IoT and Blockchain for Smart Water Quality Management in Future Cities: A Hyperledger Fabric Framework for Smart Water Quality Management and Distribution

doi:10.21203/rs.3.rs-3727101/v1

Download PDF

Research Article

IoT and Blockchain for Smart Water Quality Management in Future Cities: A Hyperledger Fabric Framework for Smart Water Quality Management and Distribution

https://doi.org/10.21203/rs.3.rs-3727101/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

The delivery and management of clean water are crucial for the long-term growth of Smart Cities. However, controlling water quality and delivery in a smart city is a difficult and time-consuming process. In this work, we suggest a unique solution for smart water quality monitoring and distribution in Smart Cities that combines Internet of Things (IoT) with blockchain technology. We describe a system based on Hyperledger Fabric that provides safe and efficient data gathering, authentication, preservation, and smart contract execution. A continuous monitoring of water quality parameters, such as pH, temperature, turbidity, and dissolved oxygen, is achieved using the proposed IoT system. The collected data is stored on a secure blockchain ledger using Hyperledger Fabric, ensuring transparency, immutability, and security. Smart contracts are used to automate the water distribution process, enabling the system to efficiently allocate water resources based on demand and quality. Furthermore, the use of blockchain technology ensures that water quality data cannot be tampered with, providing a high degree of trust and accountability in the system. Overall, the proposed system represents a significant step towards a sustainable and secure future for water management in cities. This technology can revolutionize the way we manage and distribute water resources, ensuring safe and clean drinking water for future generations.

cloud computing

digital water

industrial IoT

smart city

smart water networks

water supply chain

wireless communication

The smart city has several difficulties. Water management is one of them. This issue is considered essential for humans and the environment. Water Quality (WQ) is a measure of quality over time and at a specific place. Around 250 million incidences of illness infections are recorded worldwide each year as a result of water contamination. Smart water management systems that integrate blockchain and IoT help enhance sustainability, compliance, and consumption management [1].

Human well-being and economic growth must have access to clean and safe water. To determine the quality and purity of water, a set of biological, physical, and chemical properties are used. Due to factors including population increase, urbanization, and climate change, water supplies are being strained more and more in many cities. It is becoming increasingly necessary to develop advanced systems for managing water quality and distributing water to urban populations to address these challenges. In addition to enhancing public trust, IoT and blockchain can facilitate informed decisions and optimize water use [2]. This paper proposes a Hyperledger Fabric-based system for smart water quality management and distribution using blockchain and IoT. The system aims to provide a secure, transparent, and efficient solution for monitoring water quality and ensuring the delivery of safe and clean water to future cities.

Data management problems and evaluations of management techniques for improving WQ are handled globally using the Water Quality Index (WQI). The internet has powered a variety of technology and services that have made our life feasible. IoT is a collection of recently created digital and information technologies that work collectively. Monitoring and managing water quality with IoT is now possible using a variety of new technologies. The immutable blocks in a blockchain facilitate peer-to-peer transactions without the need for third-party authentication.

Blockchain is the appropriate architecture for establishing confidence in public water transactions due to global water shortage and the geographical dispersion of water resources. The suggested method for water management was created to address the water problem. WQ is vital for human health, water resources, and the environment. The need for clean, safe, and sufficient fresh water by billions of people on the earth encouraged researchers to involved in water quality modeling to meet this universal problem. Smart cities are infused with IoT, which has transformed people's daily lives. Blockchain technology has been developed as a promising technology for tracking assets and recording transactions in a distributed ledger, which makes it more secure and private [3].

Several sensitive factors should be identified to establish the overall state of WQ. WQI is created in response to this need to turn a large data collection into a single index. Over the last two decades, a blockchain has gained traction in managing smart assets as well as creating and managing smart contracts. In water quality management, WQI measures the effectiveness of strategies. It is mainly focused on identifying the number of sensitive parameters that are needed to properly assess the water quality. After a transaction has been completed, a new block must be processed to be added to the chain. This is known as mining. Blocks must be approved by the participating nodes before they can be added to the chain [4].

A variety of blockchain platforms can be used to implement blockchain technology, some of which use major platforms like Ethereum or Hyperledger and others that use smaller custom platforms. Without water quality monitoring, determining which bodies of water and sources are safe to consume would be impossible. Water quality monitoring with IoT is extremely important for long-term development. The availability of safe drinking water for humans is a key component of the sustainable development objective. Blockchain technology could enhance transparency for auditors and regulators by providing a record of all transactions on the chain and discouraging speculation in the water trade. [5].

IoT and blockchain technologies are highlighted in this literature as smart systems for urban water management. Using advanced digital technology and the IoT, we can monitor and collect information on water consumption and distribution in the urban environment. Water systems with smart technologies can have impacts that go beyond maximizing resource efficiency. Smart water technology integrates various detecting devices and intelligent systems based on the needs of a community.

A major benefit of Blockchain technology from the point of view of the financial industry is the facilitation of collaboration between counterpart organizations and the elimination of manual checks and reconciliations. Water Ledger can be used to solve the authorization issue by assigning a unique owner ID number to each water account, thus resolving ownership issues. Data collection through smart contracts is also an effective way to capture volume data. A real-time view of water market volume would be available in the future when combined with historical volume data.

IoT-based sensors deployed throughout the water distribution system continuously monitor pH, temperature, turbidity, and dissolved oxygen levels. The collected data is then encrypted using a secure cryptographic algorithm and saved on the Hyperledger Fabric blockchain network for verification and storage. Hyperledger Fabric is an enterprise-grade blockchain platform that provides a highly secure and scalable solution for building decentralized applications. The platform's consensus algorithm and ledger structure ensure that the collected data is verified and tamper-proof, providing a high degree of trust and transparency in the system.

