Developing Internet Of Things Systems Research Articles

Power Monitoring System Using The Internet of Things for Photovoltaic Powered Fertigation System

This research paper explains the concept of photovoltaic (PV) systems. Among renewable energy technologies, PV systems rank third after hydropower and wind in terms of usage. They find significant application in agricultural activities, contributing to income generation for daily livelihoods. However, certain plant species demand meticulous attention to achieve optimal efficiency, necessitating a larger workforce for their care. By incorporating the Internet of Things (IoT), users gain the ability to remotely monitor PV system performance through a dedicated application. The central goal of this project involves crafting a simulation of an IoT system. This system is intended for monitoring the functionality of PV systems and devising an IoT-based model for overseeing the power aspects of PV systems in conjunction with fertigation systems (watering plants with nutrient solutions). A key feature of this system is its capacity to gauge the IoT model's effectiveness by contrasting outcomes derived from hands-on observation and simulated scenarios. The outcomes of this study reveal thatthe developed IoT system effectively tracks real-time parameters like current, voltage, generated power, and solar PV temperature. It successfully establishes a link between users and the IoT infrastructure for system monitoring. Looking ahead, the gathered PV output data will be instantly accessible on mobile devices. This empowers users to assess the performance of their PV systems concerning fertigation.

Development of Observation System Using Internet of Things for Growth Improvement in Indoor Farming of Apium Graveolens Var. Secalinum Alef

This research was performed to develop an Internet of Things (IoT) monitoring system for growth condition of Apium Graveolens var. Secalinum Alef, then to determine the optimised lighting environment to optimise growth of Apium Graveolens var. Secalinum Alef. The experiment was conducted for 42 days, and the plants were grown hydroponically. Various data required to monitor the plants closely was collected daily using the developed IoT system and the data was relayed to the researcher using IoT. Based on the data collected from the experiment it is determined that the plants grown using the LED lighting system yielded more harvest compared to the plants grown using normal sunlight. The significant of this research is an IoT system to continuously monitor plant growth condition using an internet connected mobile device was developed, allowing farmers to obtain real time data of the growth conditions on their mobile devices, and it was determined that Apium Graveolens var. Secalinum Alef cultivation under LED artificial lighting performed better as compared to normal sunlight. The application of this IoT system can help farmers to lower their overhead costs and increase yields.

A Security Framework for Increasing Data and Device Integrity in Internet of Things Systems.

The trustworthiness of a system is not just about proving the identity or integrity of the hardware but also extends to the data, control, and management planes of communication between devices and the software they are running. This trust in data and device integrity is desirable for Internet of Things (IoT) systems, especially in critical environments. In this study, we developed a security framework, IoTAttest, for building IoT systems that leverage the Trusted Platform Module 2.0 and remote attestation technologies to enable the establishment of IoT devices' collected data and control plan traffic integrity. After presenting the features and reference architecture of IoTAttest, we evaluated the privacy preservation and validity through the implementation of two proof-of-concept IoT applications that were designed by two teams of university students based on the reference architecture. After the development, the developers answered open questions regarding their experience and perceptions of the framework's usability, limitations, scalability, extensibility, potential, and security. The results indicate that IoTAttest can be used to develop IoT systems with effective attestation to achieve device and data integrity. The proof-of-concept solutions' outcomes illustrate the functionalities and performance of the IoT framework. The feedback from the proof-of-concept developers affirms that they perceived the framework as usable, scalable, extensible, and secure.

Development of IoT-Based Real-Time Monitoring System and LFA to Improve the Efficiency and Performance of Wastewater Treatment Plant in Udayana University Hospital

Indonesia is one of the countries infected by the Coronavirus Disease 2019 (COVID-19) pandemic, which is caused by acute respiratory syndrome virus 2 (SARS-CoV-2). At the end of March 2020, the provincial government of Bali appointed Udayana University Hospital to handle COVID-19 patients because the province has experienced an increase in the number of positive cases. In September 2020, COVID-19 cases in Bali increased by more than 100%, resulting in a higher volume and content of hazardous liquid waste. Furthermore, hazardous liquid waste is the residue of activities that contain substances that can pollute and damage the environment and health, necessitating more efforts in managing the processing of hazardous wastewater produced by the hospital. Based on the background above, this study developed and applied an Internet of Things (IoT) based monitoring system to the Wastewater Treatment Plant (WWTP) in Udayana University Hospital. In principle, the IoT system can be used as a real-time monitoring tool and minimizes direct contact activities of officers’ WWTP sites. Moreover, the Liquid Film Aerator (LFA) was applied to improve the efficiency of WWTP. The developed IoT system successfully monitors pH, DO, and real-time temperature, and the monitoring results were presented in a web-based user interface. The result shows better power usage efficiency than conventional aeration. Furthermore, conventional aeration with a root blower requires 619.8 watts to produce 1 mg/L of DO, while LFA only requires 273.2 watts. The developed systems can be applied to other hospitals or similar wastewater plants that handle COVID-19 cases.

