RTIMS: Real-Time Indoor Monitoring Systems: A Comprehensive Review

Abstract

Real-time indoor monitoring systems (RTIMS) are a key component of modern technological infrastructures in smart and automated buildings and facilities. They enable the continuous collection, analysis, and response to environmental data under strict time constraints, ensuring optimal system performance. These systems are designed to operate with high accuracy and low latency, making them essential in situations and events where timely decision-making is critical. Their applications range from industrial automation and production line monitoring to smart cities, smart homes, and healthcare for the elderly and disabled. The significant advances in electronics, communications, and software—particularly in Internet of Things (IoT) technologies and data transfer protocols—are reflected in the diversity of real-time monitoring systems, in terms of the parameters that can be monitored, the control and command systems that can be used, and the actuators that respond to commands. In this paper, the concepts, design, components, and working methods of these systems are discussed in detail. The latest research on real-time indoor monitoring systems published over the past five years is reviewed, resulting in the selection of 143 studies that met the inclusion criteria. This review synthesizes the technologies used for data capture, transmission, processing, storage, and visualization, as well as the approaches employed for alerts and system integration. By presenting these technical insights in a structured manner, the article provides a practical reference for researchers and practitioners aiming to design and implement real-time monitoring systems more efficiently and effectively.

1. Introduction

Real-time indoor monitoring systems (RTIMS) are technologies designed to continuously collect, process, and analyze information and data from an area of interest as it is generated, enabling immediate visibility and rapid response to any malfunction or deviation in the performance of targeted systems and environments. By integrating sensors and detectors with advanced communications networks, data processing software and algorithms, artificial intelligence, deep learning, and embedded machine learning, these systems provide a clear view of system performance and can predict potential failures or actions required to maintain optimal performance. RTIMS are widely used in automation, manufacturing, transportation, healthcare, and IT, where detecting malfunctions or failures in real times is essential. Their key advantage is that they allow organizations to make proactive decisions, reduce downtime, enhance safety, and ensure smoother and more efficient operations [1,2].

The Internet of Things (IoT) has recently revolutionized information and communications technology, driving significant changes in daily life. Many devices and equipment, from homes to large industrial facilities, are now connected to the Internet. This connectivity has enabled the development of real-world monitoring systems that leverage data from these devices to display and analyze the status of the environment and provide recommendations or warnings. RTIMS applications range from simple home temperature and humidity monitoring to advanced industrial monitoring and control systems. The growing number of connected devices has significantly improved the quality of life in many areas. Real-time monitoring systems maximize the benefits of these devices by utilizing their data efficiently and enabling automatic control based on user preferences through deep learning, machine learning, and artificial intelligence algorithms. These algorithms support in-depth data analysis, optimal decision-making, actuators and equipment control, and the issuance of alerts and warnings to prevent potential risks [3].

The main objective of this article is to bring together and make sense of the current knowledge surrounding RTIMS. In this work, the technologies that power these systems are clearly explained, the benefits they offer are highlighted, and their applications across different fields are explored. Key design criteria are outlined that developers and practitioners should consider when creating or improving RTIMS solutions. By reviewing and organizing recent research, the goal of this study is to provide readers with a clear, accessible, and up-to-date understanding of how RTIMS work and why they matter.

This article presents a comprehensive review of RTIMS published over the past five years. By examining 143 research works, it synthesizes key aspects of RTIMS design and implementation, including sensing technologies, communication networks, database solutions, visualization platforms, and alerting mechanisms. The review aims to provide researchers and practitioners with a clear understanding of the technological components commonly used in RTIMS and practical guidance for selecting and integrating these components in real-time monitoring applications.

To guide this review, the following research questions were formulated: What are the key technologies and components that enable RTIMS? What benefits do these systems provide across various indoor monitoring applications? How are RTIMS applied in domains such as healthcare, industrial automation, environmental monitoring, and smart homes or buildings? What design criteria are essential for developing effective RTIMS tools? And finally, what are the current trends, challenges, and gaps in RTIMS research from 2020 to 2025? Addressing these questions provides a clear framework for the analysis and synthesis presented in this work.

The remainder of this work is structured as follows. Section 2 outlines the inclusion and exclusion criteria used in the study. Section 3 provides a detailed examination of the core technologies underlying RTIMS, including data sources, communication and data transfer layers, storage and time-series databases, analytics engines, visualization and dashboard interfaces, alerting mechanisms and escalation policies, as well as security and access control. Section 4 highlights the key benefits of RTIMS, such as enhanced error detection, reliable data storage, improved data efficiency, and standardized data handling. Section 5 explores major application areas of RTIMS across healthcare, industrial automation, environmental monitoring, and smart home or building systems. Section 6 presents the essential design criteria for RTIMS tools, covering scalability, ease of use and customizability, and integration capabilities. Section 7 reports the results, Section 8 provides the discussion, and Section 9 concludes the paper.

2. Methodological Approach

In this work, the focus is on the implemented RTIMS, which are structured into three virtual layers: the sensing and data collection layer, the data classification, analysis, and storage layer, and the visualization and user interaction layer. Additionally, the study discusses various software and hardware components that can be used to develop these systems, including both commercial solutions and research contributions in this field.

2.1. Search Strategy

This review focuses on articles published from January 2020 to September 2025, providing an overview of the most recent developments in this area. This approach provides a realistic picture of the ongoing evolution of these systems and helps readers, especially new researchers, gain a fundamental understanding of system operation, available techniques and technologies for real-time implementation, and potential challenges encountered in previous work. In searching for manuscripts and research papers, emphasis was placed on publications indexed in reputable databases and publishing houses affiliated with well-known international organizations, such as Elsevier (Scopus and ScienceDirect), IEEE (IEEE Xplore), PubMed, and MDPI. The initial search was conducted in ScienceDirect and IEEE Xplore, before being extended to the other listed databases. Several keywords were used to capture relevant research, including: indoor monitoring system, indoor environment monitoring, smart building management system, building monitoring system, indoor data monitoring, IoT-based monitoring, and IIoT. Over 670 relevant papers were retrieved within the specified period for eligibility assessment, and a subset of highly relevant papers was selected and analyzed. The review followed the PRISMA protocol, paying attention to elements that determine inclusion and exclusion criteria.

2.2. Inclusion and Exclusion Criteria

The current work focuses on the study of actual monitoring systems, as implied by the term “real-time”, and also includes the development of certain software and hardware components used in these systems. The term “indoor” specifies that the work considered takes place inside a building or enclosed structure, rather than in outside environments. Care was taken to select the most comprehensive systems for the monitoring concept, as they encompass most of the main components across the system layers—from the data generation and transfer, to analysis and storage, to display and control interfaces. Consequently, purely theoretical research or modeling studies were excluded. Based on these considerations, the following inclusion and exclusion criteria were applied:

• Inclusion Criteria

  -

  Real-time system

  -

  Integrated systems containing all main elements: data acquisition, analytics engine, and visualization/dashboard interface

  -

  Systems designed for indoor applications

  -

  Complete systems including hardware, software, and servers

• Exclusion Criteria

  -

  Simulation, theoretical research, and algorithms

  -

  Systems for outdoor environments

  -

  Partial systems (not complete, cannot implement real-time monitoring, or addressing only a specific issue)

  -

  Studies that do not provide information about the components used

Applying these, 143 papers were identified as meeting the objectives of this study. Figure 1 illustrates the flowchart of the systems selection process.

3. RTIMS Technologies

Indoor monitoring systems consist of several main parts depending on their function within the system, beginning with the process of capturing and recording data from the targeted area and ending with the display of that data to users in a meaningful way. In this section, the main components used in these systems are discussed in detail.

3.1. Data Sources

This layer of monitoring systems represents the source of raw data supplied to the system. It may include sensors, detectors, devices, or data-generating applications. The nature of this raw data depends on the specific monitoring application and can range from data tracking people and devices in buildings and workplaces, to medical and biometric data captured by wearable devices for patients, the disabled, and the elderly in healthcare applications. It can also include data from equipment and robots in production lines for automation and factory monitoring, or data captured by environmental and air quality sensors in smart home and building applications [4,5].

3.2. Communication and Data Transfer Layer

The communications layer serves as the backbone of a monitoring system, enabling data and information between the data source layer and the other layers of the system. It facilitates seamless data exchange among various components such as sensors, control units, servers, and user interfaces. The primary function of this layer is to ensure reliable, secure, and real-time transmission of raw data throughout the system architecture. This layer supports both upstream and downstream communications: from data sources to the processing and presentation layers, and from user interfaces, processing, and control layers back to the sensors and sensor nodes. Data collected from distributed sensors or field devices is sent to central or cloud servers for processing and analysis, while control commands or configuration updates are distributed to devices and sensor nodes. The choice of communication technology depends on the application type, the characteristics of the monitored environment, and technical requirements such as coverage area, data volume, transfer rate, and operating frequency. Various networking technologies can be used, including wired Ethernet, serial communication, and wireless protocols such as Wi-Fi, Zigbee, LoRa, Bluetooth, or cellular networks (such as 4G/5G) [6,7,8,9].

In modern RTIMS, Internet Protocol (IP-based communications, the Internet of Things (IoT)), and message-oriented middleware such as Message Queuing Telemetry transport (MQTT) and Open Platform Communications (OPC) are widely used to support interoperability, scalability, and remote access. Furthermore, this layer incorporates data integrity mechanisms, encryption algorithms, and error-tolerance functions to ensure accurate and secure information transmission even in challenging or unstable environments. The efficiency of the communication layer directly affects system performance, influencing anomaly detection speed, response coordination effectiveness, and long-term reliability. Therefore, designing a robust communication layer is essential to achieve high performance, responsiveness, and reliability in any monitored infrastructure [10,11,12].

3.3. Storage and Time-Series Database

In real-time monitoring systems, time series databases (TSDBs) play a critical role in managing the continuous flow of data generated by sensors, detectors, devices, and applications within the data source layer. Unlike traditional relational databases, TSDBs are specifically designed to handle time-stamped data, enabling rapid data ingestion, compression, and retrieval of time-stamped records. Using time-series databases reduces storage costs through advanced data encoding and retention mechanisms, while supporting precise queries, trend analysis, and anomaly detection in real-time data. Consequently, TSDBs enhance scalability, reliability, and responsiveness, making them essential for applications such as industrial automation, environmental monitoring, building monitoring, and smart infrastructure. For example, TimescaleDB provides tables that automatically partition data by time period to prevent index degradation and support high ingestion rates, while offering efficient data compression achieving storage savings of up to approximately 95%. Storage engines in time series databases often employ advanced compression techniques—such as delta encoding, columnar formats, and multi-stage compression pipelines—to minimize storage costs while maintaining analytical performance [13,14,15].

Table 1. A Comparison of Relational Databases, Data Warehouses, and Data Lakes in RTIMS Applications.

Feature	Relational Databases	Data Warehouses	Data Lakes
Data Type	Structured data only	Structured and semi-structured data	Structured, semi-structured, and unstructured data
Data Purpose	Operational data	Analytical and reporting data	Raw data and advanced analytics
Schema Approach	Schema on write	Schema on write	Schema on- read
Scalability	Moderate, vertical scaling	High, horizontal scaling	Very high distributed, and scalable storage
Performance	Fast for transactional queries	Optimized for analytical queries	Depending on query engine
Storage Cost	Moderate	High	Low
Data Volume	MB-GB	Hundreds of GB to many PB.	Hundreds of GB to Exabyte
Data Freshness	Real-time or near real-time	Batch, scheduled updates	Batch, streaming data
Integration Complexity	Simple	Moderate, requires (extract, transform, load)/(extract, load, transform) ETL/ELT pipelines	High, needs metadata management
Best Use Case	Operational monitoring (sensor data, logs)	Business performance analytics and reporting	Centralized raw data repository for advanced analytics and ML
Typical Tools/Examples	MySQL, SQL Server, PostgreSQL	Google BigQuery, Snowflake, Azure Synapse, Amazon Redshift	Azure Data Lake, Athena in Amazon S3, Apache Hadoop

presents a comparison of relational databases, data warehouses, and data lakes in RTIMS applications [16,17,18].