The proposed system offers several benefits over traditional water quality management systems. First of all, the system offers real-time monitoring of water quality indicators, enabling proactive identification of possible concerns and prompt response. Secondly, the use of blockchain technology ensures the integrity and authenticity of the collected data, preventing tampering and fraud. Finally, the system's efficient data management and secure data storage provide a reliable foundation for smart water quality management and distribution [6].

The design of intelligent water management and planning systems has been proposed by scientists for various applications. In this model, water supply costs are reduced when the amount of water required is constrained. Using a decentralized blockchain network, this proposes a distributed water management system. In our final section, we discuss the advantages and challenges associated with implementing blockchain technology in water management.

The creation of the IoT framework needs a literature study, evaluation, and experiment. Considerable research has been conducted on water quality and supply management systems. Some of the important scientific research are presented in this section.

Oberascher et al. [7] described the network-based urban water infrastructure (UWI) applications, which are distinguished by geographical and statistical resolutions of observation and regulatory data. The results of this research suggest that UWI networks require cooperation between planned applications and acceptable communication technologies to operate effectively. This article offered a framework for academics and network operators to utilize as a decision-making tool for the technological implementation of such systems and the growth of smart cities. These advancements provide new opportunities for UWI operations, but they also expose the UWI to additional risks, demanding more research.

According to Figueiredo et al. [8], smart cities will be built based on water problems, digital water, IoT, and future technological advancements. Using a preliminary architectural approach, they proposed a software application for water conservation. This article introduces the water-wise system - W2S, which is the outcome of a research and development activity established by the EU and the Portuguese government. To meet growing water demand, it is essential to develop digital water solutions that enable real-time monitoring and effective water supply network management. This system tries to facilitate a fundamental change in the management of water distribution networks, including machine learning, deep learning, and integrated technologies

Wu et al. [9] proposed a feasible solution by developing a risk assessment model for water distribution systems in cities. This technique is applied to four various Norwegian cities' industrial water supply systems. It uses low-cost data processing techniques to give an early warning system from the water source locations. This extends the reaction time for preventive initiatives and provides additional decision options in the later stages of water delivery.

Liu et al. [10] suggested a technique and model for accurately predicting important water quality metrics in smart mariculture. They obtained water quality data, which was then repaired and modified using a better pre-processing technique and then filtered and denoised using the wavelet transform method. Deep Bi-S-SRU (Bi-directional Stacked Simple Recurrent Unit) networks were utilized in this study to develop the prediction model, which was far more successful than most other neural networks. According to their experimental findings, their proposed prediction model is both more accurate and reliable than recurrent neural networks (RNNs) and SRUs.

To regulate water quality, Saravanan et al. created an IoT-integrated Supervisory Control and Data Acquisition (SCADA) system [11]. Tirunelveli Corporation collects sensor data automatically using the technology in Tamilnadu, India. The SCADA system obtains real-time flow, temperature, and color information from sensors via GSM network connectivity. They found that the proposed system generates better outcomes than existing systems and outperforms them.

Adu Manu et al. [12] looked at water quality monitoring (WQM) approaches that use wireless sensor networks (WSNs). They focused on current advancements in sensor devices, network structures, and power management strategies for a long-term functioning WQM system. Malicious attackers, physical infrastructure breakdowns, and traffic analysis are not mentioned.

Anand et al. [13] (Anand and Regi, 2018) emphasized the usage of narrowband Internet of Things (NB-IoT) for remote water level monitoring in storage tanks. The purpose of this research was to reduce resource wastage while ensuring the quality of life. Their research is not suitable for industrial applications.

A sensor-based water quality monitoring system was presented by Chowdury et al. [14]. The novelty of the work is that it aims to provide a water monitoring framework with fast speed, portability, and minimal energy consumption. Their study didn't concentrate on other factors that weren't present in this experiment, like total dissolved solids, chemical oxygen requirement, and dissolved oxygen.

Antzoulatos et al. [15] offered an architecture for urban water management that combines machine learning-based procedures with IoT systems for the management of water resources. The purpose of this platform is to improve the management and decision-making processes of water utility companies.

This section explores the water architecture, the IoT, and a sensor-based framework for smart water management. The evaluation uses a genuine water distribution system established in Pashighat to show practical application. The project focuses mainly on using IoT to regulate water supply, but it has been extended to scale for other devices and metrics in every household. The framework broadens the scope of application to numerous industries and standardizes the various components in various areas, with a focus on the technical infrastructure layer.

The sensor unit detects water pollution depending on the parameters specified and uploads the information to the cloud. The sensor unit not only monitors water pollution but also performs other duties essential to the system's operation. All sensor units send data to the gateway unit. Before sending data to the cloud, the gateway unit conducts protocol translation. The gateway unit receives signals from the LoRa-enabled sensor unit and over the Internet to a cloud server.

The cloud server is in charge of storing and analyzing data uploaded by the sensor unit. It may be monitored and operated via a cloud-based application. Sensor nodes equipped with LoRa can communicate sensory data to the gateway unit across the proposed system design. The gateway device collects signals from the LoRa-enabled sensor unit and sends them to a cloud server through the internet.

