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Review

A Systematic Review for Ammonia Monitoring Systems Based on the Internet of Things

by
Adriel Henrique Monte Claro da Silva
1,
Mikaelle K. da Silva
1,2,
Augusto Santos
2 and
Luis Arturo Gómez-Malagón
1,*
1
Polytechnic School of Pernambuco, University of Pernambuco, Recife 50720-001, PE, Brazil
2
Máximo SMS, Recife 52051-200, PE, Brazil
*
Author to whom correspondence should be addressed.
IoT 2025, 6(4), 66; https://doi.org/10.3390/iot6040066 (registering DOI)
Submission received: 15 September 2025 / Revised: 20 October 2025 / Accepted: 22 October 2025 / Published: 30 October 2025
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Abstract

Ammonia is a gas primarily produced for use in agriculture, refrigeration systems, chemical manufacturing, and power generation. Despite its benefits, improper management of ammonia poses significant risks to human health and the environment. Consequently, monitoring ammonia is essential for enhancing industrial safety and preventing leaks that can lead to environmental contamination. Given the abundance and diversity of studies on Internet of Things (IoT) systems for gas detection, the main objective of this paper is to systematically review the literature to identify emerging research trends and opportunities. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, focusing on sensor technologies, microcontrollers, communication technologies, IoT platforms, and applications. The main findings indicate that most studies employed sensors from the MQ family (particularly the MQ-135 and MQ-137), microcontrollers based on the Xtensa architecture (ESP32 and ESP8266) and ARM Cortex-A processors (Raspberry Pi 3B+/4), with Wi-Fi as the predominant communication technology, and Blynk and ThingSpeak as the primary cloud-based IoT platforms. The most frequent applications were agriculture and environmental monitoring. These findings highlight the growing maturity of IoT technologies in ammonia sensing, while also addressing challenges like sensor reliability, energy efficiency, and development of integrated solutions with Artificial Intelligence.

1. Introduction

Ammonia (NH3) is an inorganic compound composed of nitrogen and hydrogen, widely used in various sectors such as agriculture, the refrigeration industry, and chemical plants. In modern agriculture, ammonia is a key component in the production of nitrogen-based fertilizers, such as urea, ammonium nitrate, and ammonium sulfate. These fertilizers are essential for global agriculture, supporting food production and security. In the refrigeration industry, ammonia is used due to its high latent heat of vaporization, meaning that it can absorb large amounts of heat during phase change. This characteristic allows the use of a smaller amount of refrigerant compared to other substances, reducing acquisition costs and equipment size, thereby lowering the energy consumption and operational costs of these systems. Additionally, unlike many synthetic refrigerants, ammonia does not contribute to global warming or ozone layer depletion. Its use is aligned with international environmental regulations, making it a sustainable alternative for the industrial sector [1,2].
Other applications of ammonia include chemical synthesis and neutralization. Ammonia is used as a precursor in the production of nitric acid, which is further utilized to make explosives and plastics [3]. It is also a key ingredient in the synthesis of caprolactam, a raw material for nylon production [4], and it serves as a base to neutralize strongly acidic solutions, such as sulfuric acid, resulting in the formation of ammonium salts like ammonium sulfate [5].
Despite its utility, ammonia is a common pollutant found in wastewater, typically originating from agricultural runoff, industrial discharges, and domestic sewage [6]. Furthermore, ammonia poses significant risks, such as flammability, toxicity, and reactivity, which require rigorous measures to protect workers’ health and ensure facility safety [7].
From a flammability perspective, ammonia is classified as a flammable fluid, although its flammability occurs within a very limited range. Under atmospheric pressure, the flammability limits of ammonia in air range between 15–16% (Lower Flammability Limit—LFL) and 25–28% (Upper Flammability Limit—UFL), with an ignition temperature of 651 °C. These limits, combined with ammonia’s low heat of combustion, significantly reduce its flammable potential. According to the ANSI/ASHRAE 34-2007 standard, ammonia is classified in Group B2, which includes highly toxic and low-flammability fluids [8]. Additionally, the flammable potential of an ammonia-air mixture can be altered by various factors, including pressure, temperature, turbulence, the power of the ignition source, and the presence of other components [9].
Ammonia is a highly volatile substance, and when released into the environment, it can quickly form vapors that are dangerous if inhaled at high concentrations; it can also cause harm through skin or eye contact. Exposure to low concentrations, such as in poorly ventilated industrial environments, can cause irritation to the eyes, nose, and throat, accompanied by coughing and difficulty breathing. At higher concentrations, ammonia can cause severe lung damage, leading to pulmonary edema, asphyxiation, and even death. The World Health Organization (WHO) and other regulatory agencies define occupational exposure limits for ammonia. For example, the Permissible Exposure Limit (PEL) set by OSHA (Occupational Safety and Health Administration) is 50 ppm (parts per million) as an eight-hour time-weighted average. Exposures above 300 ppm are considered Immediately Dangerous to Life and Health (IDLH) [10].
Another critical aspect is ammonia’s reactivity with specific materials, such as metals and oxidizing agents. Violent chemical reactions can occur if it comes into contact with incompatible substances, releasing heat and toxic gases. Therefore, proper storage and prevention of cross-contamination are essential to avoid accidents [11].
Moreover, although ammonia itself does not contribute to global warming or ozone depletion, its production process can have a carbon footprint, and its indirect effects (e.g., formation of N2O) must be considered. The production of ammonia is primarily done through the Haber-Bosch process, which combines nitrogen from the air with hydrogen derived from natural gas (methane, CH4). This process is highly energy-intensive and relies heavily on fossil fuels, which release CO2 during combustion. As a result, the production of ammonia is associated with significant carbon emissions. For example, the global ammonia industry is responsible for approximately 2% of global CO2 emissions, making it a contributor to climate change indirectly through its production process [12].
As discussed, the use of ammonia presents several challenges, particularly related to its toxicity and flammability. Ammonia leaks can pose risks to human health and the environment, requiring robust safety systems that include continuous ammonia detectors, appropriate ventilation systems, and training for operational teams to handle emergencies. Given these challenges, the precise, continuous, and reliable monitoring of ammonia has become a priority for industries and regulatory authorities. For example, the guiding document for refrigeration system design, ANSI/ASHRAE Standard 15-2007, establishes safety requirements for refrigeration systems [13]. According to the standard, it is recommended to use ammonia detectors within the machinery room to protect personnel and property. Through continuous monitoring of ammonia concentration in the machinery room, alarms and protective control actions can be triggered when certain levels are reached. Proper selection, location, and operation/maintenance of ammonia detectors are essential for machinery room safety.
In this context, the Internet of Things (IoT) emerges as a revolutionary technology capable of monitoring ammonia and other toxic gases in real time [14]. IoT integrates smart sensors, wireless connectivity, real-time data processing, and advanced analytics to create autonomous and integrated monitoring systems. For gas monitoring, an IoT architecture is typically structured into three layers: the perception layer, the network layer, and the application layer. The perception layer comprises gas sensors, microcontrollers, and auxiliary sensors (e.g., for temperature and humidity). The network layer encompasses communication technologies such as Wi-Fi, Bluetooth, and LoRaWAN. Finally, the application layer includes IoT platforms for user interfaces, data visualization, data storage, and control logic. These systems enable the continuous collection of environmental data, the transmission of information to centralized platforms, and the generation of immediate alerts in case of dangerous ammonia concentrations.
Due to the importance of ammonia sensors, a large number of scientific publications are reported in the literature, and their classification has been developed considering, for example, types of sensors [15,16,17], materials [18,19,20,21], and applications [22,23]. However, to our knowledge, there is no comprehensive review available of ammonia monitoring systems based on the IoT. This systematic review article aims to provide a comprehensive and up-to-date overview of the use of IoT technologies for ammonia sensing, highlighting recent advancements, practical applications, and the challenges faced. The review covers studies published in recent years, focusing on sensors, microcontrollers, wireless communication techniques, IoT platforms, and applications across various sectors. By exploring the state of the art in ammonia sensing through IoT, this article seeks to contribute to the advancement of industrial safety, environmental protection, and operational efficiency. This systematic review provides valuable insights for researchers, engineers, and managers interested in implementing IoT solutions for ammonia monitoring, highlighting best practices, technological innovations, and opportunities for improvement. In an increasingly connected and data-driven world, IoT represents a powerful tool to address the challenges associated with ammonia management, promoting a safer and more sustainable future for industries and the communities that depend on them.

2. Materials and Methods

The methodology employed in this work was adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [24]. The process consisted of formulating research questions, followed by identification, screening, and eligibility stages. In the identification stage, relevant databases and keywords were selected. The screening stage involved applying inclusion and exclusion criteria based on titles and abstracts. Finally, in the eligibility stage, the full texts of the remaining papers were analyzed. Answers to the initial research questions were synthesized to identify limitations and gaps in the current knowledge and to propose directions for future research.

2.1. Research Questions

The research questions were formulated to identify the main technologies employed for IoT-based ammonia detection. The synthesized information provides an overview of these technologies, highlighting the main challenges and opportunities, and suggests directions for future research.
The review is guided by the following questions:
  • Query 1: What types of sensors are employed for the detection and monitoring of ammonia gas?
  • Query 2: Which microcontrollers are typically utilized to interface with ammonia gas sensors and facilitate data acquisition?
  • Query 3: What communication technologies are commonly implemented to transmit data from these sensor systems?
  • Query 4: What IoT platforms are predominantly used for the visualization and analysis of data collected from ammonia gas sensor systems?
  • Query 5: What are the primary applications of ammonia gas sensors integrated with IoT technologies?

2.2. Search Process

The search was conducted using several databases, including IEEE Xplore, Scopus, and Web of Science. The Boolean operator “AND” was used with the keywords “ammonia” and “IoT”. The following filters were applied directly within each database’s search engine: (a) Publication date between 2015 and 2025, as key IoT standards were consolidated at the beginning of this period [25]. (b) Document type limited to research articles and conference papers. (c) Language restricted to English.
The search, performed in August 2025, yielded 612 records: 188 from IEEE Xplore, 282 from Scopus, and 127 from Web of Science. An additional 15 publications were identified via Google Scholar.

2.3. Inclusion and Exclusion Criteria

Further inclusion and exclusion criteria were applied during the screening stage, as detailed in Table 1. The records from the initial search were exported to Zotero 7.0.22 (64-bit) software, and duplicates were removed, resulting in 403 publications for screening. The titles and abstracts of these publications were analyzed. Papers focused solely on the chemical synthesis and characterization of sensors, or on monitoring gases other than ammonia using IoT, were excluded as they did not address the research questions. Following this stage, 291 publications focused on monitoring ammonia using IoT technologies were selected for the eligibility assessment.

2.4. Study Selection

From the collected publications, the full text manuscripts were assessed for eligibility. During the analysis of the publications, it was observed that not all the paper reported all details about the IoT technology to answer the research questions raised in this review. For this reason, papers that were out of the scope of the research questions were excluded. In this way, 148 publications were included in the revision as shown in Figure 1.
The full texts of the 291 publications were assessed for eligibility. Many publications did not report sufficient information to answer the research questions and were consequently excluded. Papers that reported answers for at least three of the five research questions were included. A final set of 148 publications was included in the qualitative synthesis, as illustrated in the PRISMA flow diagram shown in Figure 1.

2.5. Risk of Bias

Although this systematic review followed the structured PRISMA methodology, several potential sources of bias are discussed. First, the selection of keywords may introduce bias. Using the “AND” operator with many keywords can limit the search, while using “OR” can make it unmanageably broad. To mitigate this, few keyword were tested. Second, the choice of databases (IEEE Xplore, Scopus, Web of Science) may have led to the omission of relevant studies from other sources. To address this, a supplementary search was conducted using Google Scholar. Future reviews could be improved by including additional databases. Finally, bias may arise from the subjective application of inclusion/exclusion criteria during screening and eligibility assessments. All decisions were discussed among the authors in accordance with PRISMA guidelines to minimize this risk.

2.6. Data Extraction and Synthesis

Data were extracted from the 148 included papers to answer the research questions. The extracted information included: sensor type, microcontroller, communication technology, IoT platform, and application domain. The publication type (journal or conference proceeding) and year of publication were also recorded.
Of the publications included, 94 (63.5%) were journal articles and 54 (36.5%) were conference papers as shown in Figure 2a. The earliest relevant publication was from 2017, with the annual number of publications peaking in 2023. The distribution of publications by year from 2017 to 2025 is shown in Figure 2b.

