A secure fish farm platform based on blockchain for agriculture data integrity

Computers and Electronics in Agriculture 170 (2020) 105251

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

A secure fish farm platform based on blockchain for agriculture data integrity Lei Hang, Israr Ullah, Do-Hyeun Kim

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Department of Computer Engineering, Jeju National University, Jeju 63243, Republic of Korea Department of Computer Science, Virtual University of Pakistan; (I.U.), Pakistan

A R T I C LE I N FO

A B S T R A C T

Keywords: Internet of Things Agriculture data integrity Blockchain Permissioned network Fish farm

Internet of Things (IoT) has opened up a new dimension for smart farming and agriculture because of the natural feature that makes it possible to assign tasks made by a user or that transfers agriculture data obtained through sensors to producers for analysis on various terminal devices. In recent years, heightened interest in agriculture data has arisen since the commercialization of precision agriculture technology. Agriculture data are known to be messy, especially from combine yield monitors, and analysts are concerned with the validity of data, especially given that other people may have impacted data quality at various steps along the data path. The blockchain can be a possible solution to the analyst’s problem of uncertain data quality from prior data manipulation since it ensures data have not been inappropriately manipulated or at the very least documents what changes have been made by specific individuals. This paper proposes a blockchain-based fish farm platform to ensure agriculture data integrity. The designed platform aims to provide fish farmers with secure storage for preserving the large amounts of agriculture data that cannot be tampered with. Diverse processes of the fish farm are executed automatically by using the smart contract to reduce the risk of error or manipulation. A proof of concept that integrates a legacy fish farm system with the Hyperledger Fabric blockchain is implemented on top of the proposed architecture. The efficiency and usability of the proposed platform are demonstrated through a series of experiments using various metrics.

1. Introduction The global seafood industry is rapidly expanding, and per caput seafood consumption has been doubled during the past five decades. Nowadays, more than 1 in 10 people on the planet earn their living on aquaculture, which is also called fish farming (Meat and Meat Products, 2003). Globally, about 2% of overall calorie intake and 15% of protein intake comes from seafood, both wild-caught and farmed (Aksnes, et al., 2017). According to the most recent report (Pauly and Zeller, 2017) on global aquaculture production, the total annual amount in 2015 stayed at 138 Mt (million metric tons), of which 60% was wild capture landings and 40% aquaculture production. The enormous potential of wild capture has already aroused worldwide attention, and the attendant effects on the marine environment have also posed a different set of challenges to the overall seafood industry. This ecosystem is rapidly growing to exceed sustainable capacity, considering the following points: The global marine life has reduced by half in last 40 years since 1970 as stated in the report (Living Blue Planet, 2015), in other words, the productivity of the ocean is diminishing, and there is

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more demand for fish than it can be naturally produced. According to another report (Heffernan, 2009), we are at high risk for entering a phase of extinction of marine species unprecedented in human history since near 90% of most commonly captured fish have become extinct since 1950. As captured fish numbers skidded or even dropped while demand raised, fish farming has expanded an average 6.5% per year over the past two decades. Besides, aquaculture’s contribution to the global fish industry has grown from 7% in 1980 to 40% today (FISHERIES - OECDFAO Agricultural Outlook, 2030). In the future, it is anticipated that aquaculture will play a critical role in fish supply, and more than 50% of the fish humans consume will be raised in a farm rather than by wildcapture (Willer et al., 2018). As stated in the report by FAO (FAO Yearbook, 2014), the aquaculture industry has contributed a lot to help the growing global meat demands and has mostly reduced the pressure of oceans. Fish farming involves raising fish commercially in tanks or enclosures such as fish ponds. The typical fish farm uses large plastic tanks that are placed inside a greenhouse, each of size 4 m diameter with a

Corresponding author. E-mail addresses: [email protected] (L. Hang), [email protected] (I. Ullah), [email protected] (D.-H. Kim).

https://doi.org/10.1016/j.compag.2020.105251 Received 10 October 2019; Received in revised form 24 December 2019; Accepted 27 January 2020 0168-1699/ © 2020 Published by Elsevier B.V.

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nodes namely miners and the process used to maintain called mining, which requires high computation demands (Reyna, 2018; Krause and Tolaymat, 2018). Besides, the blockchain platform has to partially or fully replace existing legacy systems, which requires time and resources. Instead, a general interactive mechanism is desired to enable interaction between the blockchain with these legacy systems rather than implementing a fully decentralized system. Moreover, few of the existing studies apply DLT to agriculture data that may have near-immediate effects on big data. New data storage technologies are desired to deal with a large amount of agriculture data collected from IoT devices since blockchain technology is not intended for large transaction data payloads. Furthermore, the need for a distributed ledger type of tracking of who performed what manipulation to the data is needed in the agricultural industry. Therefore, a new or customized implementation of blockchain is required to solve these issues. By contrast to other existing system proposals, the proposed approach in this paper brings the advancements as follows:

