Computers and Electronics in Agriculture 178 (2020) 105476
Contents lists available at ScienceDirect
Computers and Electronics in Agriculture
journal homepage: www.elsevier.com/locate/compag
Review
Integrating blockchain and the internet of things in precision agriculture:
Analysis, opportunities, and challenges
T
Mohamed Torkya,c, Aboul Ella Hassaneinb,c
a
Faculty of Computer and Information Systems-Islamic University in Madinah, KSA Higher Institute of Computer Science and Information Systems-Culture & Science City
Academy, Egypt
b
Faculty of Computers and Artificial Intelligence-Cairo University, Egypt
c
Scientific Research Group in Egypt (SRGE), Egypt
ARTICLE INFO
ABSTRACT
Keywords:
Precision agriculture
Blockchain technology
Internet of things
Challenges and solutions
Blockchain quickly became an important technology in many applications of precision agriculture discipline.
The need to develop smart P2P systems capable of verifying, securing, monitoring, and analyzing agricultural
data is leading to thinking about building blockchain-based IoT systems in precision agriculture. Blockchain
plays the role of pivotal in replacing the classical methods of storing, sorting and sharing agricultural data into a
more reliable, immutable, transparent and decentralized manner. In precision farming, the combination of the
Internet of Things and the blockchain will move us from only smart farms only to the internet of smart farms and
add more control in supply-chains networks. The result of this combination will lead to more autonomy and
intelligence in managing precision agriculture in more efficient and optimized ways. This paper exhibits a
comprehensive survey on the importance of integrating both blockchain and IoT in developing smart applica­
tions in precision agriculture. The paper also proposed novel blockchain models that can be used as important
solutions for major challenges in IoT-based precision agricultural systems. In addition, the study reviewed and
clearly discussed the main functions and strengths of the common blockchain platforms used in managing
various sub-sectors in precision agriculture such as crops, livestock grazing, and food supply chain. Finally, the
paper discussed some of the security and privacy challenges, and blockchain-open issues that obstacles devel­
oping blockchain-IoT systems in precision agriculture.
1. Introduction
There is a growing body of literature that recognizes the importance
of utilizing emerging technologies in precision agriculture (Zhang et al.,
2002; McBratney et al., 2005; Nikkil et al., 2010). Precision agriculture
is a new technology that utilizes Information Technology (IT), satellite
technology, Geographical Information System (GIS), and remote sen­
sing for enhancing all functions and services of the agriculture sector
(Khanal et al., 2017). Today, precision agriculture started to rely upon
Mobile apps (Jagyasi et al., 2013), smart sensors (Sartori and Brunelli,
2016), drones (Puri et al., 2017), cloud computing (Mekala and
Viswanathan, 2017), Artificial Intelligence (AI) (Jha et al., 2019), in­
ternet of Things (IoT)Ahmed et al., 2018), and blockchain (Ge et al.,
2017). Based on these technologies, it is become possible to process and
access real-time data about the conditions of the soil, crops, and
weather along with other relevant services such as crops and fruits
supply chain, food safety, and animal grazing.
Many statistical reports announced that precision agriculture will
add more improvement to the global economy based on the use of
advanced technology in all agriculture subsectors. According to market
research and advisory firm, the global market of precision agriculture
will grow at an average rate of 13.7 percent to reach 10.55 billion U.S.
dollar by 2025 (Xinhuanet, 2020). In addition, the global precision
farming market is evaluated to grow from USD 7.0 billion in 2020 to
USD 12.8 billion by 2025, at a Compound Annual Growth Rate (CAGR)
of 12.7% (Markets and Markets, 2020).
In precision agriculture, forecasting and predictive analytic software
systems can use agricultural data to provide farmers with guidance
about soil management, crop maturity rotation, optimal planting times,
and harvesting times, etc. For example, machine learning technology
can be integrated with remote sensing for accurate forecasting of crop
production and nitrogen levels estimation in precision agriculture
(Chlingaryan et al., 2018; Lee et al., 2010). In addition, historical crop
planting maps can be used for developing a machine learning system for
predicting annual crop planting (Zhang et al., 2019; Elavarasan et al.,
2018). Predicting crop growth in the smart greenhouse using a selflearning model and IoT data is another contribution of machine
learning in precision agriculture (Kocian et al., 2020). AgriProduction
(Dos Santos et al., 2019) is another system that able to anticipate
agricultural problems related to soil humidity, temperature, and leaf
growth based on both LoRa IoT technology and the ARIMA prediction
model.
https://doi.org/10.1016/j.compag.2020.105476
Received 25 November 2019; Received in revised form 29 April 2020; Accepted 1 May 2020
Available online 08 September 2020
0168-1699/ © 2020 Elsevier B.V. All rights reserved.
Computers and Electronics in Agriculture 178 (2020) 105476
M. Torky and A.E. Hassanein
Sensor technology and IoT can also mitigate various challenges in
precision agriculture (Tzounis et al., 2017). Agricultural monitoring
systems can provide surveillance services that maintain the plant
growing at an optimal level and early anticipate the conditions that lead
to epidemic plant disease outbreak based on wireless sensor networks
installed in the planted area (Khattab et al., 2019; Ibrahim et al., 2019).
Smart irrigation systems based on IoT and sensor technology is another
solution for the shortage of clean water resources that are necessary for
a lot of plants kinds as well as achieving optimum water-resource uti­
lization in the precision agriculture (Huong et al., 2018). Building
flexible and automated platforms able to cope with soilless needs in full
smart greenhouses using moderately saline water is an important issue
in precision agriculture that depends on combining IoT with cloud and
edge computing for mitigating this challenge.
Deep learning technology represents a recent technology in preci­
sion agriculture (Kamilaris and Prenafeta-Bold, 2018). This new tech­
nology can help in designing automated and reliable fruit detection
systems for fruit yield estimation and automated harvesting through
applying neural network models on imagery data obtained from two
modalities: color (RGB) and Near-Infrared (NIR)Sa et al., 2016) like
Mango fruit detection (Koirala et al., 2019), cotton detection and seg­
mentation (Li et al., 2017), and apple detection and segmentation (Kang
and Chen, 2019). Utilizing deep learning for visual detection and re­
cognition of weeds in grasslands is an additional contribution of deep
learning in terms of weed control in precision agriculture discipline
(Kounalakis et al., 2018).
Decision Support Systems, data analysis, and data mining become a
significant technique for managing many services in precision agri­
culture (Zhai et al., 2020). Managing smart farms through web-based
decision support systems can help in complying with many require­
ments in precision agriculture such as crop production, optimizing
farming costs, and monitoring market dynamics in more efficiency
(Narra et al., 2020). In addition, many steps have been made to improve
irrigation decisions based on various irrigation decision models that can
help the farmers to carry out critical irrigation actions and optimize
irrigation depth (Car, 2018; El Baki et al., 2018). Moreover, water loses
reduction and improving water supply efficiency can be achieved
through automating irrigation canal operations, and manipulating both
known and unknown patterns of water demands that can be recognized
by different irrigation systems in the farms (Shahdany et al., 2019).
Agricultural robotics has brought also a significant development for
various applications in precision agriculture (Pedersen et al., 2006).
The objective of agricultural robotics is more than just the use of ro­
botics for specific functions in precision agriculture, but most of the
recent the automatic agricultural vehicles are multi-function, such that
it can be used for weed detection, agrochemical dispersal, terrain le­
veling, irrigation, field supervision, as well as tree fruit production
(Cheein and Carelli, 2013; Bergerman et al., 2015). Moreover, devel­
oping a smart drone system becomes an interesting and significant
technology in precision agriculture (Mogili and Deepak, 2018). Smart
drones able to solve many big challenges in precision agriculture such
as irrigation monitoring, weed identification, crop dusting, crop mon­
itoring, pesticide spraying as well as deterring fertility levels, identi­
fying bacteria, fungus or diseases based on Infra-red radiation com­
monly reflected from sensors or thermal imagery (Smith, 2020).
Recently, blockchain represents the last new technology that can be
used for mitigating significant challenges in precision agriculture,
especially when integrated with IoT technology (Tripoli and
Schmidhuber, 2018). According to a new market intelligence report by
BIS Research, employing blockchain technology in precision agricul­
ture,and food supply chain markets is anticipated to increase from
$41.9 M in 2018 to $1.4B by 2028 (BIS Research, 2018). Blockchain can
introduce a variety of benefits and support in several applications in
precision agriculture. For instance, Smart farming, supply chain mon­
itoring and tracking (Lin et al., 2018; Bordel et al., 2018; Tse et al.,
2017; Casado-Vara et al., 2018), finance management (Chinaka, 2016),
data assurance and security (Mann et al., 2018; Xie et al., 2017; Liang
et al., 2017). The Increased utilization of the public blockchain in food
markets has also motivated the governments to restructure their legis­
lative frameworks and regulations to consider blockchain in its eco­
nomic policies. Recently, the need for blockchain in precision agri­
culture is mandatory to bridge the demand and supply gap along with
attaining sustainability in the ecosystem.
Although some reviews studies in integrating blockchain with IoT
have been introduced (Dorri et al., 2017; Fernáez-Caramés and FragaLamas, 2018), these reviews didn’t go deep into investigating the
benefits and solutions that blockchain can introduce for developing new
applications in precision agriculture (Bermeo-Almeida et al., 2018). As
a response to this limitation in the literature, this paper is one of the
first mature studies that introduces a holistic approach and a systematic
review for investigating more contributions of blockchain technology in
precision agriculture. The rest of this paper is designed as follows:
Section 2 introduces an overview of blockchain technology. Section 3
discusses the major contributions of Blockchain in IoT applications.
Section 4 discusses the major uses cases of integrating blockchain with
IoT in precision agriculture. Section 5 discusses some challenges and
open problems that obstacle building blockchain-IoT networks in pre­
cision agriculture. Finally, Section 6 summarizes the general conclu­
sion.
