Open Source Internet Of Things Research Articles

Open-source Internet of Things (IoT)-based air pollution monitoring system with protective case for tropical environments

In recent years, the escalation of industrial activities has significantly increased natural resource pollution, with air pollution becoming a major cause of diseases affecting living organisms. To address this critical environmental challenge, this study proposes a comprehensive air pollution monitoring system utilizing advanced technological instruments based on the Internet of Things (IoT). The system’s primary objective is to provide precise, rapid, and efficient measurements, enabling detailed examinations of pollutant behaviors and facilitating data dissemination. The system includes a monitoring station equipped with sensors to measure ambient temperature, relative humidity, and concentrations of pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), suspended particles (PM2.5, PM10), and ozone (O3). Additionally, it captures meteorological variables like wind speed, wind direction, and precipitation, allowing a nuanced analysis of their correlation with air pollutants. The collected data are transmitted via the Internet and visualized on a user-friendly platform accessible from any internet-enabled device. A protective case, designed with SolidWorks CAD software and fabricated using 3D printing, was validated through simulations for extreme conditions to ensures the system’s robustness in tropical climates. The cost-effective, low-energy system offers a scalable solution for monitoring air pollution, advancing understanding of pollutant behaviors, and supporting environmental management.

RADAR-IoT: An Open-Source, Interoperable, and Extensible IoT Gateway Framework for Health Research.

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.

Smart Home Automation System

This project introduces the overall framework of an economical wireless home automation system (HAS). Its primary emphasis lies in establishing an Internet of Things (IoT)-centered home automation setup, capable of automatic configuration based on environmental conditions and capable of overseeing multiple devices remotely via the internet. The objective is to design firmware for intelligent control, ensuring automated functionality with minimal human intervention to uphold the well-being of all electrical appliances in the household. Node MCU, a widely recognized open-source IoT platform, has been employed for the automation process. Various transmission modes are implemented across distinct system components to relay user commands from Node MCU to the actual device. The central control system utilizes wireless technology, enabling remote accessibility via a smartphone. The incorporation of a cloud server-based communication system enhances project practicality, providing users unrestricted access to appliances, regardless of their spatial proximity. To fortify automation, a data transmission network has been established. The user-friendly interface, cost-effective construction, and straightforward installation make it an ideal solution for managing household electrical appliances and devices. Both control and appliance status can be monitored through an Android platform. The system's objective is to enhance the living conditions of elderly and disabled individuals by offering necessary support and assistance, ultimately elevating the overall home living experience with the concept of a smart home.

Advancing Healthcare Networks: Optimizing Multi-Objective RPL for Diverse Traffic in Low-Power, Lossy Environments

RPL: Powering modern healthcare IoT Networks. The Routing Protocol for Low Power and Lossy Networks(RPL) is a protocol specifically designed for routing in networks characterized by low power and lossy connections built on IPv6. It is specifically designed for the growing use of Instantaneous IoT (Internet of Things) applications. These networks cater to the specialized requirements of Low Power (resource-constrained) and Lossy Networks(unstable networks), also known as LLNs, where routing data smoothly and prioritizing traffic types are the significant challenges. Studies have shown that standard RPL, which makes use of a set of routing rules, struggles to provide the level of performance that many IoT applications in modern-day healthcare demand. This limits RPL’s potential in scenarios where we have a mix of data traffic, which is very common in healthcare settings. To handle the diverse data traffic found in healthcare settings, our method involves distributing several instances of RPL settings throughout the network. The main objective of this strategy is to improve the effectiveness of important and critical healthcare applications. We have developed and thoroughly tested our system using Contiki, which is an open-source IoT operating system. We have three instances in our MultiInstance RPL setup, each of which is intended to handle different kinds of network traffic and provide varying Quality of Service (QoS) measures. Different sets of Objective Function (OF) rules are used by each instance to decide which route is optimal for the data it processes. We have made use of OF0 (Objective Function 0) and MRHOF (Minimum Rank with Hysteresis Objective Function). We have rigorously tested our solution. Our focus has been on metrics like delay, average energy consumption and Packet Delivery Ratio (PDR). Compared to standard RPL, our approach improved packet delivery rates and significantly reduced delays for high priority data packets. This research shows how to better address the complex data traffic needs of real-time healthcare IoT networks.