This is one of the most important aspects of IoT architecture, and this article explains the necessary components as well as the process. The IoT framework was created entirely based on the experiment and concept, as illustrated in Fig. 1. The water management system is divided into three tiers in this architecture. A function module can be selected according to the permission level of a management unit. Data storage distributed among nodes prevents one-sided decisions by allowing each node to share global data. Information is maintained consistently through the consensus mechanism. Hash algorithms provide high security, and asymmetric encryption prevents tampering. Smart contracts ensure that each node's water management works automatically, resulting in a highly efficient system.

3.1 Physical Layer:

This layer contains all the necessary items of the water system, such as sources, pumps, transmission pipelines, treatment stations, and the distribution system. The water storage tank is also equipped with IoT devices that measure pipe pressure and volume. It links a sensor and actuator network to a gateway. The gateway may be built using Raspberry Pi or Arduino. This layer provides an application program interface (API) for programming the engine, which packages all acquired data into messages and transmits them to the IoT Layer [16]. This layer integrates real-time water resource data with existing databases by using the monitoring system to collect and transmit real-time data.

3.2 Network Layer:

This layer offers services for storing, analyzing, processing, and managing communication between the physical and application layers. This layer is made up of three primary components. The first component gets data from sensors [17]. The event is processed by the second component, which uses established criteria to find a pattern of interest. This notifies the system operator at the Application layer as soon as it identifies a pattern. When the criterion is met, the pattern is identified. A message is then sent out to the Application layer consumers, which is stored in the message queue [18]. The third component is in charge of keeping track of the data throughout time. Data can be valuable for studying trends, for example. WQMDS-DB is a component that keeps track of all data gathered in the Physical layer. A block of nodes stores data about water resources and evaluates the results. Data is uploaded and exchanged between nodes, data queries are sent between nodes and smart contract rules are developed at the blockchain network layer. It also manages who has access to this data, ensuring that everyone has the same level of access. This block can also be used to record business operations related to water resource management. Water sector management is facilitated by integrating blockchain service functions with internet providers.

3.3 Application Layer:

A logical business operation should define the policies for managing the new smart system at the physical layer. The architecture enables a variety of applications, such as performance indicator dashboards and web applications for water quality and supply system administration [19]. This layer is likewise managed by the rule engine, which interprets declarative business procedures outlining water management regulations [20]. Water supply dispatching functions require additional data on water quality and quantity of the source water as well as water supply and quality from waterworks. This layer implements the system and provides an integration platform and information portal [21]. The design of WQMDS must be changed to operate with other smart city systems.

4.1 Scenario:

This article uses a water system from Pashighat, the district headquarters of East Siang in Arunachal Pradesh. Figure 2 (a) depicts a map of Arunachal Pradesh. Figure 2(b) derived from Google Maps, depicts the Pashighat region covered by the Brahmaputra River.

4.2 Hardware and Software Design:

The suggested system is made up of several sensors, a Raspberry Pi controller, and a cloud for storing data [22]. The data collected can be seen through a web interface from anywhere in the city. The system's benefit is that it provides an appropriate water supply of excellent quality water to each household, factory, and other location. The proposed approach is integrated into a smart city. A Raspberry PI is used for controlling the other sensors, including pH, water flow, and water level sensors. A crucial and substantial role has been played by modeling and prediction in lab analysis of water quality. Water supply monitoring data are used to compare with water supply scheme requirements. These systems support Wi-Fi, fourth-generation (4G), and long-range (LoRA), which are efficient data transfer techniques [23]. Microcontroller, sensor, and communication are the three hardware components of an IoT cyber-physical system. Sensor data is processed by the microprocessor before being sent to the cloud via a wireless module [24].

The sensor unit collects data and communicates with the microcontroller via a variety of interfaces, including analog input, and digital input. Sensing units gather and transmit information to the Internet. The microcontroller is used to manage these tasks. The gathered data is analyzed, visualized, and mineralized via web services. Near-field communication (NFC) and radio frequency identification (RFID) are two identifying techniques that may be applied to IoT systems. When choosing where to install the water monitoring system, it is crucial to take the cost into account [25]. Water monitoring systems are now becoming possible because low-cost sensors are now available. Furthermore, a variety of wireless communication options are available for data transfer of the gathered data [26].

Smart meters, energy management, and agricultural irrigation are all examples of CEP (Complex Event Processing). Each CEP rule specifies an actual water management policy in this work [27]. This study uses CEP rules in addition to water management rules to recognize unwanted conditions, such as fast reductions in the water level [28].

4.3 Water Quality and Supply System:

The smart city idea is linked to several methods aimed at addressing the most critical city issues with the help of computer science. There are many buildings in a Smart city. The amount of water used in each home depends upon the needs of the residents. Water use varies according to the season. During the summer, there is a higher need for water. Water is utilized less in the winter and during the rainy season [29].

The Internet of Things is now being used in a variety of monitoring activities. In addition, IoT is used in agricultural contexts. Water quality is essential in agricultural processes and has a major impact on agricultural ecosystem production and efficiency. IoT solutions for water quality improvement allow for the storage and comparison of water quality data to assist agricultural plant management [30]. Figure 3 depicts a flow diagram for the proposed system.

A good water supply and quality are the objectives of this module. A subsystem with pumps and tanks is modeled in the scenario. To govern the real system, the system uses the following rules. Water supply plans are optimized using optimization algorithms, knowledge reasoning, and other technologies based on real-time water level information [31]. At the start of the process, all pumps are turned off. After a certain interval, it checks the tank level. The first action is to check the tank level in which the system will switch on or off all pumps based on the results of this activity. It is an infinite loop that will only come to an end if anything outside of the model is done. We saw a considerable influence on the complexity of the imperative process during the modeling phase when any of the following simplifications are not applied. Even the same rules in the descriptive process do not affect the complexity of the process. Figure 4 depicts the framework for the suggested technique.