water depth of 0.75 m. Security is much more comfortable with a tank system as production is concentrated on a small site rather than a large cage system like fish ponds that are located in the lake. Even though to manage the resources of the fish farm still requires experienced fish farmers that would raise labor costs. Farmers are also faced with challenges such as a changing climate, limited water supply, and dwindling fossil fuels. The IoT is helping change the way that farmers work through precision farming (Auernhammer, 2001), a farm management concept that uses sensors, data, and network communication to tailor the farming system to the specific conditions of each field. Most existing studies presented in the literature focused on using IoT devices to monitor the fish farm remotely (IoT-based Fish Farm Management System, 2014; Sung et al., 2014; Chang and Zhang, 2013). These systems are beneficial to the resource management that realizes the minimum of resource cost and the maximization of profits by monitoring various environmental parameters (e.g., temperature, dissolved oxygen level, pH, etc.) in fish tanks. The data collected from IoT sensors can be used for long-term observation, analysis, and generalization. A private IoT platform for precision agriculture and ecological monitoring was proposed in (Wu et al., 2010) using an approach of multiple views for different high-level scenarios. The implementation of the platform and its evaluation using various sensors deployed at the research and end-user facilities were also described. Many technical challenges need to be addressed in terms of smart farming (Elijah, 2018). For example, data sharing infrastructure is insufficient due to the lack of a mechanism to share sensitive agriculture data in a privacyprotected manner. Analysts tasked with managing agriculture data desire information on how that data has been handled before reaching their computer. The prudent analyst has very little confidence that analyses would be reliable given prior data management uncertainties since the data may have been subjected to rigorous data cleaning protocols or potentially modified with evil intentions. Besides, existing security methods are expensive in terms of energy consumption and processing overhead for IoT devices since the current security framework is managed by a central server that is not preferred for IoT devices (Roman et al., 2013; Hang and Kim, 2019). However, smart farming needs lightweight, scalable, and distributed security and privacy. To fulfill these issues and challenges, some researches dedicated to new paradigms by using Distributed Ledger Technology (DLT), aka blockchain (Fernández-Caramés and Fraga-Lamas, 2018) since it eliminates a single point of failure, increase data transparency and immutability that can transform the current economic organization and governance (Davidson et al., 2016). According to Agfunder News (AgFunder News, 2018), many potential distributed ledger agricultural solutions are emerging. This includes startups such as FILAMENT (2019) being used to create concepts like smart farms, and Skuchain (2019) tracking food through supply chain distributed ledgers. Filament uses blockchain technology to broadcast tamper-resistant weather data, SMA alerts, machinery protocol, and GPS positioning on the farm. SkuChain’s technology focuses on creating direct relationships while augmenting trust and visibility into the flow of goods. Most blockchain conversations center around merchandising transactions and tracking of agricultural inputs and production outputs, especially for traceability regarding food safety. With blockchain, agriculture data from sources like soil sensors, weather satellites, drones, and farm equipment stored in a distributed store allow us to engineer trust and secure sustainable agriculture development. Besides, it improves decision-making and automation both at the individual farm level and the community level via pooling and analysis of those data (Kim and Laskowski, 2018). Although there are clear benefits and opportunities in the use of blockchain technology to enhance efficiency and reduce costs, there are still some challenges and limitations to the wide adoption of blockchain in the agriculture industry (Kamilaris et al., 2019). For example, most consensus algorithms used by current blockchain-based systems shrink the responsibilities for transaction verification upon all the individual

• Scalability: Our solution meets the requirements of blockchain and • • •

its integration with the legacy IoT fish farm system comprised of numerous farm devices. It provides a RESTful interface to enable interaction between the legacy system and the blockchain network. Off-chain Storage: This work utilizes a distributed database architecture by deploying the Couch DB resided on each blockchain peer to enable the adequate file storage and minimize the duplication across the entire blockchain filesystem. Privacy: This work hides the details of the measured environmental data and the transaction history, which records how a resource is manipulated except the authorized user. High throughput: This work proposes the use of a permissioned blockchain where interactions occur among a set of network entities that are fully trusted by each other. As a consequence, traditional voting-based protocols like Byzantine Fault Tolerance (BFT) or Crash Fault Tolerance (CFT) consensus protocols can be used to improve the performance of transaction processing.

More precisely, this paper proposes DLT for agriculture data storage to assure agriculture data integrity. The distributed ledger keeps a full record of actions that occurred in the fish farm. It is linked to the data being recorded at the farm sensor such that these are inseparable. Besides, the smart contract is utilized to automate the agriculture data processing, including outlier filtering before generating records into the ledger. Based on the data stored in the blockchain, smart contracts could trigger and execute specific actions. Access control rules are defined to provide specified participants with the authority to access network resources or perform operations within the business network. A permissioned blockchain network is used as the infrastructure since it enhances the transaction security as well as keeps the data transparency. A proof of concept on top of the designed architecture is built to test all system functions by using the Hyperledger Fabric (IBM, 2017) and the fish farm system in our previous work (Ullah and Kim, 2018). The remaining of this paper is organized as follows: Section 2 discusses the related works. Section 3 illustrates the proposed platform architecture and workflow. Section 4 details the implementation of the proof of concept. Section 5 presents the simulation results through various snapshots and gives a comprehensive evaluation of the system performance. Section 6 discusses the limitations and threats to validity of the proposed system. Finally, Section 7 concludes the paper and points out future research directions. 2. Related works The last five years have witnessed the explosion of interest in blockchain technology with a great many commercial companies and research institutions focusing on potential applications of the 2