3.4. Analytics Engine

The analytics engine is the core component of any real-time monitoring system (RTIMS), as it transforms continuous streams of raw data into indicative and actionable parameters. It is responsible for high-speed data processing, applying predefined equations, thresholds, and algorithms to detect patterns, identify errors and anomalies, and perform predictive analyses to alert and prevent future risks. Advanced computational technologies such as stream processing, machine learning, and event correlation are commonly employed to ensure that critical information is extracted and evaluated with minimal latency. These capabilities enable timely decision-making in domains such as healthcare, industrial automation, and cybersecurity, where even short delays can impact system efficiency or safety. The analytics engine is typically integrated with visualization and presentation layers, ensuring that results are displayed in an intuitive and actionable form. This integration facilitates rapid comprehension and response by human operators and supports a closed-loop control paradigm that enhances overall system responsiveness and reliability [19,20].

3.5. Visualization and Dashboard Interface

Visualization and dashboard interfaces are an essential layer in real-time monitoring systems, bridging the gap between complex digital data streams and user comprehension. They translate multidimensional and dynamic data streams into clear visual representations, such as graphs, counters, and various types of simulated metrics, that allow users to monitor system performance and detect any anomalies in real time. In healthcare application, for example, dashboards enable the continuous tracking of patient vital signs and management of available medical resources, thereby improving safety and treatment outcomes [21]. Similarly, in public health monitoring, real-time dashboards provide insights into regional or city-level disease outbreaks, supporting efficient response strategies and policy interventions [22]. The design of these interfaces emphasizes usability and interpretability, ensuring that users can quickly and accurately understand system status and trends. Modern dashboards often incorporate customizable alert thresholds, advanced search and filtering functions, and predictive analytics modules to enhance situational awareness. Increasingly, artificial intelligence and machine learning algorithms are integrated to deliver deeper analytics, trend forecasting, and anomaly prediction, further expanding the scope and efficiency of real-time decision making. Overall, well-designed visualization and dashboard systems are essential for the effectiveness of RTIMS, providing users with intuitive tools to make informed and timely decisions.

Table 2. Summary of Selected ready-to-use visualization and dashboard solutions for RTIMS.

Tool	Data Source Integration	Visualization Features	Type, Platform	Deployment Mode	Typical Applications	Scalability	Licensing	Reference
Grafana	SQL, NoSQL, InfluxDB, JSON APIs, Prometheus	Interactive dashboards, real-time metrics, alerting	Open-source dashboard	On site, Cloud	IoT monitoring, network telemetry, server performance	High	Open Source	[23]
ThingsBoard	MQTT, CoAP, HTTP	Customizable dashboards, device telemetry, alerts	IoT platform with dashboard	On site, Cloud	Industrial IoT, data monitoring	High	Open Source/Enterprise	[24]
Power BI	SQL, Excel, APIs, Azure, IoT Hub	Advanced charts, key performance indicator (KPIs), AI-driven insights	Business intelligence & analytics	On site, Cloud	Business analytics, IoT dashboards, energy monitoring	High	Commercial (subscription)	[25]
Kibana	Elasticsearch, Logstash, Beats	Real-time data visualization, log analytics	Analytics & dashboard	On site, Cloud	Log monitoring, anomaly detection, IoT	High	Open Source	[26]
Plotly Dash Enterprise	SQL databases, Excel, Parquet files, APIs	Interactive dashboards, real-time updates	Web-based visualization & dashboard	On site, Cloud	Data visualization, operations control	High	Commercial	[27]
Tableau	SQL, CSV, REST APIs	Rich interactive visuals, storyboards	Visualization & analytics	On site, Cloud	Operations dashboards, KPIs tracking	Medium–High	Commercial	[28]
Superset (Apache)	SQL databases, APIs	Charts, filters, dashboards	Dashboard	On site, Cloud	Analytics, real-time reporting	Medium	Apache License 2.0	[29]

provides a comparative overview of widely used ready-to-use visualization and dashboard solutions relevant to RTIMS. The tools included were selected based on their popularity, open-source availability, industrial adoption, and explicit support for real-time or streaming data.

3.6. Alerting and Notification System

Instant alert and notification systems are an integral part of modern monitoring architectures, enabling immediate response to potential hazards, functional deviations, or potential malfunctions in machinery, equipment, or system components. These systems continuously analyze captured data and compare it with previous inputs to assess the level of risk, ranging from normal alerts to moderate warnings or severe alarms. Alerts are triggered when pre-defined thresholds for monitored parameters are exceeded. The effectiveness of such systems depends largely on the speed, accuracy, and relevance of alert delivery. Therefore, advanced monitoring frameworks employ intelligent notification mechanisms that consider user context and minimize unnecessary interruptions to optimize response efficiency [30]. For example, platforms like Prometheus (Prometheus Bio Inc., Hangzhou, China) utilize time-series databases to record and store readings from various data sources, enabling real-time alerts through flexible and efficient query models that adapts to dynamic monitoring requirements. Furthermore, the integration of machine learning and artificial intelligence algorithms enhances alerting systems by predicting potential risks or failures before they occur, thus enabling proactive maintenance and reducing downtime—particularly in industrial automation applications. In critical domains such as patient and elderly or clinical healthcare systems, real-time alerting has proven its effectiveness and utility, with studies demonstrating that automated alert mechanisms can significantly improve outcomes, for example in sepsis management, by enabling timely intervention [31]. However, the overall performance of alerting systems strongly depends on their design and implementation. Poorly configured thresholds or excessive alert generation can lead to alert fatigue, resulting in decreased responsiveness and reduced system reliability. Therefore, continuous evaluation, calibration, and optimization of alerting parameters and protocols are essential to ensure sustained performance in real-world applications.

3.6.1. Alerting Strategy

An effective alert strategy in RTIMS is essential to detect problems promptly and enable timely responses. The system should include different alert levels, such as notifications, alerts, and alarms, to indicate the severity of a situation. Real-time data from sensors should be continuously analyzed against pre-defined thresholds to identify deviations from normal conditions. Once an event is detected, alerts can be automatically triggered via multiple communication channels defined by the operating environment, including notifications on monitoring dashboards, mobile phone and email alerts, or visual and audible alarms on the workplace. Furthermore, the alert system should support configurable alert parameters, escalation mechanisms, and historical recording to reduce false alarms and improve long-term reliability and responsiveness. For example, alerts can be categorized based on the type and severity as follows:

• Events: Any occurrence or change in the normal state that is detected by the monitoring system. These do not necessarily indicate an emergency or require immediate attention. Examples include a door opening or closing, or a scheduled system test.
• Alerts: Notifications regarding specific events that may require attention, but are not necessarily emergencies. Examples include technical issues such as low battery warnings, maintenance reminders, or notifications of system tampering.
• Alarms: High-priority events indicating an emergency or immediate risk, such as fire, or gas leaks.

It is crucial to define clear thresholds for alerts based on statistically significant metrics, such as maximum response times and expected error rates, and to categorize alerts according to their severity and impact to prioritize responses effectively. To prevent alert fatigue, alerts should trigger only in critical situations, as excessive or frequent alerts may lead to them being ignored. Common causes of overlooked alerts include:

• High volume of alerts: Excessive alert generation can make it difficult to distinguish critical from non-critical notifications.
• Lack of prioritization: Without a system to prioritize alerts based on their severity, user may treat all alerts equally, leading to desensitization.
• Repetitive alerts: Constant exposure to similar alerts can condition users to ignore them, reducing their responsiveness to new notifications.

Appropriate alert channels should be selected based on the work environment, available infrastructure, and environmental factors such as noise levels in laboratories or industrial sites, which may affect the effectiveness of audible alerts. Various channels can be employed, including email, text messages, or team communication tools such as Slack or Microsoft Teams. Audio and visual alerts can also be used where digital communication tools are impractical [32,33].

3.6.2. Escalation Policies

An escalation policy in internal monitoring systems is a structured set of rules that guides the handling of abnormal or hazardous events through alerts or alarms when these cannot be resolved automatically or during the initial response phase (by the personnel or entities responsible for addressing the problem). These systems monitor a range of internal parameters, including temperature, humidity, air quality, leaks of gases or hazardous chemicals, concentrations of certain gases, the percentage of solid particles in the air, energy consumption, and other internal factors whose deviations may indicate potential risks such as chemical leaks or fires. The primary goal of an escalation policy is to ensure that critical problems are addressed promptly to protect workers, prevent equipment damage, maintain the comfort of building occupants, and comply with public safety standards. Typically, an organization’s occupational safety policy specifies multiple response levels, the maximum time allowed before escalation, criteria for different types of alerts, and the personnel or teams responsible at each level. For example, if a temperature sensor detects an abnormal increase that the on-site control system cannot correct within a specified timeframe, the alert is escalated to the building maintenance team or the designated maintenance engineer. If the issue persists without resolution, it is further escalated to a higher management or the highest responsible authority. This structure’s approach ensures that problems are addressed efficiently by personnel with the appropriate expertise, maintaining high reliability and safety in indoor environments. Proper planning of an escalation pipeline includes defining clear procedures by documenting the path for alerts—from initial detection after a threshold is crossed to final resolution—while ensuring that each team member understands their role and responsibilities. Escalation procedures should also specify the order of notifications based on the severity and type of the alert. The time required to address each escalation level should be defined to guarantee a rapid response to critical issues [34,35]. Figure 2 illustrates a suggested three-level escalation procedure for a recoded event.

3.7. Security and Access Control

In many real-time monitoring applications, security and access control are essential elements of design and operation, particularly in environments where timely data processing and the system integrity are critical. These systems are often used to monitor the elderly and people with disabilities, network devices, industrial automation and control systems, and critical infrastructure—all of which require robust mechanisms to prevent unauthorized access and ensure data confidentiality and integrity. Weak security measures can allow unauthorized individuals or entities to gain access to sensitive information or even take control of devices and equipment, which could be misused for dangerous and malicious purposes (e.g., mobile robots and unmanned aerial vehicles). Therfore, real-time monitoring systems must balance the need for continuous data collection with strict security protocols. Implementing effective access control mechanisms, such as role-based access control (RBAC) or attribute-based access control (ABAC), ensures that only authorized personnel can access sensitive data and system functions. These mechanisms are essential for preventing unauthorized access and mitigating potential security breaches [36]. Furthermore, integrating real-time monitoring with security infrastructures simplifies the detection of security incidents as they occur. For example, combining access control systems with video monitoring enables for more effective preventive measures and supports post-incident investigations when necessary. Likewise, continuous security monitoring tools help organizations identify and mitigate threats before they escalate into serious issues. As cyber threats continue to evolve, ongoing research and development in this field remain essential to address emerging challenges and enhance the effectiveness of security protocols [37,38,39].

Naturally, RTIMS vary depending on specific requirements, existing infrastructure, equipment type, and the monitored parameters. This results in structural differences —some components mentioned above may be combined, omitted, or supplemented by additional specialized systems, depending on the application and environment. This review focusses on studies and systems that include the majority of the standard RTIMS components while excluding those lacking essential elements, such as dashboards, graphical user interfaces, data centers, or storage tools. Figure 3 illustrates a typical RTIMS architecture.