3. Results and Discussion

3.1. Types of Sensors

Ammonia gas sensing methods can be categorized as solid-state, optical, and others. Solid-state sensors are based on changes in the conductivity of a thin film of metal oxide or conducting polymer upon gas exposure. Optical methods rely on the analysis of optical absorption, with tunable diode laser absorption spectroscopy (TDLAS) and cavity ringdown spectroscopy (CRDS) being well-developed techniques. Other sensors, such as electrochemical sensors, surface acoustic wave sensors (SAWS), and field-effect transistor (FET) sensors, are also reported in the literature [16]. Additional methods and reviews on ammonia sensor technology are available [15,17,18].
Methods for sensing ammonia in water, typically for agricultural purposes, can be similarly categorized as electrochemical or spectrometric. Electrochemical methods are based on changes in an electrical variable (resistance, potential, current) due to the absorption of NH3 by a sensing material. Spectrometric methods include optical techniques to analyze absorption, fluorescence, color, and photoacoustic waves [22]. Reviews on electrochemical [26] and spectroscopic methods [27] are available.
From the analysis of the included papers, 122 used commercial sensors, 8 reported non-commercial sensors, and 18 did not specify the sensor type. The results are summarized in Table 2. The non-commercial sensors employed various techniques and materials. Solid-state sensors were fabricated using composites such as antimony-doped tin dioxide (Sb-doped SnO2) with polyaniline (PANI), and reduced graphene oxide (RGO) with tungsten trioxide (WO3) to enhance the sensitivity and selectivity to NH3 and NO2 gases under real mining conditions [28]; an array of 64 chemiresistive sensors based on semiconducting single-walled carbon nanotubes (sc-SWCNTs) [29]; a PANi solution deposited on a copper-clad chip [30]; a MEMS device based on a sputtered SnO2 thin film [31]; graphene decorated with zinc oxide (Graphene@ZnO) and laser-induced graphene decorated with polypyrrole (LIG@Ppy) [32]; and a silicon corrole-functionalized TiZnN2 (SipC-TiZnN)/p-Si heterostructure [33]. An electrochemical sensor (ammonium ion-selective electrode, ISE) was fabricated using poly (vinyl chloride) (PVC), potassium tetrakis (4-chlorophenyl)borate (KtpCIPB), ammonium ionophore I (nonactin), and bis (2-ethylhexyl) sebacate (BEHS) [34]. Finally, a spectrometric method was employed to analyze the color of sodium salicylate [35].
It means that, among the papers reporting the type of sensor—whether commercial or non-commercial—123 papers used solid-state sensors and 6 used electrochemical sensors, representing approximately 83.1% and 4.0% of the total, respectively. Only one paper employed spectroscopic techniques, and 18 papers did not report the sensor technology, representing 0.7% and 12.2%, respectively.
It is important to note that some papers employed more than one type of sensor. The prevalence of solid-state sensors is largely due to their ease of installation and low cost for the reported applications, which are mostly proof-of-concept studies. However, in industrial applications, the use of MQ-family sensors is limited due to operational constraints defined by their specifications. For instance, industrial systems require sensors with high selectivity, low power consumption, low detection limits, and minimal baseline drift. The MQ-135 is an air quality sensor with high sensitivity to ammonia, sulfides, benzene vapors, smoke, and other toxic gases, while the MQ-137 shows high sensitivity to NH3 and can also detect organic amines such as trimethylamine and cholamine. However, these sensors require heating of the metal oxide (SnO2) surface to enable adsorption/desorption reactions. Their typical detection range is 10 to 10,000 ppm, with a limit of detection of about 10 ppm and significant baseline drift over time due to variations in humidity and temperature, thus requiring frequent calibration and temperature compensation [36,37].
In comparison, electrochemical sensors offer better selectivity than solid-state sensors, do not require heating, and can achieve detection limits from low ppm to sub-ppm levels, with lower baseline drift than metal oxide sensors under many conditions, although they still require periodic calibration [38,39,40]. Moreover, electrochemical sensors are widely used in regulatory and occupational monitoring or integrated into professional instruments. In contrast, MQ-family sensors are considerably less expensive than electrochemical sensors.
As shown, the main technology used for ammonia detection is based on commercial SnO2 sensors. However, detection technologies and materials developed for other gases could also be explored for ammonia sensing. For example, in the detection of NOx gases, memristor-based gas sensors—also known as gasistors—have attracted researchers’ attention due to their low energy consumption, compact size, and high response [41,42]. The integration of a filament-based memristor heater with a carbon nanotube sensor for humidity control has resulted in ultra-low power consumption and high sensing accuracy [43]. Furthermore, for the detection of inorganic gases and volatile compounds, thin films of metal oxides such as ZnO and WO3, conducting polymers, and carbon nanotubes, among other materials, have been widely investigated [44,45]. Some of these materials were identified in the present review for ammonia detection; however, several others have not yet been explored. This suggests that future research should include the characterization of new sensing materials and the adaptation of technologies previously used for other gases to improve ammonia detection performance.
Table 2. Sensors used in ammonia detectors.
Table 2. Sensors used in ammonia detectors.
Sensor Name
(Qty of References)		TypeDatasheetReferences
MQ-135
(70)		Metal Oxide
SnO2		[36][46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115]
MQ-137
(32)		Metal Oxide
SnO2		[37][62,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]
MQ-2
(3)		Metal Oxide
SnO2		[147][49,148,149]
MQ
(4)		 [150,151,152,153]
MiCS-6814
(6)		Metal oxide[154][116,155,156,157,158,159]
MiCS-5524
(2)		Metal Oxide[160][161,162]
TGS 2602
(3)		Metal Oxide[163][164,165,166]
TGS-2444
(1)		Metal Oxide
SnO2		[167][168]
ZE03
(1)		Electrochemical[38][169]
ME3-NH3
(1)		Electrochemical[40][73]
MIX8415
(1)		Electrochemical [170]
N11 board. NH3 sensor GS +4NH3100
(2)		Electrochemical[39][171,172]
Self developed
(8)		 [28,29,30,31,32,33,34,35]
N/A
(18)		 [173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190]

3.2. Microcontrollers

A microcontroller is an integrated circuit comprising a processor, memory, and I/O peripherals on a single chip, designed to control operations in an embedded system. For IoT applications, microcontrollers require wireless capabilities. It is important to note that some microcontrollers lack native wireless technologies, necessitating the use of specific shields or additional modules for IoT applications.
Microprocessors can be classified by various parameters, but classification by core architecture indicates which have native wireless technology embedded. Table 3 classifies the microcontrollers from the selected papers based on core architecture and wireless technology. Of the 148 selected papers, 4 did not specify the microcontroller type, 84 used microcontrollers without native wireless technology, and 60 used those with native wireless technology, representing 2.7%, 56.8%, and 40.5%, respectively. As all papers described IoT systems, this indicates that at least 56.8% employed hybrid configurations using shields or additional modules for wireless communication. Microcontrollers based on the Xtensa (ESP32 and ESP8266) and ARM Cortex-A (Raspberry Pi 3B+/4) architectures were the most employed, either directly or coupled with other microcontrollers, due to their native wireless capabilities.
As observed, the distinction between technologies with and without native wireless connectivity is crucial for understanding trends in microcontroller usage. The Arduino platform, well known since its release during the 2003–2005 period, was commonly used for IoT applications around 2017 through the integration of external shields for wireless communication. This hybrid configuration combined Arduino’s ease of use with the wireless capabilities of other microcontrollers. However, this approach has several disadvantages: the boards are mechanically coupled, increasing overall size and system complexity, and leading to higher energy consumption compared with native wireless technologies, making it a suboptimal option for industrial applications.
At that time, Espressif microcontrollers had already become available on the market, and their use has since become widespread. Among them, the ESP32 has emerged as the most popular choice for IoT and semi-industrial applications due to its low cost, integrated wireless features, and reduced energy consumption, as reported in this literature review for ammonia gas monitoring.
Although there are industrial applications of ESP32 and Arduino in Programmable Logic Controllers (PLCs) [191,192] and Human–Machine Interfaces (HMIs) [193], there remains a gap between the use of these microcontrollers and their integration into industrial gas monitoring systems. It is expected that newer microcontrollers such as the Arduino Portenta (Arduino S.r.l., Monza, Italy) [194], ESP32-S3 (Espressif Systems, Shanghai, China) [195], and STM32 (WB and WL series) (STMicroelectronics, Geneva, Switzerland) [196]—designed to overcome limitations related to reliability, ease of use, and energy efficiency—may increasingly include edge intelligence capabilities, making them a promising area for future research.

3.3. Communication Technology

Communication technology used to transmit and receive data collected by sensors may include both wired and wireless technologies. From the analysis of the included papers in this review, it was observed the use of wired technologies (ethernet) and wireless technologies for short-range (Wi-Fi, bluetooth, zigbee) and medium-range (LoRaWAN) combined to cellular technologies (2G (GSM/GPRS), 4G). A brief comparison is shown in Table 4; more extensive comparisons including technologies like Z-wave, Sigfox, 5G, wirelessHART, 6LoWPAN, and ISA100.11a can be found elsewhere [197,198,199,200,201,202].
As shown in Table 5, of the 148 papers, 89 used only Wi-Fi, 7 used Bluetooth, 4 used LoRaWAN, 1 used Zigbee, 19 used hybrid cellular technologies, 5 used hybrid Wi-Fi technologies, and 2 used Ethernet, accounting for 59.5%, 4.6%, 3%, 0.8%, 7.6%, 3.8%, and 1.5% of the publications, respectively. Furthermore, 21 publications did not report the communication technology used. These results demonstrate the strong prevalence of Wi-Fi for IoT-based ammonia sensing.

3.4. IoT Platforms

An IoT platform serves as an interface between IoT devices and end-users, providing device management, connectivity, services, and data handling. Platforms can be classified by characteristics such as connectivity/device management, data storage/management, data analysis/visualization, development tools, edge/fog computing, integration, service management, and auditing and payment [203]. As sensing applications involve substantial data processing, IoT platforms typically leverage cloud computing, self-hosted systems, or dedicated clusters [204].
The tools used in the selected papers are classified in Table 6. Some tools are hybrid, depending on the application. According to the findings, 51 publications (34.5%) used the Blynk and ThingSpeak cloud platforms, 35 publications (23.6%) referenced 16 other computational tools, and 62 publications (41.9%) did not report any specific IoT platform. These results indicate a preference for the Blynk and ThingSpeak cloud platforms in gas sensing applications.

3.5. Applications

Ammonia gas sensors are utilized across various sectors. Based on the selected papers, applications were categorized as: agriculture, environmental monitoring, industrial safety, aquaculture, smart cities, healthcare, the food industry, and research/laboratory use. As shown in Table 7, the primary categories were agriculture (27.5%), environmental monitoring (33.6%), aquaculture (13.0%), and industrial safety (9.9%). The remaining 21 publications (16.0%) pertained to other categories. These findings highlight the key application areas of ammonia sensing in the agricultural and environmental sectors.

4. Conclusions

This systematic review analyzed ammonia monitoring systems based on the Internet of Things (IoT). The methodology, adapted from the PRISMA guidelines, was applied to evaluate selected papers with a focus on sensors, microcontrollers, communication technologies, IoT platforms, and application domains. The percentage of papers that addressed each research question was 87.8% for Q1 (sensors), 97.3% for Q2 (microcontrollers), 85.8% for Q3 (communication technologies), 58.1% for Q4 (IoT platforms), and 100% for Q5 (applications). The influence of missing or unreported data on conclusions regarding predominant technologies depends on the number of papers providing consistent answers. In this study, when more than 74 papers (50% of the selected set) reported the same technology, it was considered the preferred option. Specifically, for sensor type, the MQ-135 and MQ-137 were cited 70 and 32 times, respectively—corresponding to 68.9% of the selected papers—indicating that MQ-family sensors are the most widely used. Following the same analysis, the results show that the most frequently employed components were MQ-135 and MQ-137 gas sensors; Xtensa-based microcontrollers with native wireless capabilities such as the ESP32 and ESP8266; and Wi-Fi as the main communication technology. Regarding IoT platforms, cloud-based solutions were preferred and cited in 74 papers, with Blynk and ThingSpeak being the most frequently used. However, since 41.9% of the selected papers did not specify the platform used, the conclusion about the predominant platform in ammonia monitoring systems remains limited. Future research should provide detailed information on the platform technologies to allow a more accurate identification of emerging trends. The predominant application domains identified were agriculture and environmental monitoring.
Despite significant advancements, IoT-based ammonia sensing faces technical and operational challenges. Future research should address critical aspects such as sensor accuracy and reliability, which depend on calibration, environmental conditions, and interference from other gases. While most publications demonstrated proof-of-concept systems, information on long-term performance was limited. Temporal analysis is essential for evaluating not only sensor reliability but also factors like mechanical integrity and energy consumption—particularly in battery-operated sensors deployed in locations without a stable power supply.
As IoT technologies evolve, new sensors, microcontrollers, communication methods, and platforms must be evaluated for both existing and emerging applications. Future research should focus on the integration of heterogeneous IoT systems, which often use different protocols and standards, hindering interoperability and consolidated data analysis [205,206]. Integration with other emerging technologies, such as Artificial Intelligence (AI) and machine learning, could further expand potential applications by enabling leak prediction, process optimization, and proactive decision-making [207].
For example, temporal data analysis using AI can improve system reliability prediction by considering environmental variables—such as temperature and humidity—as well as operational parameters, including the presence of interfering gases or dust. Moreover, the use of data from multiple types of sensors or sensing modalities in a multimodal sensor fusion architecture allows for more accurate, reliable, and comprehensive characterization of system performance [208]. In this configuration, including sensors for different gases, machine learning algorithms such as support vector machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) and Neural Networks (NN) are used to classify and identify gases, predict concentration, and also model nonlinear patterns [28,209,210,211]. Such approaches are highly valuable not only to detect, identity and quantify the concentration of the gas, but for planning the maintenance of gas monitoring systems and developing automated calibration routines, reducing manual intervention and significantly improving detection accuracy, selectivity, and long-term stability under variable environmental conditions [212]. However, these advancements also introduce new challenges. The integration of AI capabilities directly into edge devices—such as sensors, microcontrollers, or gateways—known as edge intelligence, allows data to be analyzed and processed locally without relying on constant communication with centralized cloud servers [213,214]. This paradigm enables low-latency responses, reduced bandwidth usage, enhanced privacy, and improved energy efficiency, which are essential for scalable and autonomous industrial IoT deployments.
Furthermore, cybersecurity is a critical concern, as IoT systems are vulnerable to attacks that compromise data integrity and system functionality. Consequently, future research should focus on developing: (1) inexpensive encryption algorithms for microcontrollers (e.g., ESP32); (2) blockchain technologies to ensure the integrity of sensor data for compliance, forensic analysis, and dispute resolution; and (3) Zero-Trust Architecture (ZTA), where every device and user request is rigorously authenticated, authorized, and encrypted before being granted access to the network or data, regardless of its origin.
According to the obtained results, Wi-Fi is the main communication technology employed in academic, pilot, or semi-industrial settings; however, these systems often lack full industrial robustness, where factors such as safety, interference, and power consumption are essential for successful long-term operation. Special attention should be given to developing robust infrastructures to enhance safety and ensure system reliability. This includes using commercial sensors, industrial-grade hardware, and Industrial IoT (IIoT) solutions for real-time data transmission, which requires robust communication infrastructure, especially in remote areas or regions with limited network coverage. In this context, the industrial applicability of electrochemical sensors should be investigated more deeply. Additionally, the development of Low-Power Wide-Area Network (LPWAN) technologies, such as LoRaWAN and Narrowband IoT (NB-IoT), is crucial for optimizing data protocols and transmission intervals to maximize battery life in areas without grid power. The recent use of commercial sensors and long-distance communication technologies reported in [171,172] is a promising step in this direction.
Finally, as data from in-situ IoT ammonia sensor studies become more accessible, it is necessary to establish standardized methodologies for testing and validating the performance, reliability, and longevity of these integrated systems under real-world operational stress. This will provide clear guidelines for industry adoption and ensure the deployment of effective and trustworthy monitoring solutions.

