Low-cost Internet Of Things Research Articles

Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms

Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms

Applications of machine learning & Internet of Things for outdoor air pollution monitoring and prediction: A systematic literature review

According to the World Health Organization (WHO), air pollution kills seven million people every year. Outdoor air pollution is a major environmental health problem affecting low, middle, and high-income countries. In the past few years, the research community has explored IoT-enabled machine learning applications for outdoor air pollution prediction. The general objective of this paper is to systematically review applications of machine learning and Internet of Things (IoT) for outdoor air pollution prediction and the combination of monitoring sensors and input features used. Two research questions were formulated for this review. 1086 publications were collected in the initial PRISMA stage. After the screening and eligibility phases, 37 papers were selected for inclusion. A cost-based analysis was conducted on the findings to highlight high-cost monitoring, low-cost IoT and hybrid enabled prediction. Three methods of prediction were identified: time series, feature-based and spatio-temporal. This review’s findings identify major limitations in applications found in the literature, namely lack of coverage, lack of diversity of data and lack of inclusion of context-specific features. This review proposes directions for future research and underlines practical implications in healthcare, urban planning, global synergy and smart cities.

An Eco-Friendly and Low Cost IoT based Room Temperature Control by Fan Speed Regulation for Tropical Use

This work presents an Internet of Things (IoT) based room temperature monitoring and control system by fan speed regulation developed for use in rooms of tropical regions of West Africa. In this work, cutting-edge technologies were integrated, including IoT, and cloud- based monitoring to create a system capable of dynamically remote controlling fan speed based on real- time temperature data. The Dallas DS18B20 Waterproof Temperature Sensor serves as the cornerstone for accurate temperature monitoring. A Microcontroller (Node MCU ESP8266) with Wi-Fi Module facilitates IoT connectivity, allowing users to remotely monitor and control the system through the Blynk Cloud and the Blynk App. A 12V DC table fan, driven by a MOSFET which is being controlled through Pulse Width Modulation (PWM) by the microcontroller, enables fine- tuned speed adjustments. A 16x2 LCD display provides real-time feedback on current temperature and fan speed percentage, enhancing user awareness. The microcontroller programming involves the implementation of an adaptive algorithm for dynamic fan speed control based on the room temperature, user settings and some preset parameters conditioned for tropical region of West Africa. By dynamically adjusting fan speed based on real-time temperature data, the system optimizes energy consumption, providing sustainable and eco-friendly solutions. The circuit was designed and simulated in Proteus software, the code was written in Arduino IDE, tested on breadboard, implemented finally on Veroboard and all fitted inside a suitable box. After testing the system, it worked as expected and it was observed that the fan speed increases as the room temperature increases and vice versa. Also, the fan speed depends both on the room temperature and the set threshold value. It was also observed that the speed of the fan is at its maximum when the temperature is above 40°C. Thus, the developed system is good for room temperature control in the tropical region of West Africa.

Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science.

Real-time air quality monitoring based on locally developed unmanned aerial vehicle and low-cost smart electronic device

This article deals with the development of real-time air quality monitoring based on Unmanned Aerial Vehicles (UAVs) and low-cost Internet of Things (IoT) devices. This investigation aims to design and develop an UAV-based platform that can monitor a large number of air pollutants in real-time with high spatial and temporal resolution. The proposed environmental monitoring system consists of five main elements, namely the UAV, sensors, data storage module, programmable card, and IoT communication module. Estimated pollutants such as particulate matter (PM2.5) and toxic gases (carbon monoxide CO, nitrogen dioxide NO2, and carbon dioxide CO2) are detected by low-cost sensors. The ZigBee wireless protocol is used for communication between the PC and UAV. This work is carried out to assess the air quality in urban areas, given the heavy road traffic and the emissions of some companies. The data analyzed were collected from December 2, 2022 to January 3, 2023, in two major cities of Cameroon, Douala and Kribi. The periodic average values of the detected pollutants are 222 ± 22 μg/m3 and 85.7 ± 8.6 μg/m3 for PM2.5, 560.8 ± 1.0 ppm and 555.6 ± 1.0 ppm for CO2, 4.2 ± 0.2 ppm and 0.7 ± 0.1 ppm for NO2, and 27.6 ± 2.8 ppm and 4.5 ± 0.5 ppm for CO in Douala and Kribi respectively. This made it possible to have an air quality index (AQI) of 444.2 for Douala City and 171.3 for Kribi City. These high values indicate poor air quality during the measurement period.