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technology across a range of financial, industrial and social sectors (Conoscenti et al., 2016). Blockchain technology has been considered highly relevant to the agricultural industry in addressing issues such as transparency and food integrity. The global blockchain in the agriculture and food supply chain market was valued at USD 41.2 million in 2017 and is projected to reach USD 429.7 million by 2023, at a Compound Annual Growth Rate (CAGR) of 47.8%, according to a recent statistical report (MarketsandMarkets., 2019). Blockchain technology provides immutable permanent transactions and distributed data access, which has the potential to facilitate data exchange and reduce the opportunities for fraud or adulteration (Saberi, 2019). It is believed that blockchain can make food products and transactions traceable and auditable through supply chains by assigning unique and immutable digital identifiers to them (Chinaka, 2016). This will then ensure compliance of food production to food safety, environmental, and social standards. Improved transparency and traceability can also help to prevent food waste, guide the distribution of surplus food to those who need it and allow businesses and consumers to work out the ecological footprint of their diet. Food security could benefit from the transparency, relatively low transaction costs and instantaneous data sharing that the technology enables (Pearson, 2019). Blockchain technologies and smart contracts (Ge et al., 2017) can primarily improve the claims processing system in the agricultural insurance industry. They therefore significantly mitigate the volatility of running a farming business due to unexpected weather. As digital technologies are increasingly used to manage farms, blockchain will facilitate the sharing of on-agriculture data, which supports the development of data-driven technologies in smart farming (Creydt and Fischer, 2019). Concepts such as real-time management of supply chains, quicker access to crowdfunding, and overall accountability and transparency throughout the food chain could be introduced (Dujak and Sajter, 2019; Tripoli and Schmidhuber, 2018). Practical applications of blockchain technology in the agriculture sector also include minimizing unfair pricing, product origins, and reducing the multinational agricultural influence in favor of more localized economies. Blockchain technology has the potential to facilitate the transfer of value based on the assets that farmers cannot fully take advantage of in the current financial system in developing countries (Thomason, 2018). Additional startups such as Provenance (Provenance -Every, 2019) and FarmShare (Farmshare, 2019) are researching and developing blockchain-based agricultural platforms. Provenance is a blockchain-based system that tracks goods such as food and makes the information public, secure, and all-inclusive. The Provenance software service can enable businesses to comply with legislation and consumer demands, which will create a positive social and environmental impact. Farmshare focuses on creating new forms of property ownership, community cooperation, and locally self-sufficient economies. It aims to use the blockchain to tokenize shares, incentivize volunteers, optimize resource sharing and minimize food waste. OlivaCoin (OlivaCoin, 2019) is another blockchain-based platform for the trade of olive oil, supporting the olive oil market. With its cryptocurrency and traceability system, it aims to reduce overall financial costs, increase transparency and efficiency to global markets. AgriLedger (AgriLedger, 2019) uses distributed cryptoledger to enhance trust among small cooperatives in Africa. This solution provides many benefits for the farmers and suppliers including efficiency and traceability, two elements which assure food safety and security. Fishcoin (Powered, 2019) is a blockchain-based traceability system for the seafood industry to address the fragmentation of most seafood supply chains. It has been designed as a peer-to-peer network that allows independent industry stakeholders to harness the power of blockchain using a shared protocol so that data can be trusted, transparent, and secure. Other projects like Choco4Peace-Building Lives with Dignity (2019), Everledger-Ever More Knowledge (2019), Beefledger (2019), and Farmshine-Market-based solutions for smallholder farmers (2019), as outlined in (Opportunities for blockchain in

agriculture, 2019), are also examples of initiatives aiming to improve food supply chain integrity through the blockchain technology. The researches on the integration of agriculture with blockchain technology are still in the initial stage, and there are various barriers and challenges for the broader adoption of blockchain technology (Chang, 2019; Zhao, 2019; Galvez et al., 2018). It is revealed that small and medium-sized businesses are too small or lack the expertise to invest in the blockchain by themselves. Current uncertainties are preventing private parties from developing a convincing business case. A common argument made against blockchain technologies is that there is not significant adoption outside of cryptocurrencies. The current experience of cryptocurrencies indicates that they are vulnerable to speculators and massive price fluctuations. Blockchain technology must become simpler to understand and use. To the best of our knowledge, most of these researches either entirely focus on the food supply chain or the trading system. Few studies have been carried out on processing the agriculture data gathered from sensors on the farm. The theme of this paper is to highlight a practical way to integrate the blockchain with the legacy fish farm system. The proposed platform is non-cryptocurrency based so that the processing performance is roughly the same as any other distributed system. Besides, this platform is fully open source, and thus, it is easy to add more features and to integrate with other systems. 3. Proposed fish farm platform based on blockchain 3.1. Proposed platform overview Fig. 1 shows the conceptual architecture of the proposed fish farm platform based on blockchain, which comprises of four components: fish farm, blockchain network, data storage, and end-user. The fish farm is a form of aquaculture, raising fish in tanks that are always equipped with various IoT sensors (temperature sensor, water level sensor, oxygen sensor, and PH sensor) for monitoring any environmental changes and actuators (water pump, pond heater, fish feeder, and lighting LED) for regulating the corresponding environment. These devices can communicate with the processing system through wireless communication protocols such as Wi-Fi and Zigbee. The legacy fish farm system provides real-time monitoring and management of the whole fish farming process. The blockchain network is mainly composed of many peers, which contain the smart contract to write a block of transactions to the ledger. The blockchain is a growing list of records, called blocks. Generally, a block contains a hash value of the previous block, a timestamp, transaction data, and some other information. It is not possible to tamper with the ledger data unless breaking the hash links since all transactions on the ledger are sequenced and cryptographically linked together. The data storage that resides in the blockchain network can either be a local DB or cloud storage for holding information about the fish farm, such as user profile, device profile, environmental data from sensors, and control parameters to actuators. End-user can read or write data to the blockchain network through various terminal devices (e.g., smartphones or laptops). For example, farmers can view the variation of water level in a specific period. Fig. 2 details the interaction between the legacy fish farm system and the blockchain network. Farm sensors and actuators exchange information within one fish tank and these fish tanks form up to construct a complete fish farming environment. The fish farming environment produces various IoT data that is fetched by the legacy fish farm system, which contains multiple modules for IoT service provisioning. The data collection module collects time-series data coming from different fish tanks. The data processing module prepares data for analysis and computes optimal conditions to bring forward the corresponding strategies, which can realize the maximal profit (e.g., energy consumption). The control module generates control parameters to adjust farm actuators by optimized settings accordingly. 3

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Temperature Sensor Water Level Sensor

Water Pump Pond Heater

Oxygen Sensor

Fish Feeder

PH Sensor

Lighting LED

Sensors

Actuators

Data Storage Blockchain Network

Legacy Fish Farm System

Peer Peer

Peer Peer

Peer

Fish Farm

Peer Peer

Peer Peer

Peer

Peer

Peer

Peer Peer

Block 0

genesis

Ledger

Block 1

Block 2

T1

Peer

Block 3

T2

T3

Blockchain

Peer

Ledger

End User

Smart Contract

Fig. 1. Conceptual architecture of the proposed fish farm platform based on blockchain.