Bloom’s IoT Taxonomy towards an effective Industry 4.0 education: Case study on Open-source IoT laboratory

In the rapidly evolving landscape of Industry 4.0, the demand for skilled professionals well-versed in Internet of Things (IoT) technologies is escalating. However, a significant challenge faced in educational settings is the lack of comprehensive and effective methodologies for imparting practical knowledge and skills in IoT. This paper presents an approach for designing and implementing an Internet of Things (IoT) laboratory in which students may practice and comprehend many components of an IoT environment, such as analysis of sensor data, IoT platform development, and setup of messaging protocols. The teaching methodology adopts a Cyber-Physical System (CPS) framework, which integrates teachers, classrooms, and resources to create a comprehensive learning environment. Bloom's taxonomy is employed to assess the efficacy of the suggested technique in terms of cognitive skills and knowledge acquisition. The evaluation procedure demonstrated the advantages of a hybrid learning environment, which integrated both face-to-face and remote instruction. The case study is conducted in an engineering higher education course on first year students. The findings of this paper lay the groundwork for a remotely deliverable IoT training course, contributing to the advancement of IoT education and equipping students with the necessary skills for the evolving landscape of Industry 4.0.

Cloud Assisted IoT-Operated Small Residential Microgrid with Collusive Model Approach for Energy Trading

This article explores a strategic approach for an energy-self-sufficient and economically sustainable small residential community, powered by rooftop solar photovoltaics. This community plays the role of a microgrid (MG) with a microgrid operator (MO). It operates in two distinct modes: within the MG (peer-to-peer or P2P) and with the utility grid. The P2P transfer is experimentally performed with open-source Internet of Things (IoT) applications from the cloud. This will enable a low-cost MG operation for developing countries. Residents from the community are elected considering their support towards community welfare, and they are considered as delegates. The MO with delegates, control the energy transfer operation. This approach distributes generated energy among community members at low prices if there is energy demand, minimizing carbon footprint. The action of selling energy by a prosumer during the need of fellow resident is considered as the token of social service towards the community. A social service counter (SSC) is chosen to identify services for each prosumer in the MG. When a seller sells energy within the community, the SSC increases. This count rewards the prosumer in several ways. MO only allows prosumers to participate in energy trading with the grid during the high-demand hours of the day. Delegates play an essential role in protecting the community's interest while selling energy outside the community. They try to form a coalition among participants to reduce installation costs and maximize the cumulative payoff. A comparative study between the proposed coordination game and two competitive game approaches, namely Cournot and Stackelberg's algorithm in the restricted domain, reveals that the proposed method is well suited for a small residential MG. Shapley value is a tool that identifies each delegate's contribution during the game. The paper employed this method for the overall coordination game to identify the most acceptable payoff for individual players.

IoT-AD: A Framework to Detect Anomalies Among Interconnected IoT Devices

In an Internet of Things (IoT) environment (e.g. smart home), several IoT devices may be available that are interconnected with each other. In such interconnected environments, a faulty or compromised IoT device could impact the operation of other IoT devices. In other words, anomalous behavior exhibited by an IoT device could propagate to other devices in an IoT environment. In this paper, we argue that mitigating the propagation of the anomalous behavior exhibited by a device to other devices is equally important to detecting this behavior in the first place. In line with this observation, we present a framework, called IoT Anomaly Detector (), that can not only detect the anomalous behavior of IoT devices, but also limit and recover from anomalous behavior that might have affected other devices. We implemented a prototype of, which we evaluated based on open-source IoT device datasets as well as through real-world deployment on a small-scale IoT testbed we have built. We have further evaluated in comparison to prior relevant approaches. Our evaluation results show that can identify anomalous behavior of IoT devices in less than 2.12 milliseconds and with up to 98% of accuracy.