Agricultural transformation: A story about the Internet of Things

Precision agriculture, a sustainable solution for enhancing crop productivity, utilizes enabling technologies, primarily centred around the Internet of Things (IoT), to optimize irrigation and increase crop yields by adapting farming techniques to specific conditions. This study presents the adoption of a low-cost and eco-friendly IoT initiative in rural farms in Vietnam, which is an unexpected location. It marks the country’s first IoT start-up for agriculture during the 4.0 Industrial Revolution. To answer three research questions, we conducted a contemporary case study mixed with a couple of field experiments in a real-life context to learn how an IoT application was blended with local culture to transform traditional agriculture to a modern and digitalized industry. The pilot implementation yielded promising results, with positive feedback from farmers. Following necessary modifications, the system has been widely adopted across provinces in Vietnam. The study identified three key factors contributing to the start-up’s success: ‘Thiên thời, Địa lợi, Nhân hòa’. We believe that the start-up can assist millions of local farmers to get on the 4.0 train and that adopting IoT to initiate smart agriculture in Vietnam has sent a strong message ‘To be successful, global technology has to integrate into local culture’.

Enhancing Trust and Security in IoT Architecture for Low-Cost Microcontroller Devices using Elliptic Curve Cryptography

Abstract—In the rapidly expanding universe of the Internet of Things (IoT), low-cost microcontroller devices, such as the NodeMCU, have become ubiquitous. While these devices have transformed how we interact with the physical world, they also introduce significant security vulnerabilities. Chief among these is susceptibility to man-in-the-middle (MITM) attacks, which pose a serious risk to the confidentiality and integrity of data transmitted across these networks. This study addresses this critical issue by proposing the adoption of Elliptic Curve Cryptography (ECC), a cryptographic method known for its efficiency and strong security, despite the computational limitations of low-cost IoT devices. We conducted an experiment demonstrating the ease with which an MITM attack can intercept data from a NodeMCU device. Following this, we implemented ECC to secure data transmission, showcasing its viability as a lightweight yet robust security solution. Our research not only highlights the pressing need for enhanced security measures in the IoT ecosystem but also provides a practical framework for securing low-cost microcontroller devices against sophisticated cyber threats. Through our findings, we contribute to the development of more secure, trustworthy IoT architectures, ensuring that these devices can continue to safely serve as integral components of our digital lives. Keywords—NodeMCU, ESP8266, Data theft, Elliptic Curve Cryptography, Man-in-the-middle attack

Low-Cost Internet of Things Solution for Building Information Modeling Level 3B—Monitoring, Analysis and Management

The integration of the Internet of Things (IoT) and Building Information Modeling (BIM) is progressing. The use of microcontrollers and sensors in buildings is described as a level 3B maturity in the use of BIM. Design companies, contractors and building operators can use IoT solutions to monitor, analyze or manage processes. As a rule, solutions based on original Arduino boards are quite an expensive investment. The aim of this research was to find a low-cost IoT solution for monitoring, analysis and management, and integrate it with a BIM model. In the present study, an inexpensive NodeMCU microcontroller and a temperature and pressure sensor were used to study the thermal comfort of users in a single-family home. During the summer season, analysis of the monitored temperature can contribute to installation (HVAC) or retrofit work (for energy efficiency). The article presents a low-cost solution for studying the thermal comfort of users using a digital twin built-in BIM. Data obtained from sensors can support both the design and management processes. The main contribution of the article enables the design, construction and use of low-cost circuits (15.57 USD) even in small developments (single-family houses, semi-detached houses, terraced houses, atrium buildings). Combining IoT sensor telemetry with BIM (maturity level 3C) is a challenge that organizations will face in the near future.

A low-cost IoT solution for power availability improvement in hospitals.

Power is a crucial element in hospitals whether it's within the intensive care units, surgical bays, or medical scanning rooms. Without reliable power, patients’ lives are at risk. The power availability improvement is therefore a crucial point in healthcare. A supervising system superimposed to the main components of the power system as able to promptly signal anomalies or malfunctioning or outages can be very useful for this task. To this aim, an Internet of Things (IoT) based solution has been implemented that allows equip at low cost the various electrical components in the distribution MV/LV substations of the hospital’s power system so as to be connected to a central supervising system. The solution implements a platform based on Rapsberry pi 3.

An IoT-Based Prototype for Optimizing Agricultural Irrigation: A Case Study in the Biobío Region of Chile

This paper shows an agricultural irrigation alternative using a low-cost Internet of Things (IoT) application. This technology allows the automation and optimization of processes by monitoring, processing, and analyzing large volumes of related data, including variables related to the soil (moisture), fertilization, and irrigation, which can be visualized through the internet to enable sustainable and efficient agriculture. The study is centered on the Los Angeles, Biobío, Chile case. The main objective of the prototype is to optimize water consumption for agricultural irrigation by using only the amount that is strictly necessary for each crop by allowing real-time visualization of irrigation status, moisture data, and irrigation times while providing a history of data that can be used for further analysis.