2. Blockchain technology: an overview
The theory of Blockchain was invented by ”Satoshi Nakamoto” in
2008 to work as a public ledger of the bitcoin transactions (Nakamoto,
2008; Al-Jaroodi and Mohamed, 2019). The concept of blockchain can
be defined as a decentralized, distributed ledger for storing timestamped transactions between many computers in a peer to peer net­
work. So that any involved record cannot tamper retroactively. This
allows the blockchain users to audit and verify transactions in­
dependently and transparently. So, the blockchain can be designed as a
growing stack of records, called ”blocks”, which are connected using
cryptographic techniques. Each block must have a hash code of the
previous block, a timestamp, and a set of confirmed transactions (Zheng
et al., 2017). Simply, a blockchain is an invented way to structure data
in a decentralized manner, which structured as a book that contains an
infinite number of pages, each page (i.e. block) contains new transac­
tions in the blockchain. The blockchain ledger is controlled autono­
mously using a P2P network and a public time-stamping server
(Tapscott and Tapscott, 2016). The decentralized transparent design of
blockchain tracing and secure the transaction workflow. This feature
can solve the long-standing problem of double-spending (Chohan,
2017). In the blockchain, each block has a unique hash value that
identifies the identity of the block. The first block in the chain is named
a ”genesis block” which has no parent block as depicted in Fig. 1. The
general architecture of each block is explained in Fig. 2. Each block unit
consists of a block header and a block body. Especially, the block
header involves six components:
• Block version is a software version number indicates which con­
sensus protocol to follow.
• Markle Tree Root Hash is used to verify the hash code that iden­
•
•
•
•
2
tifies all block transactions.It recursively defined as a binary tree of
hash codes.
Timestamp is given in seconds since 1/1/1970. It is used to im­
mutably track the creation and update time of the block for block
integrity guarantee.
N-Bits identifies the target threshold of hash code that specifies the
valid block.
Nonce is an arbitrary number that can be used just once in a
cryptographic communication. It is a 4 byte field, which usually
begins with ’0s’ and grows for every hash computation.
Parent Block Hash is a 256 bit hash code that refers to the
Computers and Electronics in Agriculture 178 (2020) 105476
M. Torky and A.E. Hassanein
Fig. 1. Blockchain as a sequence of hashed blocks.
previous block. Without this component, there would be no con­
nection and chronology between blocks in the blockchain.
2.1. Blockchain features
Blockchain has four major features as depicted in Fig. 4 that make it
is better than the common centralized database systems:
On the other hand, the block body contains all the number of
transactions that are confirmed and validated within the block. All
transactions in the block are counted via Block Transaction Counter
(BTC). The block state represents who sent which data (e.g. Bitcoin) to
whom at a specific timestamp. An identified transaction between two
nodes only occurs once it is involved in a block, then the block is ver­
ified and added to the blockchain. To achieve this, the ledger must be
publicly available, this clears the importance of peer-to-peer networks
in blockchain systems (Zyskind and Nathan, 2015). Fig. 3 explains how
the blockchain system works while a user ’A’ transfers a transactional
data (e.g. bitcoin) to a user ’B’. In a P2P network, the nodes can guar­
antee the blockchain is protected and up-to-date, where, every node
stores the last updated version of the blockchain. Utilizing P2P network
architecture in the blockchain system has three main advantages:
• Decentralization: The set of transactions in blockchain are pro­
•
•
1. The user can always brows and check the blockchain status by a
blockchain explorer without depending on a third party
2. A malicious attacker is challenged to attack thousands or millions
of nodes at the same time in order to compromise the blocks
3. Blockchain is never removed because it would have to be re­
moved by all nodes in P2P network
•
cessed and validated through a distributed ledger-based on P2P
network. For example, in the bitcoin blockchain, there is no need for
the central trusted party (i.e. the central Bank). All nodes work to­
gether as peer to peer for adding and verifying block transactions in
the blockchain.
Persistency: In blockchain, it is impossible to drop or rollback an
identified transaction once it is added to an block in the blockchain.
Moreover,invalid transactions could be discovered immediately.
Anonymity: Each participant can communicate with the blockchain
by a generated virtual identity code, which does not uncover the
real identity of the participant. Hence, this feature raises several
security and privacy challenges of blockchain transactions (Kosba
et al., 2016).
Auditability: This feature specifies that each block is securely
linked to the previous block. This design makes transactions are
easily verified and tracked.
With respect to who are the miners and validators of blocks in
blockchain, who is authenticated to access blockchain system, level of
Fig. 2. Block design.
3
Computers and Electronics in Agriculture 178 (2020) 105476
M. Torky and A.E. Hassanein
Fig. 3. Blockchain system working.
network. These principles make sure the blockchain system works as
intended to do in a synchronization manner. The blockchain protocol
sets rules on three key issues: creating data blocks, verifying and vali­
dating data blocks, as well as resolving block conflicts in the chain.
Recently, there are different blockchain protocols used to verify and
add blocks to the blockchain in different ways. A newly mined block
can be inserted to the blockchain if it follows the formulated principles
stated by the blockchain protocol. Then, the peers in the network run
software to test if the block is rightful or not. An illegal block will
simply be rejected. In Table 2, the functionality of some major con­
sensus blockchain mechanisms is presented and compared in terms of
the mining process, the used technology for generating blocks, the
power save during mining process,the block verification speed, the
network structure on which each protocol is working, scalability, access
rights to users, and a platform example for each protocol. As depicted in
tale 2, the main disadvantages of old protocols such as Proof of Work
(Pow) (Zheng et al., 2018), Proof of Stake (PoS) (King and Nadal,
2012), Proof of Authority (PoA) (Bentov et al., 2014; Lin and Liao,
2017), and Proof of Burn (PoB) (Frankenfield, 2018; CoinCheckup,
2019) are that those protocols consume more powers and have low
speed during mining process. On the other side, the new protocols such
as Proof of Luck (Pol) (Milutinovic et al., 2016; Kim et al., 2015; Chen
et al., 2017), and Proof of Assignment (PoAss) (ICO Bench, 2019; IOTW,
2019) are more power savers and able to generate a new block in the
blockchain in less than 10 s. Moreover, these protocols is working based
on novel secure technologies, such as Intel Software Guard Extension
(SGX) as in Pol (Milutinovic et al., 2016; Kim et al., 2015; Chen et al.,
2017), and green mining technology as in PoAss (ICO Bench, 2019;
IOTW, 2019). This novel technologies provides more security, trusted
environments for block mining, and more power saving during block
verification and generation.
The literature introduced additional blockchain protocols that can
be used in different fields and applications, for instance, in crypto­
currency exchange (Schwartz et al., 2014; Buchman, 2016; Bach et al.,
2018; Gibbs and Yordchim, 2014; Jake Frankenfield, 2019; Chohan,
2019; Mser et al., 2018; Hileman and Rauchs, 2017; Chang and
Svetinovic, 2016), identity verification and privacy preserving (Umeh,
2016; Jeffrey Maxim, 2019; Keybase, 2019; Eric Weiss, 2019), E-gov­
ernment sector (Aman Soni, 2018; Nuss et al., 2018; Buchmann et al.,
2017; Vos et al., 2017; Pawlak et al., 2018), healthcare systems
(Brennan, 2017; Azaria et al., 2016), and energy and smart grids
(Martens et al., 2017; Pop et al., 2018).
Fig. 4. The major features of blockchain.
Table 1
A Comparison between blockchain types.
Property
Public Blockchain
Private
Blockchain
Consortium
Blockchain
Blockchain
Validation
Access Rights
Immutability and
Security
Efficiency
Centralization
Blockchain
Authority
All peers
One
organization
public or Private
tampered
Selected peers
public or Private
tampered
High
Yes
Permissioned
High
Partial
Permissioned
Public
Very tough to
tamper
Low
No
Permissionless
security and efficiency, design method, blockchain authority, block­
chain systems can be classified into three types: public, private, and
consortium blockchain which are compared in Table 1.
2.2. Blockchain protocols
Executing a specific blockchain protocol is where the magic occurs.
It allows all nodes in a P2P network to cooperate and work together
without trust requirements between them. The blockchain protocol is
responsible for verifying and securely adding blocks to the chain ac­
cording to the set of mining rules. So, the blockchain protocol is simply
a set of principles that are agreed upon by all peer nods in the P2P
4
Computers and Electronics in Agriculture 178 (2020) 105476
M. Torky and A.E. Hassanein
Table 2
A comparison between six blockchain protocols.
Measure
Pow (Zheng
et al., 2018)
PoS (King and
Nadal, 2012)
PoA (Bentov et al., 2014; Lin
and Liao, 2017)
PoB (Frankenfield, 2018;
CoinCheckup, 2019)
Pol (Milutinovic et al., 2016; Kim
et al., 2015; Chen et al., 2017)
PoAss (ICO Bench,
2019; IOTW, 2019)
Mining
Base Tech.
Handling
Power Save
Verification
Structure
Scalability
Access
Platform
puzzle
coin-based
No
No
> 100s
P2P
high
public
Bitcoin-NG (Eyal
et al., 2016)
wager
coin-based
partial
partial
< 100s
P2P
high
public
Peercoin (King and
Nadal, 2012)
puzzle + wager
coin-based
partial
No
< 100s
P2P
high
public
Coin Proz (ProzCoin, 2019)
burn coins.
coin-based
No
yes
< 100s
P2P
high
public
Slimcoin (CoinCheckup,
2019)
luck algo.
Intel SGX
yes
yes
< 10s
P2P/central
high
public/private
Hayekcoin(ANN, 2019)
hashcodes
green mining
yes
yes
< 10s
central
low
private
IOTW (IOTW, 2019)
3. An integration model between blockchain and IoT
energy and smart grids (Yang et al., 2017) is another important trend by
2030. In the subsections below, we will discuss in more details the
blockchain design patterns for IoT, the major solutions which block­
chain can provide to IoT challenges, as well as the recent blockchain
protocols in IoT.
Internet of Things (IoT) is a base technology and key player in the
digital transformation witnessed by industry 4.0 (Lu, 2017). In industry
5.0 (Daniel Sontag, 2019), IoT is predicted to have more dependence on
sensing devices, big volumes of data, and more patterns of connected
devices within different network topologies. we must call it the Internet
of Things 2.0 (Sheth, 2016). Recently, IoT 2.0 moves from devices and
data technology to Actionable Intelligence Technology (I-Scope, 2019).
Based on more integration between IoT as a base technology and other
technologies such as AI, Cloud Computing, and Data Science, the digital
transformation moves from IoT 2.0 to the Internet of Transformation
which makes the IoT communications so powerful (I-Scope, 2019;
Darwish et al., 2017; Vermesan et al., 2017; Sowe et al., 2014).