Development of a Low-Cost, Open-Source LoRA-based SCADA System for Remote Monitoring of a Hybrid Power System for an Offshore Aquaculture Site in Newfoundland

In this article a low-cost and open-source Internet of Things (IoT) based Supervisory, Control and Data Acquisition (SCADA) system for remote monitoring of the hybrid power system for an offshore aquaculture site is presented. The selected site is situated 2 km away from the coastline where there is no electrical utility infrastructure and limited communication options are available. The hardware of the designed system primarily consists of six field sensors, Arduino Leonardo as Remote Terminal Unit (RTU), LoRA (Long Range) gateway, cables, AC/DC current and voltage supplies. Arduino IDE, AWS, Influx DB, and Grafana provide the software support. The field sensors are responsible for measuring the solar, battery, inverter & generator currents, along with battery voltage and temperature. All of the field sensors except the temperature sensor send the data to RTU which further delivers it to The Things Network (TTN) cloud. With the help of influx DB, AWS cloud computing services, and Grafana, the data can be stored and visualized through interactive yet informative graphs. The graphs display the historical and live data of each sensor. Further, it also gives the option to set alarms and alerts on user-defined conditions to improve control over the hybrid power system. The complete hardware is assembled and tested in Memorial University’s Power lab. The developed system was supplied with variable current/voltage supplies and the data was logged for three continuous hours. However, the data can be stored for a much longer duration as per user’s requirement. The hardware and the results presented here are a testament that the proposed design system is capable of providing a remote monitoring solution for the offshore aquaculture site.

Autonomous open-source electric wheelchair platform with internet-of-things and proportional-integral-derivative control

<span lang="EN-US">This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers.</span>

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.

Designing and implementation of internet of things (IoT) on physico‐biological treatment of wastewater

AbstractThe use of Internet of Things (IoT) in industries is not new; however, the amalgamation of IoT and Biotechnology is still less explored. IoT in wastewater treatment comes with a full suite including cloud and its corresponding ecosystem. The available cloud‐based systems are advantageous, but costly and tightly coupled to their own cloud infrastructure. Opportunities are limited for the creation of a hybrid/multiple cloud model as it's difficult to integrate with the legacy system(s) and/or customized systems. The current investigation focuses on designing architechture of an open source IoT platform on an effluent treatment plant (ETP) protoype of textile industry. The designed prototype includes physical (adsorption) and biological (Rotating Biological Contactor, RBC, immobilized with white rot fungus) processes. IoT has been used for automated data monitoring of pH and ultrasound (for effluent flow from one tank to another, with respect to the water level, for processing). The process operated under an optimized pH (6.53±0.15) with reduction of representative wastewater quality parameters, biological & chemical oxygen demand, total dissolved solids, total suspended solids and optical density by 78.9, 52.15, 91.6, 20.3 and 88.8%, respectively. The findings demonstrated the reliability of the designed system and the analytics on treatment facilities.

AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain

Every aspect of the 21st century has undergone a revolution because of the Internet of Things (IoT) and smart computing technologies. These technologies are applied in many different ways, from monitoring the state of crops and the moisture level of the soil in real-time to using drones to help with chores such as spraying pesticides. The extensive integration of both recent IT and conventional agriculture has brought in the phase of agriculture 4.0, often known as smart agriculture. Agriculture intelligence and automation are addressed by smart agriculture. However, with the advancement of agriculture brought about by recent digital technology, information security challenges cannot be overlooked. The article begins by providing an overview of the development of agriculture 4.0 with pros and cons. This study focused on layered architectural design, identified security issues, and presented security demands and upcoming prospects. In addition to that, we propose a security architectural framework for agriculture 4.0 that combines blockchain technology, fog computing, and software-defined networking. The suggested framework combines Ethereum blockchain and software-defined networking technologies on an open-source IoT platform. It is then tested with three different cases under a DDoS attack. The results of the performance analysis show that overall, the proposed security framework has performed well.

Security Evaluation of Arduino Projects Developed by Hobbyist IoT Programmers

Arduino is an open-source electronics platform based on cheap hardware and the easy-to-use software Integrated Development Environment (IDE). Nowadays, because of its open-source nature and its simple and accessible user experience, Arduino is ubiquitous and used among hobbyist and novice programmers for Do It Yourself (DIY) projects, especially in the Internet of Things (IoT) domain. Unfortunately, such diffusion comes with a price. Many developers start working on this platform without having a deep knowledge of the leading security concepts in Information and Communication Technologies (ICT). Their applications, often publicly available on GitHub (or other code-sharing platforms), can be taken as examples by other developers or downloaded and used by non-expert users, spreading these issues in other projects. For these reasons, this paper aims at understanding the current landscape by analyzing a set of open-source DIY IoT projects and looking for potential security issues. Furthermore, the paper classifies those issues according to the proper security category. This study's results offer a deeper understanding of the security concerns in Arduino projects created by hobbyist programmers and the dangers that may be faced by those who use these projects.