An AI-Enabled Framework to Defend Ingenious MDT-Based Attacks on the Emerging Zero Touch Cellular Networks

Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON, or zero touch automation does not address this important problem. In this paper, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use-case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks.

A Model-agnostic XAI Approach for Developing Low-cost IoT Intrusion Detection Dataset

This study tackles the significant challenge of generating low-cost intrusion detection datasets for Internet of Things (IoT) camera devices, particularly for financially limited organizations. Traditional datasets often depend on costly cameras, posing accessibility issues. Addressing this, a new dataset was developed, tailored for low-cost IoT devices, focusing on essential features. The research employed an Entry/Exit IoT Network at CKT-UTAS, Navrongo, a Ghanaian University, showcasing a feasible model for similar organizations. The study gathered location and other vital features from low-cost cameras and a standard dataset. Using the XGBoost machine learning algorithm, the effectiveness of this approach for cybersecurity enhancement was demonstrated. The implementation included a model-agnostic eXplainable AI (XAI) technique, employing Shapley Additive Explanations (SHAP) values to interpret the XGBoost model's predictions. This highlighted the significance of cost-effective features like Flow Duration, Total Forward Packets, and Total Length Forward Packet, in addition to location data. These features were crucial for intrusion detection using the new IoT dataset. Training a deep-learning model with only these features maintained comparable accuracy to using the full dataset, validating the practicality and efficiency of the approach in real-world scenarios.

Correction: Internet of Things (IoT) for Smart Agriculture: Assembling and assessment of a low-cost IoT system for polytunnels

Correction: Internet of Things (IoT) for Smart Agriculture: Assembling and assessment of a low-cost IoT system for polytunnels

Overview about four different types of MQ sensors typically applied to IoT devices leak detection to flammable gases as a gas liquefied petroleum that is used on Brazilian kitchens

Considering the current scenario increasingly conducive to the use of technologies like the Internet of Things (IoT), and the growing change in the expansion of the use of connected networks and aiming to meet in the problem of LPG leak safety in the household, at places that are not have regulations that require the use sensors, both in the public and private sectors, that can mitigate operational risks in the use of flammable gases such as LPG (Liquefied Petroleum Gas) which is the most used gas for cooking food or in water heating in Brazil. This study seeks to present the consolidation of results on pre-liminary tests with four types of sensors that detect hydrocarbons and determine which of them may be more promising in the use of LPG detection, which may in the future be applied to a low-cost and Open Source IoT device.

An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment.

Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.

Software-Based Security Approach for Networked Embedded Devices

As the Internet of Things (IoT) continues to expand, data security has become increasingly important for ensuring privacy and safety, especially given the sensitive and, sometimes, critical nature of the data handled by IoT devices. There exist hardware-based trusted execution environments used to protect data, but they are not compatible with low-cost devices that lack hardware-assisted security features. The research in this paper presents software-based protection and encryption mechanisms explicitly designed for embedded devices. The proposed architecture consists of two parts: the Agent, which is designed to work with low-cost, low-end devices without requiring modifications to the underlying hardware, and the Computing Module, which is designed for slightly more computationally powerful devices. The Computing Module enables devices to write data in protected memory and continuously verifies its integrity to provide protection. Additionally, it utilizes the Agents located on the device to safeguard device applications against attacks by requesting the Agent to generate an application code signature and validating it. The proposed solution is an alternative data security approach for low-cost IoT devices without compromising performance or functionality. Our work underscores the importance of developing secure and cost-effective solutions for protecting data in the context of IoT.

An Intelligent Water Monitoring IoT System for Ecological Environment and Smart Cities.

Global precipitation is becoming increasingly intense due to the extreme climate. Therefore, creating new technology to manage water resources is crucial. To create a sustainable urban and ecological environment, a water level and water quality control system implementing artificial intelligence is presented in this research. The proposed smart monitoring system consists of four sensors (two different liquid level sensors, a turbidity and pH sensor, and a water oxygen sensor), a control module (an MCU, a motor, a pump, and a drain), and a power and communication system (a solar panel, a battery, and a wireless communication module). The system focuses on low-cost Internet of Things (IoT) devices along with low power consumption and high precision. This proposal collects rainfall from the preceding 10 years in the application region as well as the region's meteorological bureau's weekly weather report and uses artificial intelligence to compute the appropriate water level. More importantly, the adoption of dynamic adjustment systems can reserve and modify water resources in the application region more efficiently. Compared to existing technologies, the measurement approach utilized in this study not only achieves cost savings exceeding 60% but also enhances water level measurement accuracy by over 15% through the successful implementation of water level calibration decisions utilizing multiple distinct sensors. Of greater significance, the dynamic adjustment systems proposed in this research offer the potential for conserving water resources by more than 15% in an effective manner. As a result, the adoption of this technology may efficiently reserve and distribute water resources for smart cities as well as reduce substantial losses caused by anomalous water resources, such as floods, droughts, and ecological concerns.