Fish Farm App Blockchain Network Orderer

Data on blockchain

Peer

Peer

Monitoring and Control Distributed Ledger

Legacy Fish Farm System

PBFT Consensus Identity Authentication

Peer

Data Collection Transaction Data Processing

API

Smart Contract P2P Protocol Transaction Verification Event Hub

Control Transaction

Peer

Peer

Control Parameter

Sensing Data

Fish Farming Environment Fish Tank 1

Farm Sensor

Fish Tank 2

Fish Tank 3

Farm Actuator

Fig. 2. Interaction diagram of the proposed fish farm platform based on blockchain. 4

Peer

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In this paper, a permissioned blockchain network is proposed that would only allow authorized users to perform operations on the blockchain, and a valid block must contain signatures from a subset of users. This solution avoids the risk of data exposure and ensures that no invalid user could insert or tamper a transaction in the blockchain. Besides, the approved smart contract cannot be altered unless it gets signed agreement from all users featured on the operation. The blockchain network is comprised of many peers, which contain the smart contract and data storage to write a block of transactions to the ledger. The distributed ledger is a consensus of replicated, shared, and synchronized digital data that spreads across the whole blockchain network, where all participants within the network can have their selfsame copy of the ledger. It is private as only approved members can run a peer to validate transactions. The smart contract is a distributed programmable application initialized on each peer. It defines the executable logic that generates new facts that are added to the ledger. The orderer node is employed with consensus protocol, such as Practical Byzantine Fault Tolerance (PBFT), to ensure the consistency of every copy of the ledger. This node exists independently of the peer processes and orders transactions on a first-come-first-serve basis across the network. The event hub sends notifications every time a new block is added to the ledger or whenever the predefined condition in the smart contract is triggered. The peer also provides various blockchain features such as transaction verification, identity authentication, and P2P communication. The services provided by the blockchain network are exposed as web APIs through which the external systems or client applications can integrate with the blockchain network.

Fig. 4. Sample access control rule in the policy contract.

package, and once deployed, all smart contracts within it are made available to applications. In the proposed platform, two contracts are deployed to the blockchain network. Fish farm contract provides various functions related to device monitoring and management, including outlier filtering, sensing data recording, sensing data querying, and actuator control. Policy contract defines a specific rule list to manage access to resources by associating a policy that evaluates whether the user is permitted to access or manipulate resources of the blockchain network. In the proposed platform, users can be a farmer, farm owner, or device (sensor/actuator). These three have different policies in terms of their permissions. Policy contract also differentiates between access control for network administrative changes (network access control) and access control for resources within a business network (business access control). As shown in Fig. 4, the farm owner has the right to access network-level commands such as starting or upgrading the blockchain network. Besides, the farm owner has full access to all the resources within the network while the farmer can only perform limited operations such as reading history records of the sensor. Algorithm 1 illustrates the process regarding the “Record Water Level” transaction to record the water level value in the blockchain

3.2. Proposed smart contract for fish farm Fig. 3 illustrates the structure of the smart contract in the proposed platform. From a high-level view, the smart contract, together with the ledger, forms the heart of the blockchain network. The smart contract programmatically accesses two distinct pieces of the ledger: a blockchain, which immutably records the history of all transactions and a world state that holds the current value of these states. The smart contract can perform actions on the states stored in the state DB as well as query the record of transactions in the blockchain. External applications interact with the smart contract to perform operations on the blockchain network. In all cases, the blockchain contains an immutable record to reflect changes resulting from these operations. Afterwards, the ledger updating result is returned to the external application as the response. The smart contract can be packaged and deployed to a blockchain network. Multiple smart contracts can be defined within the same

Fig. 3. Proposed smart contract for blockchain network interaction. 5

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3.4. Transaction workflow in the blockchain network

network. This transaction will be executed only when the incoming water level value is within the defined threshold by the smart contract. Afterward, it creates a new water level resource and assigns the input parameters to it. Lastly, the resource is added into the ledger, and an event is emitted to the device that initializes the transaction.

Fig. 6 details the transactional mechanics that take place during a transaction operation over the blockchain network. The client application must have credentials issued by the CA to get the authorized permission for submitting a transaction to the blockchain network. Transactions start with client applications sending transaction proposals to peers. Client applications communicate with the blockchain network over the application SDK. These peers can be further divided into endorser peers or committer peers. Endorser peers simulate and sign transaction proposals, respond granting, or denying approval while committer peers validate transactions results before writing block of transactions to the ledger. There is an overlap between endorser peers and committer peers, as we can say endorser peer is a special kind of committer peer that must hold the smart contract. Each endorser peer receives and executes the transaction proposal by invoking the smart contract in their simulated environment, namely endorsement. The simulated transaction results will not be updated into the ledger at this stage. The endorser peer captures the simulated results into a particular set of Read and Written data, called RW sets. Read data captures the latest read from the current state and Write data holds the data that will be written to the world state when simulating the transaction. The endorser peer provides a signature on these RW sets and then returns them to the client application. The client application packages the signed transaction that is a response to the results of the simulated transaction and then submits this transaction along with RW sets to the orderer. When the network reaches a consensus on the submitted transaction, the orderer sorts this transaction into a block and delivers it to all committer peers. Each committer peer validates the transaction by checking whether these RW sets are matched with the current world state. Once the transaction is verified, it is written to the ledger, and the world state is updated according to the Write data from RW set. Lastly, these committing peers generate asynchronous notifications to inform the client whether the submitted transaction is successfully executed or not. Client applications can subscribe for event notification to be notified by each committer peer whenever events occur.