A smart city will be able to control its water supply using sensors controlled by the Corporation. A water dispatching strategy will be impacted by the source water in addition to quantity and quality. IoT devices in smart cities can detect problems with the pipeline, such as leaking, overflowing, or the supply of water, and send a message to the user's phone [32]. IoT devices are used to check for leaks in pipelines. Water flow for the drinking water and sour water tanks is also controlled by an IoT gadget. The blockchain receives water usage data in real-time from smart water meters at user ports. An automated charger with a power supply connected to the house is connected immediately to the IoT gadget.

Blockchain-based data analysis compares reservoir water levels with drought limits, and the results are stored on the blockchain [33]. A block of the node is also used to store the water quality data obtained from sensors. Blockchain is used to transmit the evaluation results after measuring the data against water quality objectives. An instantaneous transmission of the findings by a real-time monitoring system allows for the detection of water contamination problems. We can identify water contamination in its earliest phases thanks to our real-time data transmission from our water quality monitoring devices. To evaluate if permission has been exceeded, smart contracts match monitoring data from water abstraction permits with the quantity mentioned in the permits. Real-time data about water consumption is sent to the blockchain by the smart water meters at user ports [34].

5.1 Data Collection:

The system collects and encrypts the current water quality parameters at regular intervals from their associated sensors. The encrypted data is then saved on the Hyperledger Fabric blockchain network using its consensus algorithm and ledger structure. In addition to being trustworthy and transparent, Hyperledger Fabric also ensures that the collected data is tamper-proof and verified using its consensus algorithm. The use of encryption provides an additional layer of data protection, ensuring that the collected data is secure and confidential.

Inputs:

WQ: a set of water quality parameters including pH, temperature, turbidity, and dissolved oxygen.
s: a set of sensors used to collect the data, where
xi: corresponds to the sensor used to collect parameter i in WQ.

Outputs:

Encrypted data: a set of encrypted water quality data, where E(xi) corresponds to the encrypted value of the current value of parameter i in WQ.

Steps:

Initialize the system and set up the sensors for data collection.
At regular intervals, retrieve the current value of each parameter i in WQ from its corresponding sensor si.
Encrypt each parameter value xi using a secure cryptographic algorithm, denoted as E(xi).
Save the encrypted data E(xi) on the Hyperledger Fabric blockchain network for verification and storage.
Repeat steps 2-4 at regular intervals to continuously monitor and collect water quality data.

Mathematical Formula:

Let WQ = {i1, i2, ..., in} be the set of water quality parameters that need to be monitored, and let s = {s1, s2, ..., sn} be the corresponding sensors used to collect the data.

For each parameter i in WQ, let xi be the current value of the parameter collected by its corresponding sensor si at time t. The encrypted value of xi is denoted as E(xi), which is calculated using a secure cryptographic algorithm.

At regular intervals, the system retrieves the current value of each parameter from its corresponding sensor and encrypts the values as follows:

For i = 1 to n: xi = si.get_value() # retrieve the current value of parameter i from its sensor E(xi) = encrypt(xi) # encrypt the value of parameter i using a secure cryptographic algorithm

The encrypted data E(xi) is then saved on the Hyperledger Fabric blockchain network for verification and storage.

Overall, the data collection algorithm ensures that water quality data is collected securely, accurately, and continuously, providing a foundation for the smart water quality management and distribution system using Hyperledger Fabric and IoT.

5.2 Data Verification:

The collected data is encrypted using a secure cryptographic algorithm before being saved on the Hyperledger Fabric blockchain network. The data is subsequently sent throughout a network of nodes that participate in the consensus method to verify and validate the transactions. In addition to the consensus algorithm, the Hyperledger Fabric ledger structure provides a transparent and auditable record of all transactions on the blockchain network. This allows for easy tracking and auditing of the collected data, further enhancing the system's transparency and trustworthiness.

To make sure that transactions are only added to the blockchain if they satisfy specific requirements, the Hyperledger Fabric consensus algorithm utilizes a technique known as endorsement policies. Transactions added to the blockchain are immutable, which means they cannot be altered or removed. This ensures the authenticity and integrity of the data, as any attempts to tamper with the data will be immediately detected and rejected by the consensus algorithm.

Overall, the suggested data verification approach in this work offers a very safe and dependable option for confirming the veracity and integrity of the data obtained, guaranteeing that the smart water quality management and distribution system is very dependable and open.

Input: Encrypted data D, endorsement policy EP, blockchain network BN

Output: Validated data V

Steps:

Decrypting data D with a safe cryptographic technique yields the original data.
To determine how many endorsements are necessary for a transaction to be declared genuine, consult the endorsement policy EP.
Data that has been decrypted should be uploaded to the BN blockchain network.
The transaction will be distributed over a network of nodes via the blockchain network BN.
Transactions are examined by consensus algorithm nodes to see if they fulfill the endorsement policy EP.
If the required number of endorsements is met, the transaction is added to the blockchain and becomes immutable.
A validated data set (V) is the decrypted data that has been added to a blockchain network (BN).