Arduino IOT Based Inventory Management System Using Load Cell and NodeMCU

Nowadays, everything is made simpler with information and communication technological advancements. It is preferable to track and monitor using devices rather than perform it manually. This resulted in the rapid growth of Internet of Things (IoT) technology and relevant markets. Low cost IoT products has made access to IoT much easier and desirable. These low cost IoT devices and related technologies are widely used in areas such as educational, transportation, tracking, inventory management and many more. The use of Arduino and RFID in the inventory management system lacks in some areas including hardware limitations. In conjunction to the limitation of using an Arduino and RFID technology, this project aims to develop an IoT based inventory management system that incorporates the uses of a NodeMCU and a load cell. In comparison of the NodeMCU to an Arduino, the NodeMCU stands out with the built in Wi-Fi module with much higher processor and additional properties of it being much smaller. While the use of a load cell is much more convenient as to suit all kinds of inventory management needs compared to the use of RFID that suits better to larger scale businesses with larger inventory and massive stocks. Towards the end, this project is expected to ease inventory management by the implementation of IoT with IoT Based Inventory Management System using Load Cell and NodeMCU. The project will generate the inventory count and automatically stores data in the cloud platform. These data can be accessed with internet connection. The project also alerts users when the inventory is low or high in balance. The output of the project is that the project’s working prototype was successfully developed. Overall, the project is a success as all the objectives of the project was successfully achieved.

Prediction of Air Pollution Index Using Internet of Things and Low-Cost Sensor Measurements: A Case Study in Shah Alam, Malaysia

Background: The Air Pollutant Index (API) in Malaysia is determined by calculating sub-indices for the six main pollutants: particulate matter (PM10 and PM2.5), ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2), and nitrogen dioxide (NO2), based on the possible health implications to the public. The study focuses on the UiTM Shah Alam Internet of Things (IoT) monitoring station in Selangor, which is an urban area. Method: Data was retrieved from low-cost IoT sensors, containing datasets from February 2022 to June 2022. The study aims to develop a predictive model using a Machine-Learning approach to predict air pollutant concentrations for the following day. Results: The comparison of the three models reveals that Random Forest had the best predictive models for PM10 concentration, with root-mean-square error (RMSE) values between 10.88 and 18.15, absolute error values between 8.03 and 11.39, and relative error values between 29.67 and 31.51. The RMSE, absolute error, and relative error for SO2 were (0.26-0.39), (0.11-0.26), and (50.11%- 84.64%), respectively. The absolute error (0.003–0.004), relative error (20.83%–24.52%), and RMSE (0.004–0.005) for NO2 were measured. For CO, the relative error (26.01%-42.34%), absolute error (0.147-0.250), and RMSE (0.259-0.468) were all within allowable bounds. The O3 RMSE, absolute, and relative errors were (0.003–0.005), (0.0005-0.00006), and (26.17%–33.10%), respectively. The results of the concentration prediction for PM2.5 were as follows: RMSE: (16.65 - 26.83), absolute error (10.15 - 14.29) and relative error (31.43% - 33.09%). Conclusion: Based on the results, the study shows that PM2.5 is a significant pollutant, representing the API.

Garbage Segregation and Monitoring Using Low-Cost IoT System for Smart Waste Management

Waste management is one of the serious issues in maintaining the longevity of life. Managing waste in garbage bins not only requires a systematic garbage segregation policy but also a prevention and control mechanism for human behaviour. With the aid of the low-cost-budget Internet of Things (IoT) approach, managing garbage bins for municipal and town councils become practical, effective, and easy to be enforced. This study proposed a proof-of-concept prototype of a low-cost IoT approach using Raspberry Pi and cheap sensor modules to segregate waste into garbage bins with local alarming and remote notification systems. The systems are capable of detecting metal and non-metal waste so that segregation can be applied, and measuring waste levels in the garbage bin to prevent waste overflow. On top of that, the systems are also able to warn with loudness sound of a mistype waste scanned at the bin lid and send prompt notifications to the remote observer for further actions. The study results from a number of performance testing demonstrated the developed systems can accurately measure waste levels either to be empty, half-filled, or 80% filled when the ultrasonic ranging module sensor is placed 105° inside the bin. This is the best positioning we recommended when working with a bin of size 15 cm in height and 10 cm in width. Finally, we observed that the finest detection results were achieved when the movement of objects are scanned less than 2 cm, but not more than 4 cm away.