The integration of blockchain technology with IoT is another im­
portant contribution predicted to revolutionize the digital transforma­
tion of various domains (Makhdoom et al., 2018). Blockchain as a
distributed ledger can be verified and deployed across several hetero­
geneous IoT networks. In IoT-based environments, Blockchain is pre­
dicted to add 176B$ to the global economic value by 2025 and over 3T$
by 2030 (Fourquadrant, 2019).
Fig. 5 explains the top five predictions of integrating Blockchain
with IoT by 2030. For example, In e-governments, by 2030, most
governments will depend on digital currency in most of (maybe all of)
financial transactions. In the digital economy, by 2030, blockchain will
produce more token-based protocols for enhancing digital economy
transactions. By 2030, the Self-Sovereign Identity standard (Baars,
2016) will emerge for managing the individuals’identities locally and
internationally. One of the most promising trends where blockchain
and IoT can work together by 2030 is the global supply chain man­
agement and tracking. Moreover,Blockchain and IoT benefits in digital
3.1. Blockchain design patterns-based IoT Networks
In IoT-device communications, IoT peers collaborate and build trust
over a blockchain design pattern. Each IoT- peer device can be re­
presented by one or more nodes in a P2P network. This network is used
to broadcast IoT transactions between devices based on a blockchain
protocol. This protocol is responsible for managing, verifying, and se­
curing IoT transactions. The Effective monitoring and management of a
blockchain-IoT network system requires a special framework for
managing data traffics, and sense the events and transactions between
IoT devices. The blockchain-IoT framework should be modular enough
to enable the monitoring of a variety of IoT transaction Patterns as
discussed in (Hitarshi, 2019). Fig. 6 depicts a proposed update of the
Standard Blockchain Monitoring Framework in IoT (SBMF-IoT) when
utilized to monitor three trucks which provide food patterns to various
customers through a specific food production company. The SMBF-IoT
comprises of the following components:
1. A Monitoring Agent (MA) which embedded with each IoT truck
through the associated IoT-decentralized Application (IoT-dApp). It
can read the logs generated as a part of the transaction process.
2. A log Collection Engine (LCE) that manipulates the streaming log
information of IoT trucks and realize it for further transaction pro­
cessing.
3. The Elastic Nodes Cluster (ENC) which processes a large amount
of log data to organize and index it into matching IoT data files,
which are shared and stored as replicas.
4. A Visualization Platform (VP) consumes the transactional IoT
data collated by ENC and provides effective insight into the block­
chain nodes and network statistics.
Leveraging the SBMF-IoT as a design standard for managing and
monitoring blockchain-IoT transactions will help in achieving the fol­
lowing:
• Providing transparent end to end IoT transactions.
• Providing more control in the performance and throughput of the
blockchain network.
• Installing non-invasive monitoring routines that can be dynamically
enabled for each on-boarded IoT peer and also support a common
network provider model.
The blockchain is a distributed ledger that will perform a major role
in how IoT-devices will be linked together directly/indirectly between
each other. Integrating blockchain within IoT communications require
Fig. 5. Top five predictions of integrating blockchain and IoT by 2030.
5
Computers and Electronics in Agriculture 178 (2020) 105476
M. Torky and A.E. Hassanein
Fig. 6. Standard Blockchain Monitoring Framework in IoT (SBMF-IoT): A proposed Model.
to identify the design pattern on which the communication occurs: the
communication may be in three basic design patterns: from IoT device
to IoT device, from IoT device to the blockchain, or a hybrid design. The
key benefits of utilizing blockchain as a design base for IoT transactions
are building trust, reducing cost, and accelerating transactions.
-IoT to IoT Design Pattern: In this design pattern, blockchain work
as only data storage for IoT- devices. Only some bytes of data are re­
gistered in the blockchain whereas the IoT communications occur
outside the blockchain. This design would be helpful in scenarios where
the IoT interactions are taking place with low latency. Fig. 7 explains
how two IoT-cars are communicating based on IoT-IoT design pattern
via a Management Hub component which allows the IoT devices to
store data in the blockchain.
-IoT to Blockchain Design Pattern: In this design pattern, all IoT
interactions go through blockchain. It works as a data storage and
transaction monitor and manager. This design guarantees that all IoT
communications are traceable as defined in the blockchain. Moreover,
it increases the independence of IoT nodes such that each device can
interact directly with the blockchain. This design approach is effective
when the transactions happen between a variety of IoT devices in dif­
ferent domains. However, assimilating a large number of IoT transac­
tions and data volumes in blockchain will lead to bandwidth and la­
tency problems. So the scalability is one of the well-known dilemmas of
blockchain (Tasca and Widmann, 2017). Fig. 8 explains the three types
of interactions between three IoT-devices with the blockchain, IoTbuilding,IoT-energy node, and IoT-Truck. Although the three nodes
Fig. 8. Multi IoT-devices to Blockchain-design pattern: A proposed model.
appear that they are heterogeneous, based on this design pattern, the
blockchain is used as a storage repository for heterogeneous data as
well as a transaction monitor, and a verifier for different communica­
tion patterns.
-Hybrid Design Pattern: In the early years of IoT communications,
the disparate endpoints of IoT-devices were not doing a lot of data
analysis and processing. Now, the endpoints are talking to each other
with more interdependence and processing more data, this a promising
approach is called Edge Computing (Satyanarayanan, 2017). The in­
tegration between IoT and other technologies, including Fog Com­
puting (Chiang and Zhang, 2016), Artificial Intelligence (AI) (Vermesan
et al., 2017), and blockchain (Li et al., 2018) leads to a hybrid design
pattern. In this pattern, the data needs to be manipulated just on the
IoT-device and does not need to be transmitted back to the cloud. Also,
AI will become a key player in this design pattern. such that it will help
IoT devices to make critical decisions. Blockchain can be leveraged for
providing trust and security as missed links in the edge computing
approach. The hybrid IoT network requires more security and relia­
bility. Moreover, it requires a lot of transmitted data volumes and dif­
ferent IoT transaction patterns which can be managed by the Block­
chain. Fog Computing can play a major role in this design pattern. It can
remedy the limitations of blockchain and IoT regarding energy con­
sumption and computing power, such that fog computing-based devices
such as gateways, and data sensors require a little computation power.
These advantages make fog computing a key player for reducing the
Fig. 7. IoT- device to IoT- device- design pattern: A proposed model.
6
Computers and Electronics in Agriculture 178 (2020) 105476
M. Torky and A.E. Hassanein
Fig. 9. Hybrid design pattern: A proposed model.
amount of bandwidth and latency. Moreover, it accelerate blockchain
mining operations. Fig. 9 explains the architecture of the hybrid design
pattern which integrates IoT, Cloud Computing, Fog Computing, and
Blockchain in a coherent design model. This figure depicts how
blockchain can be utilized to work as a data repository and transactions
monitor and verifier for two different heterogeneous fog networks that
are managed by a cloud. The strength of this design pattern here is that
blockchain is not only dedicated to store and verify data from different
IoT devices as explained in the previous design pattern but also to store
and verify data from different heterogeneous and complex fog and
cloud networks.
the other side, Precision agriculture network systems are challenged by
network performance problems such as the communication speed with
a huge number of heterogeneous devices and sensors, the power con­
sumption of these devices, bandwidth and latency as well as limited
data storage. So, this section explains how blockchain technology can
makes IoT communications are more secure, transparent and tamper­
proof. Moreover, it will improve digital agricultural processes with realtime data monitoring in accelerated end-to-end transaction processes.
Hence, blockchain technology can introduce good solutions to the se­
curity and performance of IoT networks in the precision agriculture
systems.
3.2. Challenges of IoT networks and blockchain solutions
3.2.1. IoT security challenges and blockchain solutions
By 2025, it is predicted that will be more than 21 billion IoT nodes
(more than 75 million IoT devices in agriculture sector lonely) which
will motivate the attackers for executing a variety of IoT attack patterns
(Norton, 2019). In 2016, Mirai malware (Kambourakis et al., 2017) has
been considered as the first IoT malware that can infect linked devices
such as Digital Video Recorder (DVR), IP cameras, and home router.
This malware is able to turn the affected devices into a botnet for ex­
ecuting Denial of Service (DDoS) attacks (Kolias et al., 2017). Another
example is IoT-reaper (Gary Davis, 2019), which appeared in 2017. It
Building robust IoT networks in the precision agriculture systems is
challenged by a lot of network security threats, and communication
performance dilemmas in this type of networks (Makhdoom et al.,
2018). For example, farmers need to secure supply chain systems for
trading their crops and protecting their smart greenhouse networks
against cyber attacks. Moreover, they need a trusted environment to
manage and access their IoT-agricultural machines without threats to
identity, privacy, and integrity of data processed by those devices. On
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M. Torky and A.E. Hassanein
Signature Algorithm (ECDSA) is 20 bytes (Ziegler et al., 2015). With
160-bit address space, blockchain can derive and assign addresses
spaces about 1.46 * 10 48 for IoT nodes (Khan and Salah, 2018). Hence,
the possibility of address collision is roughly 10 48 , which is secure
enough to assign a GUID (Global Unique Identifier) to IoT devices. With
blockchain technology, there is no need for a central authority for
providing and generating limited Internet Assigned Number Authority
(IANA) (Ziegler et al., 2015). Furthermore, blockchain able to derives
4.3 billion addresses spaces compared to IPv6. So, Blockchain provided
a magical solution for solving the scalability and security challenges in
IoT.
2) Managing Identity of Things. Generating identities and mana­
ging access for IoT devices is considered another benefit of blockchain
in IoT security. Blockchain can be utilized for granting trustworthy
identity creation, ownership monitoring and tracking of goods, pro­
ducts, and services. Data transparency and end-to-end process tracking
is another benefit of robust blockchain. For example, Trust-Chain
(XXXX, YYYY) has been proposed for verifying and managing trusted
transactions while maintaining the integrity in distributed IoT net­
works. Each Block in the Trust-Chain represents a transaction between
two IoT participants and creating new transactions is depending on the
hash codes of the previous transaction. Trust-Chain records are cryp­
tographically authenticated and signed by both parties and connected
in a directed graph. The main advantage of Trust-Chain beside security
is each agent in the Trust-Chain system monitors the interactions by
others and collects records to compute trustworthiness levels. Block­
chain also can provide control functions for trustworthy and decen­
tralized transactions in the three design patterns explained previously.