Author Contributions

Conceptualization, L.A.G.-M.; methodology, A.H.M.C.d.S. and L.A.G.-M.; formal analysis, A.H.M.C.d.S. and L.A.G.-M.; data curation, A.H.M.C.d.S. and L.A.G.-M.; writing-review and editing, A.H.M.C.d.S., M.K.d.S., A.S. and L.A.G.-M.; supervision, L.A.G.-M., funding acquisition, L.A.G.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Níıvel Superior-Brazil (CAPES) under Grant 001, and in part by the Fundação de Amparo a Ciência e Tecnologia do Estado de Pernambuco (FACEPE) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-Brazilian Agencies (FACEPE BPP-0075-3.05/24).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Acknowledgments

During the preparation of this manuscript, the authors used ChatGPT (GPT-5, OpenAI) for the purposes of checking grammar, spelling, and clarity of English language usage. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

Adriel Henrique Monte Claro da Silva and Luis Arturo Gómez-Malagón declare no conflicts of interest. Mikaelle K. da Silva and Augusto Santos are employee of an organization or company (Máximos SMS).

References

  1. Pearson, A. Refrigeration with Ammonia. Int. J. Refrig. 2008, 31, 545–551. [Google Scholar] [CrossRef]
  2. Abas, N.; Kalair, A.R.; Khan, N.; Haider, A.; Saleem, Z.; Saleem, M.S. Natural and Synthetic Refrigerants, Global Warming: A Review. Renew. Sustain. Energy Rev. 2018, 90, 557–569. [Google Scholar] [CrossRef]
  3. Hocking, M.B. Ammonia, Nitric Acid and Their Derivatives. In Modern Chemical Technology and Emission Control; Springer: Berlin/Heidelberg, Germany, 1985; pp. 205–233. [Google Scholar] [CrossRef]
  4. Huang, J.; Liu, Q.; Wu, W.; Ma, Y.; Huang, J. Industrial Process and Modern Technical Adaptations for Nylon 6 Monomer Caprolactam: A Mini Review. Mater. Sci. 2024, 30, 47–52. [Google Scholar] [CrossRef]
  5. Busca, G. Bases and Basic Materials in Industrial and Environmental Chemistry: A Review of Commercial Processes. Ind. Eng. Chem. Res. 2009, 48, 6486–6511. [Google Scholar] [CrossRef]
  6. Karri, R.R.; Sahu, J.N.; Chimmiri, V. Critical Review of Abatement of Ammonia from Wastewater. J. Mol. Liq. 2018, 261, 21–31. [Google Scholar] [CrossRef]
  7. Khudhur, D.A.; Tuan Abdullah, T.A.; Norazahar, N. A Review of Safety Issues and Risk Assessment of Industrial Ammonia Refrigeration System. ACS Chem. Health Saf. 2022, 29, 394–404. [Google Scholar] [CrossRef]
  8. ANSI/ASHRAE Standard 34-2007; Designation and Safety Classification of Refrigerants. ASHRAE—American Society of Heating, Refrigerating and Air-Conditioning Engineers: Atlanta, GA, USA, 2007.
  9. Pfahl, U.J.; Ross, M.C.; Shepherd, J.E.; Pasamehmetoglu, K.O.; Unal, C. Flammability Limits, Ignition Energy, and Flame Speeds in H2–CH4–NH3–N2O–O2–N2 Mixtures. Combust. Flame 2000, 123, 140–158. [Google Scholar] [CrossRef]
  10. Occupational Safety and Health Administration. Ammonia. 2024. Available online: https://www.osha.gov/chemicaldata/623 (accessed on 14 April 2025).
  11. National Center for Biotechnology Information. PubChem Compound Summary for CID 222, Ammonia. Available online: https://pubchem.ncbi.nlm.nih.gov/compound/Ammonia (accessed on 17 April 2025).
  12. Liu, X.; Elgowainy, A.; Wang, M. Life Cycle Energy Use and Greenhouse Gas Emissions of Ammonia Production from Renewable Resources and Industrial By-Products. Green Chem. 2020, 22, 5751–5761. [Google Scholar] [CrossRef]
  13. ASHRAE Standard 15-2013; Safety Standard for Refrigeration Systems. ASHRAE—American Society of Heating, Refrigerating and Air-Conditioning Engineers: Atlanta, GA, USA, 2013.
  14. Alsamrai, O.; Redel-Macias, M.D.; Pinzi, S.; Dorado, M.P. A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things. Sustainability 2024, 16, 4353. [Google Scholar] [CrossRef]
  15. Timmer, B.; Olthuis, W.; Berg, A.V.D. Ammonia Sensors and Their Applications—A Review. Sens. Actuators B Chem. 2005, 107, 666–677. [Google Scholar] [CrossRef]
  16. Kwak, D.; Lei, Y.; Maric, R. Ammonia Gas Sensors: A Comprehensive Review. Talanta 2019, 204, 713–730. [Google Scholar] [CrossRef]
  17. Bielecki, Z.; Stacewicz, T.; Smulko, J.; Wojtas, J. Ammonia Gas Sensors: Comparison of Solid-State and Optical Methods. Appl. Sci. 2020, 10, 5111. [Google Scholar] [CrossRef]
  18. Aarya, S.; Kumar, Y.; Chahota, R.K. Recent Advances in Materials, Parameters, Performance and Technology in Ammonia Sensors: A Review. J. Inorg. Organomet. Polym. Mater. 2020, 30, 269–290. [Google Scholar] [CrossRef]
  19. Bannov, A.G.; Popov, M.V.; Brester, A.E.; Kurmashov, P.B. Recent Advances in Ammonia Gas Sensors Based on Carbon Nanomaterials. Micromachines 2021, 12, 186. [Google Scholar] [CrossRef]
  20. Norizan, M.N.; Siti Zulaikha, N.D.; Norhana, A.B.; Syakir, M.I.; Norli, A. Carbon Nanotubes-Based Sensor for Ammonia Gas Detection—An Overview. Polimery 2021, 66, 175–186. [Google Scholar] [CrossRef]
  21. Hizam, S.M.M.; Al-Dhahebi, A.M.; Mohamed Saheed, M.S. Recent Advances in Graphene-Based Nanocomposites for Ammonia Detection. Polymers 2022, 14, 5125. [Google Scholar] [CrossRef] [PubMed]
  22. Insausti, M.; Timmis, R.; Kinnersley, R.; Rufino, M.C. Advances in Sensing Ammonia from Agricultural Sources. Sci. Total Environ. 2020, 706, 135124. [Google Scholar] [CrossRef]
  23. Ni, J.-Q.; Heber, A.J. Sampling and Measurement of Ammonia at Animal Facilities. Adv. Agron. 2008, 98, 201–269. [Google Scholar] [CrossRef]
  24. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, 71. [Google Scholar] [CrossRef]
  25. Sorri, K.; Mustafee, N.; Seppänen, M. Revisiting IoT Definitions: A Framework towards Comprehensive Use. Technol. Forecast. Soc. Change 2022, 179, 121623. [Google Scholar] [CrossRef]
  26. Ryu, H.; Thompson, D.; Huang, Y.; Li, B.; Lei, Y. Electrochemical Sensors for Nitrogen Species: A Review. Sens. Actuators Rep. 2020, 2, 100022. [Google Scholar] [CrossRef]
  27. Guo, X.; Chen, J.; Shen, Y.; Li, H.; Zhu, Y. Evolution of the Fluorometric Method for the Measurement of Ammonium/Ammonia in Natural Waters: A Review. TrAC Trends Anal. Chem. 2024, 171, 117519. [Google Scholar] [CrossRef]
  28. Li, Y.; Guo, S.; Wang, B.; Sun, J.; Zhao, L.; Wang, T.; Yan, X.; Liu, F.; Sun, P.; Wang, J.; et al. Machine Learning-assisted Wearable Sensor Array for Comprehensive Ammonia and Nitrogen Dioxide Detection in Wide Relative Humidity Range. InfoMat 2024, 6, e12544. [Google Scholar] [CrossRef]
  29. Dargie, W.; Wen, J.; Panes-Ruiz, L.A.; Riemenschneider, L.; Ibarlucea, B.; Cuniberti, G. Monitoring Toxic Gases Using Nanotechnology and Wireless Sensor Networks. IEEE Sens. J. 2023, 23, 12274–12283. [Google Scholar] [CrossRef]
  30. Alure, S.M.; Tonpe, R.V.; Jadhav, A.D.; Sambare, S.T.; Pagare, J.D. Drainage Toxic Gas Detection System Using IoT. Adv. Intell. Syst. Comput. 2020, 1077, 161–167. [Google Scholar] [CrossRef]
  31. Gupta, K.; Kishore, K.; Jain, S.C. Quality Assessment and Drift Analysis of IoT Enabled Ammonia Sensor. In Proceedings of the 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 20–22 September 2017; pp. 171–176. [Google Scholar] [CrossRef]
  32. Santos-Betancourt, A.; Carlos Santos-Ceballos, J.; Salehnia, F.; Ayoub Alouani, M.; Romero, A.; LuisRamirez, J.; Vilanova, X. IoT Platform Enhanced with Neural Network for Air Pollutant Monitoring. IEEE Trans. Instrum. Meas. 2024, 73, 1–11. [Google Scholar] [CrossRef]
  33. George, J.; Vikraman, H.K.; Ghuge, R.S.; Reji, R.P.; Jayaraman, S.V.; Magna, G.; Paolesse, R.; Sivalingam, Y.; Di Natale, C.; Mangalampalli, K.S.R.N. Self-Powered, Photovoltaic-Driven NH3 Sensor: Ultra-High Selectivity, High Sensitivity, and IoT-Enabled Real-Time Monitoring with Novel Organic Molecule Functionalized TiZnN2/p-Si Heterostructure. Small 2025, 21, 2502324. [Google Scholar] [CrossRef]
  34. Agir, I.; Yildirim, R.; Nigde, M.; Isildak, I. Internet of Things Implementation of Nitrate and Ammonium Sensors for Online Water Monitoring. Anal. Sci. 2021, 37, 971–976. [Google Scholar] [CrossRef]
  35. Deepak, K.S.; Balapure, A.; Priya, P.R.; Kumar, P.; Saiand Dubey, S.K.; Javed, A.; Chattopadhyay, S.; Goel, S. Development of a Microfluidic Device for the Dual Detection and Quantification of Ammonia and Urea from the Blood Serum. Sens. Actuators Phys. 2024, 369, 115174. [Google Scholar] [CrossRef]
  36. Datasheet MQ-135. Available online: https://www.winsen-sensor.com/d/files/PDF/Semiconductor%20Gas%20Sensor/MQ135%20(Ver1.4)%20-%20Manual.pdf (accessed on 6 March 2025).
  37. Datasheet MQ-137. Available online: https://www.winsen-sensor.com/d/files/semiconductor/mq137.pdf (accessed on 6 March 2025).
  38. Datasheet ZE03. Available online: https://www.winsen-sensor.com/d/files/ze03-electrochemical-module-manualv2_5.pdf (accessed on 6 March 2025).
  39. Datasheet GS +4NH3100. Available online: https://ddscientific.com/products/gs-4nh3-100-electrochemical-sensor-ammonia-nh3?srsltid=AfmBOorVXwlTqBlA4q7pbFzt-NEj-RL1LjAHjGG_7lysQNfKmnrieTeR (accessed on 6 March 2025).
  40. Datasheet ME3-NH3. Available online: https://www.winsen-sensor.com/d/files/me3-nh3-0-100ppm(ver1_3)-manual.pdf (accessed on 6 March 2025).
  41. Lee, D.; Yun, M.J.; Kim, K.H.; Kim, S.; Kim, H.-D. Advanced Recovery and High-Sensitive Properties of Memristor-Based Gas Sensor Devices Operated at Room Temperature. ACS Sens. 2021, 6, 4217–4224. [Google Scholar] [CrossRef]
  42. Ali, M.; Lee, D.; Ahmad, I.; Kim, H.-D. Recent Progress in Memristor-Based Gas Sensors (Gasistor; Gas Sensor + Memristor): Device Modeling, Mechanisms, Performance, and Prospects. Sens. Actuators Rep. 2025, 9, 100269. [Google Scholar] [CrossRef]
  43. Chae, M.; Lee, D.; Kim, H. Dynamic Response and Swift Recovery of Filament Heater-Integrated Low-Power Transparent CNT Gas Sensor. Adv. Funct. Mater. 2024, 34, 2405260. [Google Scholar] [CrossRef]
  44. Khorramifar, A.; Karami, H.; Lvova, L.; Kolouri, A.; Łazuka, E.; Piłat-Rożek, M.; Łagód, G.; Ramos, J.; Lozano, J.; Kaveh, M.; et al. Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors. Sensors 2023, 23, 5716. [Google Scholar] [CrossRef] [PubMed]
  45. Yadav, V.; Arkoti, N.K.; Gautam, S.K.; Kuppireddy, S.; Yendrapati, T.P.; Modem, S.; Narayana, C.; Lee, H.-D.; Siddhanta, S.; Jayarmaulu, K. Recent Advances in Nanoporous NOx Gas Sensors: Synergizing Raman Spectroscopy, IoT, and Machine Learning for High-Performance Detection. Nanoscale 2025, 17, 20704–20733. [Google Scholar] [CrossRef] [PubMed]