4. Benefits of Using RTIMS

RTIMS provides a range of important benefits that enhance the accuracy, reliability, and usability of indoor monitoring data. By continuously collecting and analyzing real-time information, RTIMS help ensure the early detection of abnormal conditions, improve the consistency and security of stored data, and support efficient data processing across various applications. Moreover, RTIMS contributes to the standardization of data formats and reporting practices, helping organizations integrate diverse data sources more effectively. The following subsections discuss these key benefits in detail, including error and anomaly detection, reliable data storage, improved data efficiency, and the role of standardization in ensuring interoperability.

4.1. Detecting Errors and Data Anomalies

Detecting anomalies in data received from the sources (sensors, detectors, servers, etc.) by RTIMS is one of the most important benefits and advantages of these systems, as it ensures the accuracy, reliability, and consistency of information within the system. Incorrect or corrupted data can negatively impact the overall integrity of a system. When a system’s databases receive distorted or corrupted data, it may be mixed with clean data, making it difficult to identify the source of the problem or error. Identifying corrupted data is particularly challenging when it exists in small quantities in databases and data warehouses. Early detection of errors and anomalies helps prevent incorrect decisions that could increase risks, exacerbate problems, or cause system damage. It also prevents system failures by detecting abnormal data resulting from physical defects. Furthermore, it helps prevent security breaches that may arise from incorrect or corrupted data intentionally transmitted for malicious purposes. Anomaly detection enhances data quality and reliability, enabling organizations to trust the results of their analytical and operational processes, which supports faster decision-making based on analysis outcomes. Data anomaly detection also enables rapid respond to potential problems, reducing downtime and minimizing associated financial losses. Additionally, it supports predictive maintenance by anticipates failures and directly addressing their root causes [40].

4.2. Reliable Data Storage

Reliable data storage—whether through databases, data warehouses, or data lakes—plays a critical role in the effectiveness and integrity of a data monitoring system. A robust storage infrastructure ensures consistent data availability, accuracy, and protection from loss or corruption, which is vital for long-term analysis in real-time monitoring systems. Databases provide structured and interactive storage, allowing efficient querying and retrieval of up-to-date information essential for operational decision-making. Data warehouses integrate and consolidate large amounts of historical and current data from multiple sources, enabling advanced analysis of system effectiveness, identifying performance weaknesses or potential trouble spots, and supporting proactive maintenance and strategic planning. Data lakes offer the flexibility to handle unstructured or semi-structured data captured from various devices and equipment in multiple formats and sizes, such as logs, sensor outputs, and multimedia files generated by cameras, optical, and audio sensors. This makes them ideal for big data analytics and machine learning applications within monitoring systems. For example, some monitoring systems transmit video files when anomalies are detected or a potential security breach occurs. This volume of data can be large and difficult to process using conventional databases. Integrating these storage solutions enables organizations to maintain continuous monitoring of operations, make decisions based on archived and real-time data, and improve overall performance [41,42].

4.3. Data Efficiency

The use of RTIMS enhances data efficiency, providing significant benefits that directly improve system performance, scalability, and decision-making accuracy. This improvement results from how data is collected, processed, stored, and transmitted, as well as from the application of effective filtering algorithms and data compression techniques. These mechanisms ensure that only relevant information is used, reducing duplication, lowering storage costs, and increasing data processing speed. Enhanced data efficiency allows users to establish connections between datasets captured from different sources, enabling a deeper understanding and more comprehensive analysis of the system’s status. Efficient data utilization helps identify relationships among interconnected issues, determine the causes of persistent problems, and uncover causal factors that might otherwise go unnoticed. Furthermore, RTIMS allows users to define criteria and thresholds for controlling system performance, automatically sending alerts and warnings if these criteria are not met or if predefined thresholds are exceeded, ensuring data integrity and consistency. The use of monitoring systems also accelerates the detection of data anomalies and immediate failures in system components, enabling rapid intervention and improving operational reliability. Data optimization also supports scalability, as real-time monitoring systems can handle increasing data loads without significantly expanding infrastructure resources. Efficient data handling further reduces energy consumption by minimizing unnecessary data transfers and repeated storage [43].

4.4. Data Standardizations

Currently, due to the development and widespread use of sensors and devices in RTIMS, differences exist in the types, sizes, and timestamps of data generated by these devices, primarily due to the lack of standardized protocols regulating data specifications. Additionally, devices that use different technologies to measure the same factors often produce data in different units of measurement. Sometimes, these differences arise from devices of different origins using distinct unit systems, as is the case between certain units in Europe and North America, for example. The benefits of RTIMS become evident through the use of standardization algorithms, which convert varying units into a specific, unified format. This standardized format serves as the basis for system performance control processes and the determination of alert and warning thresholds within the monitoring system. Data is standardized by converting it into a standardized format defined by user-predefined rules. Real-time monitoring tools continuously record data from multiple sources and then convert it into the required format. This process ensures efficient data usage and facilitates the integration of data from all locations operating under the same standardization system, promoting consistency and interoperability [44,45]. Figure 4 illustrates the advantages of standardization in RTIMS.

4.5. RTIMS Drawbacks and Implementation Challenges

Despite their numerous advantages, RTIMS also present several challenges. First, these systems often rely on extensive sensor deployments and continuous data transmission, which may increase energy consumption and maintenance costs. Additionally, real-time data processing requires robust network connectivity and computational resources, making system performance vulnerable to latency, bandwidth limitations, or hardware failures. Privacy and security concerns also arise due to the constant monitoring of indoor environments, especially in healthcare and smart-building applications, where sensitive personal data may be exposed or intercepted. Furthermore, integrating heterogeneous devices, protocols, and platforms can complicate system interoperability and increase deployment complexity. These limitations highlight the importance of designing RTIMS with careful consideration of reliability, cost, security, and scalability [46,47,48].

5. RTIMS Application

RTIMS have become essential technologies for enhancing safety, comfort, and efficiency in modern buildings. These systems continuously collect, process, and analyze data from various sensors to monitor environmental conditions, human activity, and equipment performance within indoor spaces. Their applications range from home automation and smart buildings to patient and healthcare monitoring, industrial automation and control systems, and environmental monitoring. These main applications include various sub-applications such as temperature, humidity, and air quality monitoring; indoor object movement and positioning monitoring and tracking; building health monitoring; and asset or employee tracking systems. By providing immediate feedback and automated responses, RTIMS play a vital role in optimizing indoor environments and supporting intelligent decision-making. In the following sub-sections, the main types of RTIMS applications and significant related works from the period covered in this review will be discussed in details.

5.1. RTIMS for Patients and Healthcare

Patient monitoring and healthcare systems have seen significant advancements, driven by the tremendous growth in the medical electronics and communications industry and innovations in wearable technologies, the Internet of Things, machine learning, and artificial intelligence. This evolution is reflected in performance efficiency and multitasking, expanding from basic health tracking to comprehensive real-time health management platforms that provide continuous monitoring and personalized care.

Today, the use of wearable devices such as smart watches or fitness management devices has become widespread. These devices record various vital signs, including temperature, heart rate, blood pressure, blood oxygen level, and physical activity. Some devices incorporate artificial intelligence algorithms that process this data, enabling early detection of any abnormalities or deficiencies in body functions. This allows user and healthcare provider to access live vital data and make critical decisions in a timely manner [49]. Significant advancement in IoT technologies have also enabled the development of interconnected health monitoring systems, where devices communicate seamlessly and provide comprehensive health data for remote patient monitoring. This allows healthcare providers to track patients’ health status and vital signs in real time, improving service delivery and enabling rapid interventions when necessary [50]. Similarly, the proliferation of mHealth applications on smartphones has expanded access to healthcare services. These applications offer several important features, such as symptom tracking, medication reminders, dosage information, and virtual consultations, allowing users to proactively manage their healthcare, especially when access to healthcare providers is limited. During the COVID-19 pandemic, these applications played an important role in monitoring systems, providing guidance, and reducing the burden on healthcare facilities [51].

Despite these advances, challenges remain, including data privacy, access and disclosure, device compatibility across different sources, and user interaction with graphical interfaces [52]. Addressing these challenges is essential to enhance the effectiveness of healthcare monitoring systems. Examples of recent work in patient monitoring and healthcare include the following.

Vijayalakshmi et al. proposed IoT-based intelligent health care monitoring system for long-time screen users [53]. The system integrates AI, IoT, and natural language processing technologies (NLP) to maximize performance. It consists of four layers: (1) sensors for data collection, (2) data analytics engine to analyze health parameters and user behavior, (3) a virtual assistant based on large language models (LLMs) for user-computer interaction, and (4) a user interface module for generating on-screen interactions and indications. Sensors include the MAX30100 heart rate sensor (Shenzhen Electronics, Shenzhen, China), the FlexiForce A401posture sensor (Sri Electronics & Embedded Solutions, Coimbatore, India), the MPU-6050 inertial measurement unit (InvenSense Inc., Sunnyvale, CA, USA), the Tobii Eye Tracker 4C (Tobii, Stockholm, Sweden), DHT11 (Aosong Electronics Co., Ltd., Guangzhou, China) temperature and humidity sensor, the Shimmer3 GSR Electrodermal Activity Sensor (Shimmer, Dublin, Ireland), the TSL256 ambient light sensor (ams-OSRAM AG, Premstaetten, Austria), and the HC-SR501 motion sensor (Sunrom Electronics, Ahmedabad, Gujarat, India). Data is processed and analyzed using machine learning (ML) and deep earning (DL) algorithms, such as random forest classifier (RF), support vector machine (SVM), convolutional neural networks (CNN), K-means clustering, and long short-term memory (LSTM) Networks. The virtual assistant is uses NLP components including natural language understanding (NLU), natural language generation (NLG), and dialogue management. The user interface provides a dashboard for displaying real-time health metrics, personalized recommendations, and system notifications. Test results demonstrated effectiveness in promoting overall health and enhancing user well-being.

Lopes et al. developed a contactless health monitoring system (CHM) for patients and elderly individuals in nursing homes [54]. The system monitors vital signs such as elevated body temperature, heart rate, and respiratory rate in real time using IR thermal imaging sensors and Doppler radar without invasiveness or direct contact. It consists of three main parts: the CHM IoT Edge devices above patients’ beds to capture measurements, a cloud AI engine with broker and data storage, and a web- or mobile-based GUI. The computing platform uses a Raspberry Pi 4 model B (Raspberry Pi Ltd., Cambridge, UK) with a quad-core ARM Cortex-A72 CPU. Sensors include the Texas Instruments AWR1642 chipset (Texas Instruments Incorporated, Dallas, TX, USA) as a Doppler radar, the FLIR Lepton 3.5 infrared thermal camera (Teledyne FLIR LLC, Wilsonville, OR, USA) for measuring body temperature, the SGP30 (Sensirion AG, Stafa, Switzerland) gas sensor for measuring volatile organic compounds (TVOCs) and eCO₂ concentration, the DHT22 (Aosong Electronics Co., Ltd., Guangzhou, China) humidity & temperature sensor, and SPS30 optical sensor (Sensirion AG, Stafa, Switzerland) to measure particle matter (PM) in air concentration. System performance was validated against reference devices, with errors below 10% in 96%, 74%, and 52%, of cases for body temperature, heart rate, and respiratory rate measurements, respectively. Challenges included selecting appropriate data transfer protocols and integrating the system with the nursing home’s network.