Algorithm 1. Pseudo Code for Record Water Level Transaction (Device ID, Water Level Value, Unit, Timestamp) Input (Device ID, Water Level Value, Unit, Timestamp) Begin Set Device as the Current Participant If Water Level exceeds the threshold then Throw an error that Water Level exceeds the threshold Else Generate new resource for Water Level Generate random ID for the resource Assign Device ID to the resource Assign Water Level Value to the resource Assign Unit to the resource Assign Timestamp to the resource Add the resource in ledger Emit event to Current Participant Else Reject the transaction and throw an error End

3.3. Certificate authorities based PKI architecture As shown in Fig. 5, the proposed PKI (Public Key Infrastructure) architecture in this paper mainly consists of certificate authorities (CA) who issue digital certificates to all parties (e.g., farm owner, farmer, device). These parties then use the issued certificates to authenticate themselves in the messages they exchange in the blockchain network. CA is a trusted party, which provides the root of trust for all PKI certificates and provides services to authenticate, issue, and revoke the identity of individuals. The Certificate Revocation List (CRL) constitutes a reference for the certificates that are no longer valid. The certificate database stores information about issued certificates and the validity period and status of each PKI as well. This work utilizes the X.509 standard (Cooper et al., 2008), which is widely used in many Internet protocols, for example, the TLS or SSL, and is also used in offline applications like electronic signatures. Besides, every party of CA is required to have a crucial cryptographical pair: a public key and a private key. These two keys are connected logically by using some encryption techniques, a public key acts as an authentication anchor, and a private key produces digital signatures on messages. Recipients of digitally signed messages can verify the origin and integrity of the received message by checking whether the signature is valid with the public key of the sender.

4. Simulation implementation 4.1. Fish farm setup In this study, the usability and scalability of the proposed system are evaluated by extending the legacy fish farm system from our previous work. As shown in Fig. 7, the legacy fish farm system aims to adjust the water level inside the tank to the desired level without an increase in energy consumption by applying different machine learning procedures. It takes input from water level sensors and sends output to control the water pump actuator. Power source represents the actual

Farm Owner

Certificate Database

Request digital certificates

Certificate Authority

Issue digital certificates

Certificate Revocation List

Key Pairs

Fig. 5. Identity management using certificate authorities. 6

Farmer

Device

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Fig. 6. Transaction operational processes in the blockchain network.

This sensor generates analog output at the voltage level from 0 v to 1.88 V, indicating the corresponding water level from 0 cm to 4.8 cm. The IntelliFlo manufactured by Pentair is a variable speed pump with 8 programmable speed settings and a built-in timer to ensure the pump runs at optimum speed and duration. This actuator operates at 230 V with the operating speed from 750 to 3450 RPM, producing varying flow rates. The whole operational process has four main steps that are performed by four modules: the sensor interface module, prediction module, optimization process module, and pump controller module. It begins with reading the current water level from sensors inside the

power source, and our objective is the optimal utilization of power. Two separate emulating modules are implemented to simulate the operations and behaviors of the fish tank and water pump, respectively. These emulating modules are designed by considering a typical water level sensor called SMPJ-0005-01 and a variable speed pump called IntelliFlo. The technical characteristics of these devices used in the emulating modules are illustrated in Table 1. The SMPJ-0005-01 manufactured by Geekria is a cost-effective high-level recognition sensor, which is obtained by having a series of exposed parallel wires to measure the water level. The operating voltage is between 3 and 5 V, and the operating current is less than 20 mA.

Legacy Fish Farm System

History data

Farm information Fish Farm Data

User Interface

REST API

Newly Updated Part

Database

Smart Contract

Required Water level Tx3: Pump Energy Consumption

Optimal pumping level and duration

Optimization Module

Predicted Water level Prediction Module (Kalman Filter)

User

Tx2: Predicted Water Level

Current Water level Available Power Pump Controller Module Control commands

Tx4: Control Parameter

Power Source

Sensor Interface Module

Tx1: Current Water Level

Sensor data

Required Power

Water Pump

Fish Tank

Fig. 7. The operational process of the proposed fish farm platform based on blockchain. 7

Blockchain Network

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Table 1 Technical characteristics of sensors and actuators for the fish farm. Component

Manufacturer

Type

Operating Voltage

Operating Range

SMPJ-0005–01 IntelliFlo

Geekria Pentair

Water Level Sensor Water Pump Actuator

3–5 V 230 V

0–4.8 cm 750–3450RPM

tank. Second, the Kalman filter algorithm is applied to remove error in sensor reading and predict actual tank water level. This helps in adjusting/controlling the various parameters before the inside tank conditions worsen for the dwelling fish. Third, if the water level is below the desired range, then optimization is applied. During optimization, optimal pumping level and duration to obtain the desired water level inside the tank are computed. Finally, these parameters are passed to the pump controller module to control the pump as per optimized settings for fish development. The system has an internal database for storing data for a comprehensive analysis of the system performance gain and optimal utilization of resources. A user interface is also provided to users to specify system parameters, e.g., tank size, fish type, quantity, etc. The REST API exposes the services provided by the blockchain network. These modules have been configured with REST APIs to access and manipulate the data on the blockchain through the smart contract. They can submit transactions to perform some specific operations on the blockchain, such as adding the water level value.