  46. Siregar, G.A.W.; Sofina, D.; Hayatunnufus; Harahap, L.A.; Ramadhani, R. Implementation of Rabbit Urine Ammonia Level Detection System with Internet of Things. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Medan, Indonesia, 25–26 July 2024; IOP Publishing: Bristol, UK, 2024; Volume 1413, p. 012052. [Google Scholar] [CrossRef]
  47. Aubakirov, A.; Makhanov, K.; Issainova, A.; Glazyrina, N.; Razkhova, B.; Satybaldina, D. An IoT-Based Air Pollution Monitoring System for Smart City. In Proceedings of the 2024 4th International Conference on Computer Systems, ICCS 2024, Hangzhou, China, 20–22 September 2024; pp. 156–161. [Google Scholar] [CrossRef]
  48. Ahmareza, D.; Widiyanto, A.; Nugroho, S. Automatic Odor Control System in Broiler Chicken Coops Using MQ-135 and DHT 11 Sensors. E3S Web Conf. 2024, 500, 03017. [Google Scholar] [CrossRef]
  49. Nagrale, N.K.; Nagrale, V.N.; Nagrale, A.N. Modern Food Grain Storage with RFID Security Cover and Innovative Technology Using IoT. In Proceedings of the 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET), Nagpur, India, 7–8 June 2024; pp. 1–6. [Google Scholar] [CrossRef]
  50. Bachewar, B.; Gadiya, A.; Badagandi, H.; Ahuja, M.; Kadu, A. ChemiRover—Integrated IoT and ML for Hazardous Area Monitoring. In Proceedings of the 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 24–28 June 2024; pp. 1–6. [Google Scholar] [CrossRef]
  51. Abid, M.A.; Amjad, M.; Munir, K.; Siddique, H.U.R.; Jurcut, A.D. IoT-Based Smart Biofloc Monitoring System for Fish Farming Using Machine Learning. IEEE Access 2024, 12, 86333–86345. [Google Scholar] [CrossRef]
  52. Prasad, R.; Raj, S.; Akash, M.; Shashni, H.; Subeesh, A.; Chauhan, N. Implementation of IoT and Fuzzy Logic Driven Real-Time Smart Storage Monitoring System. In Proceedings of the 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 24–28 June 2024; pp. 1–7. [Google Scholar] [CrossRef]
  53. Soliman-Cuevas, H. ZamPen Chick Monitoring Using Wireless Sensor Network. In Proceedings of the 2024 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa, 7–8 March 2024; pp. 232–236. [Google Scholar] [CrossRef]
  54. Islam, M.D.S.; Saha, T.; Mir, M.D.S.; Ankon, M.H.; Chisty, N.A. Design and Implementation of IoT-Based Manhole Monitoring System. In Proceedings of the 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON), Rajshahi, Bangladesh, 12–13 September 2024; pp. 502–507. [Google Scholar] [CrossRef]
  55. Johar, G.M.; Adha, F.J.; Hajamydeen, A.I.; Raya, L.; Alkawaz, M.H. The Efficiency in Controlling and Monitoring a Poultry Farm Based on Internet of Things (IoT). In Proceedings of the 2024 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2024, Shah Alam, Malaysia, 29 June 2024; pp. 303–307. [Google Scholar] [CrossRef]
  56. Bhuiyan, Z.W.; Haider, S.A.R.; Haque, A.; Uddin, M.R.; Hasan, M. IoT Based Meat Freshness Classification Using Deep Learning. IEEE ACCESS 2024, 12, 196047–196069. [Google Scholar] [CrossRef]
  57. Sundar Ganesh, C.S.; Akshaya Prasaath, V.; Arun, A.; Bharath, M.; Kanagasabapathy, E. Internet of Things Enabled Air Quality Monitoring System. In Proceedings of the International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023—Proceedings, Coimbatore, India, 14–16 June 2023; pp. 934–937. [Google Scholar] [CrossRef]
  58. Karuna, G.; Ram Kumar, R.P.; Gopaldas, S.; Parvathaneni, V.; Lokesh, T. Air Quality and Hazardous Gas Detection Using IoT for Household and Industrial Areas. E3S Web Conf. 2023, 391, 01146. [Google Scholar] [CrossRef]
  59. Rai, A.; Rai, A.; Upadhyay, A. Smart Wearables for Coal Mine Workers. In Proceedings of the ViTECoN 2023—2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Vellore, India, 5–6 May 2023. [Google Scholar] [CrossRef]
  60. Alekhya, K.; Sravya, P.D.; Naik, N.C.; Lakshminarayana, B.J. Ambient Air Quality Monitoring System. In Proceedings of the 2023 International Conference for Advancement in Technology, ICONAT 2023, Goa, India, 24–26 January 2023. [Google Scholar] [CrossRef]
  61. Zhang, H.; Luo, Q.; Chen, X.; Deng, X.; Zou, J.; Han, H.; Liu, Y. Development of a Programmable and Scalable Smart IoT Cultivation Apparatus. In Proceedings of the International Conference on Internet of Things and Machine Learning (IoTML 2023), Singapore, 15–17 September 2023; Volume 12937, p. 1293704. [Google Scholar] [CrossRef]
  62. Jebari, H.; Mechkouri, M.H.; Rekiek, S.; Reklaoui, K. Poultry-Edge-AI-IoT System for Real-Time Monitoring and Predicting by Using Artificial Intelligence. Int. J. Interact. Mob. Technol. 2023, 17, 149–170. [Google Scholar] [CrossRef]
  63. Srinivasan, C.; Sridhar, P.; Pradeepa, J.; Thulasiragavan, P.V.; Yuvaraj, R. Internet of Things Towards the Implementation of a Smart City. In Proceedings of the 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), Salem, India, 19–20 June 2023; pp. 984–988. [Google Scholar] [CrossRef]
  64. Fathurohman, M.A.A.; Sumitra, I.D.; Daud, A.R. Integration of Wireless Sensor Network and IoT for Enhanced Broiler Closed-House Monitoring: A Case Study at Broiler Teaching Farm. In Proceedings of the 2023 9th International Conference on Signal Processing and Intelligent Systems (ICSPIS), Bali, Indonesia, 14–15 December 2023; pp. 1–8. [Google Scholar] [CrossRef]
  65. Sai Prasad, M.V.; Sumalatha, A.; Rani, K.S.; Nicy, M.; Meenakshi, C.; Babu, D.C. IoT Based Smart Poultry Management System. In Proceedings of the 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India, 14–15 December 2023; pp. 1–6. [Google Scholar] [CrossRef]
  66. Valov, N. Project-Based Teaching of Students with Development of a Smart Control of the Free Range Chicken House. In Proceedings of the 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Istanbul, Turkiye, 8–10 June 2023; pp. 1–7. [Google Scholar] [CrossRef]
  67. Dhanalakshmi, K.S.; Jeyanathan, J.S.; Anjineyulu, K.C.; Babu, K.M.; Mahendra, M.; Reddy, N.P. Cloud IoT Based Poultry Environment Analysis System. In Proceedings of the 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2–4 February 2023; pp. 76–81. [Google Scholar] [CrossRef]
  68. Chaganti, P.C.V.; Sukesh, K.S. Smart IoT Solutions for Subsurface Gas Monitoring and Safety in Critical Infrastructures. In Proceedings of the 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, 11–13 December 2023; pp. 303–308. [Google Scholar] [CrossRef]
  69. Banupriya, N.; Gokul, G.; Naveen, K.S.; Narayani, S.; Mohamed, A.A. Wearable IoT Gas Monitoring for Sewers Safety. In Proceedings of the 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, 11–13 December 2023; pp. 268–272. [Google Scholar] [CrossRef]
  70. Malik, P.K.; Duggal, A.S.; Aluvala, S.; Sahithi, R.; Geetanjali; Gehlot, A. Development of a Low-Cost IoT Device Using ESP8266 and Atmega328 for Real-Time Monitoring of Outdoor Air Quality with Alert. In Proceedings of the 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE), Ghaziabad, India, 23–24 November 2023; pp. 125–129. [Google Scholar] [CrossRef]
  71. Pramono, T.B.; Qothrunnada, N.I.; Asadi, F.; Cenggoro, T.W.; Pardamean, B. Water Quality Monitoring System for Aquaponic Technology Using the Internet of Things (IoT). Commun. Math. Biol. Neurosci. 2023, 2023, 120. [Google Scholar] [CrossRef]
  72. Santos, R.C.; Lopes, A.L.N.; Sanches, A.C.; Gomes, E.P.; da Silva, E.A.S.; da Silva, J.L.B. Intelligent Automated Monitoring Integrated with Animal Production Facilities. Eng. Agric. 2023, 43, e20220225. [Google Scholar] [CrossRef]
  73. Lashari, M.H.; Karim, S.; Alhussein, M.; Hoshu, A.A.; Aurangzeb, K.; Anwar, M.S. Internet of Things-Based Sustainable Environment Management for Large Indoor Facilities. PEERJ Comput. Sci. 2023, 9, e1623. [Google Scholar] [CrossRef]
  74. Murugeswari, R.; Jegadeesh, P.; Kumar, G.N.; Babu, S.N.; Samar, B. Revolutionizing Poultry Farming with IoT: An Automated Management System. In Proceedings of the 2023 4th International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India, 23–24 March 2023; pp. 22–27. [Google Scholar] [CrossRef]
  75. Charieth, F.M.; Parengal, H.; Ashifa, K.M.; Prabhakaran, S.; Arya, C.F. Air Purification by Chemical Photocatalysis by Means of Titanium Dioxide: A Smart Air Purification System. Eur. Chem. Bull. 2022, 11, 146–152. [Google Scholar] [CrossRef]
  76. Rakib, M.; Haq, S.; Hossain, M.I.; Rahman, T. IoT Based Air Pollution Monitoring & Prediction System. In Proceedings of the 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh, 26–27 February 2022; pp. 184–189. [Google Scholar] [CrossRef]
  77. Islam, R.; Hossain, M.I.; Rahman, M.S.; Kabir, S.; Sohan, M.S.R.; Shufian, A. Smart IoT System for Automatic Detection and Protection from Indoor Hazards: An Experimental Study. In Proceedings of the 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, 16–18 September 2022; pp. 112–117. [Google Scholar] [CrossRef]
  78. Siddika, A.; Hossen Faysal, M.A.; Rasel Ahmed, M.; Rahaman, M.M.; Ali, M.; Ahmed Foysal, M.F. A Data Analysis Technique to Find the Environmental Effect on Egg Production in the Poultry Farm Using ML and IOT. In Proceedings of the 2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED), Sukabumi, Indonesia, 28–29 July 2022; pp. 1–6. [Google Scholar] [CrossRef]
  79. Gkogkidis, A.; Tsoukas, V.; Papafotikas, S.; Boumpa, E.; Kakarountas, A. A TinyML-Based System for Gas Leakage Detection. In Proceedings of the 2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), Bremen, Germany, 8–10 June 2022; pp. 1–5. [Google Scholar] [CrossRef]
  80. Naik, A.; Koushik, K.A.; Poorna Vikas, A.S.; Reddy, S.; Manjunatha, N.M.; Sooda, K. A Poultry Farm Monitoring and Control System. In Proceedings of the 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 16–17 October 2022; pp. 1–5. [Google Scholar] [CrossRef]
  81. Mani, G.; Viswanadhapalli, J.K.; Sriramalakshmi, P. AI Powered IoT Based Real-Time Air Pollution Monitoring and Forecasting. In Proceedings of the Journal of Physics: Conference Series, Chennai, India, 23–25 September 2021; Volume 2115, p. 012016. [Google Scholar] [CrossRef]
  82. Megantoro, P.; Aldhama, S.A.; Prihandana, G.S.; Vigneshwaran, P. IoT-Based Weather Station with Air Quality Measurement Using ESP32 for Environmental Aerial Condition Study. Telkomnika Telecommun. Comput. Electron. Control. 2021, 19, 1316–1325. [Google Scholar] [CrossRef]