Hasan et al. proposed a low-cost IoT-based health physiological signal monitoring system capable of continuously monitoring electrocardiograms (ECG), electromyograms (EMG), electroencephalograms (EEG), and electroencephalograms (EOG) [55]. The system includes a portable analog front end (AFE) and sensor unit for acquisition, filtering and amplification, a Wi-Fi router, SQL database server, and a graphical user interface (GUI). The ESP32-WROOM (Espressif Systems, Shanghai, China) IoT microcontroller handles initial data processing and transmission to the IoT cloud over Wi-Fi. The Eclipse Mosquitto message queuing telemetry transport (MQTT) broker (Eclipse Foundation AISBL, Brussels, Belgium) transmits data from the sensing unit to a computer for SQL storage. The GUI, developed in JavaScript, displays the system information in a web browser. The experimental results validated the system’s accuracy compared to conventional certified biomedical devices.

Other approaches for patient and healthcare monitoring are reported in [56,57,58,59].

5.2. RTIMS for Industrial Automation

Industrial automation monitoring systems have become an essential part of modern manufacturing, and in many cases, this type of real-time monitoring application preceded others. Their development has been driven by the need for direct, real-time visibility of production lines, predictive analytics to guide workers and anticipate hazards or failures, and operational efficiency to optimize resource use. Traditional supervisory control and data acquisition (SCADA) systems commonly used in factories have evolved to incorporate cloud computing, the Industrial Internet of Things (IIoT), and deep learning and machine learning technologies, enabling smarter and more efficient industrial automation monitoring solutions [60].

Industrial Internet of Things (IIoT)-enabled sensors and edge computing allow massive data to be collected and analyzed without sending all of it to the cloud. This approach reduces response times by processing data closer to the source, saves bandwidth by minimizing cloud data transfer, enhances security by storing sensitive data on-site, and ensures continued operation even if the cloud connectivity is lost [61]. Additionally, connecting industrial control systems to the cloud enables remote management and maintenance, which can be critical in industrial sites with challenging operating environments [62].

Artificial intelligence, machine learning, and deep learning algorithms facilitate anomaly detection in sensor and detector data, enabling the identification and prediction of potential faults. This reduces production downtime, prevents fault accumulation, and lowers repair and maintenance costs [63]. Examples of recent work in industrial automation RTIMS include:

Kim et al. developed a multi-point real-time monitoring system for a plant factory to monitor and analyze plant growth. The system consists of sensor nodes, network communication, and a monitoring dashboard [64]. A printed circuit board (PCB) connects the main microcontroller with the sensors. An Arduino Uno R3 (Arduino LLC., Boston, MA, USA) serves as a node controller, integrated with the Xbee Zigbee TH wireless module for communication (Digi International, Inc., Hopkins, MN, USA) between the slave nodes and the master node, which includes the main server using a Wemos D1 R1 Wi-Fi module (Espressif Systems, Shanghai, China). Sensors include the DHT22 (Aosong Electronics Co., Ltd., Guangzhou, China) humidity & temperature sensor, MD0550 Airflow speed sensor (Modern Device, Brooklyn, NY, USA), CM1107 Carbon dioxide sensor (Cubic Sensor and Instrument Co., Ltd., Wuhan, China), DS18B20 Nutrient solution temperature sensor (Analog Devices, Inc., Wilmington, NC, USA), and the DFR0300 Electrical conductivity sensor (DFRobot, Shanghai, China). Recorded data from slave nodes is transmitted to the master node and forwarded to the visualization server via Wi-Fi using JSON (JavaScript Object Notation) format every 5 s. The dashboard supports authorized login, sensor data viewing, weather information integration, real-time synchronization, and report generation. Tests over one month in the factory demonstrated that the system effectively analyzes the internal environment for plant growth and optimizes environmental facility operations through a closed control loop.

Al-Okby et al. proposed an IoT-based ambient monitoring system for automated laboratories and industrial location [65]. Hybrid IoT-based sensor nodes record the environmental and location data and transmit them to the monitoring server via Wi-Fi network. Sensors include the SGP30 (Sensirion AG, Stafa, Switzerland) gas sensor for VOCs and CO₂ concentration, the SGP41 (Sensirion AG, Stafa, Switzerland) environmental sensor for nitrogen oxide and VOC indexes, SHT41 (Sensirion AG, Stafa, Switzerland) for temperature and relative humidity, PMSA003 (Nanchang Panteng Technology Co., Ltd., Nanchang, China) for particulate matter, and the DWM3000 transceiver module (Qorvo Inc., Greensboro, NC, USA) for distance calculation. All sensors are integrated with the ESP32-S3 (Espressif Systems, Shanghai, China) microcontroller, which transmits data to a Python based monitoring server using UDP. The server monitors environmental parameters, gas concentrations, and triggers alarms if thresholds are exceeded. Movable sensor nodes are tracked using UWB measurements. Tests with various VOC types and volumes demonstrated accurate chemical leakage detection with approximately 0.5-m localization accuracy in line-of-sight (LoS) conditions.

Xia et al. developed an augmented reality and indoor positioning-based real-time indoor monitoring system for factories called the Augmented Indoor Mobile Production Monitoring System (AIMPMs) [66]. The system integrates augmented reality with indoor positioning, using a Microsoft HoloLens2 (Microsoft Corporation, Redmond, WA, USA) for visualization. The positioning system combines UWB modules with the JY90X (Shenzhen Rainbowsemi Electronics Co., Ltd., Shenzhen, China) inertial measurement unit for improved localization accuracy. The recorded data is transmitted to a HP workstation Z8 (HP Inc., Palo Alto, CA, USA), which serves as the main server. The system was tested in a hydraulic cylinder factory using a 54 UWB base station and 176 UWB tags. AIMPM allows staff to monitor production lines and working conditions in real time. Results indicate that AIMPM significantly improves human physiological and mental fatigue levels compared to previous systems, creating a highly efficient human–machine interaction model with direct human control. Similar approaches for RTIMS applications are reported [67,68,69,70].

5.3. RTIMS for Environmental Parameters

Indoor air quality (IAQ) monitoring and the measurement of environmental factors to improve indoor air quality have become essential in nearl every home and workplace. These systems have rapidly developed due to advances in microelectronics, low-cost sensors, general communications technologies, and particularly the Internet of Things (IoT), as well as the proliferation of deep learning and artificial intelligence (AI) systems. The integration of environmental sensors with IoT and AI-based analytics systems enables effective monitoring with analytical and predictive capabilities at a reasonable cost. Recent reviews show a clear shift from isolated, laboratory-based devices to networked, cloud-connected sensor nodes that continuously record measurements of environmental parameters such as temperature, relative humidity, volatile organic compounds (VOCs/TVOCs), carbon dioxide, fine particulate matter (PM₁/PM_2.5/PM₁₀), and nitrogen oxides (NOx) for real-time monitoring in buildings and public spaces. Distributed IoT architectures allow continuous, spatially accurate monitoring, while remote dashboards display all recorded data and sensor locations, supporting occupant health monitoring [71,72,73].

Real-time indoor environmental monitoring is among the most widely applied RTIMS use cases. Selected works from the review period include:

Liu et al. proposed an IoT based multi-points indoor air quality monitoring system capable of measuring temperature, humidity, PM2.5, and CO₂ concentration [74]. The system uses three sensors: the SHT30 (Sensirion AG, Stafa, Switzerland) for temperature and relative humidity, PMS5003 (Nanchang Panteng Technology Co., Ltd., Nanchang, China) for PM2.5 measurements, and the S8 0053 (Senseair AB, Delsbo, Sweden) sensor for CO₂ concentration. The sensors are connected to a Cortex-M3 32-bit STM32F103C8T6 (STMicroelectronics, Geneva, Switzerland) microcontroller via I2C (SHT30) and UART (S8 0053 and PMS5003). Data is transmitted to the cloud using the integrated DRF1609H (Shenzhen DTK Electronics Co., Ltd., Shenzhen, China) Zigbee module. The user interface, accessible via website or mobile app, is designed with a WYSWYG approach. It allows authorized users to monitor live data, view archived records, export CSV files for the required data, and receive warning and alert notifications when user-defined thresholds are exceeded. Tests revealed that Zigbee signals were strongly attenuated by indoor walls, and the authors suggest using the LoRa communication protocol to overcome this limitation.

Kadir et al. developed a cloud-based indoor air quality monitoring system using IoT technologies [75]. The system consists of a portable sensor module and a cloud server. The sensor module used the ESP-WROOM-32 (Espressif Systems, Shanghai, China) IoT microcontroller for sensors data preprocessing via I2C bus; with two environmental sensors: BME680 (Bosch Sensortec, Reutlingen, Germany), and the CCS811 (ScioSense B.V., Eindhoven, The Netherlands). The sensor module is powered by a rechargeable Panasonic Li-ion NCR18650B (Panasonic Corporation, Kadoma, Japan) battery (3400 mAh, 3 V to 4.2 V) operational voltage. Data is transmitted to the cloud via Wi-Fi. The InfluxDB Cloud 2.0 (InfluxData, San Francisco, CA, USA) collects the data, and the Amazon S3 serves as the database (Amazon Web Services, Inc., Seattle, WA, USA). The web-based Freeboard.io allows real-time data visualization and GUI configuration to access multiple data sources [76]. Tests confirmed that the sensors reliably measured environmental parameters under varying conditions.

Kim et al. proposed an indoor monitoring system based on edge computing, using an NVIDIA’s Jetson Nano board (NVIDIA Corporation, Santa Clara, CA, USA) for high-performance edge computing [77]. The data collection unit interfaces with 14 environmental sensors through UART, USB, I2C, RS485, Digital TTL, and Ethernet protocols. Sensors include the BME680, the BMP388 digital pressure sensor (Bosch Sensortec, Reutlingen, Germany), the SCD30 (Sensirion AG, Stafa, Switzerland) temperature, humidity, and CO₂ sensor, the SGP30 (Sensirion AG, Stafa, Switzerland) TVOC, and eCO₂ sensor, the SHT35 (Sensirion AG, Stafa, Switzerland) temperature, and humidity, the BH1750 (ROHM Co., Ltd., Kyoto, Japan) Illumination sensor, the SEN0376 (DFRobot, Shanghai, China) alcohol sensor, the SEN0321 (DFRobot, Shanghai, China) ozone sensor, the SEN50135 (Mecca Solution Co., Ltd., Daegu, Republic of Korea) motion sensor, the PMS5003 (Nanchang Panteng Technology Co., Ltd., Nanchang, China) particulate matter sensor, and the S-pH-01A (Seeed Technology Co., Ltd., Shenzhen, China) pH sensor. All sensors are integrated on a single PCB. Data visualization is implemented with Telegraf (InfluxData, Inc., San Francisco, CA, USA), InfluxDB, and Grafana (Grafana Labs., New York, NY, USA). The collected data is preprocessed, normalized, and analyzed using a trained bidirectional long short-term memory (LSTM)-based prediction model. Data is transmitted via MQTT, and AI models implemented with TensorFlow and Keras (Google Brain Team, Mountain View, CA, USA) run on the Jetson Nano GPU. CPU. Tests demonstrated superior performance compared to traditional LSTM, gated recurrent unit (GRU), and bidirectional GRU methods. Similar RTIMS approaches for environmental monitoring are reported in [78,79,80,81,82].

5.4. RTIMS for Smart Home/Building Monitoring

Smart home and building RTIMS are widely used applications today, directly linked to improving the quality of life for occupants. The widespread availability of modern communications technologies and the Internet of Things, along with the development of building management and monitoring applications for computers and mobile devices, allows users to receive essential information about their indoor environment and to control systems such as air conditioning, ventilation, lighting, and energy management to achieve optimal conditions [83]. Recent integrations of AI, machine learning, and deep learning—implemented via edge computing or cloud processing—have enhanced these systems [84]. These capabilities enable intelligent monitoring that predicts potential risks, provide guidance and warnings, schedules maintenance to prevent damage, optimizes resource usage, and achieves significant energy savings [85].