Una arquitectura IoT para ciudades inteligentes basada en la plataforma FIWARE

The Internet of Things (IoT) is a concept that has been gaining considerable popularity today. It attempts to represent everyday things that are connected to the internet, but in reality it is much more than that. The overall aim is to interconnect the physical with the digital world, as the physical world is measured by sensors which translates into actionable data, and also the data can be translated into commands to be executed by actuators. Currently, the number of designed IoT architectures has increased considerably as a result of different approaches, standards and use cases. This leads to difficulties in understanding, selecting and using these architectures. In this work, an IoT architecture based on the FIWARE platform is proposed with the aim of facilitating the development of smart cities. With the proposal made, it was possible to integrate the main elements to be considered in this technology, thus offering a basis that serves as a guide, both for developing IoT systems and for creating more specific architectures that respond to the particular characteristics of a given application.

Out-of-Things Debugging: A Live Debugging Approach for Internet of Things

Context: Internet of Things (IoT) has become an important kind of distributed systems thanks to the wide-spread of cheap embedded devices equipped with different networking technologies. Although ubiquitous, developing IoT systems remains challenging. Inquiry: A recent field study with 194 IoT developers identifies debugging as one of the main challenges faced when developing IoT systems. This comes from the lack of debugging tools taking into account the unique properties of IoT systems such as non-deterministic data, and hardware restricted devices. On the one hand, offline debuggers allow developers to analyse post-failure recorded program information, but impose too much overhead on the devices while generating such information. Furthermore, the analysis process is also time-consuming and might miss contextual information relevant to find the root cause of bugs. On the other hand, online debuggers do allow debugging a program upon a failure while providing contextual information (e.g., stack trace). In particular, remote online debuggers enable debugging of devices without physical access to them. However, they experience debugging interference due to network delays which complicates bug reproducibility, and have limited support for dynamic software updates on remote devices. Approach: This paper proposes out-of-things debugging, an online debugging approach especially designed for IoT systems. The debugger is always-on as it ensures constant availability to for instance debug post-deployment situations. Upon a failure or breakpoint, out-of-things debugging moves the state of a deployed application to the developer's machine. Developers can then debug the application locally by applying operations (e.g., step commands) to the retrieved state. Once debugging is finished, developers can commit bug fixes to the device through live update capabilities. Finally, by means of a fine-grained flexible interface for accessing remote resources, developers have full control over the debugging overhead imposed on the device, and the access to device hardware resources (e.g., sensors) needed during local debugging. Knowledge: Out-of-things debugging maintains good properties of remote debugging as it does not require physical access to the device to debug it, while reducing debugging interference since there are no network delays on operations (e.g., stepping) issued on the debugger since those happen locally. Furthermore, device resources are only accessed when requested by the user which further mitigates overhead and opens avenues for mocking or simulation of non-accessed resources. Grounding: We implemented an out-of-things debugger as an extension to a WebAssembly Virtual Machine and benchmarked its suitability for IoT. In particular, we compared our solution to remote debugging alternatives based on metrics such as network overhead, memory usage, scalability, and usability in production settings. From the benchmarks, we conclude that our debugger exhibits competitive performance in addition to confining overhead without sacrificing debugging convenience and flexibility. Importance: Out-of-things debugging enables debugging of IoT systems by means of classical online operations (e.g., stepwise execution) while addressing IoT-specific concerns (e.g., hardware limitations). We show that having the debugger always-on does not have to come at cost of performance loss or increased overhead but instead can enforce a smooth-going and flexible debugging experience of IoT systems.

The Use of Internet of Things technology to develop a smart farm prototype for pig farming

This research aims to develop a smart farm prototype using Internet of Things (IoT) technology. The objectives are 1) to design and construct environment monitor and control systems of the housing and 2) to design and build an IoT feeding control system. The development is under the system development process at the pig farms in Nakhon Si Thammarat province, Thailand, using open-source hardware and software. This system utilizes a ESP8266 Wi-Fi microcontroller, which functions to connect different sensors data to server and control the actuators. The MQTT protocol for data transferred over a secure wireless local network. Node-RED for designing the flow of data is stored on the server using a NoSQL database such as InFluxDB. In this research, it was found that the measured temperature was an average of 28 °C ranging from 24.43 °C to 34.34 °C. The humidity was an average of 92%. The feeding control system is following the instructions 100% of the time. Users can access data and control the system via a web application with a smartphone or a computer in real-time. Additionally, the collected information is processed and analyzed as big data for forecasting or studying climate change around the study point in the future. The overall system performance evaluation was at the highest level achieving on average 4.50 out of 5 with a standard deviation of 0.47 from the users. Therefore, the developed IoT system could be used as a prototype and expanded to a larger farm or other kinds of farms.