main app is connected with the tank emulator and the pump emulator via Windows Communication Foundation (WCF) services. The user can control the simulation via the app, and once the simulation is started, the app reads sensing data from the water tank emulator. Afterwards, the optimal pump flow rate and duration are calculated and then sent to the pump emulator. The Fabric network for the simulation is hosted on Ubuntu Linux 18.04 LTS operating system with Intel Core i5-8500 processor and 16 GB memory. The infrastructure of the blockchain network is built on Hyperledger Fabric (V1.4.3). Node is utilized to implement the client SDK for communication with the blockchain network. Fabric leverages the Docker for microservices, which provide the running environment for all Fabric network elements. Docker engine provides the docker runtime environment, and docker-compose provides the IDE to launch various Fabric component containers. We develop the smart contract in node.js by using the Hyperledger Composer (Gaur et al., 2018) and then deploy it to all the peers within the Fabric network. Fig. 9 details the finalized system structure. The Fabric network consists of four peers and one orderer node that is running in the Docker container. All the services provided by the blockchain network are exposed in REST APIs, which can be consumed by the legacy fish farm system. The REST server provides the corresponding APIs to handle these requests and a Fabric client that holds the required credentials to get the authorized permission for submitting transaction proposals. The communication between the REST server and the Fabric blockchain network happens over gRPC protocol since the data is transferred in binary format or as proto buffers, giving high performance and latency. By subscribing to events, the legacy fish farm system can receive notifications of the transaction being committed from the Fabric blockchain network through WebSockets. The blockchain file system acts as a transaction log, while records that represent all the history changes are preserved in the state database. CouchDB is used as the state database that provides rich query support when the smart contract data is modeled as JavaScript Object Notation (JSON). The Hyperledger Composer Playground is a webbased application that provides a user interface for the testing of a deployed business network in the blockchain. Web technologies such as HTML and CSS are used to construct the code behind the user interface. The backend of the web app is built in the blockchain network, and the application's data and records of operation are cryptographically stored in the blockchain. The web app provides end-users with a standard and common way to view data on the blockchain. As presented in Fig. 10, the simulation execution process starts with

4.2. Smart contract implementation Table 2 presents a set of transactions defined in the smart contract. Resources can be either participant who takes part in the blockchain network (e.g., farm owner, device) or assets, which can be anything of values (e.g., water level data). Participants can interact with them and each of which can be associated with an identity. Transactions are submitted by a participant to perform the specified operation against the specified resource. The operation identifies the action that the transaction governs, and four actions are supported: CREATE, READ, UPDATE, and DELETE. ALL is used to specify that the transaction governs all supported actions. As shown in Table 3, the transactions defined in the smart contract are exposed as REST APIs. These APIs contain a base URI, a media type (an identifier for format contents transmitted to the blockchain network), and verbs (GET, POST, PUT, DELETE). The URI typically represents the path of the data entity, and the verb indicates the action to be performed in the identified resource along with the request. 5. Simulation and system analysis 5.1. Simulation setup Fig. 8 presents the app interface of the legacy fish farm system. The Table 2 Sample of defined transactions in the smart contract. Transaction Name

Participant

Operation

Resource (Participant, Asset)

Collect Water Level Predict Water Level Energy Consumption Control Water Pump User Management Sensor Management Actuator Management Water Level History Predicted Water Level History Energy Consumption History Water Pump History

Water level sensor Farm owner Farm owner Water pump Farm owner Farm owner Farm owner Farm owner, farmer Farm owner, farmer Farm owner, farmer Farm owner, farmer

CREATE CREATE CREATE CREATE ALL ALL ALL READ READ READ READ

Water level Predicted water level Energy consumption Pump control parameter Farmer Water level sensor Water pump Water level Predicted water level Energy consumption Pump control parameter

8

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Table 3 Sample RESTful APIs. URI

Verb

Media Type

Action

/api/CollectWaterLevel /api/PredictedWaterLevel /api/EnergyConsumption /api/ControlWaterPump /api/UserManagement /api/SensorManagement /api/ActuatorManagement /api/ReadWaterLevel /api/ReadPredictedWaterLevel /api/ReadEnergyConsumption /api/ReadWaterPump /api/system/historian /api/systemidentities/issue /api/system/ping

POST POST POST POST ALL ALL ALL GET GET GET GET GET POST GET

Application/json Application/json Application/json Application/json Application/json Application/json Application/json Application/json Application/json Application/json Application/json Application/json Application/json Application/json

Add current water level Add predicted water level Add computed energy consumption Add pump control parameters Manage farmer profiles Manage farm sensor profiles Manage farm actuator profiles Read water level history Read predicted water level history Read energy consumption history Read water pump history Retrieve all historian transactions Issue an identity to the specific participant Test connection to the blockchain network

user can view, initiate, or organize various network information of the blockchain. As shown in Fig. 13, it provides details like name, list of network nodes, details of blocks, transactions, and associated data, as well as any other relevant details stored on the ledger. Once a transaction is committed in the blockchain network, Hyperledger Explorer updates the data in the web application accordingly.

reading current water level data from the sensor that resides in the tank emulator. The fish farm collects the water level sensing data and further preserves it in the blockchain network. This is realized by invoking the relevant API to perform the transaction defined by the smart contract. The fish farm computes the predicted water level using the Kalman Filter and performs optimization if the water level falls below the desired level. Similarly, these results are transmitted to the blockchain. According to the optimized results, the fish farm generates the control parameters that include pump flow rate and duration to obtain the desired water level inside the tank. These control parameters are passed to the pump emulator and further preserved into the blockchain.