Despite these advances, challenges remain. Widespread adaption is limited by compatibility and standardization issues between different products and systems within buildings, data quality and transmission methods, and the integration of older with newer systems. Cybersecurity and privacy risks associated with building sensors are also critical considerations for the success of smart building applications [86]. The COVID-19 pandemic highlighted the importance of these systems, emphasizing the need to manage the indoor environmental parameters, remotely control factors such as ventilation, temperature, and occupancy, and reduce physical contact, whether in homes, private buildings, or in healthcare facilities. This led to a re-evaluation of resilience and public health within building monitoring systems and smart cities [87]. Selected works during the review period include:

Anik et al. proposed a low-cost, scalable IoT-based indoor environmental monitoring system called Building Data Lite (BDL) [88]. The system consists of portable sensor nodes and a central server. Sensor nodes integrate a Raspberry Pi Zero (Raspberry Pi Ltd., Cambridge, UK) with built-in WiFi and an MCP3008 analogue-to-digital converter (ADC) to digitalized the analog sensors measurements. Three prototypes were tested: the first two included DHT11 (Aosong Electronics Co., Ltd., Guangzhou, China) temperature and humidity sensors, MQ2 (Winsen Electronics Technology, Zhengzhou, China) smoke, gas, and CO sensors, GL5528 light sensor (Shenzhen Chenxinhong Electronics Co., Ltd., Shenzhen, China), the SW420 (Handson Technology Enterprise, Masai, Johor, Malaysia) vibration sensor, the HC-SR501 (Handson Technology Enterprise, Johor, Malaysia) passive infrared motion sensors, flame sensors, and microphones. Each sensor node stores data locally in a MariaDB database (MariaDB Corporation Ab., Helsinki, Finland) to prevent any data loss during communication interruptions, creating temporary hourly files for unsent readings. The third prototype included an Enviro Plus sensor array (Pimoroni Ltd., Sheffield, UK), with BME280 (Bosch Sensortec, Reutlingen, Germany) temperature, pressure, humidity sensors, the LTR-559 (Lite-On Technology Corporation, Taipei City, Taiwan) light and proximity sensor, the MICS6814 (SGX Sensortech SA, Neuchâtel, Switzerland) analog gas sensor, and the ADS1015 (Texas Instruments Incorporated, Dallas, TX, USA) analog to digital converter. The central server is web-based, receiving data via Wi-Fi, using PHP v7, with a front-end in CSS, HTML, and JavaScript (Netscape Communications Corporation, Mountain View, CA, USA). A dynamic GUI was implemented with AJAX, JQuery and Chart.js libraries. Experiments in 12 households with 48 sensor nodes showed that the proposed system is functional, portable, and scalable.

Azman et al. proposed an IoT-Based RTIMS for restroom hygiene monitoring [89]. The system helps improve the cleaning efficiency by notifying staff only when cleanliness thresholds are exceeded, particularly during high ammonia levels and poor indoor air quality. It uses the InfluxDB (InfluxData, San Francisco, CA, USA) time series database and MQTT protocol over Wi-Fi for real-time monitoring and long-term trend analysis. The sensing layer consists of two sensor nodes: a hygiene monitoring node (ESP32-SOLO-1, BME680 environmental sensor, MQ-137 ammonia sensor) and a counting system node which was used for detecting user presence and movement (ESP32-SOLO-1 with IR motion sensor). Data is transmitted to the cloud via MQTT broker in JSON format, with ESP32 devices as clients, and the database, dashboard, and notification devices as subscribers. Trend analysis is performed using probability density function (PDF), supported by correlations from BME680 and MQ137 readings. Notifications are delivered via Telegram mobile app, while Grafana (Grafana Labs, New York City, NY, USA) visualizes the data. Practical experiments improved restroom hygiene management, reduced operating costs, and enhanced user comfort and safety.

Vaheed et al. proposed an IoT-based air pollution monitoring system for smart building [90]. The system includes three main hardware components: a ZPHS01B (Winsen Electronics Technology, Zhengzhou, China) multi-channel gas sensor module (laser PM sensor, IR CO₂ sensor, electrochemical sensors for carbon monoxide, a NO₂ sensor, VOC sensors, and temperature and humidity sensors), a Raspberry Pi 4 MCU (Raspberry Pi Ltd., Cambridge, UK) to process sensor data, and a JioFi Wi-Fi router (Reliance Jio Infocomm Limited, Mumbai, India) to transmit data to the cloud. Software components include Google Firebase (Google LLC., Mountain View, CA, USA) for cloud-based data storage and analysis and a Python-based web portal for visualization. Practical tests over one month demonstrated effective monitoring and real-time alerts when air quality index exceeds a user-defined threshold. Similar RTIMS applications for smart homes and building monitoring are reported in [91,92,93,94,95].

6. RTIMS Tools Design Criteria

The design of real-time indoor monitoring systems (RTIMS) requires careful consideration of multiple factors and conceptual frameworks to achieve the maximum possible benefit and ensure that the intended objectives are met. The following sections will address the most important factors that must be taken into account when designing these systems.

6.1. Scalability

Scalability is a key factor in the design of RTIMS. The system should be able to grow alongside the expansion of the underlying infrastructure over time. In recent years, rapid developments in electronics, sensors, and device manufacturing have led to successive generations of components featuring higher specifications, improved quality, and increased data volumes. Many devices have evolved from transmitting simple raw data to sending large, high-resolution multimedia files—particularly in RTIMS applications related to object tracking, localization, and robotics. Therefore, these significant advancements must be considered during the design phase, along with the possibility of upgrading or replacing specific components in the sensor layer. The system should be capable of both vertical scalability (increasing processing power and computational load capacity) and horizontal scalability (increasing the number of nodes or components) without compromising overall performance. Furthermore, high flexibility should be ensured to accommodate various equipment types, sizes, and data quality levels generated by different devices [96]. Figure 5 illustrates the concept of vertical and horizontal scalability in RTIMS.

6.2. Ease of Use and Customizability

Ease of use is a critical aspect of any data monitoring system, as it directly influences user adoption, operational efficiency, and overall effectiveness. RTIMS should therefore be designed to ensure simplicity and intuitiveness on the frontend, regardless of the complexity of the backend infrastructure. Therefore, a clear and well-structured graphical user interface (GUI) or dashboard is essential, particularly in light of user privacy considerations and the diverse nature of users and their data requirements. A carefully designed system with usability in mind allows users of all backgrounds—from engineers and technicians to non-technical staff—to access real-time data, interpret it effectively, and respond appropriately to alerts and warnings without requiring extensive technical training or prior experience. Moreover, the user interface must ensure that processed data is transformed into clear graphical representations that can be easily understood by both specialists and non-specialists. The system design should support customization to accommodate specific monitoring requirements, such as tailored metrics, alerts, and dashboards, while adapting to the unique needs of individual users or user groups. For example, in a research center with multiple research teams working across different disciplines, the monitoring system should allow each research team to view parameters relevant to their specific research activities on a dedicated dashboard. It should also implement appropriate permission levels and access control mechanisms, defining which users can view, edit, or manage particular data sets within each research team [97,98].

6.3. Integration Capabilities

The compatibility of a real-time monitoring system with the existing infrastructure at the monitored site is an important factor in system design. RTIMS must be capable of seamless integration with available instrumentation, equipment, systems, devices, and software applications. This requires support for diverse communication protocols, data formats, and platform architectures used within the existing environment [99,100].

7. Results

The works selected for this review were examined based on several factors to ensure alignment with the objective of facilitating the study and selection of real-time indoor monitoring systems (RTIMS). The first factor considered was their suitability for indoor environments. This is particularly important given the challenges of operating monitoring systems efficiently indoors, where walls, equipment, and other obstacles can affect performance—especially for radio-frequency tracking and positioning systems integrated to monitor the movement of people and objects within buildings. The second factor was the inclusion of the key elements of real-time monitoring systems, particularly data storage and the presence of graphical user interfaces (GUIs), which are essential functional components.

The selected works, published from the beginning of 2020 to 30 September 2025, were summarized based on several aspects: the types of sensors and devices in the data-source layer; the networks and protocols used for data transmission; the data storage approach; the presence of an analysis engine or cloud computing for data processing; the type of GUI employed; the alert and warning mechanisms implemented; and the application domain of each system. These details are presented in

Table 3. Summary of selected RTIMS.