Moreover, it allows to remote asset management and instant data ver­
ification from end to end between IoT devices.
3) IoT Transaction Verification:
Blockchain can perform an important function in managing the
authentication and authorization of IoT systems. With blockchain, all
IoT transactions mad by devices are registered on the distributed ledger
and can be monitored and tracked securely. Each IoT transaction
communicated with the blockchain system will always be crypto­
graphically proofed by the valid sender that holds a unique PK and
GUID. Hence, this will be helpful in ensuring the authentication and
integrity of the triggered transaction. Blockstack (Ourad et al., 2018) is
a common blockchain technique that utilizes JSON Web Token (JWT)
for authenticating IoT-transactions easily. As depicted in the proposed
model in Fig. 11, Blockstack can be used to authenticate the access to a
smart greenhouse. The authentication with Blockstack requires a spe­
cial communication between a decentralized application (DApp) of an
IoT greenhouse and the Blockstack Browser.
The authentication process can be explained through eight steps:
doesn’t intrinsic depend on breaking prosaic passwords like Mirai does,
but instead avails the vulnerabilities in various IoT devices and exclude
them into a botnet cram. Due to the limitation to unstandardized se­
curity models for IoT, Other attacks can execute data forgery routines,
data block and encryption. Ransomware (Yaqoob et al., 2017) encrypt
victim’s data and ask for a ransom to decrypt it can work in different
ways with IoT devices. It needs to identify the correct IoT-device
owners to ask for ransom money. Moreover, plenty of IoT devices are
operated by other devices; hence, the attacker needs to get the In­
dustrial Control Systems (ICS) to penetrate the target device and en­
crypt the data. Shamoon 2 and DuQu-2 (Makhdoom et al., 2018) are
another IoT attack models that target ICS of IoT devices based on its
ability to replicate across different operating systems and IoT devices.
In 2012, King Saudi Arabia announced that there were about 15 gov­
ernmental proxies and communities have been attacked with Shamoon
2 (Smith, 2019). Shamoon removed data within 35,000 devices and
hijacked the computer’s boot record, which prevents the computer from
being rebooted. Hence, the development of future IoT communication
systems requires to meet the following security requirements:
1. IoT devices have to operate securely in an authenticated manner.
2. Data integrity should be ensured versus data forgery, alteration
and unauthorized access.
3. The IoT-device codes have to be secure against Tamper proof.
4. All IoT-devices have to be authenticated within the IoT system
before installed in the network.
5. IoT Networks have to be tamper-resistant regarding both software
and hardware tamper.
6. IoT data should be encrypted with efficient and secure key gen­
erator systems in which the breaked key can be updated as and
when required
7. IoT systems have to ensure user security such as, ID management,
enrollment, authentication, authorization, and non repudiation.
8. IoT system has to be immunized against unauthorized access to
private IoT data and network.
By the coming generations of blockchain systems and the advance in
developing untraditional smart contracts and distributed ledger, it is
expected that blockchain will provide magic solutions for many chal­
lenges in IoT networks security and achieve the required security re­
quirements. Fig. 10 depicts the key solutions, which blockchain can
grant for IoT-networks security (Khan and Salah, 2018). In the fol­
lowing, these key solutions of blockchain that can mitigate the security
challenge in IoT networks are discussed in some details.
1) Extending Address Space: The limitation of IPv6 address space
is a big scalability challenge for addressing IoT devices. IPv6 has a 128bit address space, whereas Blockchain has a 160-bit address space. So,
with blockchain addressing, the generated PK by Elliptic Curve Digital
1. A user (e.g. farmer) signs in with blockstack by requesting a new
access to the smart greenhouse by establishing a new connection
with its decentralized application (DApp).
2. The DApp then sends the sign in data to the function
”Redirect _To _Sign ()” for processing the access request.
3. The function ”Redirect _To _Sign () ” asks the blockstack browser to
generate a new Authentication Request Token for the user’s access re­
quest.
4. The blockstack browser responds bycreating new JWT authentica­
tion response token that being forwarded to the DApp.
5. The DApp then manipulates the JWT authentication response token
by calling the function Habndle _Pending _Sign () for verifying the
issued JWT authentication response token by the blockstack
browser.
6. After the function Habndle _Pending _Sign () has verified the JWT
token, it forwards the JWT verification result to the DApp for re­
sponding the user’s access request.
7. The DAppm then grants the user the required access data to access
greenhouse system.
Fig. 10. Key security solutions of Blockchain for IoT.
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Fig. 11. Blockstack authentication protocol: A proposed model.
different bandwidth options.
2. IoT platform layer:It is sometimes called network layer. It defines
the various communication protocols and networks used for con­
nectivity and edge computing. So, it is responsible for recording and
analyzing the transmitted data from sensors and other physical
endpoints through session protocols between IoT gateways and IoT
platform.
3. Application Layer. Is responsible for providing IoT services to the
end users through several applications such as mobile applications,
public web or back-office applications. It depend on HTTP protocol
to exchange data between the IoT platform and those applications.
8. The user (e.g. farmer) inputs the granted access data and establishes
the connection with his greenhouse system.
4) Securing IoT Communications:
To provide secure communication between IoT devices, the classical
protocols such as ”Hypertext Transfer Protocol (HTTP)”, or ”Extensible
Messaging and Presence Protocol (XMPP)” must be replaced with more
secure protocols such as ”Datagram Transport Layer Security (DTLS)”
or ”Transport Layer Security (TLS)” for providing secure communica­
tion (Nguyen et al., 2015; Kothmayr et al., 2013). However, DTLS or
TLS protocols have some drawbacks in terms of computation time or
memory requirements as they considered heavy and complex protocols.
Moreover, these protocols have some problems with centralized gov­
ernance and control of key generation and distributions using the
common PKI protocol. Using blockchain can remove these problems
and enhance key management between IoT devices by assigning each
device a unique GUID and PKI pair once installed and connected to the
blockchain network (Khan and Salah, 2018). With blockchain, new
secure communication enhancement can be conceived such that there is
no need to handshake phase to exchange PKI certificates as in DTLS or
TLS protocols. Therefore, blockchain is the best solution to cover run­
time computing and memory management requirements for achieving
secure communications between IoT devices. Moreover, the firmware
of the IoT devices can be hashed into a Blockchain continually for de­
tecting IoT Malware and alert the device owners to take the necessary
security measures, or auto defends against the detected malicious bot.
Instruction verification and authentication is another benefit of block­
chain for securing communication between IoT devices. For instance,
the sender node hashes a message that wants to forward to another IoT
node and add the hash code into a Blockchain. On the other hand, the
receiver node then hashes the same message. The verification rule state
that, if the hash value matches the hash value on the Blockchain, then
the received message has not tampered in the transit.
The IoT performance problems in the precision agriculture networks
can be summarized in five challenges as depicted in Fig. 12. Blockchain
can provides also good solutions for the those challenges:
(1) Blockchain and Sensing problem. This problem is concerned
with the perception layer in IoT layer model. IoT sensors are embedded
in a lot of agricultural machines, such as agriculture tractors,smart
greenhouses, farming devices, etc. These sensors continuously emit data
about the working status and permit IoT nodes to send and receive data
from each other via the cloud. Blockchain can be used for defining
communication rules between these sensors as well as managing all
M2M transactions. For instance, IOTA (Internet of Things Application)
(Popov, 2016) is a promising update of the blockchain platforms which
specially designed to facilitate a large number of transactions between
IoT devices using IOTA ledger and Directed Acyclic Graph (DAG). Al­
though IOTA is still new, it can provide magic solutions for the sensor
3.2.2. IoT performance challenges and blockchain solutions
Due to the huge number connected IoT devices in the precision
agriculture networks, IoT system have to harmonize a future large
number of network topologies and process big data volumes with high
level of throughput. So, the performance of IoT networks in the preci­
sion agriculture systems represents another big challenge. The IoT
performance problems result from some limitations in the traditional
IoT layer model that involves three basic layers. The endpoints and
gateway layer, The IoT platform layer, and the Application layer.
1. Endpoints and Gateway layer: It is sometimes called perception
layer that involves several IoT devices such as sensors, controllers,
and mobile devices. in this layer, the physical endpoints send IoT
messages to the IoT platform through a gateway. Each sensor con­
nects to the gateway through one of several data link protocols with
Fig. 12. IoT performance challenges in the precision agriculture networks.
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communications and the scalability challenge in the precision agri­
culture.
(2) Blockchain and Energy Consumption Problem: This problem
is concerned with the Network layer in IoT layer model. Due to the
increasing of IoT network systems in the precision agriculture, IoT
devices are predicted to be low-power devices. Moreover, IoT com­
munications in precision agriculture networks are depending on
Wireless technology which consumes far more energy than wired con­
nections Such as Fiber (5G? IoT, 2019). Although fiber is safer, faster,
more reliable, and energy-efficient than wireless, pushing for 5G tech­
nology everywhere is cheaper than to depend on fiber. But un­
fortunately, this will greatly grow energy consumption. Recent IoT
devices in precision agriculture networks consume energy via data
centers, M2M communication, embodied energy (e.g. mining, manu­
facturing, and transporting), and Obsolescence of digital technologies.
But with the decentralization feature of Blockchain, it can introduce
some solutions to handle the energy consumption problem. The private
blockchain can be utilized for ensuring the proportion between the high
computation power along with the high bandwidth connection for IoT
nodes (Makhdoom et al., 2018). Blockchain will grant the resilience
needed for the smart grids in the future. Blockchain-based flexible
power systems will witness low-carbon power produced at scale-not
only by large utilities, but also by renewable power sources. Blockchain
will enable us to measure how much energy IoT-devices and sensors
consume in real-time. Blockchain can maximize three energy trends
decarbonization, digitization, and electrification (Maher Chebbo,
2019). All renewable energy-based processes such as energy selling/
payments transaction, and energy contracts can take place on a
blockchain immediately. Blockchain can provide techniques for col­
lecting and storing data from countless distributed sources as well as
processing it in real-time. Blockchain will also maximize the elec­
trification by providing decentralized energy ledgers for monitoring a
large number of batteries and a variety of energy sensors used in the
precision agriculture networks.