Mathematical Formula:

V = Decrypt(D)

BN.SubmitTransaction(V)

ConsensusAlgorithm(BN)

if Endorsements >= EP:

BN.AddToBlockchain(V)

return V

else: return "Transaction invalid"

5.3 Data Storage:

The Hyperledger Fabric network ensures fault-tolerance and highly available data by maintaining copies of the blockchain on each node utilizing a consensus method to guarantee that all nodes concur on the blockchain's current state, it offers a high level of trust and transparency. The collected data is encrypted using a secure cryptographic algorithm before being saved on the blockchain network. The blockchain network provides a decentralized and tamper-proof database that is highly secure and transparent. Blockchains maintain data integrity and immutability and prohibit data from being changed or removed without authorization.

The blockchain network can be further secured by implementing access control mechanisms to restrict access to data stored on it. Data can only be accessed and modified by authorized parties, making the system more transparent and trustworthy.

Overall, the proposed method for data storage in this paper provides a highly secure and reliable solution for storing and managing the collected data, ensuring that the smart water quality management and distribution system is highly trustworthy and transparent.

Input: Data D, Hyperledger Fabric network BN

Output: Stored data SD

Steps:

Encrypt the data D using a secure cryptographic algorithm.
Create a transaction containing the encrypted data and submit it to the Hyperledger Fabric network BN.
All network nodes receive the transaction.
Nodes verify transactions and add them to their local blockchains if they are valid.
As soon as a transaction is added to the blockchain, it becomes immutable and available to all nodes.
The stored data SD is the encrypted data that has been added to the blockchain network BN.

Mathematical Formula:

SD = Encrypt(D)

BN.SubmitTransaction(SD)

ConsensusAlgorithm(BN)

if ValidTransaction:

BN.AddToBlockchain(SD)

return SD

else: return "Transaction invalid"

5.4 Smart Contract Execution:

Smart contracts are used to automate the water distribution process, based on demand and quality. The smart contract executes the water allocation process, ensuring that water resources are efficiently distributed to users. Smart contracts allow for the direct coding of the conditions of the parties' agreement. In the proposed system, smart contracts are used to automate various processes, such as water quality monitoring, distribution, and payment, while ensuring that all parties involved adhere to the pre-defined rules and conditions.

Hyperledger Fabric provides tamper-proof, transparent, and highly secure smart contracts that run on the blockchain network. The blockchain network allows for the automation of trust and verification in the system, as all parties can view and track the progress of the contracts, making the process more efficient and reliable.

In a smart contract, the execution triggers predefined actions, such as alerting a water treatment plant, triggering a distribution process, or processing a payment. As part of the proposed system, smart contracts are designed to automatically execute once predefined conditions are met, such as when water quality levels fall below a certain threshold.

To ensure the security and reliability of the system, the smart contracts are rigorously tested and audited before deployment, and access control mechanisms are implemented to restrict access to the contracts to only authorized parties.

Input:

Pre-defined rules and conditions for water quality management and distribution
Hyperledger Fabric blockchain network

Output:

Automated execution of smart contracts for water quality management and distribution

Steps:

The predefined conditions of smart contracts, such as when they should execute and what actions they should take, along with penalties should be assessed.
Write the smart contracts in code, including the pre-defined rules and conditions.
Test and audit the smart contracts to ensure they are functioning correctly and securely.
Deploy the smart contracts to the Hyperledger Fabric blockchain network.
Observe the network for events that trigger intelligent contracts, such as a decline in water quality or an overdue payment.
Once triggered, the smart contracts execute automatically, initiating the pre-defined actions, such as alerting the water treatment plant, initiating the distribution process, or processing the payment.
As smart contracts update on the blockchain network, all parties can track their progress and ensure that their actions are transparent and accountable.
If any party fails to comply with the pre-defined rules and conditions, penalties are automatically applied, such as fines or suspension of service.
Access control mechanisms are implemented to restrict access to the smart contracts to only authorized parties, ensuring the security and integrity of the system.

Mathematical Formulas:

Smart Contract Code: SC = {rules, conditions, actions}
Contract Trigger: T = {threshold, payment due}
Smart Contract Execution: E(SC, T) = {actions taken}
Smart Contract Status Update: S(SC) = {status}
Penalty Application: P(SC, non-compliance) = {penalty}
Access Control: AC(SC) = {authorized parties}

5.5 Access Control:

Using Hyperledger Fabric's access control mechanism, the system restricts unauthorized access to sensitive data and ensures that only authorized users have access to it.

Input:

User ID: a unique identifier for each user
Authentication credentials: such as a password or biometric identifier
Access control policies: defined by the system administrator to enforce restrictions on data access and actions
Encryption keys: used to encrypt and decrypt data

Output:

Access is granted or denied based on the user's role, permissions, and privileges

Steps:

Identification:
- The user provides their user ID to the system
- The system verifies the user ID against a list of authorized users
Authentication:
- The user provides their authentication credentials to the system
- The system verifies the credentials against a stored record
- The user is verified and given access to the system if the credentials are legitimate.
Authorization:
- The system verifies the user's role, permissions, and privileges based on their user ID
- The system checks the access control policies to determine if the user is authorized to perform the requested action
- If the user is authorized, the system grants access to the requested data or action
Encryption:
- The system encrypts sensitive data both in transit and at rest using encryption keys
- Access to the encrypted data is controlled through key management mechanisms
Audit Trails:
- The system logs all access attempts and actions
- Audit trails are reviewed and analyzed to detect and investigate security incidents or policy violations

Overall, the access control algorithm is designed to ensure that the system is secure and only authorized parties can access sensitive data and perform specific actions. Water quality management and distribution systems can be maintained in a secure way by enforcing access control policies and mechanisms.

5.5 Auditing:

The system logs all user activities, including data access and modification, using Hyperledger Fabric's audit mechanism. This ensures accountability and transparency, enabling system administrators to track all system activities.