Data Sources, Sensors	Network, Communication Protocols	Data Storage	Analytical Engine, ML, AI	Visualizations	Notifications Channels	Applications	Reference No., Year
SGP30, SGP41, SHT41, PMSA003I, MaUWB_ESP32S3	Wi-Fi, UWB	Microsoft SQL	Cloud	Python-based	Dashboard	Automation	[65], 2025
MD0550, CM1107, DHT22, DS18B20, DRF300	Wi-Fi, Zigbee	MariaDB	Apache Web Server	Web-based dashboard	Dashboard	Automation	[64], 2023
PMS5003, MH-Z19C, BME280, LTR-559ALS-01, MICS6814	Wi-Fi	MySQL, SD card	PHP server	PHP GUI	Dashboard, LCD display	Automation, Environmental	[101], 2024
BLE beacons, accelerometer	Wi-Fi, Bluetooth	Cloud database	Cloud	Revit software, Building Information Modeling (BIM)	Dashboard	Automation	[102], 2024
PowerScout 48 HD, Logitech C922 Pro HD, RealSense D435i, SVPRO Fisheye camera	Modbus Wireless	MongoDB	Web server	Web-based GUI	Dashboard	Automation	[20], 2021
JY90X IMU, LiDAR, UWB, HoloLens2	Wi-Fi, UWB	Cloud database	Web server	Web-based GUI	Dashboard	Automation	[66], 2024
DHT11, CCS811, GP2Y	Wi-Fi	ThingsBoard platform	Thingsboard Rule Engine	Thingsboard dashboard	Dashboard, Telegram	Environmental	[103], 2024
PMS5003, SHT31, S8 0053	Zigbee, Modbus	Cloud database	Cloud	Web-based GUI, dashboard	Mobile app.	Environmental	[74], 2021
SGP41, SGP30, SHT40, PMSA00I	Wi-Fi, MQTT	Microsoft SQL	Cloud	Web-based dashboard	Dashboard, Email, SMS	Environmental	[104], 2024
DHT11, MQ2, KY-026, MPU-6050	Wi-Fi	Cloud based	Blynk cloud based	Mobile app.	Mobile app., LEDs, Speaker	Environmental	[105], 2020
DHT11, MQ135	Wi-Fi, MQTT	MySQL	Cloud	Web-based Dashboard	Dashboard, Mobile app.	Environmental	[106], 2020
MQ2, MQ9, PMS7003,	Wi-Fi	Web-based	Cloud, ML, AI	Web-based Dashboard	Dashboard	Environmental	[107], 2023
BME680, CCS811	Wi-Fi	InfluxDB	Amazon Cloud	Freeboard GUI	Dashboard	Environmental	[75], 2021
SPS30, TGS2602, BME680, K30	LoRa, Wi-Fi	Cloud database	Cloud	MATLAB, web-based Dashboard	Dashboard, Mobile App.	Environmental	[108], 2024
DHT22, SGP30, PMSA003I, MQ9	LoRa-MQTT	Cloud database	Cloud	Web-based Dashboard	Dashboard	Environmental	[109], 2024
MQ5, DHT11, Water Level Sensor	Bluetooth	SQL database.	Cloud	Web-based dashboard	Dashboard, Mobile app.	Environmental	[110], 2020
Si7006, CCS811, PMS7003	Wi-Fi, MQTT	MySQL	Node-red	Grafana web-based	Dashboard	Environmental	[111], 2021
DHT11, MQ2, MQ135, MQ4, MQ7	Wi-Fi	ThingSpeak cloud-based	Web server	Web-based ThingSpeak, LCD	Dashboard, Email, SMS	Environmental	[112], 2024
MH-Z19B, TGS2611, PMSDS011, BMP280, SHT30, DS18B20	Wi-Fi, MQTT	Microsoft SQL	Web server	Web-based dashboard	Dashboard	Environmental	[113], 2022
MQ135, GP2Y1014	Wi-Fi	Blynk cloud	Blynk cloud	Web-based Dashboard, Mobile app.	Dashboard, Mobile app.	Environmental	[114], 2020
DHT22, PMS5003, MQ6, MQ9	Wi-Fi	Blynk cloud	Blynk cloud	Web-based Blynk, LCD	Dashboard, mobile app.	Environmental	[115], 2025
BMP180, MQ7, MQ4, HR202	Bluetooth	Google Firebase Database	Android app.	Python	Mobile app.	Environmental	[116], 2023
MQ135, DHT11	Bluetooth	Cloud-based	Mobile app	Mobile app	Dashboard, mobile app, Email	Environmental	[117], 2024
MQ135, DHT11, LM35,	Wi-Fi	Blynk cloud	Blynk cloud	Web-based Blynk, LCD	Dashboard, web, mobile app.	Environmental	[118], 2023
DGS-CO968-034, DGS-H2S968-036, DGS-O3968-0424, DGS-NO2968-043, DGS-SO2968-038, RD200M, SPS30, SVM30	Wi-Fi	Blynk cloud	Blynk cloud	Web-based Blynk, LCD	Dashboard, mobile app.	Environmental	[119], 2020
DHT22, FC22-1, MQ7, SGP30	Wi-Fi	ThingSpeak cloud-based	ThingSpeak platform	Mobile app.	Mobile app.	Environmental	[120], 2020
MQ5, DHT22	Wi-Fi	ThingSpeak cloud-based	Web server	Web-based ThingSpeak	Visual, Email	Environmental	[121], 2021
ENS160, PMS5003, ΒΜΕ280	Wi-Fi	InfluxDB, SD	Cloud	Grafana web-based	Dashboard, LCD display	Environmental	[122], 2024
DHT11, MQ5, MQ135	ZigBee	Alibaba Cloud	Alibaba Cloud	WeChat app, OLED	Dashboard, buzzer, mobile app.	Environmental	[123], 2024
DHT11, MQ135	Wi-Fi	ThingSpeak cloud-based	Web server	Web-based ThingSpeak	Dashboard, mobile app.	Environmental	[124], 2023
DHT11, MQ135, BMP180, MG-811	Wi-Fi	ThingSpeak cloud-based	Web server	Web-based ThingSpeak, OLED	Dashboard, mobile app.	Environmental	[125],2020
PMS5003, ENS160, SHTC3	Wi-Fi	ThingSpeak cloud-based	Web server	Web-based ThingSpeak	Dashboard, Email	Environmental	[126], 2024
SEN0232, DHT11, AHT10,	Wi-Fi, LoRa, MQTT	InfluxDB	Node-Red	Node-Red dashboard, Grafana web-based	Dashboard	Environmental	[127], 2025
TGS2600, TGS2602, Cozir-Blink5000, PID-AH2, PMS5003, Grove Multichannel Gas Sensor V2, BH1750, MPL3115A2	Wi-Fi, MQTT, GSM, Ethernet	microSD, Cloud-based	Cloud-based	Dashboard, mobile app	Dashboard, mobile app	Environmental	[81], 2024
BME680, SGP30, MS5803-05BA	Wi-Fi, Bluetooth	Microsoft SQL	Cloud-based	Dashboard	Dashboard	Environmental, Automation	[82], 2021
SCD30, SHT35, BME680, SGP30, BH1750, BMP388, SEN0376, SEN0321, SEN50135, C930, PMS5003, S-pH-01A, Air Korea	Wi-Fi, MQTT	InfluxDB, Cloud-based	Cloud-based	Grafana	Dashboard	Environmental	[77], 2023
SN-GCJA5, SCD40,	Wi-Fi, MQTT	InfluxDB	Telegraf, InfluxDB Cloud-based	InfluxDB Dashboard	Dashboard	Environmental	[128], 2025
SEN55	Wi-Fi	ThingsBoard Cloud based	ThingsBoard platform	ThingsBoard Dashboard	Dashboard, mobile app	Environmental	[129], 2024
AGS01DB, AGS10, AGS02MA, SGP30, SEN55, BME680, SGP40, CCS811, ENS160, iAQ-Core	Wi-Fi, MQTT	ThingsBoard Cloud based	ThingsBoard platform	ThingsBoard Dashboard	Dashboard	Environmental	[130], 2024
BME680, ENS160, PGS1004, SGP41, MS5803-05BA	Wi-Fi	Microsoft SQL	Cloud	Web based Dashboard, GUI App.	Dashboard, LCD	Environmental, Automation	[131], 2024
MQ7, MQ135, DHT11,	Wi-Fi	MySQL	Cloud	Web-based Dashboard, Mobile app	Dashboard, mobile app	Environmental	[132], 2023
BME680, SCD30,	Wi-Fi, MQTT	InfluxDB	InfluxDB Cloud-based, Node-Red	Grafana	Dashboard	Environmental	[133], 2023
SHT41, VEML7700, IMP34DT05, SFA30, MICS_VZ_89TE, SEN54, SCD30, 3SP_NO2_5F, 3SP_CO_1000	Wi-Fi, MQTT	MySQL	Cloud	Web-based Dashboard	Dashboard	Environmental	[17], 2024
MH-Z19D	Wi-Fi	ThingSpeak cloud-based	Web server	Web-based ThingSpeak	Dashboard, mobile app	Environmental	[134], 2025
SCD30, SEN54, Grove Multichannel V2, SFA30	Wi-Fi	MySQL, BlueHost cloud based	Web server	Web based Dashboard	Dashboard	Environmental	[135], 2025
BME680, BH1750, SCD30, PMS7003T	Wi-Fi	ThingSpeak cloud-based	Web server	Web-based ThingSpeak	Dashboard	Environmental	[136], 2024
DHT11, MQ135	Wi-Fi, GSM	Cloud based	Cloud server	Web-based Dashboard, Mobile app	Dashboard, SMS, visual alarm	Environmental	[78], 2020
BME680	Wi-Fi	MySQL	Web server	Web-based GUI	Dashboard	Environmental	[80], 2021
PM900M, MHZ19C, ZP07, ZE27-O3, ZE07-CO, MiCS- 2714, ZE08CH2O	Wi-Fi, LoRa, MQTT	Cloud based	Cloud server	Web-based Dashboard	Dashboard, mobile app.	Environmental	[79], 2024
MQ2, MQ7, MQ8, MQ135, DHT11	Wi-Fi	ThingSpeak cloud-based	Web server	Web-based ThingSpeak, LCD	Dashboard	Environmental	[137], 2024
MAX30102	Optical signals, OOK modulation	MariaDB	Nginx and Apache web based	web application	Dashboard	Healthcare	[56], 2024
DHT22, AD8232 ECG, SpO2	Wi-Fi, GSM	Cloud based	Cloud server	Google Data Studio dashboard, LCD	Dashboard, LCD, SMS	Healthcare	[138], 2022
DS18B20, SpO2, ECG, Max30100	Wi-Fi, ZigBee, Bluetooth	local server, cloud server	AI based risk level prediction	Local monitoring device, web application	Dashboard, mobile app.	Healthcare	[139], 2025
DHT22, AD8232 ECG,	Wi-Fi, MQTT	Ubidots cloud	Cloud server	Ubidots cloud	Dashboard, Email	Healthcare	[140], 2024
AD8232	Wi-Fi, MQTT	Cloud based	Cloud based	Web based dashboard, Android mobile app.	Dashboard, mobile app.	Healthcare	[58], 2020
AD8232, NEO 6M, ADXL345, DS18B20, MAX32664, TCRT1000	Wi-Fi, MQTT	Google cloud	Cloud based	ThingSpeak, Blynk Cloud	Dashboard, mobile app.	Healthcare	[141], 2021
AD8232	Wi-Fi, MQTT	Ubidots cloud	Cloud server	Ubidots cloud	Dashboard	Healthcare	[57], 2025
ECG, EMG, EEG, EOG.	Wi-Fi, MQTT	SQLite3	Web browser	Web-based Dashboard	Dashboard	Healthcare	[55], 2024
DHT22, SPS30, FLIR Lepton3.5, SGP30	Wi-Fi	Public database	Cloud AI engine	Web-based Dashboard, Mobile app.	Dashboard, mobile app.	Healthcare	[54], 2024
MAX30100, DHT11, MPU6050, Tobii Eye Tracker 4C, FlexiForce A401, TSL256, HCSR501	NA	Local centralized data store	AI based	Dashboard	Dashboard	Healthcare	[53], 2024
MAX30100, MQ135, DS18B20, DHT11, GP2Y1010	Wi-Fi	Google spreadsheet	ThingSpeak, Blynk, IFTTT	Web-based ThingSpeak, Blynk mobile app.	LCD, Dashboard, mobile app. Email, SMS	Healthcare	[59], 2023
Senseair S8	Wi-Fi, MQTT	ThingSpeak cloud-based	Web server	Web-based ThingSpeak	Mobile app., LED, Dashboard, Voice	Healthcare	[142], 2025
CK-101, LM35,	Wi-Fi	Google Firebase, MySQL	Web server	Web-based dashboard, mobile app.	Dashboard, mobile app.	Healthcare	[143], 2021
MP503, KY-037, BME280, PPD42NS, TSL2561	Wi-Fi, MQTT, Kafka	Cloud based	Web server	Web-based dashboard, mobile app.	Dashboard, mobile app., LCD	Healthcare	[144], 2023
TCRT 5000	Wi-Fi	Google Firebase	Google Firebase	Web-based dashboard, mobile app.	Dashboard, mobile app.	Healthcare	[145], 2024
DS18B20, SpO2	Wi-Fi	ThingSpeak cloud-based	MATLAB, ThingSpeak web server	Web-based dashboard, mobile app.	Mobile app., Dashboard, Buzzer	Healthcare	[146], 2025
XKC-Y25-T12V, MAX30102, OX-201, W1209,	Wi-Fi, MQTT	Cloud based	Web server	Web-based dashboard	Dashboard	Healthcare	[147], 2022
MAX30105, GY-906, DHT11	Wi-Fi	Cloud based	Web server	Hand-held, SPPMS web-based dashboard, BLYNK mobile app.	Dashboard, mobile app., LCD	Healthcare	[148], 2022
MAX30100, DS18B20, SHT30	Wi-Fi, MQTT	Cloud based	Node-Red cloud based	Node-Red dashboard, mobile app.	Dashboard, mobile app.	Healthcare	[149], 2024
AD8232, MQ135, LM35,	Wi-Fi	Google Sheets Cloud based	Google Cloud Server	Figma and Bravo Studio based dashboard	Dashboard, Email, mobile app.	Healthcare	[150], 2021
AD8232, DHT11, MQ gas sensor, MAX30100, ADXL335,	Wi-Fi, MQTT	Cloud based	Web server	Local PC, LCD, web-based dashboard	LCD, Dashboard	Healthcare	[151], 2023