(3) Blockchain and Networks Complexity Problem: This problem
is also concerned with the Network layer in IoT layer model. IoT sys­
tems in precision agriculture are designed based on un heterogonous
Network topologies which lead to complex communications. The IoTfarming machines have to communicate and interact seamlessly
through different platforms and infrastructures. It’s possible, but it can
be hard, expensive,and time-consuming. In response to these chal­
lenges, Blockchain can help in acquiring and managing data based on
secure standard-based and decentralized networks. As we mentioned
previously in Section 3.1, blockchain can manage the communications
between IoT devices through three design patterns, this lead to more
simplicity in IoT communications, supporting edge processing at
farming machines endpoints, and decrease the latency of data trans­
mission across the precision agriculture networks.
(4) Blockchain and bandwidth and latency Problem: Another
important IoT performance problem against building precision agri­
culture networks is the bandwidth and latency of device communica­
tions. In IoT communications, the data traffic always originates outside
the data centers. Hence, immediately, the communication has to be
installed in the highest latency between large numbers of scattered IoT
devices. A large number of different IoT devices need to be updated
every small time period. Moreover, device communication needs lots of
routing and multiple levels of packet inspection. Replacing the data
center with blockchain will mitigate these problems. The decen­
tralization feature of blockchain will make the workload more dis­
tributed closer to the endpoints. Hence, the IoT communication will be
constructed inefficient bandwidth and low latency. However, the de­
centralized public blockchain may take control of the public nodes is
very difficult such that there is a great chance that the IoT device with
the smallest bandwidth will cause network bottleneck. In addition, due
to the growth of blockchain size, the need for computing power,
Table 3
Cloud versus Blockchain as a data storage medium.
Cloud-based Storage
Blockchain-based Storage
Centralized Trust by the cloud provider
Centralized Design
Not protected against data alteration
Single point of failure
Vulnerable to un-authorized
data propagation
User data is managed by
Decentralized Trust in the network
Decentralized Design
Protected against data alteration
multi points of failure
data propagation are based on
smart contracts
User data is replicated between all
peers
and controlled by smart contracts.
The transactions are transparent
to user identities.
Provide edge/fog computing at IoT
endpoints
so it is a good option for IoT networks.
Less expensive infrastructure
cloud provider
The transactions aren’t transparent
regarding users identities
Not ideal for low latency and
high availability for IoT networks
Costly design infrastructure
storage,and bandwidth is very importantMakhdoom et al., 2018). On
the other hand, with a private blockchain, these limitations can be re­
moved, such that the private Blockchain can process over 1,000
transactions per second on Ethereum or Bitcoin (Preethi, 2017). Hence,
a huge number of heterogeneous agriculture transactions can be pro­
cessed using the coming blockchain systems based on processing digital
asset tokens.
(5) Blockchain and Limited Data Storage Problem: Due to the
rapid growth of IoT-precision agriculture networks, large volumes of
data have to be stored and managed through flexible repositories. The
existing cloud-based storage has limited solutions to manipulate the
large scale of different patterns of IoT data. This limitation based on the
requirements of real-time data monitoring, high availability, scalability,
security, and low latency (Sharma et al., 2017 Sep). In response to these
limitations of cloud-based storage, Blockchain-based storage will enable
IoT endpoints to make more data analysis and manipulation in realtime. Satyanarayanan (2017). Table 3 explains the major differences
between cloud-based storage and blockchain-based storage. The P2P
design of blockchain makes the trust is replicated among all peers.
Hence, if any tamper occurred in any node, all peers in the system will
detect it rejects its procedure, moreover, the current status of block­
chain will not be modified or tampered. Moreover, the cloud is also
vulnerable to unauthorized data sharing, whereas, blockchain gives
users the freedom to set access rules without dependence on a third
party or a cloud service provider. Hence, blockchain is also a very good
option for data availability and security (Makhdoom et al., 2018).
4. Blockchain opportunities in IoT-based precision agriculture
The IoT growth in the last few years has granted many opportunities
for enhancing the precision agriculture sector. The witnessed increase
in using mobile-broadband access devices, smart networks, analyzing
big data volumes, and AI have provided the stakeholders with some
magic tools in developing precision agriculture systems. Blockchain is
one of the most promising technologies that can provide untraditional
solutions in smart agriculture (Lin et al., 2017). Blockchain can be used
in managing warehouses, soils, and supply chains more intelligently. It
can be utilized as a key tool to transmit real-time data about crops and
livestock. Moreover, it can be used for food safety, logistics, monitoring,
as well as managing payment transactions (Sam, 2018). Blockchain
adoption in smart agriculture and food supply chain market is predicted
to grow at a CAGR of 47.8% by 2023 (Report Linker, 2018). Moreover,
utilizing blockchain in the supply chain market is evaluated to be $60.8
million in 2018 and is forecasted to reach $ 429.7 million by 2023
(Report Linker, 2018). In the following subsections, the major use cases
of utilizing blockchain in developing IoT-precision agriculture networks
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proving quality and ethicality (Caro et al., 2018). This can provide
consumers and stakeholders more trust in the agricultural products,
ensuring food safety, and reducing food frauds (Sylvester, 2019). In the
supply chain process, blockchain can provide key five services:
1. Data Recording. Blockchain is able to work as a distributed storage
unit for all information moving between supply chain nodes
2. Monitoring. Blockchain is able to track purchases, orders, updates,
receipts, shipment notifications, or other trade-related transactions
(Li and Wang, 2018).
3. Verifying. Blockchain can be utilized for verifying transactions or
certain properties of physical products, such as identifying if a food
product is an organic or fair trade (Platform with duplicated and
shared bookkeeping, 2018).
4. Assigning and Linking. Blockchain can be used to link the physical
products to bar codes, serial codes, digital tags like RFID, etc.
5. Sharing. Blockchain can be utilized for disseminating information
about manufacturing procedures, delivery, assembly and main­
tenance of agricultural products with suppliers and vendors.
Fig. 13. Five Use Cases of Blockchain in the precision agriculture.
Moreover, blockchain can provide three advantages to the shippers:
Transparency Enhancement, Greater Scalability, where Blockchain
virtually enables stakeholders from any endpoint to access the supply
chain system, and Better Security, where blockchain could potentially
protect the system against any tamper and data alteration during the
product lifecycle.
Table 4 compares between blockchain-based supply chain and tra­
ditional supply chain system in terms of achieving data integrity, the
way in which data security is managed, the possibility of data ver­
ification, and government regulations for authenticating processes and
activities in the supply chain system.
Fig. 15 explains a proposed architectural model in which blockchain
can manage agricultural transactions process between resource de­
mands and resource suppliers in a supply chain system. The main role
of blockchain in this model is storing new supply chain transactions
between many customers’ demands and many suppliers’ products. The
customers can create new demand as a new transaction through the
decentralized customers’orders engine, and the suppliers can offer new
product as a new transaction through the decentralized suppliers’products
engine. The smart contract works here as an authenticating protocol
between the two engines for verifying and protecting the transaction
data patterns between suppliers and customers, then store the valid
transactions as a new block in the blockchain.
3) Land Registration: (Barbieri and Gassen, 2017). It can be de­
fined as the process of determining, recording and sharing transactional
information about rights, value, and use of land pieces (Anand et al.,
2016). The current classical land registry system has a lot of limitations.
These systems don’t provide the full authentication for all land trans­
actions between peoples, organizations, and governments. It is esti­
mated that about(70–80%) of land transactions worldwide are not
formally registered in any national system (Anand et al., 2016).
With Blockchain technology, we became to have a big chance to
solve these problems. Blockchain can enhance data security and ensure
the authenticity of land registration records. With the transparent, de­
centralized public ledger it becomes easy to store time-stamped land
transactions and historical rights of land pieces. Since blockchain does
not rely on a single data center, It is able to auto-reject any illegal land
transaction. It is able to detect tamper proofs on registered records and
protect all land transactions and registry details based on ”colored
coins. The colored coins-based protocol can be applied to land regis­
tration by representing the ownership of a piece of land by a single or
multiple tokens. The metadata associated with the token can be used to
monitor public registry information such as size, GPS coordinates, year
built etc. Verifying and tracking the ownership of each token can be
executed across the internet using a blockchain explorer software. Proof
of Concept (Luckas, 2019) with Ethereum (Vujicic et al., 2018) is a good
will be discussed in subSection 4.1. Moreover, some of the prominent
blockchain platforms that can be used in managing different sectors in
precision agriculture are investigated and discussed in subSection 4.2.
4.1. Blockchain usecases in the precision agriculture
Although the research in adopting blockchain technology in the
precision agriculture is in its early stages, the research trend explained
major five use cases of applying blockchain in the precision agriculture
as summarised in Fig. 13. Using blockchain technology in the precision
agriculture can introduce new contributions and improve many func­
tions such as monitoring and traceability, transparency, and efficiency
at levels of the farmers and the consumers.
1) Farm Overseeing: building smart farms based on IoT sensors
(e.g. temperature, humidity, light, crop maturity sensors, etc.) Enable
the farmers and stakeholders to digitize the obtained agricultural data
from the sensors for different purposes. Utilizing blockchain here will
provide more rapid and smooth communication between sensor net­
works. For example, blockchain can be used for monitoring crop storage
techniques for preventing post-harvest losses. Moreover, it can be uti­
lized for tracking CO2 concentration for avoiding mold growth invasion
(Sam, 2018). Traditional sensors able to detect the potential for losses
3 5 weeks earlier than traditional monitoring techniques do, how­
ever, 52% of the nation’s essential fruits and vegetables being thrown
out due to missing the control and monitoring over the supply chain.
Blockchain can be utilized also for providing secure communication in
Smart Greenhouse Farming (SGF) as depicted in the proposed model in
Fig. 14 (Patil et al., 2017). The figure explains a farm overseeing fra­
mework that involves four subsystems: smart greenhouse farm, private
blockchain, cloud storage, overlay network and the end-users (farmer
(s)). The SGF is equipped with several IoT sensors (e.g. humidity sen­
sors, light sensors, water level sensors, and CO2 sensors) as well as some
actuators (such as, LED light, Fan, Heater and sprinkling). All transac­
tions that can be occurred between IoT devices in the SGF can be stored
and mined in a private blockchain. Moreover, the secure data com­
munications between SGF, blockchain, cloud storage and end-user (i.e.
farmers who can remotely access its SGF using smart devices such as
mobiles and computers) can be managed through an overlay network.