  83. Ahuja, V.K.; Kotamraju, S.K.; Kavya, K.C.S.; Dangi, M.; Ahammad, S.H. Implementation of an Energy Efficient Framework for Air Quality Monitoring in the Cremation Center Based on Improved Chacha20 Stream Cipher for Secure Data Transmission. In Proceedings of the 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 17–18 December 2021; pp. 1146–1153. [Google Scholar] [CrossRef]
  84. Shyamalaprasanna, A.; Velnath, R.; Dhivya, K.T.; Aishwarya, S.; Saravana, G.; Srimathi, R. Monitoring and Controlling of Industrial Sewage Outlet Using IoT. In Proceedings of the 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 8–9 October 2021; pp. 1–5. [Google Scholar] [CrossRef]
  85. Korlepara, N.S.D.P.; Chandra, G.P.; Seshagiri, B.; Raju, V.S.N.N.; Veeraiah, N.; Pragaspathy, S. ¬Novel Freshness Indication Packaging Technology for Frozen Shrimps. In Proceedings of the 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 8–9 October 2021; pp. 1–5. [Google Scholar] [CrossRef]
  86. Gomes, J.B.A.; Rodrigues, J.J.P.C.; Rabêlo, R.A.L.; Tanwar, S.; Al-Muhtadi, J.; Kozlov, S. A Novel Internet of Things-Based Plug-and-Play Multigas Sensor for Environmental Monitoring. Trans. Emerg. Telecommun. Technol. 2021, 32, e3967. [Google Scholar] [CrossRef]
  87. Shobha, R.; Sadiq Mafas, S.; Karthic Raja, B.; Sangeeth Ajay, M.B.; Elavarasi, R. IoT Based Air Quality Monitoring and Poisonous Gas Detection System. Int. J. Electr. Eng. Technol. IJEET 2021, 12, 143–151. [Google Scholar]
  88. Ullas, S.; Upadhyay, S.; Chandran, V.; Pradeep, S.; Mohankumar, T.M. Control Console of Sewage Treatment Plant with Sensors as Application of IOT. In Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, Kharagpur, India, 1–3 July 2020. [Google Scholar] [CrossRef]
  89. Rozie, F.; Syarif, I.; Al Rasyid, M.U.H. Design and Implementation of Intelligent Aquaponics Monitoring System Based on IoT. In Proceedings of the 2020 International Electronics Symposium (IES), Surabaya, Indonesia, 29–30 September 2020; pp. 534–540. [Google Scholar] [CrossRef]
  90. Kodali, R.K.; Pathuri, S.; Rajnarayanan, S.C. Smart Indoor Air Pollution Monitoring Station. In Proceedings of the 2020 International Conference on Computer Communication and Informatics (ICCCI-2020), Coimbatore, India, 22–24 January 2020; pp. 535–539. [Google Scholar] [CrossRef]
  91. Syahrorini, S.; Rifai, A.; Saputra, D.H.R.; Ahfas, A. Design Smart Chicken Cage Based on Internet of Things. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Lumpur, Malaysia, 7–8 November 2020; Volume 519. [Google Scholar] [CrossRef]
  92. Boppana, L.; Lalasa, K.; Vandana, S.; Kodali, R.K. Mongoose OS Based Air Quality Monitoring System. In Proceedings of the IEEE Region 10 Annual International Conference, Proceedings/TENCON, Kochi, India, 17–20 October 2019; pp. 1247–1252. [Google Scholar] [CrossRef]
  93. Budiman, F.; Rivai, M.; Nugroho, M.A. Monitoring and Control System for Ammonia and pH Levels for Fish Cultivation Implemented on Raspberry Pi 3B. In Proceedings of the 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia, 28–29 August 2019; pp. 68–73. [Google Scholar] [CrossRef]
  94. Vijayalakshmi, J.; Puthilibhai, G.; Siddarth, S.R.L. Implementation of Ammonia Gas Leakage Detection & Monitoring System Using Internet of Things. In Proceedings of the 2019 Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 12–14 December 2019; pp. 778–781. [Google Scholar] [CrossRef]
  95. Gunawan, T.S.; Sabar, M.F.; Nasir, H.; Kartiwi, M.; Motakabber, S.M.A. Development of Smart Chicken Poultry Farm Using RTOS on Arduino. In Proceedings of the 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), Kuala Lumpur, Malaysia, 27–29 August 2019; pp. 1–5. [Google Scholar] [CrossRef]
  96. Sai, K.B.K.; Mukherjee, S.; Sultana, P.H. Low Cost IoT Based Air Quality Monitoring Setup Using Arduino and MQ Series Sensors with Dataset Analysis. Procedia Comput. Sci. 2019, 165, 322–327. [Google Scholar] [CrossRef]
  97. Muladi, M.; Sendari, S.; Widiyaningtyas, T. Real Time Indoor Air Quality Monitoring Using Internet of Things at University. In Proceedings of the 2018 2nd Borneo International Conference on Applied Mathematics and Engineering, BICAME 2018, Balikpapan, Indonesia, 10–11 December 2018; pp. 169–173. [Google Scholar] [CrossRef]
  98. Sitaram, K.A.; Ankush, K.R.; Anant, K.N.; Raghunath, B.R. IoT Based Smart Management of Poultry Farm and Electricity Generation. In Proceedings of the 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 13–15 December 2018; pp. 1–4. [Google Scholar] [CrossRef]
  99. Hamadi, H.; Satrio, K.A.; Harsono, D. Design and Development of CO, CO2, CH4, and NH3 Gas Detection Equipment Based on Iot. Res Mil. 2023, 13, 5295–5304. [Google Scholar]
  100. Tsoukas, V.; Gkogkidis, A.; Boumpa, E.; Papafotikas, S.; Kakarountas, A. A Gas Leakage Detection Device Based on the Technology of TinyML. Technologies 2023, 11, 45. [Google Scholar] [CrossRef]
  101. Brito, R.C.; Ferrareze, C.V.; Favarim, F.; Oliva, J.T.; Todt, E. A Novel System for Ammonia Gas Control in Broiler Production Environment. In Proceedings of the 2020 3rd International Conference on Information and Computer Technologies (ICICT), San Jose, CA, USA, 9–12 March 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 336–340. [Google Scholar] [CrossRef]
  102. Zakaria, N.A.; Zainal, Z.; Harum, N.; Chen, L.; Saleh, N.; Azni, F. Wireless Internet of Things-Based Air Quality Device for Smart Pollution Monitoring. Int. J. Adv. Comput. Sci. Appl. 2018, 9, 65–69. [Google Scholar] [CrossRef]
  103. Srinivas, C.S.; Mohan Kumar, C. Toxic Gas Detection and Monitoring Utilizing Internet of Things. Int. J. Civ. Eng. Technol. 2017, 8, 614–622. [Google Scholar]
  104. Demetillo, A.T.; Tudio, N.J.G.; Solite, J.C.; Dagsa, L.M.; Sajonia, E.R.B.; Balamad, A.D.B. IoT-Enabled Aquaculture Surveillance System for Enhanced Fisheries Resource Management. In Proceedings of the 2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC), Puducherry, India, 7–8 December 2023; pp. 1–6. [Google Scholar] [CrossRef]
  105. Chakraborty, A.; Gupta, R.D.; Roy, D.; Kabir, Z.; Bablu, S.I.; Gupta, R.D. Radiation Monitoring and Environmental Assessment in Precision Agriculture: Development of an Integrated IoT Device with Comprehensive Data Analysis Capabilities. In Proceedings of the 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh, 13–15 December 2023; pp. 1–6. [Google Scholar] [CrossRef]
  106. Hambali, M.F.H.; Patchmuthu, R.K.; Wan, A.T. IoT Based Smart Poultry Farm in Brunei. In Proceedings of the 2020 8th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia, 24–26 June 2020; pp. 1–5. [Google Scholar] [CrossRef]
  107. Megantoro, P.; Pramudita, B.A.; Vigneshwaran, P.; Yurianta, A.; Winarno, H.A. Real-Time Monitoring System for Weather and Air Pollutant Measurement with Html-Based Ui Application. Bull. Electr. Eng. Inform. 2021, 10, 1669–1677. [Google Scholar] [CrossRef]
  108. Godinho, A.; Vicente, R.; Silva, S.; Coelho, P.J. Wireless Environmental Monitoring and Control in Poultry Houses: A Concetual Study. IoT 2025, 6, 32. [Google Scholar] [CrossRef]
  109. Rosmiati, M.; Wijaya, R.; Hanifa, F.H.; Hidayat, R.; Winata, A.Y.; Maulana, M.A. IoT-Based Cattle Pen Monitoring and Mobile App.lication Interface for WS Farm: Enhancing Livestock Management Through Real-Time Data. Instrum. Mes. Métrologie 2025, 24, 161–169. [Google Scholar] [CrossRef]
  110. Antad, S.; Giri, V.; Bachewar, B.; Barsude, S.; Gadiya, A.; Badagandi, H. Real-Time Gas Monitoring and Anomaly Detection in Petroleum Industry Using IoT and Machine Learning. Int. J. Comput. Digit. Syst. 2025, 17, 1–17. [Google Scholar] [CrossRef]
  111. Rajendrakumar, P.M.; Shwetha, P.; Reddy, P.; Tejasri, T.; Piruthiviraj, P. IoT Cloud-Based Air Quality Monitoring System with Arduino. In Proceedings of the 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS), Karnataka, India, 23–24 April 2025; IEEE: Piscataway, NJ, USA, 2025; pp. 1–6. [Google Scholar] [CrossRef]
  112. Ullas, S.; Maheswari, B.U.; Ponnekanti, S.; Kumar, T.M.M. Automated System to Optimize the Process and Energy Consumption for Sewage Treatment Plant Based on Gas Emission by Using Sensors and IoT. IEEE Access 2025, 13, 115972–115989. [Google Scholar] [CrossRef]
  113. Singh, K.; Momin, K.; Nishal, M.; Sultania, C.; Rao, M. Agri-Guard: IoT-Based Network for Agricultural Health Monitoring with Fault Detection. In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security, Porto, Portugal, 6–8 April 2025; SCITEPRESS-Science and Technology Publications: Setúbal, Portugal, 2025; pp. 223–230. [Google Scholar] [CrossRef]
  114. Wang, L.; Li, K. Design and Implementation of Intelligent Pet House Environment Monitoring System Based on IoT. In Proceedings of the 2025 6th International Conference on Electrical, Electronic Information and Communication Engineering (EEICE), Shenzhen, China, 18–20 April 2025; pp. 694–699. [Google Scholar] [CrossRef]
  115. Raja, G.B.; Vigneashwaran, R.; Rohan, M.; Praveen, N.B.; Vijay, N.P. IoT-Based Sewage Atmosphere and Health Monitoring for Sanitation Workers with Alerting System. In Proceedings of the 2025 7th International Conference on Inventive Material Science and Applications (ICIMA), Shenzhen, China, 18–20 April 2025; pp. 855–860. [Google Scholar] [CrossRef]
  116. Khadim, H.J.; Obaed, F.K.; Ajeel, N.S. Wireless Sensing Network for Implementation of Air Quality Monitoring System and Indoor Air Quality Index Application. Iraqi Geol. J. 2024, 57, 210–220. [Google Scholar] [CrossRef]
  117. Fahrurrozi, I.; Wahyono; Sari, Y.; Sari, A.K.; Usuman, I.; Ariyadi, B. Integrating Random Forest Model and Internet of Things-Based Sensor for Smart Poultry Farm Monitoring System. Indones. J. Electr. Eng. Comput. Sci. 2024, 33, 1283–1292. [Google Scholar] [CrossRef]
  118. Singh, A.; Banerjee, A.; Chatterjee, S.; Das, H.; Das, N.; Saha, S.; Mandal, H. Sustainable Smart Trash Bin Based Waste Segregation and Collection System. In Proceedings of the 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT), Hooghly, India, 28–29 September 2024; pp. 1–6. [Google Scholar] [CrossRef]
  119. Saad, H.; Zaidi, N.F.N.; Isa, N.M.; Shahbudin, S.; Othman, N.; Masrie, M. Incorporating IoT into STEM Education through PBL-Chicken Farming Issues. In Proceedings of the 2024 IEEE 15th Control and System Graduate Research Colloquium, ICSGRC 2024, Shah Alam, Malaysia, 17 August 2024; pp. 187–192. [Google Scholar] [CrossRef]
  120. Singh, H.; Kajla, A.K. Optimizing Ammonia Concentration in Poultry Houses Through Random Forest Prediction and Lora-Wan Monitoring & Controlling System. SSRN 2024. [Google Scholar] [CrossRef]