MAX30100, DHT11, MQ7, MQ135, DS18B20, AD8232	Wi-Fi, MQTT	MS Excel data logging	MATLAB, Web server	Web-based dashboard	Dashboard	Healthcare	[152], 2025
MAX30100, LM35, AD8232	Wi-Fi, GSM	Google Firebase, SQL database	Python-Django web framework	Web-based dashboard	Dashboard	Healthcare	[153], 2021
MPU6050, DS18B20, SEN15219	Wi-Fi	Google Firebase	Web server	Web-based dashboard mobile app., Display	Dashboard, mobile app.	Healthcare	[154], 2022
NEO-6M	Wi-Fi, LoRa, MQTT	SQLite	Web server	Web-based dashboard	Dashboard,	Healthcare, Automation	[155], 2025
HTU21S, CCS811	Wi-Fi, MQTT	PostgresSQL	ThingsBoard platform	ThingsBoard Dashboard	Dashboard	Healthcare	[156], 2021
MPU6050, APDS-9008	GSM	OneNet cloud-based database	OneNet cloud platform	Web-based dashboard	Dashboard	Healthcare, Automation	[157], 2021
MAX30102, SW-420	Wi-Fi	NA	Web server	Web-based dashboard, LCD	Dashboard, LCD, LEDs, buzzers	Healthcare	[158], 2025
MAX30100, MLX90614, MQ135	Wi-Fi	Cloud based	Cloud based	Web-based dashboard mobile app., LCD Display	Dashboard, mobile app, LCD	Healthcare	[159], 2024
LM35, DHT11, MQ2, SpO2	Wi-Fi	Cloud based	Cloud based	Web-based dashboard, mobile app.	Dashboard, mobile app., buzzer	Healthcare	[160], 2024
LM35, MAX30100, AD8232	Wi-Fi, GSM	ThingSpeak cloud-based	Web server	Web-based ThingSpeak, OLED Display	Dashboard, mobile app., SMS, Display	Healthcare	[161], 2023
ADXL335, AD8232, EMG Myoware	Wi-Fi, UDP	ThingSpeak cloud-based	Web server, IFTTT	Web-based ThingSpeak, LCD Display, mobile app.	Dashboard, mobile app., LCD, buzzer	Healthcare	[162], 2022
TVOC, CO2, PM, T, RH	LAN, Wi-Fi, Si-Fox, NB-IoT	AWS Cloud based	Amazon EC2 cloud	Web-based dashboard, mobile app.	Dashboard, mobile app., SMS	Healthcare	[163], 2020
GP2Y1014AU0F, DHT11, MiCS4514, MQ131, MICS5524, MG811, MS1100	Wi-Fi, GSM, MQTT	ThingSpeak cloud-based, SD card	Web server	Web-based ThingSpeak, LCD Display, mobile app.	Dashboard, mobile app., SMS	Healthcare, Automation	[164], 2023
DHT11, MQ135, Soil moisture	Wi-Fi, MQTT	Cloud based	Cloud based	Web-based dashboard	Dashboard	Smart home and building	[165], 2023
DHT11, MQ135, KY-018,	Wi-Fi, MQTT	Cloud based	Node-RED, PageKite	Web-based PageKite	Dashboard	Smart home and building	[166], 2024
BME680, MQ137	Wi-Fi, MQTT	InfluxDB	InfluxDB, Node-Red	Web-based dashboard, mobile app.	Dashboard, mobile app., SMS	Smart home and building	[89], 2025
ZPHS01B multi-channel gas sensor	Wi-Fi	Google Firebase	Web server	Python Web Portal	Dashboard	Smart home and building, Healthcare	[90], 2022
DHT11, MQ135, KY-018, KY026	Wi-Fi, MQTT	Cloud based	Node-RED, PageKite	Web-based PageKite	Dashboard	Smart home and building	[91], 2025
SHT31, RC522, SGP30	Wi-Fi, RFID	Local database	Web server	Web-based dashboard, LCD	Dashboard, LCD, buzzer	Smart home and building	[167], 2025
HCSR501	Wi-Fi, MQTT	Blynk cloud based	Blynk app.	Blynk mobile app.	Mobile app.	Smart home and building	[168], 2025
LM35, DHT11, MQ2, MAX30105	Wi-Fi, MQTT	Cloud based	IBM Cloud Bluemix, Node-Red	Web-based dashboard	Dashboard	Smart home and building	[169], 2021
DHT11, HC-SR04	Wi-Fi, MQTT	Cloud based	Cloud based	Web-based dashboard	Dashboard, buzzer	Smart home and building	[170], 2022
MQ135, MQ7, DHT22, BMP180, PMS7003,	Wi-Fi	OpenStack	Cloud based	Web-based dashboard	Dashboard	Smart home and building	[171], 2020
MQ135, MQ2, MQ9, DHT11	Wi-Fi	Google Sheets	Web server, IFTTT	Web-based dashboard	Dashboard	Smart home and building	[172], 2025
HCSR505, MQ5, DHT11, MH-Z14,	Wi-Fi	ThingSpeak cloud-based	Web server	Mobile app.	Mobile app.	Smart home and building	[173], 2024
DHT11, GL5528, SW420, HC-SR501, MQ2	Wi-Fi	MySQL, MariaDB	Web server	Web-based dashboard	Dashboard	Smart home and building	[88], 2022
MA5990–0, Si7021, TSic-206	Wi-Fi	Cloud based	Web server	Web-based dashboard	Dashboard	Smart home and building	[174], 2024
KY005, DHT22, DS3231	Wi-Fi	Google Firebase	Google Firebase	Web-based dashboard	Dashboard	Smart home and building	[94], 2025
SCD30, SFA30, PMS5003, MiCS-VZ-89TE, RD200P2, Netatmo	Wi-Fi, Bluetooth	Cloud based	Cloud based, Node-RED	Web-based dashboard	Dashboard	Smart home and building	[175], 2023
DHT11, MQ-135	Wi-Fi, MQTT	Cloud based	Cloud based	Mobile app., LCD Display	Mobile app., LCD, buzzer	Smart home and building	[176], 2023
TGS4160, DHT11, BH1750	Wi-Fi	OneNET cloud	OneNET cloud platform	OneNET view Dashboard, mobile app.	Dashboard, mobile app., buzzer	Smart home and building	[177], 2025
DHT11, MQ-135, GL5528,	Wi-Fi	Cloud based, MySQL	Cloud based, AI	Web-based dashboard, mobile app.	Dashboard, mobile app., buzzer	Smart home and building	[178], 2024
DHT22, NEO-6M,	Wi-Fi	Cloud based	Cloud based ThinkSpeak and Blynk Platform, AI	Web-based dashboard, mobile app.	Dashboard, mobile app.	Smart home and building	[179], 2024
DHT11, TSOP1838, HC-SR501, ESP32-CAM	Wi-Fi	Cloud based	Blynk IoT Cloud	Web-based dashboard, mobile app.	Dashboard, mobile app.	Smart home and building	[180], 2023
HTU21D, CCS811	Wi-Fi, MQTT	ThingsBoard Cloud based, PostgreSQL	ThingsBoard platform	ThingsBoard Dashboard	Dashboard	Smart home and building	[181], 2024
DHT11, MQ2	Bluetooth	Cloud based	Cloud based	Mobile app., LCD Display	Mobile app., LCD	Smart home and building	[182], 2021
ZE08-CH2O, DHT11, MQ7, MQ2	Wi-Fi, MQTT	Cloud based, MySQL	Tencent cloud based	Web-based Dashboard	Dashboard	Smart home and building	[183], 2024
MQ135, T6615, PPD42NS, gp2y0a21yk0f	Wi-Fi, MQTT	ThingsBoard Cloud based, PostgreSQL, Raspberry Pi database	ThingsBoard platform	ThingsBoard Dashboard, mobile app.	Dashboard, mobile app.	Smart home and building	[184], 2023
Moisture sensor	Bluetooth	MongoDB Atlas cloud based	Cloud based	Mobile app., LCD Display	Mobile app., LCD Display	Smart home and building	[185], 2025
BME680, CJMCU-811, Enviro+, MQ2, SDS011	Wi-Fi	Micro SD, Triple store	RESTful API web server	Web-based dashboard, LCD	Dashboard, LCD	Smart home and building	[186], 2021
FC-28, DHT11	Wi-Fi	ThingSpeak cloud-based, SD card	ThingSpeak platform	Web-based ThingSpeak, Blynk mobile app.	Dashboard, mobile app.	Smart home and building	[187], 2023
DHT11, HC-SR501, MQ2	Wi-Fi	Blynk cloud based	Blynk cloud based	Blynk mobile app.	Mobile app.	Smart home and building	[188], 2025
BME680, CCS811, SCD30, SPS30, VEML7700	Wi-Fi	Blynk cloud based	Blynk cloud based	Blynk mobile app.	Mobile app.	Smart home and building	[189], 2020
DHT11	Wi-Fi, MQTT	Google Firebase	Back-end python server	Web-based dashboard	Dashboard	Smart home and building	[190], 2024
BME680, ZE08, PMS7003	Wi-Fi, MQTT	Cloud based	Node-Red	Web-based dashboard	Dashboard	Smart home and building	[191], 2020
SDS011, SCD30	Wi-Fi	ThingSpeak cloud-based	ThingSpeak platform	Web-based ThingSpeak	Dashboard	Smart home and building	[192], 2023
DHT11, MQ2, LDR, Ultrasonic, YF-S201, TDS, pH	Wi-Fi, GSM	ThingSpeak cloud-based	ThingSpeak platform	Web-based ThingSpeak, LCD	Dashboard, LCD, SMS	Smart home and building	[193], 2023
DHT22, MH-Z16, PPD42NS, MQ135, LDT0-028	Wi-Fi	Azure SQL cloud-based	Microsoft Azure	Web-based dash plotly	Dashboard	Smart home and building	[194],2021
MQ7, MQ135, GP2Y10101AU0F, DHT11	Wi-Fi	MySQL	Web server	Web-based dashboard	Dashboard	Environmental	[195], 2023
DHT11, B-LUX-V30B,	NB-IoT, MQTT	NA	OneNET Platform	Web-based dashboard	Dashboard	Environmental	[196], 2021
BME680	Wi-Fi	ThingSpeak cloud-based	ThingSpeak platform	Web-based ThingSpeak	Dashboard	Environmental	[197], 2022
MQ135, MQ9	Wi-Fi	ThingSpeak cloud-based	ThingSpeak Platform, AI	Web-based ThingSpeak	Dashboard, buzzer	Environmental	[198], 2024
BME680, SGP40	Wi-Fi	Microsoft SQL	Cloud based middleware	Web-based dashboard	Dashboard	Environmental	[199], 2021
MQ135, MQ7, DHT11, GP2Y101	Wi-Fi	ThingSpeak cloud-based	ThingSpeak, Python based AI	Web-based ThingSpeak	Dashboard	Environmental	[200], 2022
DHT22, MQ135, MQ9, MQ2, MQ3	Wi-Fi	ThingSpeak cloud-based	ThingSpeak, MATLAB, IFTTT	Web-based ThingSpeak	Dashboard, Email, SMS	Environmental	[201], 2023
DHT11, MQ135	Wi-Fi	Cloud based	Cloud based	Arduino IoT Cloud	Dashboard, mobile app.	Environmental	[202], 2025
DHT11, DHT22,	Wi-Fi, LoRa	InfluxDB	Things Network cloud platform	Web-based Grafana	Dashboard, mobile app.	Environmental	[203], 2021
DHT22	Wi-Fi, GSM, MQTT	InfluxDB	Kapacitor cloud based	Web-based Grafana	Dashboard	Environmental	[204], 2020
DHT11, MQ9, MiCS-6814, PPD42	Wi-Fi	Google Firebase	Web server	Web-based dashboard	Dashboard	Environmental	[205], 2023
MQ2, MQ4, MQ135, DTH22	Wi-Fi	ThingSpeak cloud-based	ThingSpeak platform	Web-based ThingSpeak	Dashboard	Environmental	[206], 2020
SHT41, VEML7700, ST-IMP34DT05, SCD30, SEN54, SFA30	Wi-Fi, MQTT	MySql cloud-based	Python schedulers, Flask web-API	Web-based Grafana	Dashboard	Environmental	[207], 2024
SDS021, ZE07-CO, DHT22	Wi-Fi, MQTT	MYSQL	Python based	Web-based dashboard, mobile app.	Dashboard, mobile app.	Environmental	[208], 2020
DHT11, MQ135	LoRa	ThingSpeak cloud-based	ThingSpeak platform	Web-based ThingSpeak	Dashboard	Automation	[69], 2020
SEN0232, SEN-14262,	Wi-Fi, MQTT, RF	MongoDB local database	Thingspeak platform	Web-based ThingSpeak	Dashboard	Automation	[209], 2022
AHT10, MH-Z19, LDR, LM393	Wi-Fi	ThingSpeak cloud-based	ThingSpeak platform	Web-based ThingSpeak, mobile app.	Dashboard, mobile app.	Automation	[210], 2024
DW3000	Wi-Fi, UWB	MySql cloud-based	Python based	Python based	Dashboard	Automation	[211], 2025
KY-037, DHT11, ESP32-CAM	Wi-Fi	MySQL, SD card, MongoDB	Spring Framework	Web-based dashboard	Dashboard	Automation	[212], 2023
Proximity	Profinet	Cloud based	Node-Red platform	Web-based dashboard	Dashboard	Automation	[213], 2023
MLX90640, DHT22	Wi-Fi	SD-Card, Cloud based	Python based	Web-based dashboard	Dashboard	Automation	[214], 2025
Aqara T1, JY-GZ-03AQ, CGS2, ZPHS01B	Wi-Fi, ZigBee	MariaDB	Xiaomi’s global IoT platform	Web-based dashboard	Dashboard	Automation	[215], 2025
DW1000, Trimble X7, DS18B20, SHT31, IEC-EN60825-1	Wi-Fi, UWB, Bluetooth	Central server, Cloud based	Point Cloud Data	Web-based dashboard	Dashboard	Automation	[216], 2025
DHT22, BMP180	Wi-Fi	ThingSpeak cloud-based	ThingSpeak, MATLAB	Web-based ThingSpeak	Dashboard	Automation	[217], 2024