On the other hand, Blockchain can be used for monitoring water sys­
tems that serve a set of smart farms (Lin et al., 2017) at the same time.
2) Supply Chain: Monitoring processes of the supply chain using a
public blockchain ledger adds a great value to agricultural goods and
promote the transparency of supply chain processes. Blockchain enables
consumers to track agricultural machinery, crops, and livestock for
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M. Torky and A.E. Hassanein
Fig. 14. Blockchain-based smart greenhouse farming model: A proposed model.
10B 15B annually (Galvin, 2017). Utilizing Blockchain in the food
industry, consumers will be able to verify the source and safety of their
food in seconds. Blockchain could be used to tell consumers that the
fruit and vegetables were grown with herbicide. It will provide realtime tracking, authenticating, securing and monitoring functions for the
food supply chain process (Ge et al., 2017; Galvez et al., 2018).
Blockchain also can introduce good solutions for detecting food fraud
and enhance provenance transparency such as organic food verifica­
tion. Although utilizing blockchain technology in food safety is in its
early stage, some interesting benefits of integrating blockchain and IoT
can be summarized as follows (Tian, 2016):
Table 4
A comparison between blockchain-based supply chain and traditional supply
chain.
Comparative item
Blockchain-based supply
chain
Traditional supply chain
Data integrity
Data Security
Data Verification
Government regulations
Tamper proof
Decentralized protection
Achieved
using authentication
protocol
No tamper proof
Centralized protection
Not achieved
without authentication
protocol
integration example of colored coins in managing the ownership and
land transactions (Alexandru and Chami, 2019). The proposed model in
Fig. 16 explains how blockchain can be utilized to manage the land
registry process in an automated form based on NEM blockchain (New
Economy Movement)Bach et al., 2018) and ”InterPlanetary File System
(IPFS)” (Benet, 2014). NEM is a P2P cryptocurrency blockchain laun­
ched in 2015 as a novel platform depends on the colored coin XEM
which can be mined using Proof of Importance (POI) algorithm (Bach
et al., 2018). InterPlanetary File System (IPFS) is a distributed ubiqui­
tous file system that allows users/devices to not only receive but host
P2P shared content of land pieces information (Benet, 2014). The land
registry process is based on NEM multi-signature accounts that ask N
out of M (e.g. 10 out of 30) proofs to create certain land transactions
process. The system issue NEM mosaics which are used as confirmation
tokens. The system uses a dashboard to monitor all land transaction
updates and present them in a visualized form through web APP. The
core logic layer is responsible for managing the communications be­
tween the dashboard, API, and IoT land location sensor and IPFS. It also
enables to create logical NEM account for land pieces or other real
estates through NEM server layer.
4) Food Safety (Lin et al., 2018): In the food industry, Blockchain
could transform the entire process and introduce untraditional solutions
for food problems. Several studies reported that almost 1 in 10 people
in the world fall ill after eating contaminated food every year. More­
over, 68.2% of food safety events happened in China was caused by
illegal activities, and the cost of food fraud incidents reaches to
1. Food tracking and monitoring management.
2. Reducing the agri-food loses and logistic costs
3. Detecting food fraud and verifying product information.
4. Protecting agri-food safety information based on integrating RFID
technology with blockchain and IoT
5. Maintaining safety and quality of food products.
6. Facilitating the communication between all stakeholders of food
industry process.
Integrating IoT sensors and electronics chips such as RFID tags are
evolving rapidly. So, companies will be able to attach IoT sensors to
food products to track and detect food fraud incidents and potential
failures. Forwarding these data from IoT sensors to the blockchain can
provide standardization, transparency and traceability to the supply
chain and help food stakeholders to detect temper-proof. Walmart’s
blockchain is a good example of tackling the food safety of Mango and
Pork in the food supply chain process (Kamath, 2018). The proposed
model in Fig. 17 explains a general architecture model of how block­
chain can be used in the food safety during the major four stages of food
supply chain process: food production, food processing, food shipping,
and food distribution.
5) Real Time Remittance for small farms: Farmers may need to use
a public payment system for receiving real rime remittances from the
agricultural organizations or from the governments. With mobile block­
chain systems (Xiong et al., 2018), small farmers become able to execute
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M. Torky and A.E. Hassanein
Fig. 15. Managing supply chain process using blockchain technology: A proposed model.
Fig. 16. Managing Land registry and administration using blockchain technology: A proposed model.
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Fig. 17. Managing food safety using blockchain technology: A proposed model.
real-time payments for goods, crops, agricultural services. Moreover, they
can receive agricultural remittances through a blockchain mobile APP.
Mobile blockchain will make all real-time transactions are faster, trans­
parent and keep farmer information protected. With mobile blockchain
APPs, the farmer can get the following benefits (David, 2019):
Table 5,6 summarizes the research work in the five blockchain-use
cases in the precision agriculture.
Fig. 19 depicts the research progress with respect to a number of
published papers for each use case. The research analysis result con­
firms that blockchain has more contributions to supply chain proce­
dures than other agriculture use cases. Moreover, farm overseeing, land
registry and real-time agricultural remittance transfer still in the early
stages of maturity, whereas food safety tracking become a real appli­
cation of blockchain technology.
• Cryptocurrency Applications enable farmers to execute payment
transactions with crop traders and receive real time remittances.
• Electronic Wallet APPs that enable farmer to store and manage their
digital assets and money.
• Digital crop tracker apps to provide farmers updated information
•
•
4.2. Blockchain platforms in precision agriculture
about the trade rates, cryptocurrency-based transactions, crop
market dynamics and a portfolio of various agricultural variables.
Retail APPs allowing its Farmer to execute payment transactions
through digital currencies
Smart Contracts or self-executable protocols APPs allow the farmer
to detect any fraud or illegal operations regarding their digital crops
or digital assets
The rapid growth of using blockchain in precision agriculture leads
to developing some platforms that can be used for various agricultural
activities. This subsection discusses the most common five blockchain
platforms in smart agriculture.
1) Provenance is founded by Jessi Baker in 2013 as the first
blockchain platform that supports supply chain activities (Digital Social
Innovation, 2019). Provenance enables producers, consumers and re­
tailers to track their products during all stages of the product’s life
cycle. It enables every physical product to authenticated by ”a digital
passport” that confirms its authenticity and origin for preventing selling
fake goods. With Provenance’s trust engine, the participants able to
verify their supplier transactions for better supply chain integrity. They
can also turn their digital certifications into data-backed marks for the
customers to review, then it is forwarded to the blockchain to be stored
in a secure, and genuine form. Provenance enables stakeholders to
share honest stories about their goods and products in a trustworthy
mode. Producers and consumers can show the traceability of each
product item through the Provenance’s tracking tool (Provenance,
2019). Moreover, issuing a digital asset for a specific physical product
using Provenance and connect to it via a protected tag, e.g. an NFC, will
decrease the traceability time from days to seconds, reducing good
frauds, providing faster recalls, improving transparency, protecting
Integrating blockchain with a remote sensing satellite, data, and
mobile money techniques can ensure transparent secure transactions,
automated payment remittance transfer between smart farms
(Sylvester, 2019). The farmer can execute all financial transactions
through a mobile wallet account created on a mobile blockchain such as
COIN22 (Sylvester, 2019). As depicted in the proposed model in Fig. 18,
farmers can exchange, buy, sell their digital crops through the wallet
App created in COIN22 blockchain. When a new transaction occurs
between two or more farmers, it should be verified by other farmers
who work as verifiers for the new transactions. If the transaction is
valid, a new digital token is issued regarding this transaction and stored
in a new block that will added to the COIN22-blockchain. On the other
hand, based on satellite data sent to COIN22-blockchain, farmers can
trace some major indexes of their farms (e.g. soil, water, drought in­
dexes, etc) by using a digital tracer App connected with COIN22.
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M. Torky and A.E. Hassanein
Fig. 18. The architectural model of real time financial transactions between smart farms: A proposed model.
brand valueFilatov, 2019). OriginTrail (Konic, 2019) is a similar
blockchain platform that developed for data integrity and validation in
supply chain activities.
2) AgriDigital is a cloud-based blockchain platform founded in
2015 by a team of Australian farmers and agribusiness professionals (Xu
et al., 2019) AgriDigital makes the agriculture supply chain simple, easy
and secure between farmers and consumers. The farmers and stake­
holders are able to manage their contracts, deliveries, orders and pay­
ments all in one place and in real-time. The AgriDigital platform has
five core subsystems (Agridigital, 2019):
based transactions, trusted expertise, such that more than 1500 com­
pany and technical experts prefer to work with IBM blockchain.
Moreover, it can bring together regulators, suppliers, consumers, and
professionals to work with each other in IBM Blockchain Ecosystem
(IBM, 2019). IBM Blockchain enables equal visibility of activities and
reveals where an agricultural asset is at any point in time, who owns it
and what condition it’s in (Tripoli and Schmidhuber, 2018; Kamath,
2018).
4) Foodcoin (Bitcoin Wiki, 2018) is a new blockchain ecosystem
consists of 1000 Eco Farms. It designed to create a global marketplace
of food and agricultural products. The Foodcoin system is working
through the use of smart contracts and verifying food transactions
based on a cryptocurrency called Foodcoin (FOOD). In order to develop
the FOOD ecosystem, the FOOD tokens will be created on the Ethereum
blockchain, where it can be used as a colored token currency
(Rosenfeld, 2012) to purchase or sell food products. The implementa­
tion of the FoodCoin Ecosystem is based on seven subsystems (Bitcoin
Wiki, 2018):
1. Transactions. Through this subsystem, farmers and stakeholders
able to easily buy and sell several goods
2. Storage. In which, the accounts, payments, orders, delivers, and
other sensitive information are digitized and stored
3. Communications, through which, the farmer and consumers can
build the connections patterns.
4. Finance, through which, all financial transactions and virtual
currency transfers between farmers and consumers can performed.