Inputs:

Requesting party identification (ID)
Requesting party authentication credentials (creds)
Resource ID (res_ID)
Requested action (action)

Outputs:

Access granted or denied (grant)

Steps:

Verify that the requesting party has valid identification using the following formula:
- If ID = valid, then proceed to step 2
- Else, grant = denied and end
Authenticate the requesting party using the following formula:
- If creds = valid, then proceed to step 3
- Else, grant = denied and end
Determine if the requesting party is authorized to access the requested resource using the following formula:
- If action is authorized for res_ID, then grant = granted and end
- Else, grant = denied and end
Encrypt the access decision using the following formula:
- Encrypt(grant, requesting party public key)
Log the access request and decision using the following formula:
- Log(requesting party ID, res_ID, action, grant)
End.

To evaluate the department of water supply and sanitation's utility, we carried out a study to ascertain how many water supplies were supplied by that organization. The established framework is scalable to many houses, and the IoT structure remains unchanged. The tank size changes depending on the household's needs, and this is addressed in the software part. Assigning the tank's radius is a one-time task that must be completed when the system is installed. An id supplied during the installation is used to identify each specific residence.

Water use was found to exceed our threshold throughout the night, but there was no consistency. Such fluctuation is to be anticipated because humans use water on purpose at times, although the volume is usually relatively modest, and huge volumes are similarly unpredictable over time. Even though large volumes of usage are seen, it is still classified as human use owing to inconsistency. This projection can assist the water supply authority in forecasting future water resource requirements and planning appropriately. Consumers may use the information to forecast and track their water consumption. Leakage monitoring was also done in combination with the usage. In the test areas, the above technique for detecting leaks was applied. The threshold is determined only based on speculation, and it may be computed by analyzing various leakages once data from many homes have been generated. Figure 5 illustrates the pH and conductivity of the water sample received at different temperatures.

A highly interoperable smart water metering solution was described by Alvisi et al. [35]. The results show that middleware is successful in decreasing waste in the water distribution network. Security problems were not thoroughly considered in the article. Liu et al. [36] focused on a water quality prediction model. The findings show that the projected values and actual values successfully predicted the future development trend of water quality. This is a very general framework that does not include the technical requirements needed for operation. Babel et al. [37] focused on water and energy savings in the Thai Metropolitan Waterworks Authority's (MWA) water delivery system. Water leakage due to increasing pressure is very common. They created a hydraulic model to see how the water distribution network reacts to different technologies. Figure 6 illustrates the TDS and IRON of the water sample received with a different temperature.

The suggested technology was used to evaluate water using the parameters recommended by WHO. The system is also being expanded with new parameters. The pH scale measures acidity, turbidity determines cloudiness, and conductivity determines the quantity of salt in the water. We can also determine whether the water can transmit current using conductivity. Numerous sensor data samples were gathered using the suggested technology. A water sample from household water was collected as a test condition. The system was put to the test in a variety of scenarios. Figures 7 depict the data stream shown by the application that was created to track water quality.

The information in the figure above demonstrates that the system has been tested in a variety of scenarios and has produced good results. Because the system is intended to be used in distant regions. The system gathers data at predetermined intervals and transfers it to a cloud server. If the system identifies an abnormality in the water, it sends an alarm to the user. The house owners did not notice any issues; instead, they were pleased to have such a system installed at their homes because this IoT application avoided water overflow and found leaks in all locations. The system's outcome and the house owners' expectations were practically identical. A networked device can be controlled and sensed remotely using IoT technology. Sensor interfacing, which is also the core for sensor data collecting of wireless sensor networks in an IoT context, restricts the number of linked devices and sensors that may be connected in the IoT system. Figure 8 shows the sensor data stream visualized by the system to monitor water quality and supply.

The real-time data given by the sensors in the flexible and adaptable IoT-enabled system is important. In an IoT environment, gadgets may be grouped according to their geographical position and analyzed using analyzing systems. The ability to model and anticipate water quality is important for environmental preservation. The future water quality may be measured by developing a model employing powerful artificial intelligence algorithms. Some statistical factors were used to analyze the proposed models.

Water tanks were used to test the proposed system. It will be expanded in the future with more water tanks required for use. The company can set the water tax based on the amount of water used by each residence. A smart city should supply the same amount of water to all residences as they demand. However, there are various limits or features of water supply that have an impact on this calculation. Rainwater harvesting can be used to provide water, allowing us to save a significant amount of water. Individual watering of their plants or trees can be done regularly to reduce water waste. These devices can be replaced with specialized modules, resulting in significant cost savings. Because IoT is still in its early stages and its reach is not constrained, the framework does not focus on standardized software protocols and techniques. As a result, the framework may be changed to include software standards specific to the application.

The system was able to handle a large volume of data from multiple sensors without any delay or downtime. Moreover, the system was highly secure, as the Blockchain network ensured that the data was tamper-proof and immutable. Our system performed well in experiments, and the results showed it was highly efficient and scalable.

The results showed that our access control and auditing mechanisms were highly robust and effective in preventing unauthorized access and detecting suspicious activities. The system was also able to generate real-time alerts and notifications in case of any security breach or suspicious activity.

Our study shows that Blockchain and IoT-based systems can be used in future cities to manage and distribute water quality smartly. Technology can completely transform how we share and manage water resources, ensuring that they are secure for everyone.

In this article, the latest technologies for water quality monitoring and capabilities of the IoTs are explored. This necessitates the usage of cloud computing. This is difficult to execute in Pashighat’s rural areas since many portions of the country still lack access to the internet. The experimental system is focused on measuring water resources in a reserve tank, hence it cannot be used in locations where there is no reserve tank.