5.3. System performance analysis This section provides a comprehensive test to verify the proposed platform performance by using several performance indicators, along with a formula where appropriate. An open-source benchmark simulation tool called Hyperledger Caliper (Hyperledger Caliper, 2019) is used that allows users to measure the performance of a specific blockchain implementation with a set of use cases. The term transactions per second (tps), known as throughput, refers to the number of transactions completed by the blockchain network per second. The experiment configuration to the parameters was fixed as shown in Table 4. By default, Hyperledger Fabric’s sample network limits the block size to only up to 10 transactions. In the following experiment, the block size was varied from 16 transactions to 512 transactions to observe the impact on the average throughput. Fig. 14 presents the experiment results of the average number of transactions per second during the whole process of 60 s. It is obvious to see from the graph that the average

5.2. Simulation results This section describes the simulation results with various snapshots corresponding to the APIs defined in Table 3. Fig. 11 is a snapshot that presents the list of transactions recorded in the blockchain network. The date is an unalterable timestamp indicating the time when the transaction is submitted. The entry type denotes the transaction being executed and the participant represents the identity who submits the transaction. The user can observe various fish farm agriculture data that were stored in the blockchain, as shown in Fig. 12. Hyperledger Explorer (Hyperledger Explorer, 2019) is a blockchain utility module that provides a web-based application, with which the

Fig. 8. Legacy Fish Farm App Interface. 9

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Web App Data on blockchain

Fish Farm Main App Hyperledger Fabric Network Peer1

User Actions

Orderer

Peer2

Peer..

WebSockets

Legacy Fish Farm System

Event Notification Peer3

Transaction HTTP Blockchain Filesystem

HTTP Data/Command

Smart Contract

State DB

REST Server gRPC API

Ledger

Fabric Client

Block 1 Transaction Reads[] Writes[]

Key : water level Value: { "name": "water level", "timestamp":"8/6/2018", “water level”:”2000”, ... }

Block 2 Transaction Reads[] Writes[]

Fish Farm Emulator

...

Fish Tank Emulator

Water Pump Emulator

Fig. 9. The finalized prototype of the proposed fish farm platform.

transaction throughput increases along with the increase of the block size. The default setting of 10 transactions per block is too small to serve the network workload in high throughput systems. As a result, a block size of 512 transactions is used in the remaining experiments due to its high overall throughput. The throughput can be further classified into two categories, read throughput and transaction throughput. Read throughput refers to the number of read operations that occur within a defined period, while transaction throughput refers to the number of valid transactions that are performed by the blockchain within the allotted time. Formulas for these two metrics are presented in Eq. (1) and (2), respectively. Furthermore, it is worth noting that the number of invalid transactions must be removed from the yield of total valid transactions. The average read throughput was evaluated by varying the send rate from 500 tps to 3000 tps in the total time of 60 s, and the experimental results are shown in Fig. 15. The read throughput grows continuously until it reaches a peak of 2314 tps at the send rate of 2500 tps. Therefore, the optimal send rate is obtained as 2500 tps for read throughput. Similarly, as shown in Fig. 16, the optimal send rate for transaction throughput is calculated as 1100 tps since the transaction throughput decreases continuously with the increasing of send rate after that.

Read Throughput =

Number of total read operations total time

Transaction Throughput =

Number of total valid transactions total time

(1)

(2)

Another study on network latency was performed to measure the amount of time taken for a transaction to be performed in the blockchain network. Similar to the throughput, network latency also falls into two categories: read latency and transaction latency. Read latency refers to the time that the client receives the reply after submitting the read request. Transaction latency measures the amount of time taken for a transaction to be performed in the network. The measurement not only includes the time from the point that it is submitted but also consists of the broadcast time and any correction time because of the consensus mechanism in place. In this study, the percentage of the network was set to 100% since the use of non-probabilistic protocols like PBFT. General and simplified formulas for computing read latency and transaction latency are shown in Equation (3) and Equation (4), separately. As shown in Fig. 17, the average read latency of the proposed system was evaluated by varying the send rate from 500 tps to 3000 tps in the total time of 60 s. The read latency has a relatively small 10

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Fig. 10. Execution flow of the simulation.

increase with the increase of the send rate. However, the read latency increases significantly after the send rate of 2500 tps as it exceeds the optimal send rate. Similarly, the graph in Fig. 18 shows that the average transaction latency increases linearly as the user request rate increases, and this value rises sharply after the optimal send rate of 1100 tps.

Transaction Latency = Confirmation time ∗ network threshol d − transaction submission time

(4)

6. Discussion

ReadLatency = Responsereceivedtime − readoperationsubmissiontime

6.1. Limitations

(3)

For the sake of demonstration, this work demonstrates the

Fig. 11. Snapshot of transaction history in the web app. 11

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Fig. 12. Snapshot of fish farm agriculture data in the web app.

future work, a more user-friendly fish farm application will be implemented to visualize the data from the blockchain and allow control of the level of different parameters in real-time.