5. Remit, through which, the real time remittances issued to the
farmers can be transferred.
1. Distributed database in a distributed ledger
2. The Foodcoin (FOOD)
3. Multi-functional crypto wallet called (WALLOK).
4. Payment system called (DiPay).
5. Participant verification called DIGID
6. Multi-signature (Multisig)-based smart contracts systems called
(Smaco).
7. Food Product authentication system called (Product Orgin ID
(PRORID))
The main feature of AgriDigital is the creation of digital assets in the
form of tokens. These tokens represent the physical agricultural goods
(e.g. tons of grains). Since the digital asset transfer from farmer to the
consumer along the supply chain, an immutable data of the physical
asset is created using proof of concept protocol (Luckas, 2019). Once a
digital asset is issued, users can use the application layer of AgriDigital
platform to send/receive data.
3) IBM Blockchain is one of the most common blockchain plat­
forms that is used in agricultural logistics. It is a good choice for opti­
mizing agricultural transactions and global trade relationships (Cook,
2018). What makes IBM blockchain is a popular platform for agri­
cultural stakeholders are: the high security, multi-cloud flexibility-
There are also similar blockchain platforms that can be utilized in
food industry and supply chain such as ’Ambrosus’, ’TE-Food’, and
’Ripe.io’,Cook, 2018).
5) AppliFarm is a leading blockchain platform which founded in
2017 by Neovia combines (Neovia, 2019). It can be utilized for
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M. Torky and A.E. Hassanein
Table 5
Summary of the research work of adopting blockchain in smart agricultures.
Use Case
Research
Blockchain Contribution
Year
Farm Overseeing
Lin et al. (2017)
2017
Farm Overseeing
Patil et al. (2017)
Farm Overseeing
Supply Chain
De Clercq et al. (2018)
Caro et al. (2018)
Supply Chain
Dujak and Sajter (2019)
Supply Chain
Li and Wang (2018)
Supply Chain
Platform with duplicated and shared bookkeeping (2018)
Supply Chain
Toyoda et al. (2017)
Supply Chain
Leng et al. (2018)
Supply Chain
Saberi et al. (2019)
Supply Chain
Chen et al. (2017)
Supply Chain
Lu and Xu (2017)
Supply Chain
Xie et al. (2017)
Land Registration
Barbieri and Gassen (2017)
Land Registration
Anand et al. (2016)
Land Registration
Vos et al. (2017)
Land Registration
Chavez-Dreyfuss (2016)
Food Safety
Lin et al. (2019)
Food Safety
Lin et al. (2018)
Food Safety
Tse et al. (2017)
Food Safety
Tian (2016)
Monitoring system for water
distributions
Tracing system for securing
sensor communications in the farms
Future farming tech. in agriculture 4.0.
AgriBlockIoT mechanism for
managing Agri-Food supply chain.
Blockchain application in
supply chain and logistics
Monitor system
for agricultural products.
Provenance system
for supply chain trust
Blockchain system
for the Post Supply Chain
Double Blockchain chain system
for securing public transactions
Review of blockchain
in supply chain
Supply Chain system for
Quality Management
Monitoring system the product origins
in supply chains
Secure blockchain system
for tracking products
A Blockchain model
for managing digital land registry
Blockchain system for
managing land admiration activities
Analysis model for applying blockchain
in Land Administration
”Proof of concept”:
a blockchain in the Swedish land registry
Blockchain system for preventing food data
tampering
Food tracking technique based on
IOT and blockchain
Blockchain usecases in securing
food supply chain process
Tracking system for an agri-foo
supply chain in China
Research
Blockchain Contribution
Year
Food Safety
Galvez et al.
(2018)
Blockchain in
2018
Real Time Agricultural
Remittance
Chinaka (2016)
Real Time Agricultural
Remittance
Holotiuk (2019)
food Sypply chain
Blockchain for managing
agricultural products in
Africa
Blockchain impacts in
financial transactions
2018
2018
2019
2018
2018
2017
2018
2019
2017
2017
2017
2017
2016
2017
2016
2019
2018
2017
2016
5. Challenges and open issues
Table 6
Summary of the research work of adopting blockchain in smart agricultures
(Cont—).
Use Case
2017
Adopting blockchain in precision agriculture is in its early phases.
Most agricultural projects are less than two years old, and none of those
projects are recently more than 1,000 beneficiaries. Moreover, pilot and
small scale blockchain projects are started in a limited number of
countries around the world. 93% of these projects are either in concept
stage or have started a small pilot and 7% of these projects don’t have
available information (Galen et al., 2019). Fig. 20 summarises the
current status of blockchain-based projects in the precision agriculture.
In precision agriculture, Merging blockchain technology with IoT as
well as other techniques such as Radio Frequency Identification (RFID)
(Ali and Haseeb, 2019), Cyber-Physical Systems (CPS) (Hu et al., 2012),
and 4G/5G broadband communications (Dahlman et al., 2016; Agiwal
et al., 2019) faces several important challenges and requires more re­
search work (Fernáez-Caramés and Fraga-Lamas, 2018). Developing
IoT networks and applications in precision agriculture based on
blockchain technology is a complex process and leads to more chal­
lenges and open questions. The next subsection discusses these chal­
lenges in some details.
2016
2017
providing digital proof of animal welfare, and livestock grazing (Carole,
2018). It is able to track livestock data within the animal production
sector. Placing linked tags around the cows’necks, that identifies the
areas in which they graze, enough digital data can be gathered to
guarantee high-quality grazing. For instance, presence in grazing, the
number of actual pasture days, pasture changes, etc. Moreover, the
Applifarm system can ensure the dependency of monitored livestock
farms with animal welfare requirements. Moreover, animal welfare
digital data is integrated into the AppliFarm platform and accessible by
stakeholders at any time. AppliFarm is well ahead of the market and
already operational for #meat, #dairy and #cattle. Another application
of Applifarm is the guarantee of tracked livestock farms provides GMOfree feed for their animals.
5.1. Privacy and security challenges
Although the P2P design and time stamping-based transactions of
blockchain make IoT networks are more secure against a lot of attacks,
blockchain-based IoT systems and networks still suffer from four major
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M. Torky and A.E. Hassanein
Fig. 19. Research progress of blockchain in precision agriculture use cases.
attacks: Denial of Services (DoS) attacks (Kolias et al., 2017), Sybil
Attacks (Zhang et al., 2014), Eclipse Attacks (Nayak et al., 2016), and
Routing Attacks (Apostolaki et al., 2017). DoS is a cyber-attack in which
the adversary tries to compromise the availability of an IoT device or a
network device to the authenticated users. For example, the attacker
can create this attack by sending a fake connection to the blockchain
network to deceives the authenticated users and prevents them to mine
the valid tokens of a digital asset. DoS can target blockchain-based IoT
systems through different types such as Distributed Denial of Services
(DoS), in which the adversary uses thousands of fake IP address to flood
IoT nodes with streams of bits to make it unable to correctly respond.
The common instances of DDoS attacks are SYN flooding, smurf, and
Fraggle (Bhattacharyya and Kalita, 2016).
An application-layer DDoS attack (Beitollahi and Deconinck, 2012)
is another form of DDoS attack where the adversary target applicationlayer functions and features in blockchain-based IoT systems. For ex­
ample, disrupting the farming functions of farmers’accounts for drop­
ping the smart wallet system in COIN22 blockchain (Sylvester, 2019).
DDoS Extortion (Dragomiretskiy, 2018), A Challenge Collapsar (CC)
attack (Chun-Tao et al., 2012), Permanent Denial-of-Service (PDoS) or
Phlasing attack (Leyden, 2019), Shrew Attack (Mahjabin, 2018), Slow
Read Attack (Park et al., 2015), Mirai Botnet (Kolias et al., 2017),
Teardrop Attack (Shekhar, 2016), and RUDY attack (Najafabadi et al.,
2016) are another examples of DoS attacks that can be executed on
blockchain-based IoT networks in e-agriculture systems.
There are some techniques to limit the probability of success of DoS
attacks but it is very tough to completely remove it. One of these
techniques is Application Front-End Hardware (O’Dell, 2009). It can be
used as smart hardware which works as a firewall for IoT networks for
verifying data packets before traffic reaches the blockchain system.
Blackholoing and Sinkholing (Xie and Ettema, 2016) is another DoS
defense technique. Blackhole Routing can be utilized for protecting
blockchain-based IoT system in e- agricultural against DoS attacks
where all packet traffic to the attacked IoT device, DNS or IP address is
sent to a ”black hole” (e.g. non-existent server). on the other hand,
Sinkholing Routing can be used to routes traffic to a valid IP address of
IoT device or server which analyzes traffic and rejects bad packets,
however, Sinkholing technique is not efficient for most severe attacks.
Fig. 20. Recent status of blockchain projects in precision agriculture.
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M. Torky and A.E. Hassanein
Intrusion Prevention System (IPS) (Rodas and To, 2015) is another DoS
defense technique that works efficiently on attack recognition, how­
ever, it is not able to block behavior-based DoS attacks.
One of the serious attacks that targets blockchain-based IoT net­
works in precision agriculture systems is Sybil attacks (Zhang et al.,
2014). In this attack scenario, the attacker seeks to use fake IoT nodes
or fake sensors by duplicating their identities to set up fake connections
in the blockchain-based IoT networks. The honest IoT nodes become
unable to distinguish between valid and valid connections. Detecting
such attacks is a very hard task, but unfortunately, they do occur. For
example, the Swiss-based company Chainalysis that provides block­
chain services created over 250 fake Bitcoin nodes to harvest in­
formation about transactions propagating over the network (Caffyn,
2019). Although there are some countermeasures can be used for de­
creasing the chance of succeeding Sybil attacks, there isn’t a way to
eliminate them. Verifying node identities and detecting fake nodes
using Finite Automat can be utilized in IoT networks for detecting Sybil
nodes (Meligy et al., 2017). Sybil Guard (Yu et al., 2008), Sybil Infer
(Danezis and Mittal, 2009) are other techniques that can be utilized to
defend against Sybil attack. moreover, Sybil resistant for bitcoin
(Bissias et al., 2014) can be used to address Sybil attacks, denial-ofservice attacks, and timing-based inference attacks. Sybil belief (Gong
et al., 2014) also is another Sybil attacks defender which based on a
semi-supervised learning algorithm.