Monitoring and Control Framework for IoT, Implemented for Smart Agriculture.

To mitigate the effects of the lack of IoT standardization, including scalability, reusability, and interoperability, we propose a domain-agnostic monitoring and control framework (MCF) for the design and implementation of Internet of Things (IoT) systems. We created building blocks for the layers of the five-layer IoT architecture and built the MCF's subsystems (monitoring subsystem, control subsystem, and computing subsystem). We demonstrated the utilization of MCF in a real-world use-case in smart agriculture, using off-the-shelf sensors and actuators and an open-source code. As a user guide, we discuss the necessary considerations for each subsystem and evaluate our framework in terms of its scalability, reusability, and interoperability (issues that are often overlooked during development). Aside from the freedom to choose the hardware used to build complete open-source IoT solutions, the MCF use-case was less expensive, as revealed by a cost analysis that compared the cost of implementing the system using the MCF to obtain commercial solutions. Our MCF is shown to cost up to 20 times less than normal solutions, while serving its purpose. We believe that the MCF eliminated the domain restriction found in many IoT frameworks and serves as a first step toward IoT standardization. Our framework was shown to be stable in real-world applications, with the code not incurring a significant increase in power utilization, and could be operated using common rechargeable batteries and a solar panel. In fact, our code consumed so little power that the usual amount of energy was two times higher than what is necessary to keep the batteries full. We also show that the data provided by our framework are reliable through the use of multiple different sensors operating in parallel and sending similar data at a stable rate, without significant differences between the readings. Lastly, the elements of our framework can exchange data in a stable way with very few package losses, being able to read over 1.5 million data points in the course of three months.

Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis

Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitoring the environment. Such systems have opened up environment monitoring opportunities to citizens while simultaneously confronting them with challenges related to sensor accuracy and the accumulation of large data sets. Analyzing and interpreting sensor data itself is a formidable task that requires extensive computational resources and expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is presented, which combines a real-time environmental sensing device with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit—a resource-efficient, portable and modular device designed and evaluated for indoor and outdoor environmental monitoring, and (2) exploreR—an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without the need for coding. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by lowering technological barriers.

Development and performance analysis of a ZigBee and LoRa-based smart building sensor network

A wireless sensor network employing ZigBee and LoRa (Long Range) communication protocols for integration into smart building energy management systems is presented in this article. The design and implementation details are provided, and the performance parameters of the communication network are defined and analyzed based on the test results obtained from different configurations. The developed embedded system can be used in smart environments so that the room temperature, humidity, lighting systems, and so on can be automatically monitored and controlled. By customizing the embedded code, a variety of Internet of Things (IoT) applications can be introduced owing to their scalability. Taking advantage of the complementing low-power and long-range features of ZigBee and LoRa communication technologies, a system comprising an end device, a multi-protocol gateway, and a central data collector (CDC) unit is developed. The end device collects temperature and humidity as well as light intensity data using low-power sensors and sends the data to the gateway via the LoRa wireless transceiver module. The gateway was designed as an intermediate device that allows data exchange between the LoRa and ZigBee transceiver modules. It receives sensor data from the end device via LoRa and sends them to the CDC unit via a ZigBee-based XBee S2 commercial wireless transceiver module. Sensor data are monitored in the CDC unit by using an open-source IoT software platform. A commercial STM32 integrated circuit (IC) was used as a microcontroller unit for the end device and gateway. Performance parameters such as communication range and throughput data were studied for both the ZigBee and LoRa wireless transceiver modules.