.

Table 3 provides a consolidated summary of selected RTIMS reported in the literature. The works included in the table were selected based on their relevance to real-time indoor data acquisition, processing, and visualization, as well as their clear architectural descriptions. The table is structured to highlight the essential components of each system, including: (i) indoor data sources or sensing devices, (ii) communication protocols, (iii) storage and data management solutions, (iv) analytical or processing engines, (v) visualization or dashboard tools, and (vi) notification and alerting mechanisms. These columns were chosen to capture the key architectural decisions that influence performance, scalability, and usability in RTIMS.

8. Discussion

One of the biggest challenges this work faced was the sheer number of non-executive works related to monitoring systems—potentially reaching thousands—across various research archives and databases. Therefore, the search was limited to a small number of reputable publishers and databases to narrow down the process. Furthermore, inclusion criteria were tightened to exclude non-executive works such, as studies that did not provide sufficient information about the tools, software, and equipment used to implement RTIMS. Such studies offer limited practical guidance for readers seeking to develop complete systems from scratch, as they lack detailed and transparent information on the components and methodologies employed. Additionally, studies focusing solely on the development of models or algorithms without real-time implementation were excluded. This includes work on algorithmic modeling, data analysis methods, or similar approaches that were not deployed in operational monitoring systems and had no practical application. Finally, studies that concentrated on developing individual components rather than comprehensive monitoring systems incorporating all the main elements identified in the selection criteria were also excluded.

The results presented in Table 3 provide a clear picture of the prevailing trends in data monitoring system applications. They also offer valuable information about the most common components of these systems, which can serve as a reference for implementing the required RTIMS. The dominance of certain sensor and detector manufacturers in this market is evident (see Figure 6). The sensors and detectors varied according to the different RTIMS used and the targeted applications. Medical sensors were widely used to measure vital parameters in patient monitoring and healthcare applications. For example, various sensors were used to measure body temperature, blood pressure, heart rate, blood oxygen saturation, and similar parameters. Environmental monitoring applications focused on sensors for temperature, relative humidity, atmospheric pressure, concentrations of chemical gases and vapors, and the degree of air pollution. For example, the DHT11 and DHT22 temperature and relative humidity sensors are widely used in many of the included works: the DHT11 accounts for approximately 21% of the sensors used, while the DHT22 represents about 8%. Both sensors are deployed together in roughly 29% of the applications. MQ-series gas sensors appear in about 24% of the sensors used, and the Bosch BME680 environmental sensor is used in approximately 7% of the sensors. This widespread adaption is likely due to factors such as affordability, ease of use, and broad availability.

The selected references in Table 3 were categorized into four main types of applications. Figure 7 shows that environmental monitoring accounts for the largest share of RTIMS studies, approximately 41%. This prominence is likely due to the widespread availability of low-cost environmental sensors, which facilitates research and experimentation in this area. Patient monitoring and healthcare applications, as well as smart home and building systems, each represent around 24% of studies, reflecting both the accessibility of sensors and the growing adoption of AI-driven technologies in these domains. In contrast, industrial automation applications constitute the smallest portion (≈11%), likely because of limited researcher access to industrial sites and the sensitivity of operational data, which can restrict data sharing and public reporting. These factors collectively explain the uneven distribution of RTIMS applications in the literature.

Given the diverse applications of the selected RTIMS, a variety of communication technologies and protocols were employed to enable data transfer between the different system components, such as Wi-Fi, Bluetooth, LoRa, ZigBee, GSM, NB-IoT, and others. In some of the selected projects, more than one technology or protocol was used within a single system, depending on coverage requirements and the distribution of sensor nodes and sensing devices in indoor environments. Notably, Wi-Fi was the dominant technology in most of the selected references, accounting for approximately 74%. This wide adoption of Wi-Fi can be attributed to its ubiquitous presence in indoor environments, its relatively low cost, high reliability, and ease of integration with existing infrastructure. Additionally, Wi-Fi offers sufficient data rates and coverage for most RTIMS applications, making it a practical and convenient choice for both researchers and practitioners. Figure 8 shows the distribution of communication protocols used in the selected references included in this review.

Data storage components are indispensable in RTIMS, ranging from simple SD cards to various types of databases, depending on the nature of the collected data, its frequency, and the way it is processed. In the current study, a variety of data storage solutions were employed, with some works utilizing more than one type of data store. In total, 157 data storage instances were identified across the selected works. Cloud-based databases constituted the majority of these systems. They have become increasingly prevalent in real-time indoor monitoring systems due to the competitive availability of cloud services offered by major providers such as Microsoft, Amazon, and Google. These services simplify the transition from experimental prototypes to fully operational systems by providing scalable, reliable, and easily deployable database solutions. Cloud databases offer high data security and advanced cybersecurity measures, making unauthorized access or breaches difficult to occur. In addition, they provide a wide range of features that facilitate system development, including automated backup, remote access, real-time synchronization, and integration with analytics and AI tools. The combination of accessibility, robustness, and security has made cloud-based databases a preferred choice in both academic research and industrial applications, supporting efficient data management and seamless system operation in RTIMS.

As shown in Table 3, cloud-based databases were used in approximately 26% of the data storage instances, ThingSpeak cloud-based databases in approximately 17%, and SQL-based relational databases in approximately 20%. Figure 9 illustrates the distribution of data storage types in the selected references included in this review.

Dashboards and graphical user interfaces (GUIs) are a pivotal element in RTIMS, enabling users to visually and intuitively understand the information collected by sensors or received from databases. These interfaces present data in the form of charts, graphs, and key performance indicators (KPIs), facilitating rapid tracking of changes and supporting timely decision-making. Table 3 presents the use of various data display tools. In many cases, a single system employs more than one type of user interface or dashboard; therefore, the total number of interfaces and dashboards reached 171 across the 143 selected works. Web-based dashboards and GUIs built using web development technologies such as HTML, CSS, and JavaScript were used in approximately 37% of the visualization’s tools. Mobile-optimized applications were implemented in about 19% of the visualization’s tools, and ThingSpeak web-based dashboards were used in roughly 15% of the used visualizations tools. Figure 10 illustrates the types of RTIMS visualizations tools used in the included references.

Since warning and alerting to hazards, malfunctions, and other critical issues is one of the most important functions of RTIMS, a variety of notification channels were employed, and in many cases, a single system uses more than one type of warning or alerting mechanism. In total, 245 notification channels were identified across the 143 indexed works reviewed. These channels range from simple audio and visual alarms to integrated warnings within user interfaces, mobile and tablet applications, and messages sent via email and text. Table 3 shows that dashboards and graphical user interfaces were used for instructions and alerts in the majority of works (≈54%), followed by mobile applications in approximately 24% of the channels used. Warnings were also displayed on screens integrated with sensor nodes in about 7% of the channels. Figure 11 illustrates the types of warning and notifications channels used in this review.

9. Conclusions

Real-time indoor monitoring systems are an integral part of modern infrastructure in buildings, hospitals, laboratories, industrial sites, and other facilities. These systems enable users to continuously monitor equipment and processes within buildings and take timely action based on alerts and warnings. By doing so, they help prevent future problems and malfunctions and enhance the quality of life for RTIMS users. This review provides a detailed examination of such systems, focusing particularly on developments from the last five years. It covers 143 research papers on RTIMS, encompassing systems implemented with various technologies. Key information was extracted from the reviewed works, including sensor types, communication networks and protocols, database types, visualization and dashboard tools, and the warning and alarm channels employed. This review offers a comprehensive overview of how RTIMS function and the technologies used in their implementation. The review revealed that the prevailing trend in RTIMS focuses on the use of low-cost environmental sensors, such as the DHT11 and DHT22 series of temperature and humidity sensors, which constituted 29% of the types used in the projects reviewed. Similarly, chemical gas and vapor sensors from the MQ family were used, representing approximately 24% of the sensors employed in the selected projects. The review also highlighted a clear trend towards utilizing existing and widespread communication infrastructure, with Wi-Fi technology being used in approximately 74% of the total communication technologies employed in the current review. Furthermore, the study demonstrated a strong emphasis on dashboard use, accounting for 54% of the visualization tools used, followed by mobile applications at 24%. The study also showed a trend among researchers to use cloud storage more widely, as cloud databases constituted about 26% of the types used, while SQL-based databases accounted for 20%, followed by ThingSpeak at 17%. Additionally, the review provides insights into different database types, ready-to-use dashboard tools, and the design of warning systems and escalation rules, which are essential for researcher seeking to understand how to select and implement these systems effectively in real-time applications.

Looking ahead, there is still a significant gap for improvement in RTIMS to keep pace with the rapid advancements in artificial intelligence and machine learning. For example, future efforts could explore ways to make data processing faster and more accurate, particularly by developing AI models that run efficiently on small peripherals rather than being centrally processed. Another important step is improving the integration of different sensors and communication protocols, as real-world indoor environments often rely on a combination of technologies. Because RTIMS involves sensitive information, there is also a clear need for better privacy protection and more secure data transmission. Finally, the field can benefit from integrating emerging tools such as 5G/6G connectivity, digital twins, and intelligent analytical engines to create systems that adapt more reliably to changing conditions. Focusing on these aspects will help make RTIMS more scalable, reliable, and practical for different applications.