Keywords

1 Introduction

Augmented Reality (AR) is an innovative technology that transforms the way people interact with their surroundings. It involves the overlay of digital information on the physical environment, enhancing user’s perception of the real world with added value content. AR provides promising applications in the realm of Smart Cities, where it can be used to improve the quality of life for citizens and increase operations efficiency [1, 2].

Smart Cities have a complex structure and are considered as a system of systems with constant and real-time information exchange. In this dynamic environment, citizens have the need to interact and exchange information using user-friendly and accessible means. In this context, the design and development of sophisticated software tools with a human-centric approach is a necessity [3]. The Internet of Things (IoT), as the cornerstone of Smart Cities, provides ubiquitous connectivity and real-time data exchange with established protocols. Thus, IoT can act as an enabler for the integration of AR applications in the Smart City context [4]. The combination of AR and IoT can create meaningful experiences in the urban realm [5].

AR technology provides added value services in human-computer and human-to-human interactions. Studies regarding AR applications indicate a high adoption rate from users and showcase the AR advantages in usability and effectiveness over web-based and mobile applications [6]. Moreover, the usability of AR applications is not limited to specific target groups indicatively young or experienced users, which designates AR as the optimal interaction means for a broad area of use cases [7].

Smart Cities improve citizens’ quality of life by monitoring and controlling critical indicators, such as water quality [1]. Water quality is a critical issue in urban environments, and data streams should be collected and analyzed in real-time. IoT systems are advancing, and water quality monitoring is gaining the attention of many researchers [8, 9]. By integrating AR technologies and IoT systems, city officials and maintenance workers can access visual representations of water quality data in the field, allowing them to quickly identify and address any issues that may arise [10].

The use of AR in water quality monitoring has also the potential to significantly improve the efficiency of these systems. AR can visualize data more intuitively and interactively, making it easier for city officials and maintenance workers to understand and respond to changes in water quality. Additionally, AR provides user-friendly on-site operation support while enabling seamless remote assistance from experts [11].

Nonetheless, in order to harness the full potential of AR technology, a holistic approach to develop robust AR HCIs for configuring IoT devices’ and handling data streams is needed [12]. To this end, this paper proposes a novel system architecture that enables citizens and administrators to interface with IoT water management systems in the Smart City context. The system automatically connects to the underlying IoT infrastructure and establishes connections via IoT protocols such as Message Queuing Telemetry Transport (MQTT) and Long Range Wide Area Network (LoRaWAN). The AR application enables on-site operations, including real-time data monitoring, system monitoring and IoT device configuration. In order to evaluate the system, as the literature proposes [13], the research team developed a pilot system. The pilot system utilizes the proposed architecture and unveils its potential.

2 Architecture Overview

The proposed architecture consists of three layers, namely the communication/data layer, the hardware layer and the application layer. The communication/data layer holds the IoT Gateways and MQTT Servers as well as the Data Sources that constitute the IoT system. The hardware layer includes the microcontroller, the sensors and the equipment for the IoT infrastructure. Lastly, the application Layer entails the AR application of the proposed architecture (Fig. 1).

Fig. 1.
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Proposed system architecture

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2.1 Communication/Data Layer

LoRaWAN and MQTT are two well-known IoT communication protocols. LoRaWAN is a communication protocol designed for low-power and long-range communication of IoT devices. It operates in the sub-gigahertz frequency bands, providing coverage over long distances and penetrating through building walls making it suitable for use in urban areas. LoRaWAN utilizes a star-of-stars topology in which gateways connected to the internet relay messages between the end devices and a central network server. LoRaWAN is used in Smart Cities due to its ability to support large amounts of connected devices with low-bandwidth requirements and its high scalability factor. LoRaWAN has demonstrated reliable performance in smart city applications such as waste management, environmental monitoring, and transportation [14, 15].

MQTT is a publish-subscribe lightweight messaging protocol. It is designed to be used on top of TCP/IP and has a small footprint, making it well-suited for resource-constrained devices and low-bandwidth networks. MQTT is often used in smart cities due to its ability to handle high numbers of connected devices and its efficient use of bandwidth [16].

Both LoRaWAN and MQTT are suitable communication protocols in smart cities due to their ability to support a large number of connected devices and their bandwidth management efficiency. However, they have some notable differences. LoRaWAN utilizes a spread spectrum modulation technique in the sub-gigahertz frequency bands, which allows for long-range communication and strong penetration through obstacles. In contrast, MQTT is based on TCP/IP and has a smaller footprint, making it well-suited for resource-constrained devices. As it is evident, both LoRaWAN and MQTT could be used in smart city. LoRaWAN could be used for long-range communication and MQTT could be utilized for resource-constrained devices or for direct device-to-device communication. This approach could provide a robust and flexible communication infrastructure for a smart city.

A time-series database is a specialized type of database optimized for storing and querying time-tagged data. Time-series databases are well-suited for storing data collected from IoT devices, due to the efficient querying and analysis of data. In addition, they provide fast response times regardless of the data volumes and they are used to store sensor readings, energy usage, and traffic patterns. As a last step, data analytics identify trends and patterns and is used to optimize the systems’ performance and make data-driven decisions.

2.2 Hardware Layer

The Hardware Layer of the system architecture consists of a microcontroller equipped with water-quality sensors. Additionally, a LoRaWAN antenna and a Wi-Fi module are connected to the microcontroller. The microcontroller serves as the primary processing unit for the system, utilizing the sensors for data acquisition and the LoRaWAN antenna for wireless communication with the IoT infrastructure. The Wi-Fi module allows for the establishment of a secure MQTT connection, enabling efficient data transfer, for on-site operations.