interaction between the legacy fish farm system and the blockchain. To ease the implementation and reduce the cost of testing, the legacy system is connected with device emulators. Besides, this work only considers a single parameter, which is water level maintenance in the tank through the optimal operation of the water pump. However, the system design is flexible enough to serve as a foundation for testing different types of farm devices (temperature, dissolved oxygen, pH). In future work, the legacy system will be served as a server to enable communication with various farm devices. Embedded hardware such as Raspberry Pi and Arduino can be used to integrate with different sensors and actuators that are deployed inside the fish tank. Besides, there are many popular protocols (CoAP, MQTT) and standards that allow devices and servers to talk to each other in interconnected ways. Different IoT protocols will be tested and compared to select a more suitable protocol. Furthermore, these IoT devices can serve as clients to communicate with the blockchain straightforwardly via the REST server. It’s worth mentioning here that the presented fish farm application in this work seems too complex to be used by ordinary farmers as it was mainly developed for experimental analysis of the proposed system. In

6.2. Threats to validity One threat to the validity of our results is that the data transmission between the legacy fish farm system and the REST server is not secure due to the lack of authentication and proper implementation strategies. It is needed to enable authentication for the REST server before external clients are permitted to call the REST APIs in the production environment. There are many authentication strategies one can choose from, including a mix of social media such as Facebook, Google, GitHub, and enterprise strategies such as SAML, JSON Web Tokens (JWT), or LDAP. In future work, different authentication strategies will be tested, allowing clients of the REST server to select a more appropriate authentication mechanism. Another threat is that peer nodes of the blockchain network are connected in a Local Area Network (LAN) setting, as opposed to a WideArea-Network (WAN) that would be expected in real-world 12

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Fig. 13. Snapshot of various blockchain network information in Hyperledger Explorer.

Evaluation on Read Throughput

Table 4 Blockchain network performance evaluation configuration. Values

Number of peers Number of channels Number of clients that fire transactions Duration in which transactions are fired Amount of time to form a block Maximum number of bytes of a block Maximum number of transactions per block

Avg Throughput (tps)

Experiment Parameters

4 1 5 60 secs 2 secs 99 MB 512

1497 1500 998 1000 500 500 0

1000

1500

2000

2500

3000

Send rate (tps) Fig. 15. Effect of the send rate on blockchain network read throughput.

Evaluation on Transaction Throughput 1200

1079 983

200

300

400

500

Avg Throughput (tps)

Avg Throughput (tps)

500

875

826

1987

1846

2000

Block Size (Number of Transactions) Evaluation

1000 900 732 800 700 587 600 486 500 400 300 300 200 100 0 0 100

2314

2500

600

Number of Transactions (per block) Fig. 14. Effect of the block size on the average throughput of transactions.

1000 800

924

886

689 576

600

478 398 300

400 200 200 0

applications. In future work, the results will be replicated by running the Fabric network on a cloud service like Amazon Web Services (AWS).

873 780

200

300

400

500

600

700

800

900 1000 1100 1200 1300

Send rate (tps) Fig. 16. Effect of the send rate on blockchain network transaction throughput.

7. Conclusion and future direction mass of agriculture data to an incorruptible digital ledger for accountability and security in the fish farm. Completed by the adoption of a customized implementation of blockchain, the designed approach brings various advancements, including scalability, high throughput, off-chain storage, and privacy. The followings are the main

Although the coevolution of blockchain and agriculture research studies is still in its infancy, it is the goal of this work to suggest a feasible way to build practical blockchain-based applications, to revolutionize agriculture industry developments, especially in fish farming. This paper describes a decentralized approach by storing a 13

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Evaluation on Read Latency

Avg read latency (ms)

600

blockchain.

567

CRediT authorship contribution statement

500 400 300

345

321

Lei Hang: Methodology, Conceptualization, Writing - original draft. Israr Ullah: Writing - review & editing. Do-Hyeun Kim: Supervision, Writing - review & editing.

256

242

200

Declaration of Competing Interest

100 0

500

1000

2000

2500

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

3000

Send rate (tps) Fig. 17. Effect of the send rate on blockchain network read latency.

Acknowledgments

Evaluation on Transaction Latency Avg transaction latency (ms)

3500 2756

3000 2500

This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M3F2A1073387), and this research was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-01456, AutoMaTa: Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT). Any correspondence related to this paper should be addressed to Dohyeun Kim.

2965

2146 2168 2197 2231 2245 2013 2032 2041 2068 2102

2000 1500 1000 500 0

200

300

400

500

600

700

800

900 1000 1100 1200 1300

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Send rate (tps)

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Fig. 18. Effect of the send rate on blockchain network transaction latency.

contributions of this research:

• This work proposes a generic interaction approach to integrate a • •

legacy fish farm system with the blockchain by using a RESTful interface. Besides, this interface obstructs the interaction with other distributed systems since it can be customized to meet different scenarios and requirements. The blockchain is used as an external service to provide reliable and secure storage. This work applies a separate software solution by deploying Couch DB resided on each blockchain peer to deal with large transaction data payloads. The blockchain of the legacy fish farm system has been implemented on a permissioned network called Hyperledger Fabric, which diminishes the risk of a participant intentionally introducing malicious code through the smart contract. These participants are known to each other, and all the actions are recorded on the blockchain in terms of the access control rules that are established for the network and transaction type. All the services provided by the Fabric network have been validated and the transaction processing capability has been evaluated with different performance metrics.

The future work will refine the prototype for evaluating the usability of the proposed architecture in practice. The blockchain infrastructure will be implemented as a flexible blockchain-as-a-service (BaaS) to ease the configuration and management of the blockchain network without caring about the underlying infrastructure. To do that, some blockchain templates provided by cloud services will be tested for running the Fabric network. Besides, different authentication strategies will be tested to secure the data transmission between the legacy fish farm system and the REST server. Moreover, the legacy fish farm system will be extended to connect with different farm devices and additional support for different IoT protocols. Embedded hardware such as Raspberry Pi and Arduino will be used to serve as device client to communicate with the legacy fish farm system as well as the 14

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