  121. Sridhar, K.; Radhakrishnan, P.; Swapna, G.; Kesavamoorthy, R.; Pallavi, L.; Thiagarajan, R. A Modular IOT Sensing Platform Using Hybrid Learning Ability for Air Quality Prediction. Meas. Sens. 2023, 25, 100609. [Google Scholar] [CrossRef]
  122. Dequilla-Pabiania, M.; Marasigan, J.A.; Mercado, N.P.; Rivera, L. A Low-Cost Electronic Food Nose IoT-Based Fish Quality Monitoring System. Chem. Eng. Trans. 2023, 106, 487–492. [Google Scholar] [CrossRef]
  123. Rahman Alvi, K.M.; Mondal, S. Ammonia & CO2 Gas Detection of Poultry Farms and Compost Plants by Low-Cost Smart Sensing System. In Proceedings of the 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh, 13–15 December 2023; pp. 1–6. [Google Scholar] [CrossRef]
  124. Azman, F.I.; Fazdli, M.S.F.M.; Saleh, N.L.; Noor, A.S.M.; Ali, A.M. Residential College’s Smart Restroom Monitoring System. In Proceedings of the 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), Istanbul, Turkiye, 25–27 July 2023; pp. 1–6. [Google Scholar] [CrossRef]
  125. Bashyam, S.; Pramodini, V.; Ashik, A.; Prasanth, V.S. IoT-Based Garbage Gas Detection System. In Proceedings of the 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 26–28 May 2023; pp. 1–4. [Google Scholar] [CrossRef]
  126. Srinuanjan, K.; Kruakuanphet, A.; Phongwisit, P.; Yindeesuk, W.; Kamoldilok, S. Electronic Nose Ammonia Gas Monitoring via IoT System for Chlorella sp. Cultivation. In Proceedings of the SPIE Future Sensing Technologies 2023, Yokohama, Japan, 18–21 April 2023; p. 123271F. [Google Scholar] [CrossRef]
  127. Perdanasari, L.; Etikasari, B.; Rukmi, D.L. Control System for Temperature, Humidity, and Ammonia Levels in Laying Hens Farms Based on Internet of Things. IOP Conf. Ser. Earth Environ. Sci. 2023, 1168, 012053. [Google Scholar] [CrossRef]
  128. Lukman, L. Early Detection of Ammonia Gas Levels in the Air Using IoT-Based SVM. J. Pendidik. Tambusai 2023, 7, 3750–3757. [Google Scholar] [CrossRef]
  129. Mia, M.H.; Mahfuz, N.; Habib, R.; Hossain, R. An Internet of Things Application on Continuous Remote Patient Monitoring and Diagnosis. In Proceedings of the 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), Paris/Créteil, France, 8–10 December 2021; pp. 1–6. [Google Scholar] [CrossRef]
  130. Listyarini, S.; Warlina, L.; Sambas, A. The Air Quality Monitoring Tool Based on Internet of Things to Monitor Pollution Emissions Continuously. Environ. Ecol. Res. 2022, 10, 824–829. [Google Scholar] [CrossRef]
  131. Zamzari, N.Z.; Kassim, M.; Yusoff, M. Analysis and Development of IoT-Based Aqua Fish Monitoring System. Int. J. Emerg. Technol. Adv. Eng. 2022, 12, 191–197. [Google Scholar] [CrossRef] [PubMed]
  132. Azman, F.I.; Saleh, N.L.; Ali, A.M.; Sali, A.; Noor, A.S.M. Restroom Hygiene Smart Monitoring System Incorporating Lora Technology. In Proceedings of the TENCON 2022 IEEE Region 10 Conference (TENCON), Hong Kong, China, 1–4 November 2022; pp. 1–6. [Google Scholar] [CrossRef]
  133. Wang, C.; Liu, H. Design and Implementation of Animal Laboratory Environmental Monitoring System Based on Internet of Things. In Proceedings of the 2022 2nd International Conference on Electrical Engineering and Control Science (IC2ECS), Nanjing, China, 16–18 December 2022; pp. 1235–1238. [Google Scholar] [CrossRef]
  134. Sangeetha, K.; Kanthimathi, M.; Monisha, S.R.; Reethika, M.; Amirthabowmiya, M. Poultry Farm Control and Management System Using Wireless Sensor Networks. In Proceedings of the 2022 1st International Conference on Computational Science and Technology (ICCST), Chennai, India, 9–10 November 2022; pp. 712–715. [Google Scholar] [CrossRef]
  135. Udanor, C.N.; Ossai, N.I.; Nweke, E.O.; Ogbuokiri, B.O.; Eneh, A.H.; Ugwuishiwu, C.H.; Aneke, S.O.; Ezuwgu, A.O.; Ugwoke, P.O.; Christiana, A. An Internet of Things Labelled Dataset for Aquaponics Fish Pond Water Quality Monitoring System. Data Brief 2022, 43, 108400. [Google Scholar] [CrossRef]
  136. Dinesh, D.; Mowshik, A.N.; Meyyappan, M.; Kowtham, M. Analysis of Universal Gas Leak Detector of Hazardous Gases Using IOT. Mater. Today-Proc. 2022, 66, 1044–1050. [Google Scholar] [CrossRef]
  137. Kruakuanphet, A.; Phongwisit, P.; Yindeesuk, W.; Kamoldilok, S.; Srinuanjan, K. Multi-Range Ammonia Gas Sensor Control and Monitor via IoT System. In Proceedings of the 2022 26th International Computer Science and Engineering Conference (ICSEC), Sakon Nakhon, Thailand, 21–23 December 2022; pp. 182–185. [Google Scholar] [CrossRef]
  138. Onibonoje, M.O. IoT-Based Synergistic Approach for Poultry Management System. In Proceedings of the 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Toronto, ON, Canada, 21–24 April 2021; pp. 572–576. [Google Scholar] [CrossRef]
  139. Lukman; Achmad, A.; Syarif, S. Prediction of Ammonia Contamination Levels in Wastewater Management Plant Using the SVM Method. In Proceedings of the 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia, 21–22 July 2021; pp. 250–255. [Google Scholar] [CrossRef]
  140. Sanger, J.B.; Sitanayah, L.; Ahmad, I. A Sensor-Based Garbage Gas Detection System. In Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 27–30 January 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 1347–1353. [Google Scholar] [CrossRef]
  141. Pereira, W.F.; Fonseca, L.d.S.; Putti, F.F.; Goes, B.C.; Naves, L.d.P. Environmental Monitoring in a Poultry Farm Using an Instrument Developed with the Internet of Things Concept. Comput. Electron. Agric. 2020, 170, 105257. [Google Scholar] [CrossRef]
  142. Zaini, A.; Kurniawan, A.; Herdhiyanto, A.D. Internet of Things for Monitoring and Controlling Nutrient Film Technique (NFT) Aquaponic. In Proceedings of the 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, Indonesia, 26–27 November 2018; pp. 167–171. [Google Scholar] [CrossRef]
  143. Khaowdang, S.; Suriyachai, N.; Imman, S.; Suwannahong, K.; Wongcharee, S.; Kreetachat, T. Application of Internet of Things Technology for Ventilation and Environmental Control in Conventional Open-Air Pig Housing Systems in Thailand. AgriEngineering 2025, 7, 165. [Google Scholar] [CrossRef]
  144. Febriana, S.D.; Mutiara, G.A.; Erfianto, B. E-Sniffer: Raw Meat Freshness Detection Tool Based on Odor Classification and Fuzzy Logic Utilizing Gas Fusion Sensor. Instrum. Mes. Métrologie 2025, 24, 97–109. [Google Scholar] [CrossRef]
  145. Azman, F.I.; Saleh, N.L.; Hashim, F.; Sali, A.; Ali, A.M.; Noor, A.S.M. An IoT-Based Hygiene Monitoring System in the Restroom. IEEE Access 2025, 13, 119348–119361. [Google Scholar] [CrossRef]
  146. Traya, M.A.; Azucena, E.T.; Budok, J.G.F.; Guanzon, G.S.; Naputol, A.T.; Ituriaga, J.I. Development of an IoT-Based System with AI-Driven Robotic Feeder and Rice Hull Management for Chickens. In Proceedings of the 2025 IEEE International Conference on Robotics and Technologies for Industrial Automation (ROBOTHIA), Kuala Lumpur, Malaysia, 12 April 2025; IEEE: Piscataway, NJ, USA, 2015; pp. 1–7. [Google Scholar] [CrossRef]
  147. Datasheet MQ-2. Available online: https://www.winsen-sensor.com/d/files/PDF/Semiconductor%20Gas%20Sensor/MQ-2%20(Ver1.4)%20-%20Manual.pdf (accessed on 6 March 2025).
  148. Krishnamurthy, K.T.; Managuli, M.; Malipatil, S.; Bagyalakshmi, K.; Salake, S.V.; Kadalagi, P.S.; Patil, S.B. IoT Based Poultry Farm Smart Management System. In Proceedings of the 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), Chikkaballapur, India, 18–19 April 2024. [Google Scholar] [CrossRef]
  149. Chigwada, J.; Mazunga, F.; Nyamhere, C.; Mazheke, V.; Taruvinga, N. Remote Poultry Management System for Small to Medium Scale Producers Using IoT. Sci. Afr. 2022, 18, e01398. [Google Scholar] [CrossRef]
  150. Lehaa, T.; Dhanasekaran, D.; Kumar, P.; Murali Prasad, A.; Samyuktha, P.R. Smart Poultry Farm: An IoT-Based Environmental Monitoring and Control System. In Proceedings of the 2025 7th International Conference on Inventive Material Science and Applications (ICIMA), Namakkal, India, 28–30 May 2025; IEEE: Piscataway, NJ, USA, 2025; pp. 1083–1090. [Google Scholar] [CrossRef]
  151. Indrawati, E.M.; Munawi, H.A.; Suwardono, A.; Santoso, R.; Maulana, F.I. Smart Technology Intervention for Integrated Gurami Fish Cultivation Based on IoT*. In Proceedings of the 2024 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia, 28–30 November 2024; pp. 198–202. [Google Scholar] [CrossRef]
  152. Sai Prasad, M.V.; Sumalatha, A.; Rani, K.S.; Meenakshi, C.; Nicy, M.; Babu, D.C. Cloud-Based IoT Solution for Enhanced Poultry Farm Management. In Proceedings of the 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), Noida, India, 21–23 February 2024; pp. 1–5. [Google Scholar] [CrossRef]
  153. Islam, S. An Intelligent System on Environment Quality Remote Monitoring and Cloud Data Logging Using Internet of Things (IoT). In Proceedings of the International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018, Rajshahi, Bangladesh, 8–9 February 2018. [Google Scholar] [CrossRef]
  154. Datasheet MiCS-6814. Available online: https://www.sgxsensortech.com/content/uploads/2015/02/1143_Datasheet-MiCS-6814-rev-8.pdf (accessed on 6 March 2025).
  155. Aserkar, A.A.; Godla, S.R.; Baker El-Ebiary, Y.A.; Krishnamoorthy; Ramesh, J.V.N. Real-Time Air Quality Monitoring in Smart Cities Using IoT-Enabled Advanced Optical Sensors. Int. J. Adv. Comput. Sci. Appl. 2024, 15, 840–848. [Google Scholar] [CrossRef]
  156. Mulling, L.F.; Lindino, C.; Oyamada, M.S. Calibration of a Metal Oxide Sensor for Ammonia Detection Targeting IoT Solutions. In Proceedings of the 2023 XIII Brazilian Symposium on Computing Systems Engineering, SBESC, Porto Alegre, Brazil, 21–24 November 2023. [Google Scholar] [CrossRef]
  157. Mapili, M.G.A.; Rodriguez, K.A.D.; Sese, J.T. Smart Air Filtration System Using IoT and Kalman Filter Algorithm for Indoor Air Quality and Plant Monitoring. In Proceedings of the 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET), Shah Alam, Malaysia, 6 November 2021; pp. 309–314. [Google Scholar] [CrossRef]
  158. Nasution, T.H.; Hizriadi, A.; Tanjung, K.; Nurmayadi, F. Design of Indoor Air Quality Monitoring Systems. In Proceedings of the 2020 4th International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2020—Proceedings, Medan, Indonesia, 3–4 September 2020; pp. 238–241. [Google Scholar] [CrossRef]