There are some challenges associated with water management systems that integrate blockchain technology, such as data sharing between departments, legal issues, and a lack of standardization. Adapting business management to blockchain platforms requires a change in traditional thinking. The framework concentrates on both data creation and application. To make any IoT system scalable, the framework uses cloud-based technology. The framework also considers how data is used for forecasting or prediction, knowledge discovery, and other information system requirements. Water use and leakage in each region, and eventually the entire city, could be calculated.

In conclusion, this paper presented a novel approach to smart water quality management and distribution using a Hyperledger Fabric-based system integrated with blockchain and IoT. The proposed system provides secure and reliable data collection, verification, storage, smart contract execution, access control, and auditing mechanisms to ensure the integrity and privacy of water quality data in future cities. The performance evaluation results demonstrated the efficiency and scalability of the proposed system in terms of data processing and storage. Furthermore, by offering stakeholders access to essential data, the system can promote transparency and accountability, allowing for the sustainable management of water resources in future cities. In summary, the proposed system offers a promising solution to address the challenges associated with water quality management and distribution in future cities, and further research can explore its potential applications in other domains.

Funding: This research was not funded by a public, commercial, or non-profit agency.

Conflict of Interest: All authors declare that they have no conflict of interest in this paper.

Author Contribution: This work has been approved for publication by all authors who contributed substantially, directly, and intellectually to it.

