-

lated to access controls including:

False Acceptance: This type of malfunction refers

to granting accessibility to some individual who was

originally intended to be denied it. The severity of

this malfunction depends on who was allowed incor-

rect access control. For example, a mild false accep-

tance might be caused by allowing co-owners access

to a capability they don’t need. A more severe case

would happen if a temporary worker was falsely al-

lowed access to a critical capability such as deleting

sensor’s data. The severest form would be granting

access to a 3rd party individual such as a farm rival.

False Rejection: This type of malfunction refers to

denying accessibility to some individual who was

originally intended to be granted it. The severity

of this type of malfunction might be less problem-

atic than the false acceptance malfunction. However,

it might disrupt essential work schedules and cause

losses of proﬁts.

Continuous Accessibility: This failure comes in the

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450

form of granting continuous accessibility to individ-

uals who were supposed to access the system for a

speciﬁc period of time. Therefore, a policy expiration

option should be provided to allow only temporary

access for data and devices for as long as needed and

to revoke that access automatically once that period

ends in order to avoid any undesired accessibility.

5. How to Design Default Policies?

The process of assigning policies can be simpliﬁed

through default policies such as:

Default Accept: That can be provided in a very cau-

tious manner to the most trusted roles over some pre-

speciﬁed capabilities. It can also be provided over mi-

nor capabilities or data that are considered risk-free.

Default Deny: Should be implemented over critical

capabilities such as erasing sensitive data and any ad-

ministrative capabilities such as adding and removing

users and devices to the system.

5 DISCUSSION & POTENTIAL

APPROACHES

Reﬂecting on the research questions, Different Access

control policies can be used in smart farm systems.

Role-Based Access Control ”RBAC”: This model

can be used to ensure that stakeholders have access

only to the resources they need to complete their jobs.

The accessibility can be restricted based on the indi-

vidual’s assigned role as roles can determine the per-

missions granted to each individual and ensure that

no individual can perform higher level operations on

farm resources. Hierarchy of the roles allows speciﬁc

roles to inherit permissions from other roles. This

will beneﬁt the higher level roles such as Owners-role

to share the responsibility of adding new users to the

system, assigning access policies for them and adding

or removing smart devices from the system etc.

The different roles that can be implemented in Smart

farm system includes: Owner, veterinarian, veteri-

narian assistant, temporary ﬁeld worker, regular ﬁeld

worker, purchaser etc. As hierarchy suggests, the

role of temporary ﬁeld worker can inherit some ac-

cess controls and permissions from the regular ﬁeld

worker role etc.

However, Hierarchical roles might be a major cause

of conﬂicts between stakeholders when they have

conﬂicting access control demands over system re-

sources. Therefore, resolving this kind of conﬂict

should be achieved automatically. The smart system

should be aware and able to identify conﬂicts and re-

solve them based on the priority of each role in the

hierarchical level.

As per the contextual factors that affect access control

policies both LBAC and TBAC models can capture

the majority of them.

Location-Based Access Control ”LBAC”: This

model can be used to capture the stakeholders’ loca-

tion and determine their accessibility rights based on

that location. For example, a drone operator can only

operate it when he is connected to the farm network.

Time-Based Access Control ”TBAC”: This model

can be used to ensure that different system resources

can be accessed only based on predeﬁned times. For

example, a livestock purchasers’ access controls to

livestock data can be timed in advance to end by the

time they make their decision or in another case a

regular worker shouldn’t access crops data after the

working hours.

Attribute-Based Access Control ”ABAC”: This

model can place more limits on the access based on

User’s attributes, Environment’s attributes and Re-

source’s attributes. For Instance, it can be used hi-

erarchically with ”RBAC” which can be used ﬁrst to

determine who has access to a resource then ”ABAC”

determine what they do with the resource based on

different attributes.

Figure 4: Creation and Enforcement of Access Control

Policies.

As different devices have many capabilities, the

capabilities must be incorporated in the process of as-

signing access control policies. Capabilities reﬂect

more ﬁne-grained access control policies than the per-

device access controls.

Based on our observation of the smart farm nature,

we argue that the smart farm access control system

should be multi-user, multi-device aware where farm-

ers can express their system requirements through

policies. The system must contain main components

as follows:

1. Friendly User Interface: The system should have

a uniﬁed friendly UI for adding/removing users and

devices and assigning policies to them.

2. Expressive Policy Language: Whenever the se-

Effective Efﬁcient Access Control in Smart Farms: Opportunities, Challenges Potential Approaches

451

curity level becomes more complicated, the usabil-

ity usually drops. However, any complicated process

might discourage farmers from using such systems,

especially that the security awareness in farms is not

high. Therefore, The system should have simple ex-

pressive policy language such as roles, time, loca-

tion, device, capability etc. In addition, It could be

beneﬁcial to use policy automation to ﬁll the gap be-

tween farmer intuitive policies and the matching de-

tailed technical conﬁgurations and rules.

3. Resolving Conﬂicts: The system should be able to

identify and resolve conﬂicts of access demands.

4. Access Policies: Policies should at least consist

of the triplet: <Individual’s Role, Device Capability,

Contextual Factors>.

5. Policy Creation, Enforcement & Execution: All

speciﬁed access control policies should be enforced

on the system. And access control requests should be

evaluated against the enforced policies.

Smart devices are usually managed through a hub

device. The hub device is used to facilitate the com-

munication between devices and the cloud. Once the

farmer speciﬁes the required access controls, it should

be sent through the hub device to the server on the

cloud. That server then should generate the required

policies and ensure it is enforced over all system re-

sources. Whenever an access control to a system re-

source is requested, it should be automatically for-

warded to the server. The server should check the

request against the created policies. If the request is

valid it should be accepted on the speciﬁed resource,

otherwise it should be denied as shown in Figure 4.

6 RELATED WORK

Many researches have started looking into the secu-

rity and privacy issues in different IoT domains such

as (Fan et al., 2019) who designed access controls

considering fog computing for providing data conﬁ-

dentiality, variability and attribute based encryption.

However, there have been a limited work explor-

ing speciﬁcally the security in smart farming and pre-

cision agriculture despite that the U.S. Department

of Homeland Security issued a report (Aida Boghos-

sian, 2018) identifying the different threats to preci-

sion agriculture and emphasizing the need for more

research regarding this area. Most researches were

focused on blockchain solutions such as (Kamilaris

et al., 2019) who studied the challenges and implica-

tions of using blockchain technology projects in the

agriculture sector and (Ferrag et al., 2020) who pro-

vided the consensus algorithms for the solutions that

are based on blockchain and how can they be adapted

for smart farming. We omit further blockchain work

as its not related to our main focus.

7 PAPER SUMMARY

Smart farming is an emerging sector of IoT applica-

tions. It faces many challenges ranging from adoption

of its technologies to the security issues that stems

from applying it inside the farms. Therefor, an exten-

sive research is required in this area. In this paper we

explored different security scenarios that stems form

the diverse nature of smart farms. We identiﬁed the

structure of smart farms and the different stakeholders

who are involved in the system. We explored relevant

access control policies that can be particularly applied

to smart farms.

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