  159. Marques, G.; Pitarma, R. A Cost-Effective Air Quality Supervision Solution for Enhanced Living Environments through the Internet of Things. Electronics 2019, 8, 170. [Google Scholar] [CrossRef]
  160. Datasheet MiCS-5524. Available online: https://www.sgxsensortech.com/content/uploads/2014/07/1084_Datasheet-MiCS-5524-rev-8.pdf (accessed on 6 March 2025).
  161. Fatkhurrahman, J.A.; Sari, I.R.J. Affordable Metal Oxide Gas Sensor as Environmental Friendly Method for Ammonia Gas Measurement. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Yogyakarta, Indonesia, 29–30 October 2019; Volume 366. [Google Scholar] [CrossRef]
  162. Prafanto, A.; Firdaus, M.B. Implementation of Naïve Bayes Gaussian Algorithm for Real-Time Classification of Broiler Cage Conditions. J. App.l. Data Sci. 2025, 6, 1551–1562. [Google Scholar] [CrossRef]
  163. Datasheet TGS2602. Available online: https://www.figarosensor.com/product/docs/TGS2602-B00%20%280615%29.pdf (accessed on 6 March 2025).
  164. Suhud, A.; Hanafi, N.; Purnomo, D.S. Smart Cat Litter Box with Ammonia Gas Level Controller Using IoT-Based Fuzzy Logic. In Proceedings of the 2024 International Electronics Symposium, IES 2024, Denpasar, Indonesia, 6–8 August 2024; pp. 317–321. [Google Scholar] [CrossRef]
  165. Eriyadi, M.; Notosudjono, D.; Setiana, H.; Yakin, M.A.A.A. Low-Cost Mobile Air Quality Monitoring Based on Internet of Things for Factory Area. Indones. J. Electr. Eng. Comput. Sci. 2023, 32, 545–554. [Google Scholar] [CrossRef]
  166. Panjagal, S.B.; Ramaiah, G.N.K. Odorsense: Measuring, Assessment and Alerting the Health Effects of Odor Pollution. 3C Tecnol. 2021, Special Issue, 97–113. [Google Scholar] [CrossRef]
  167. Datasheet TGS-2444. Available online: https://www.figaro.co.jp/en/product/docs/tgs2444_product_infomation_rev02.pdf (accessed on 6 March 2025).
  168. Kumar, M.; Mini, S.; Panigrahi, T. A Scalable Approach to Monitoring Air Pollution Using IoT. In Proceedings of the 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 30–31 August 2018; pp. 42–47. [Google Scholar] [CrossRef]
  169. Damdam, A.N.; Ozay, L.O.; Ozcan, C.K.; Alzahrani, A.; Helabi, R.; Salama, K.N. IoT-Enabled Electronic Nose System for Beef Quality Monitoring and Spoilage Detection. Foods 2023, 12, 2227. [Google Scholar] [CrossRef]
  170. Yoon, S.U.; Choi, S.M.; Lee, J.H. A Study on the Development of Livestock Odor (Ammonia) Monitoring System Using ICT (Information and Communication Technology). Agriculture 2021, 12, 46. [Google Scholar] [CrossRef]
  171. Provolo, G.; Brandolese, C.; Grotto, M.; Marinucci, A.; Fossati, N.; Ferrari, O.; Beretta, E.; Riva, E. An Internet of Things Framework for Monitoring Environmental Conditions in Livestock Housing to Improve Animal Welfare and Assess Environmental Impact. Animals 2025, 15, 644. [Google Scholar] [CrossRef] [PubMed]
  172. Bordignon, F.; Pravato, M.; Trocino, A.; Xiccato, G.; Marinello, F.; Pezzuolo, A. Environmental Gradients and Hen Spatial Distribution in a Cage-Free Aviary System: Internet of Things-Based Real-Time Monitoring for Proactive Management. Animals 2025, 15, 1225. [Google Scholar] [CrossRef] [PubMed]
  173. Abinaya, T.; Lshwarya, J.; Maheswari, M. A Novel Methodology for Monitoring and Controlling of Water Quality in Aquaculture Using Internet of Things (IoT). In Proceedings of the 2019 International Conference on Computer Communication and Informatics (ICCCI-2019), Coimbatore, India, 23–25 January 2019. [Google Scholar] [CrossRef]
  174. Suriasni, P.A.; Faizal, F.; Hermawan, W.; Subhan, U.; Panatarani, C.; Joni, I.M. IoT Water Quality Monitoring and Control System in Moving Bed Biofilm Reactor to Reduce Total Ammonia Nitrogen. Sensors 2024, 24, 494. [Google Scholar] [CrossRef]
  175. Rahul, S.G.; Rajkumar, R.; Reddy, Y.K.; Koushik, N.; Manikannta, V.S.; Subitha, D. Integrating IoT and Deep Learning for Smart Aquaculture Management in Freshwater Aquariums. In Proceedings of the 2nd International Conference on Sustainable Computing and Smart Systems, ICSCSS 2024, Coimbatore, India, 10–12 July 2024; pp. 321–326. [Google Scholar] [CrossRef]
  176. Xu, Y.; Wu, L.; Fan, Y.; Guo, Q. Intelligent Space Disinfection System Based on Microcontroller. In Proceedings of the International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), Nanchang, China, 17–19 March 2023; Volume 12700. [Google Scholar] [CrossRef]
  177. Xinyu, S.; Liangzhe, C.; Jinxiang, W.; Yaya, X.; Xijia, H.; Qing, Y. Research on Intelligent Ammonia Detection System Based on NB-IOT Networking Technology. In Proceedings of the 2023 IEEE 3rd International Conference on Data Science and Computer Application (ICDSCA), Dalian, China, 27–29 October 2023; pp. 460–464. [Google Scholar] [CrossRef]
  178. Shafkat, A.; Islam, E. Iot Based Biofloc Automation and Monitoring for Smart Fish Production. In Proceedings of the 2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), Yogyakarta, Indonesia, 9–10 August 2023; pp. 158–162. [Google Scholar] [CrossRef]
  179. Benhmad, T.; Ben Abdennour, A.; Darghouthi, A.; Belgacem Rhaimi, C. Remote Control of Environmental Parameters in Rabbitry Based on IoT. Internet Things Cyber-Phys. Syst. 2022, 2, 111–119. [Google Scholar] [CrossRef]
  180. Wang, Y.; Ho, I.W.-H.; Chen, Y.; Wang, Y.; Lin, Y. Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation. IEEE Internet Things J. 2022, 9, 14366–14374. [Google Scholar] [CrossRef]
  181. Jayarajan, P.; Annamalai, M.; Jannifer, V.A.; Prakash, A.A. IOT Based Automated Poultry Farm for Layer Chicken. In Proceedings of the 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 19–20 March 2021; Volume 1, pp. 733–737. [Google Scholar] [CrossRef]
  182. Mohd Redzuan, A.A.A.; Saparudin, F.A.; Mohd Shah, N.S.; Noor Azeb, M.M.A.; Shamsuddin, M.K.A. Wireless Ammonia Sensor System for Distributed Wireless Monitoring Platform Using Heltec Wifi LoRa 32 (V2). Prog. Eng. Appl. Technol. 2021, 2, 502–513. [Google Scholar]
  183. Lashari, M.H.; Memon, A.A.; Shah, S.A.A.; Nenwani, K.; Shafqat, F. IoT Based Poultry Environment Monitoring System. In Proceedings of the 2018 IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018, Bali, Indonesia, 1–3 November 2018; pp. 1–5. [Google Scholar] [CrossRef]
  184. Kalamaras, S.D.; Tsitsimpikou, M.-A.; Tzenos, C.A.; Lithourgidis, A.A.; Pitsikoglou Dimitra, S.; Kotsopoulos, T.A. A Low-Cost IoT System Based on the ESP32 Microcontroller for Efficient Monitoring of a Pilot Anaerobic Biogas Reactor. Appl. Sci. 2025, 15, 34. [Google Scholar] [CrossRef]
  185. Xu, Y.; Jin, J.; Zeng, S.; Zhang, Y.; Xiao, Q. Development and Evaluation of an Iot-Based Portable Water Quality Monitoring System for Aquaculture. Inmateh-Agric. Eng. 2023, 70, 359–368. [Google Scholar] [CrossRef]
  186. Kamruzzaman, S.M.; Sakib, F.S.; Rahman, L.M.; Ahmed, T.; Alam, M.D.S.; Shakir, M.B.; Pavel, M.I. Sense-IT: An Aquaculture-Specific Autonomous Data Acquisition and Monitoring System. In Proceedings of the 2022 International Electronics Symposium (IES), Surabaya, Indonesia, 9–11 August 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 404–409. [Google Scholar] [CrossRef]
  187. Tamim, A.T.; Begum, H.; Shachcho, S.A.; Khan, M.M.; Yeboah-Akowuah, B.; Masud, M.; Al-Amri, J.F. Development of IoT Based Fish Monitoring System for Aquaculture. Intell. Autom. Soft Comput. 2022, 32, 55–71. [Google Scholar] [CrossRef]
  188. Shandikiri, R.; Erfianto, B. Internet of Things: Water Quality Classiffication Based on Estimation of Dissolved Oxygen Solubility and Unionized Ammonia for Smallscales Freshwater Aquaculture. Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control J. 2021, 6, 259–268. [Google Scholar] [CrossRef]
  189. Khaoula, T.; Abdelouahid, R.A.; Ezzahoui, I.; Marzak, A. Architecture Design of Monitoring and Controlling of IoT-Based Aquaponics System Powered by Solar Energy. Procedia Comput. Sci. 2021, 191, 493–498. [Google Scholar] [CrossRef]
  190. Chang, C.-C.; Wei, C.-H.; Lin, M.-T.; Hwang, S.-C.J. Machine Learning Approach to IoT- Based Water Quality Monitoring. In Proceedings of the 2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2023, Tainan, Taiwan, 2–4 June 2023; pp. 182–186. [Google Scholar] [CrossRef]
  191. Opta. Available online: https://docs.arduino.cc/hardware/opta/ (accessed on 19 October 2025).
  192. NORVI IIoT ESP32 Industrial Controller. Available online: https://norvi.io/norvi-iiot-esp32-industrial-controller/ (accessed on 19 October 2025).
  193. ESP32-S3 HMI with LVGL Support. Available online: https://norvi.io/esp32-s3-hmi-with-lvgl-support/ (accessed on 19 October 2025).
  194. Arduino’s Series of High-Performance Industry-Rated Boards. Available online: https://www.arduino.cc/pro/hardware-product-family-portenta-family/ (accessed on 19 October 2025).
  195. ESP32-S3 Designed for AIoT Applications. Available online: https://www.espressif.com/en/products/socs/esp32-s3 (accessed on 19 October 2025).
  196. STM32 Wireless MCUs. Available online: https://www.st.com/en/microcontrollers-microprocessors/stm32-wireless-mcus.html (accessed on 19 October 2025).
  197. Patchmuthu, R.K.; Wan, A.T.; Suhaili, W.S. Exploring Data Security and Privacy Issues in Internet of Things Based on Five-Layer Architecture. Int. J. Commun. Netw. Inf. Secur. IJCNIS 2022, 12. [Google Scholar] [CrossRef]
  198. Ferro, E.; Potorti, F. Bluetooth and Wi-Fi Wireless Protocols: A Survey and a Comparison. IEEE Wirel. Commun. 2005, 12, 12–26. [Google Scholar] [CrossRef]
  199. Reyneke, M.; Mullins, B.; Reith, M. LoRaWAN & The Helium Blockchain: A Study on Military IoT Deployment. Int. Conf. Cyber Warf. Secur. 2023, 18, 327–337. [Google Scholar] [CrossRef]
  200. Shilpa, B.; Radha, R.; Movva, P. Comparative Analysis of Wireless Communication Technologies for IoT Applications. In Artificial Intelligence and Technologies; Raje, R.R., Hussain, F., Kannan, R.J., Eds.; Lecture Notes in Electrical Engineering; Springer: Singapore, 2022; Volume 806, pp. 383–394. [Google Scholar] [CrossRef]
  201. Yadav, V.; Kumar, L.; Kumar, P. Evolution and Development of Wireless Communication System. In Proceedings of the 2019 International Conference on Computing, Power and Communication Technologies (GUCON), New Delhi, India, 27–28 September 2019; pp. 53–57. [Google Scholar]
  202. Poursafar, N.; Alahi, M.E.E.; Mukhopadhyay, S. Long-Range Wireless Technologies for IoT Applications: A Review. In Proceedings of the 2017 Eleventh International Conference on Sensing Technology (ICST), Sydney, NSW, Australia, 4–6 December 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–6. [Google Scholar] [CrossRef]