Open-source Internet of Things remote aquatic environmental sensing

This paper details the physical and hardware design of a flexible open-source IoT (Internet of Things) platform for environmental sensing. The application is a remote water quality monitoring buoy that can be deployed in calm, shallow near-shore aquatic environments with fresh or brackish water. The system’s development has been informed by experience through conducting multiple actual water quality studies over a prolonged period. The system runs an Arduino Mega 2560 microcontroller using off-the-shelf Adafruit lux and temperature sensors. A light attenuation turbidity sensor is adapted and integrated into the design. A TinySine 3G GSM module transmits data to a server that is displayed via a ThingsBoard IoT dashboard. The system is stable over time, provides reliable remote sensor readings, has low energy consumption, and is powered by renewable energy (up-cycled batteries). The hardware aspires to be general-purpose so that future environmental monitoring applications can repurpose the electronics by adding new compatible sensors and modifying the physical design to match the requirements.

Over-the-air firmware update for IoT devices on the wild

Internet of Things (IoT) has been gaining a lot of attention in the last few years and despite the large amount of investments, there is still much to be developed and refined. Software and firmware updates over-the-air (OTA) are a crucial part for IoT solutions longevity. This paper presents the design and implementation of an OTA firmware update method based on the standard architecture recently proposed by the Internet Engineering Task Force (IETF). The proposed solution is evaluated through a testbed using 20 constrained IoT devices and an open source IoT cloud platform, as well as on a set of devices deployed in a real world application. Results show that the proposed solution is suitable for constrained devices and has little impact on the network traffic.

Development of a Wireless Sensor Network and IoT-based Smart Irrigation System

This study proposes a smart irrigation system (SIS) using the drip method, which was designed and implemented using wireless sensor networks and an open-source Internet of Things (IoT) cloud computing platform (“Thingspeak.com”) for data collection, storing, data analytics, and visualization. The methodology incorporates the integration of hardware and software components to make irrigation decisions based on web resources like the weather forecast from “weather.com” and sensor values from soil samples. The data collected are then analyzed at the edge server and updated every 15 minutes. Based on the threshold value, the system starts pumping water or stops the irrigation process depending on the irrigation schedule. A web application was developed to display the result so that we could monitor and control the system using an android application edge or a web browser. Based on the data recorded and measured, the data are in comma-separated values (CSV) format and contain 143731 entries with 10 columns. The sample size used contains 5722 rows and 6 columns from the result of our machine learning algorithms using Microsoft Excel and Jupyter Notebook to process and evaluate the performance of the drip SIS, considering the soil moisture, soil temperature, sunlight, rain, and pump. The results confirm the threshold metrics classification evaluation, and some of the metrics computed from our confusion matrix are shown in the classification summary results, showing the accuracy to be 89%, the misclassification rate (error rate) is equal to 10%, the sensitivity is equal to 79%, the specificity is equal to 93%, and the precision of the model is 81%. The evaluation, when compared to K-nearest neighbours using K = 6 and K = 1, shows the prediction accuracy to be 97% and 98%. The results indicate the system is highly efficient and reliable in performing irrigation and managing water resources and can be adopted in rural areas to boost agricultural productivity.

An Accurate Detection Approach for IoT Botnet Attacks Using Interpolation Reasoning Method

Nowadays, the rapid growth of technology delivers many new concepts and notations that aim to increase the efficiency and comfort of human life. One of these techniques is the Internet of Things (IoT). The IoT has been used to achieve efficient operation management, cost-effective operations, better business opportunities, etc. However, there are many challenges facing implementing an IoT smart environment. The most critical challenge is protecting the IoT smart environment from different attacks. The IoT Botnet attacks are considered a serious challenge. The danger of this attack lies in that it could be used for several threatening commands. Therefore, the Botnet attacks could be implemented to perform the DDoS attacks, phishing attacks, spamming, and other attack scenarios. This paper has introduced a detection approach against the IoT Botnet attacks using the interpolation reasoning method. The suggested detection approach was implemented using the interpolation reasoning method instead of the classical reasoning methods to handle the knowledge base issues and reduce the size of the detection fuzzy rules. The suggested detection approach was designed, tested, and evaluated using an open-source benchmark IoT Botnet attacks dataset. The implemented experiments show that the suggested detection approach was able to detect the IoT Botnet attacks effectively with a 96.4% detection rate. Furthermore, the obtained results were compared with other literature results; the accomplished comparison showed that the suggested method is a rivalry with other methods, and it effectively reduced the false positive rate and interpolated the IoT Botnet attacks alerts even in case of a sparse rule base.