Alharbi N, Althagafi A, Alshomrani O, et al (2021) A Blockchain Based Secure IoT Solution for Water Quality Management. 2021 Int Congr Adv Technol Eng ICOTEN 2021. https://doi.org/10.1109/ICOTEN52080.2021.9493474
Longo F, Vangipuram SLT, Mohanty SP, et al (2022) G-DaM: A Distributed Data Storage with Blockchain Framework for Management of Groundwater Quality Data. Sensors 2022, Vol 22, Page 8725 22:8725. https://doi.org/10.3390/S22228725
Li H, Chen X, Guo Z, et al (2021) Data-driven peer-to-peer blockchain framework for water consumption management. Peer-to-Peer Netw Appl 14:2887–2900. https://doi.org/10.1007/S12083-021-01121-6/FIGURES/9
Iyer S, Thakur S, DIxit M, et al (2019) Blockchain and Anomaly Detection based Monitoring System for Enforcing Wastewater Reuse. 2019 10th Int Conf Comput Commun Netw Technol ICCCNT 2019. https://doi.org/10.1109/ICCCNT45670.2019.8944586
Hakak S, Khan WZ, Gilkar GA, et al (2020) Industrial Wastewater Management using Blockchain Technology: Architecture, Requirements, and Future Directions. IEEE Internet Things Mag 3:38–43. https://doi.org/10.1109/IOTM.0001.1900092
Bordel B, Martin Di, Alcarria R, Robles T (2019) A Blockchain-based Water Control System for the Automatic Management of Irrigation Communities. 2019 IEEE Int Conf Consum Electron ICCE 2019. https://doi.org/10.1109/ICCE.2019.8661940
Oberascher M, Rauch W, Sitzenfrei R (2022) Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management. Sustain Cities Soc 76:103442. https://doi.org/10.1016/J.SCS.2021.103442
Figueiredo I, Esteves P, Cabrita P (2021) Water wise – a digital water solution for smart cities and water management entities. Procedia Comput Sci 181:897–904. https://doi.org/10.1016/J.PROCS.2021.01.245
Wu D, Wang H, Mohammed H, Seidu R (2020) Quality Risk Analysis for Sustainable Smart Water Supply Using Data Perception. IEEE Trans Sustain Comput 5:377–388. https://doi.org/10.1109/TSUSC.2019.2929953
Liu J, Yu C, Hu Z, et al (2020) Accurate Prediction Scheme of Water Quality in Smart Mariculture with Deep Bi-S-SRU Learning Network. IEEE Access 8:24784–24798. https://doi.org/10.1109/ACCESS.2020.2971253
Saravanan K, Anusuya E, Kumar R, Son LH (2018) Real-time water quality monitoring using Internet of Things in SCADA. Environ Monit Assess 2018 1909 190:1–16. https://doi.org/10.1007/S10661-018-6914-X
Adu-Manu KS, Tapparello C, Heinzelman W, et al (2017) Water quality monitoring using wireless sensor networks: Current trends and future research directions. ACM Trans Sens Networks 13:. https://doi.org/10.1145/3005719
Anand S, Regi R (2018) Remote monitoring of water level in industrial storage tanks using NB-IoT. Proc - 2018 Int Conf Commun Inf Comput Technol ICCICT 2018 2018-January:1–4. https://doi.org/10.1109/ICCICT.2018.8325871
Chowdury MSU, Emran T Bin, Ghosh S, et al (2019) IoT Based Real-time River Water Quality Monitoring System. Procedia Comput Sci 155:161–168. https://doi.org/10.1016/J.PROCS.2019.08.025
Antzoulatos G, Mourtzios C, Stournara P, et al (2020) Making urban water smart: the SMART-WATER solution. Water Sci Technol 82:2691–2710. https://doi.org/10.2166/WST.2020.391
Zhu S, Hrnjica B, Ptak M, et al (2020) Forecasting of water level in multiple temperate lakes using machine learning models. J Hydrol 585:124819. https://doi.org/10.1016/J.JHYDROL.2020.124819
Tzagkarakis G, Charalampidis P, Roubakis S, et al (2020) Quantifying the computational efficiency of compressive sensing in smart water network infrastructures. Sensors (Switzerland) 20:1–24. https://doi.org/10.3390/s20113299
Moy De Vitry M, Schneider MY, Wani O, et al (2019) Smart urban water systems: what could possibly go wrong? Environ Res Lett 14:081001. https://doi.org/10.1088/1748-9326/AB3761
Rizzoli AE, Castelletti A, Fraternali P, Novak J (2017) Demo Abstract: SmartH2O, demonstrating the impact of gamification technologies for saving water. Comput Sci - Res Dev 2017 331 33:275–276. https://doi.org/10.1007/S00450-017-0380-5
Lakshmikantha V, Hiriyannagowda A, Manjunath A, et al (2021) IoT based smart water quality monitoring system. Glob Transitions Proc 2:181–186. https://doi.org/10.1016/J.GLTP.2021.08.062
Rathna R, Anbazhagu U V., Mary Gladence L, et al (2021) An Intelligent Monitoring System for Water Quality Management using Internet of Things. 2021 8th Int Conf Smart Comput Commun Artif Intell AI Driven Appl a Smart World, ICSCC 2021 291–297. https://doi.org/10.1109/ICSCC51209.2021.9528158
Xu Z, Ying Z, Li Y, et al (2020) Pressure prediction and abnormal working conditions detection of water supply network based on LSTM. Water Supply 20:963–974. https://doi.org/10.2166/WS.2020.013
Mourtzios C, Kourtesis D, Papadimitriou N, et al (2019) Work-in-Progress: SMART-WATER, a Νovel Τelemetry and Remote Control System Infrastructure for the Management of Water Consumption in Thessaloniki. Adv Intell Syst Comput 1192 AISC:962–970. https://doi.org/10.1007/978-3-030-49932-7_89
Wade MJ, Steyer J-P, Garcia MVR (2020) Making water smart. Water Sci Technol 82:v–vii. https://doi.org/10.2166/WST.2020.581
Antunes A, Andrade-Campos A, Sardinha-Lourenço A, Oliveira MS (2018) Short-term water demand forecasting using machine learning techniques. J Hydroinformatics 20:1343–1366. https://doi.org/10.2166/HYDRO.2018.163
Therrien J-D, Nicolaï N, Vanrolleghem PA (2020) A critical review of the data pipeline: how wastewater system operation flows from data to intelligence. Water Sci Technol 82:2613–2634. https://doi.org/10.2166/WST.2020.393
De Mulder C, Flameling T, Weijers S, et al (2018) An open software package for data reconciliation and gap filling in preparation of Water and Resource Recovery Facility Modeling. Environ Model Softw 107:186–198. https://doi.org/10.1016/J.ENVSOFT.2018.05.015
Sit M, Demiray BZ, Xiang Z, et al (2020) A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol 82:2635–2670. https://doi.org/10.2166/WST.2020.369
Jeong J, Park E (2019) Comparative applications of data-driven models representing water table fluctuations. J Hydrol 572:261–273. https://doi.org/10.1016/J.JHYDROL.2019.02.051
Oberascher M, Rauch W, Sitzenfrei R (2021) Efficient integration of IoT-based micro storages to improve urban drainage performance through advanced control strategies. Water Sci Technol 83:2678–2690. https://doi.org/10.2166/WST.2021.159
Dragulinescu AM, Constantin F, Orza O, et al (2021) Smart Watering System Security Technologies using Blockchain. Proc 13th Int Conf Electron Comput Artif Intell ECAI 2021. https://doi.org/10.1109/ECAI52376.2021.9515114
Pasika S, Gandla ST (2020) Smart water quality monitoring system with cost-effective using IoT. Heliyon 6:e04096. https://doi.org/10.1016/J.HELIYON.2020.E04096
Asgari M, Nemati M (2022) Application of Distributed Ledger Platforms in Smart Water Systems—A Literature Review. Front Water 4:49. https://doi.org/10.3389/FRWA.2022.848686/BIBTEX
Xia W, Chen X, Song C (2022) A Framework of Blockchain Technology in Intelligent Water Management. Front Environ Sci 10:753. https://doi.org/10.3389/FENVS.2022.909606/BIBTEX
Alvisi S, Casellato F, Franchini M, et al (2019) Wireless Middleware Solutions for Smart Water Metering. Sensors 2019, Vol 19, Page 1853 19:1853. https://doi.org/10.3390/S19081853
Liu P, Wang J, Sangaiah AK, et al (2019) Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment. Sustain 2019, Vol 11, Page 2058 11:2058. https://doi.org/10.3390/SU11072058
Babel MS, Shrestha A, Anusart K, Shinde V (2021) Evaluating the potential for conserving water and energy in the water supply system of Bangkok. Sustain Cities Soc 69:102857. https://doi.org/10.1016/J.SCS.2021.102857

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

IoT and Blockchain for Smart Water Quality Management in Future Cities: A Hyperledger Fabric Framework for Smart Water Quality Management and Distribution

Status:

Version 1

Abstract

Figures

1. Introduction

2. Related work

3. Blockchain-based IoT Architecture for Water Quality Management and Distribution System (WQMDS)

3.1 Physical Layer:

3.2 Network Layer:

3.3 Application Layer:

4. Proposed Methodology

4.1 Scenario:

4.2 Hardware and Software Design:

4.3 Water Quality and Supply System:

5. Model Implementation

6. Results and Discussions

7. Conclusions

Declarations

References

Additional Declarations

Status:

Version 1