1) Eclipse Attack (Nayak et al., 2016) is another attack in which
the adversary aims to obscure certain nodes from the entire P2P net­
work. The attack scenario monopolizes an IoT node’s connections so
that it cannot receive data from any nodes other than the attacking
nodes. In contrast to Sybil attacks, Eclipse attacks mainly target single
IoT nodes rather than the entire network at once. The attacking node
could easily execute double-spending transactions with the blockchain
system. This can easily be done by sending a transaction showing proof
of payment to the victim IoT node, obscuring it from the network, then
finally sending another transaction to the entire network spending the
same tokens again. In this way, the victim node becomes isolated and
only receives data from the malicious nodes until to drop it (Wst and
Gervais, 2016). There have been multiple countermeasures discussed
that may reduce the success of eclipse attacks. Most of them propose
how a specific IoT node locally stores IP addresses that it will use to
later reconnect to the network or set an upper limit on the number of
incoming TCP connections (Marcus et al., 2018). However, there is no
effective way to completely remove the Eclipse attacks (Fantacci et al.,
2009).
2) Routing attack is the last attack model that target blockchainbased IoT networks. The attacker seeks to intercept messages propa­
gating between IoT devices and tamper these messages before sending
them to the peers in the network (Wallgren et al., 2013). Routing at­
tacks can be executed through two models: 1) Portioning attacks, in
which the adversary divides the network into two or more disjoint
groups by hijacking a bridge node between two groups of networks. 2)
Delay attack in which the adversary intercepts the propagated mes­
sages, tamper with them and finally propagate them again to the net­
work (Apostolaki et al., 2017). Routing attacks cannot completely be
prevented, however, there are some countermeasures that may reduce
its success on blockchain-based IoT networks. For example, diversifying
the connections between IoT nodes based on dynamic topologies will
prevent the attacker to hijack the bridge nodes between two sub-net­
works topologies (Lu, 2014). Another technique is to monitor the net­
work features such as Round-Trip Time (RTT) (Tun and Thein, 2008)
and detect anomalous patterns.
3) Privacy preserving is another challenge in blockchain-based IoT
network systems (Fernáez-Caramés and Fraga-Lamas, 2018). All IoT
devices and participants are authenticated by their public key or a hash
value with the blockchain. The anonymity is not ensured and, since all
transactions are shared publicly, there is a likelihood for the attacker to
analyze such transactions and infer the real identities of the IoT nodes.
This makes the privacy is a complex challenge and open problem. For
addressing privacy challenges, some limited techniques are proposed
for mitigating privacy issues. Permission blockchain (Nikkil et al.,
2010) can be utilized for issuing digital certificates for identifying the
identities of IoT devices. Extracting device signature automatically can
be used for authenticating IoT applications and its users (Dorri et al.,
2017). Blockchain-based multi-level mechanisms (Li and Zhang, 2017)
can be utilized also to define a set of access lists and access rights for
authorizing user/device access. Privacy can also be improved through
zero-knowledge proof techniques such as Zerocoin (Miers et al., 2013)
and Zerocash (Sasson et al., 2014). However, these techniques still
vulnerable to other attack patterns (Peng, 2012).
5.2. Blockchain size and energy consumption
Due to the continuous transactions with blockchain, blockchain
tends to grow rapidly and block size will increase. This leads to larger
download times and the need for a larger memory space for mining
purposes (Fernáez-Caramés and Fraga-Lamas, 2018). Moreover, a lot of
IoT devices must store large volumes of data that are not interesting to
them. this leads to a waste of computational power and resources. This
issue can be addressed by using lightweight blockchain (Gruber et al.,
2018). However, this approach requires designing a hierarchical and
centralized blockchain system.
An alternative approach to address the block size growth is miniblockchain (França, 2019). This approach is working through the use of
an accounting tree, which registers only the recent status of every node
linked to the blockchain. Also, block size has to be scaled according to
the bandwidth limitations. Many small transactions would increase
energy consumption, while a few large ones may involve big payloads
that cannot be processed by some IoT devices (Fernáez-Caramés and
Fraga-Lamas, 2018). Moreover, energy consumption is a major factor in
blockchain-based IoT computing since most IoT nodes are powered by
batteries. Therefore, energy efficiency is a major aspect of keeping longtime computing of IoT nodes. Energy consumption can result during
blockchain mining operations (Truby, 2018) and P2P communication
(Zhou et al., 2014). Proof of Stake (King and Nadal, 2012) and Proof of
Space (Dziembowski et al., 2015) are good algorithms for solving en­
ergy-consuming problems during mining processes.
Mini-blockchain (França, 2019) is also a good choice to reduce
energy consumption during P2P communications. Moreover, the cryp­
tographic techniques such as Scrypt (Asolo, 2018) or Myriad and multialgorithm mining (CoinBrief, 2018) are faster and thus can be utilized
for reducing mining energy consumption.
5.3. Complex technical challenges
There are still unsolved technical challenges that can oppose de­
signing blockchain-based IoT network systems in precision agriculture
(Nikkil et al., 2010). Some of these challenges can be summarized as
follows:
1) Storage capacity and scalability. In the context of IoT-based eagricultural applications the scalability limitations are much difficult to
address. Blockchain may appear to be not appropriate for IoT models.
With IoT communication, IoT nodes can send gigabytes (GBs) of data in
real-time. This attribute represents a solid barrier against the block­
chain and IoT integration model. The known blockchain platforms can
only manipulate a few numbers of transactions per second. So this could
lead to a potential bottleneck for IoT systems. Moreover, blockchain is
not designed to store large amounts of data, So, scaling blockchain to
serve thousands of different heterogeneous devices is a big technical
challenge (Khan and Salah, 2018).
2) Blockchain Forking. Forking is a common technical problem
with blockchain. It occurs when two peers (i.e. miners) add two right
blocks to the chain at the same time. This situation is called ”Blockchain
forking” (Spanos et al., 2017). All known blockchain protocols solve the
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M. Torky and A.E. Hassanein
issues that obstacles developing blockchain-IoT systems in precision
agriculture.
Before this study, a lot of research work discussed the integration
between IoT and blockchain as abstract technologies, but this study is
one of the first research attempts that investigated how this integration
between IoT and blockchain can be implemented in precision agri­
culture domain. Further research work is needed to be carried out in
order to validate the proposed blockchain solutions and implement the
conceptual models introduced in this study. Moreover, additional re­
search should focus on investigating more blockchain platforms used in
precision agriculture and conducting a comparison study under some
major criteria in deeper analysis and assessment. Finally, more research
work is also needed to examine more closely the link between block­
chain and IoT in additional use cases in precision agriculture tech­
nology.
forking problem with a simple rule: The longest chains of blocks is the
right one. When we have blockchain fork, some IoT-peers will start
mining through one branch of the chain, and the other peers mine
through the other chain. Definably, one chain will have more miners
than the other, and as such, will insert new blocks to their chain faster.
The rest of the miners will then switch over to the longer chain and the
forked chain will always die out. Hence, A specific IoT device miner
must be programmed to doesn’t execute a transaction on a forked
blockchain. The Conventional rule said that it is therefore wise to wait
for 6 blocks to really verify and confirm a specific transaction
(Cryptohelp, 2018). Blockchain forking can create confusion, fake
transactions, technical challenges, and economic uncertainty. Some
new blockchains platforms such as Tezos (Fernandes and Alexandre,
2019) suggest formal design structures of blockchain systems to de­
crease the risk of occurring blockchain forking.
3) Latency and Throughput. Due to developing many topologies of
IoT networks, developing blockchain-based IoT networks requires rapid
processing for a variety of transaction patterns per time unit. This re­
presents a great challenge in IoT networks regarding throughput. For
example, Bitcoin’s blockchain can execute a maximum of 7 transactions
per second (Vukolic, 2015) while VISA network (VisaNet) able to pro­
cess 100,000 transactions per minute (Vermeulen, 2016). Latency is
another challenge related to the time required by the blockchain al­
gorithm to create a novel block in the chain. For example, in the case of
Bitcoin, block creation times take a 10-min mean according to Poisson
distribution (Fernáez-Caramés and Fraga-Lamas, 2018), while latency
requires only a few seconds in the case of VISA blockchain (Vermeulen,
2016). Hence, developing an optimal blockchain-based on IoT network
with rapid throughput and low latency represents an open problem.
4) Multi-chain Management. In some cases, professionals and
blockchain engineers need to configure more than one blockchain
platform for different purposes in an IoT network (Lee and Kim, 2008).
For example, in smart agricultural system, Foodcoin (Bitcoin Wiki,
2018) is used to manage food supply chain transactions, COIN22
(Sylvester, 2019) is used to manage financial transactions between
farmers, and AppliFarm (Neovia, 2019) can be used to monitor animal
welfare, livestock grazing. Configuring more blockchain platforms in a
standalone system leads to more security, privacy, scalability, oper­
ability, and monitoring problems. Moreover, the collaborative im­
plementations and the use of collaborative standardization regarding
consensus protocols, authentication, authorizations, and encryption
algorithms are needed for managing multi-chain platforms (FernáezCaramés and Fraga-Lamas, 2018).
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ­
ence the work reported in this paper.
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6. Concluding remarks
This survey study was designed to investigate the importance of
integrating the Internet of Things (IoT) and blockchain technologies in
developing smart systems, and applications in precision agriculture.
This technological integration has shown that blockchain can introduce
novel solutions for chronic security and performance challenges in IoTbased precision agricultural systems. The significant findings of this
study can be summarized into four contributions.
Firstly, the study reviewed considerable solutions by which block­
chain can solve many security and performance challenges of IoT-based
network systems in precision agriculture. Secondly, the study proposed
new blockchain models that can be implemented in the most important
five uses cases in precision agriculture. Using these models, blockchain
can be integrated with IoT for mitigating many challenges in internet of
farms and crop overseeing, supply chain, food safety, land registration,
and financial transaction between farmers with each other or between
farmers and agricultural organizations. Thirdly, the study reviewed and
discussed the most common blockchain platforms used to digitally
manage various subsectors in precision agriculture, such as crops
overseeing, livestock grazing, and food supply chain. Finally, the study
discussed some of security and privacy challenges, and blockchain-open
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