2.3 Application Layer

At the application layer an AR mobile application has been developed using the Unity game engine. Unity's ability to simulate real-world environments, along with scripting capabilities and cross-platform support make it a powerful and widely-used tool for researchers [17]. The application’s functionalities are divided into two sub-categories, the AR utilities and the IoT interconnections. Additionally, the application categorizes users into two types, citizens and administrators. The provided functionalities differ based on the user type.

AR Utilities.

For the AR utilities’ development, the research team utilized the Vuforia SDK. The Vuforia SDK provides a powerful and versatile set of tools for creating AR applications that include image recognition and tracking which was used in this research. An image target was used for the initiation of the AR experience. As the application recognizes an image target, 3D objects and graphs regarding the hardware and the sensor readings are presented at the AR layer.

IoT Interconnection.

The AR application automatically establishes a connection to the IoT infrastructure. Once identified, the image target initiates the AR experience. The image target contains the unique ID of each hardware device, which is necessary to retrieve the corresponding data from the database or to connect to the MQTT server. In addition to the image target, the application uses location data from the mobile phone to determine if the user is on-site and near the device. In that case, both MQTT and LoRaWAN connections are possible. The MQTT protocol provides a direct connection between the AR application and the microcontroller, enabling real-time data exchange. On the other hand, the LoRaWAN infrastructure provides access to the database that holds historical data.

3 Pilot System

The research team developed a pilot system in order to demonstrate the applicability of the proposed architecture. The pilot system follows the proposed system architecture and the three layers were implemented from the ground up. As mentioned above, in each mode, the application is able to connect to the IoT infrastructure with either MQTT or LoRaWAN protocols. Each protocol provides specific information to the application.

Communication

The pilot system utilizes the LoRaWAN protocol through the LoRaWAN antenna on the microcontroller, for the communication layer. LoRaWAN gateways were already available near the pilot system’s installation point. For the MQTT connection, the local network was utilized. Lastly, a local time series data base was implemented in order to store the data.

Hardware

Regarding the hardware, an esp32 microcontroller and four water quality sensors namely pH, electric conductivity, ORP and temperature, were used. The microcontroller provides built-in Wi-Fi connection capabilities which are used for the MQTT protocol implementation. In addition to the sensors a LoRaWAN antenna module, the RN2483A, was connected to the microcontroller to connect to the Lora Gateway.

Application

The Augmented Reality (AR) application utilizes a user-friendly interface with self-explanatory features for user navigation. There are two modes in the application namely, citizens’ mode and administrators’ mode. Users can switch between modes in order to get access to different features. It is worth noting that in the pilot system, the application does not provide user profiles and allows mode switching without verification. In order to initiate the AR experience from the mobile device, the user identifies the image target of the water monitoring device. Once the image target is recognized, the application links the image target with the corresponding ID. At this stage, the application connects to the underlying LoRaWAN server and retrieves the stored location of the hardware device. The application then compares the GPS location of the mobile device to the stored location to determine if the user is near the device. If the user is on-site, the AR application will automatically connect to the MQTT server, which is hosted on the hardware device.

The citizen’s mode provides access to the sensor readings. Citizens are able to see the real-time value of each critical water quality metric. In addition, a graph that shows past readings can be displayed (Fig. 2).

Fig. 2.
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AR applications for citizens

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In contrast, the mode for administrators includes additional functionality for device configuration and information pertaining to the Internet of Things (IoT) infrastructure. The “Device Configuration” tab provides information about the hardware and allows for modification of sensor reading intervals and data transmission intervals while the “IoT Information” tab provides information about the IoT infrastructure (Fig. 3).

Fig. 3.
figure 3

AR application for Administrators

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4 Conclusion

This paper has proposed a novel system architecture for an AR-enabled interface for IoT water management systems in Smart Cities. The proposed architecture utilizes the strengths of both AR and IoT technologies to improve the quality of life for citizens and increase the efficiency of city operations. Moreover, the system is designed with a human-centric approach, which is crucial in Smart Cities, making the system user-friendly and intuitive. This is an important aspect of the proposed architecture as it allows citizens to have access to information about the water quality in a convenient way and allows administrators to access the information about the hardware and the IoT infrastructure in a more efficient way.

The proposed system architecture is composed of three layers: communication/data, hardware and application. The communication/data layer includes IoT Gateways, MQTT Servers, and Databases that constitute the IoT system. The hardware layer incorporates the microcontroller, sensors, and communication hardware. The application layer includes the AR application. The proposed architecture enables citizens and practitioners to interface with IoT water quality monitor systems in a user-friendly way. The system automatically connects to the underlying IoT devices and establishes connections via the respective IoT protocol. The AR application enables on-site or remote operations, including real-time data monitoring, system monitoring, and IoT device configuration.

According to the literature, AR is a useful technology in the Smart City ecosystem [18], nonetheless, a system architecture for the integration of AR and IoT technologies for water management systems in Smart Cities is missing. The proposed system architecture has the potential to significantly improve the efficiency and effectiveness of IoT water management systems in Smart Cities. Lastly, the architecture can be used as a blueprint for other Smart City applications and will pave the way for future research in this field. It has the potential to improve the transparency of Smart Cities and enable citizens to have a more active role in the management of their city's resources.

With that being said, the system architecture is in a preliminary phase and there are some key technological limitations as well as some research gaps that need further examination from the research community. First of all, safety and privacy issues are not in consideration at the current stage. If the system would be used in Smart Cities both issues should be solved prior to wide adoption. Additionally, the pilot system was created in order to evaluate the system, but in the current stage, only the technical limitations were examined. Future research regarding the usability and the scalability of the system will provide insight into the potential of the proposed architecture, in real-world scenarios.