  203. Asemani, M.; Abdollahei, F.; Jabbari, F. Understanding IoT Platforms: Towards a Comprehensive Definition and Main Characteristic Description. In Proceedings of the 2019 5th International Conference on Web Research (ICWR), Tehran, Iran, 24–25 April 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 172–177. [Google Scholar] [CrossRef]
  204. Shwe, T.; Aritsugi, M. Optimizing Data Processing: A Comparative Study of Big Data Platforms in Edge, Fog, and Cloud Layers. Appl. Sci. 2024, 14, 452. [Google Scholar] [CrossRef]
  205. Jamshed, M.A.; Ali, K.; Abbasi, Q.H.; Imran, M.A.; Ur-Rehman, M. Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review. IEEE Sens. J. 2022, 22, 5482–5494. [Google Scholar] [CrossRef]
  206. Panda, S.; Mehlawat, S.; Dhariwal, N.; Kumar, A.; Sanger, A. Comprehensive Review on Gas Sensors: Unveiling Recent Developments and Addressing Challenges. Mater. Sci. Eng. B 2024, 308, 117616. [Google Scholar] [CrossRef]
  207. Chowdhury, M.A.Z.; Oehlschlaeger, M.A. Artificial Intelligence in Gas Sensing: A Review. ACS Sens. 2025, 10, 1538–1563. [Google Scholar] [CrossRef] [PubMed]
  208. Narkhede, P.; Walambe, R.; Mandaokar, S.; Chandel, P.; Kotecha, K.; Ghinea, G. Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion. Appl. Syst. Innov. 2021, 4, 3. [Google Scholar] [CrossRef]
  209. Singh, S.; S, S.; Varma, P.; Sreelekha, G.; Adak, C.; Shukla, R.P.; Kamble, V.B. Metal Oxide-Based Gas Sensor Array for VOCs Determination in Complex Mixtures Using Machine Learning. Microchim. Acta 2024, 191, 196. [Google Scholar] [CrossRef]
  210. Peng, S.; Zhu, J.; Liu, Z.; Hu, B.; Wang, M.; Pu, S. Prediction of Ammonia Concentration in a Pig House Based on Machine Learning Models and Environmental Parameters. Animals 2022, 13, 165. [Google Scholar] [CrossRef]
  211. Li, X.; Guo, J.; Xu, W.; Cao, J. Optimization of the Mixed Gas Detection Method Based on Neural Network Algorithm. ACS Sens. 2023, 8, 822–828. [Google Scholar] [CrossRef]
  212. Ferrer-Cid, P.; Barcelo-Ordinas, J.M.; Garcia-Vidal, J.; Ripoll, A.; Viana, M. Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. IEEE Internet Things J. 2020, 7, 3124–3132. [Google Scholar] [CrossRef]
  213. Zhou, Z.; Chen, X.; Li, E.; Zeng, L.; Luo, K.; Zhang, J. Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing. Proc. IEEE 2019, 107, 1738–1762. [Google Scholar] [CrossRef]
  214. Nayak, S.; Misra, S.; Basu Majumder, S. Machine Learning-Based Edge Intelligence for Reliable Gas Monitoring Networks. IEEE Sens. J. 2025, 25, 35268–35277. [Google Scholar] [CrossRef]
Iot 06 00066 g001
Figure 1. PRISMA flow diagram.
Figure 1. PRISMA flow diagram.
Iot 06 00066 g001
Iot 06 00066 g002
Figure 2. Distribution of publications by (a) type and (b) publication year.
Figure 2. Distribution of publications by (a) type and (b) publication year.
Iot 06 00066 g002
Table 1. Inclusion and exclusion criteria used in the screening process.
Table 1. Inclusion and exclusion criteria used in the screening process.
Inclusion CriteriaExclusion Criteria
Publications from 2015–2025Duplicates
Research manuscripts published in journals or conference proceedingsBooks, book chapters, reviews, magazines, abstracts
Written in EnglishWritten in other languages
Within the scope of the research queriesOut of the scope of the research questions
Table 3. Microcontrollers used in ammonia detectors.
Table 3. Microcontrollers used in ammonia detectors.
Group
(Qty of References)		Microcontrollers
Identified		Native Wireless
(Qty of References)		References
Xtensa (Espressif)
(99)		ESP32: ESP-32 CAM, WROOM-32, Mappi 32, TTGO, Heltec Lora.
ESP 8266: Wemos D1 Mini, ESP 12E/F, NodeMCU.		Yes (99)[29,33,35,46,48,49,50,51,53,54,55,56,57,58,60,61,62,63,64,65,66,67,68,69,70,71,72,74,77,80,82,83,84,85,86,89,90,91,92,96,97,101,103,104,105,106,107,108,109,110,111,113,114,115,116,117,119,120,121,122,123,124,125,126,128,129,131,134,135,137,139,140,141,142,144,145,148,149,150,151,152,155,156,158,159,161,162,164,165,169,174,177,179,180,182,184,185,186,187,189]
ARM Cortex-M
(14)		STM32: STM32F103C8T6, STM32F103RCT6, STM32L431RCT6, F401RE, B-L072Z-LRWAN1.
GD32F303RGT6
Arduino Nano 33 BLE		No (11)
Yes (3)
B-L072Z-LRWAN1/Nano 33 BLE		[18,28,47,61,67,79,100,114,121,124,132,133,176,177]
AVR (8-bit)
(61)		ATmega328P: Arduino UNO/Nano.
ATmega2560 (Mega), ATmega1281, Arduino Pro Mini		No (61)[30,34,48,49,51,53,57,59,60,62,63,65,68,69,70,71,73,75,76,77,78,80,81,87,89,93,94,95,96,97,98,103,104,105,106,111,112,116,122,128,129,130,134,136,138,139,140,142,150,152,153,162,165,166,173,174,175,177,179,181,190]
ARM Cortex-A
(27)		Raspberry Pi 5/4/4B/3B+/2/Zero/pico W
NVIDIA Jetson AGX Xavier		Yes (15) (Raspberry Pi 3B+/4/5)
No (12)		[31,32,52,55,56,62,73,81,83,88,89,93,99,102,110,112,113,118,121,124,127,146,157,178,182,183,188]
MSP430 (16-bit)
(1)		MSP430G2553No (1)[86]
Hybrid/Special
(2)		Waspmote Gases PRO v3
Zolertia RE-Motes		Yes (2)[29,168]
N/A
(4)		 [143,170,171,172]
Table 4. Comparison of wireless technologies [197].
Table 4. Comparison of wireless technologies [197].
ParameterWi-Fi *
[197,198,199]		Bluetooth
[198]		ZigBee
[200]		LoRaWAN ** [200]2G (GSM/GPRS) *** [201]4G (LTE ****)
[202]
Range100 m10 m10–100 m5 km (Urban)
15 km (rural)		500 m–25 km15 km
Data Rate31.4 Mbps732 kbps20, 40, 250 Kbps250 bps-50 kbps64 kbps100 Mbps-1 Gbps
Operating Frequency2.4 GHz, 5 GHz2.4 GHz2.4 GHz, 868 MHz, 915 MHz868 MHz, 915 MHz, 430 MHz1.8 GHz2–8 GHz
* Wi-Fi: wireless fidelity; ** LoRaWAM: Long Range Wide Area Network; *** GSM/GPRS: GSM: Global System for Mobile communication/General Packet Radio Service; **** LTE: Long Term Evolution.
Table 5. Communication technologies used in ammonia detectors.
Table 5. Communication technologies used in ammonia detectors.
Communication Technology
(Qty of References)		References
Wi-Fi
(89)		[30,31,32,33,34,48,49,51,53,54,55,57,58,59,61,64,65,66,67,68,69,70,71,72,74,76,77,80,81,82,84,85,86,87,89,90,91,92,95,96,97,99,101,102,103,105,106,108,109,110,111,113,114,115,116,119,123,126,128,129,131,133,134,137,139,140,141,143,145,146,148,149,151,152,158,159,161,165,166,169,174,175,178,179,180,184,185,187,189]
Bluetooth
(7)		[28,75,79,100,144,156,188]
Lorawan
(4)		[120,132,136,182]
Zigbee
(1)		[29]
Cellular technologies 2G (GSM/GPRS) and 4G.
(7)		[98,104,130,153,168,176,183]
Cellular technologies 2G (GSM/GPRS) and 4G coupled to other technologies (Wi-Fi, zigbee, lorawan, bluetooth, ethernet)
(12)		Cellular technologies + Wi-Fi: [60,107,173,181,186]
Cellular technologies + zigbee: [138,170]
Cellular technologies + lorawan or RF868: [73,171,172]
Cellular technologies + lorawan + ethernet: [47]
Cellular technologies + Wi-Fi + bluetooth: [63]
Wi-Fi coupled to others (bluetooth, zigbee, lorawan, ethernet)
(5)		Wi-Fi + ethernet: [121]
Wi-Fi + bluetooth: [125,155]
Wi-Fi + lorawan: [124]
Wi-Fi + zigbee + ethernet: [62]
Ethernet
(2)		[78,94]
N/A
(21)		[35,46,50,52,56,83,88,93,112,117,118,122,127,135,142,150,157,162,164,177,190]
Table 6. IoT platforms used in ammonia detectors.
Table 6. IoT platforms used in ammonia detectors.
Category
(Qty of References)		ToolReferences
Cloud platform
(74)		Alibaba[61,114,176]
Aliyun[133]
AWS[73,106,155]
Firebase[30,53,81,84,101,107,127,178,187]
Losant[92]
Blynk[33,49,50,54,57,58,66,69,70,71,74,80,91,110,119,126,131,137,169]
Blynk/ThingSpeak[67,129,157]
Blynk/Thingspeak/AWS[105,111]
ThingSpeak[34,35,48,51,59,60,72,76,77,83,87,90,96,102,104,116,122,128,139,146,148,158,159,168,175,179,181]
VK Cloud[47]
Ubidot[134]
Google Cloud[62]
In.IoT[86]
Kaggle[135]
oneNET[185]
Self-hosted
(11)		Grafana[123]
Thingsboard[143,171,174,180]
NodeRed[32,99,124,132,145]
Thinger.io[165]
Other
(1)		GoDaddy[120]
N/A
(62)		 [28,29,31,46,52,55,56,63,64,68,75,78,79,82,85,88,89,93,94,95,97,98,100,103,108,109,112,113,115,117,118,121,125,130,136,138,140,141,142,144,148,149,150,151,152,153,156,161,162,164,166,170,172,173,177,182,183,184,186,188,189,190]
Table 7. Application of ammonia detectors.
Table 7. Application of ammonia detectors.
Category
(Qty of References)		ApplicationsReferences
Agriculture
(45)		Animal farming: chicken farming, poultry farming, rabbitry farming.
Agroindustry, onion farming, livestock odor.		[48,52,53,55,61,62,64,65,66,67,72,74,78,80,91,95,98,101,105,106,108,109,113,117,119,120,123,127,134,137,138,141,143,146,148,149,150,152,162,170,171,172,179,181,183]
Environmental
(46)		Air and water quality, odor pollution, landfill, sewage and biogas facilities, wastewater management, sewage treatment plant.[32,34,46,47,57,58,60,70,75,81,82,83,86,87,88,90,92,96,97,102,107,111,116,121,125,128,130,136,139,140,145,153,155,157,158,159,164,165,166,168,174,180,182,184,188,190]
Industrial Safety
(16)		Hazardous area, gas leakage, industrial ambient, mines, industrial sewage outlet.[28,29,50,59,73,76,77,79,84,94,100,103,110,112,115,132]
Aquaculture
(17)		Aquaculture, aquaponics.[51,71,89,93,104,122,131,135,142,151,173,175,178,185,186,187,189]
Smart Cities
(7)		Smart Trash Bin, smart room, manhole, sewers, Subsurface, gas drainage.[30,54,63,68,69,118,124]
Healthcare
(5)		Medicine, Animal laboratory, Space disinfection, Pet house[35,114,129,133,176]
Food Industry
(5)		Freshness food, Food industry.[49,56,85,144,169]
Research & Labs
(7)		Laboratory, quality assessment.[31,33,99,126,156,161,177]
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da Silva, A.H.M.C.; da Silva, M.K.; Santos, A.; Gómez-Malagón, L.A. A Systematic Review for Ammonia Monitoring Systems Based on the Internet of Things. IoT 2025, 6, 66. https://doi.org/10.3390/iot6040066

AMA Style

da Silva AHMC, da Silva MK, Santos A, Gómez-Malagón LA. A Systematic Review for Ammonia Monitoring Systems Based on the Internet of Things. IoT. 2025; 6(4):66. https://doi.org/10.3390/iot6040066

Chicago/Turabian Style

da Silva, Adriel Henrique Monte Claro, Mikaelle K. da Silva, Augusto Santos, and Luis Arturo Gómez-Malagón. 2025. "A Systematic Review for Ammonia Monitoring Systems Based on the Internet of Things" IoT 6, no. 4: 66. https://doi.org/10.3390/iot6040066

APA Style

da Silva, A. H. M. C., da Silva, M. K., Santos, A., & Gómez-Malagón, L. A. (2025). A Systematic Review for Ammonia Monitoring Systems Based on the Internet of Things. IoT, 6(4), 66. https://doi.org/10.3390/iot6040066

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