Agent-Oriented Software Engineering Methodologies: Analysis and Future Directions

The Internet of Things (IoT) facilitates in building cyber-physical systems, which are significant for Industry 4.0. Agent-based computing represents effective modeling, programming, and simulation paradigm to develop IoT systems. Agent concepts, techniques, methods, and tools are being used in evolving IoT systems. Over the last years, in particular, there has been an increasing number of agent approaches proposed along with an ever-growing interest in their various implementations. Yet a comprehensive and full-fledged agent approach for developing related projects is still lacking despite the presence of agent-oriented software engineering (AOSE) methodologies. One of the moves towards compensating for this issue is to compile various available methodologies, ones that are comparable to the evolution of the unified modeling language (UML) in the domain of object-oriented analysis and design. These have become de facto standards in software development. In line with this objective, the present research attempts to comprehend the relationship among seven main AOSE methodologies. More specifically, we intend to assess and compare these seven approaches by conducting a feature analysis through examining the advantages and limitations of each competing process, structural analysis, and a case study evaluation method. This effort is made to address the significant characteristics of AOSE approaches. The main objective of this study is to conduct a comprehensive analysis of selected AOSE methodologies and provide a proposal of a draft unified approach that drives strengths (best) of these methodologies towards advancement in this area.

Design and Simulation of IoT Systems Using the Cisco Packet Tracer

Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants; steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SOx emissions, converter head temperature, NOx emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.

Accuracy and Reliability of Data in IoT System for Smart Agriculture

This paper presents the design and development of low-cost Internet of Things (IoT) system for remote monitoring of agriculture ecosystem. The aims of the research are to evaluate the accuracy and reliability of data collected and transmitted with developed IoT system and access from remote location. These data are then compared with the readings of the conventional stand-alone sensors or meters. The system is set in the laboratory and utilises low cost microcontroller, sensors, Wi-Fi network communication, cloud storage, mobile and web application. Microcontroller Arduino Uno ATMega328P is used with ESP8266 Wi-Fi module to enable the device to be connected to the Internet. The sensors used in this system are selected base on the plant’s growth factor. All data collected from the sensors are sent to the Cloud platform such as ThingSpeak and Blynk. Periodical monitoring is carried out on laptop and mobile phone. The results showed that the data taken from the soil moisture, soil temperature, light intensity, surrounding temperature and humidity are accurate and reliable.

A Preliminary Component Model for IoT

The enthusiasm of IT entrepreneurs in producing Internet of Things (IoT) systems is undeniable as currently, the number of connected devices is enormously increasing. Many research has been done to efficiently develop IoT systems. IoT systems are usually engineered from scratch. IoT component models have been introduced but lack of generic development framework or model that supports high reusability and loose coupling in dealing with the heterogeneous devices that can hinder its development. Thus, an IoT component model is proposed. Meta-modelling has been used to define the component model where the specific interaction and composition standard in a component are abstracted. IoT component model is intended to develop a prototype for IoT development. With this IoT prototype, IoT system developers will not need to develop everything from scratch every time, as generic components can be reused even when it is applied in different domains or during system enhancement is required. Smart home IoT system has been selected as a case study to evaluate our prototype tool. In this study, we provide an alternative way to develop IoT software in component-based software engineering method. A prototype has also been developed to assist reusability and reduce coupling between modules.

Internet of things based industrial environment monitoring and control: A design approach

In this research an internet of things (IoT) system is designed for the purpose of industrial environment monitoring and control. The system is mainly composed of control and sensor units. Control unit has the responsibility of managing the received data form the sensor unit and then executing the developed control algorithm based on the measured parameters. NodeMCU development kit is used as the core of the control unit. The senor unit contains gas and temperature sensors utilized for measuring the temperature and concentration of toxic gases in the monitored space. A buzzer has also been embedded in the sensor unit for alerting the occupants acoustically in the danger situations. If the monitored temperature gets higher or lower than the set levels, the air conditioning system will be automatically operated. Similarly, the fan (ventilation) system will be operated if the level of toxic gases becomes high. A mobile application, based on Blynk platform, is then developed to enable the wireless monitoring and control of the environment by the in charge people. In addition to the automatic and wireless control the manual control capability is considered in the developed IoT system.

Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems

A mismatch between staffing ratios and service demand leads to overcrowding of patients in waiting rooms of health centers. Overcrowding consequently leads to excessive patient waiting times, incomplete preventive service delivery and disgruntled medical staff. Worse, due to the limited patient load that a health center can handle, patients may leave the clinic before the medical examination is complete. It is true that as one health center may be struggling with an excessive patient load, another facility in the vicinity may have a low patient turn out. A centralized hospital management system, where hospitals are able to timely exchange patient load information would allow excess patient load from an overcrowded health center to be re-assigned in a timely way to the nearest health centers. In this paper, a machine learning-based patient load prediction model for forecasting future patient loads is proposed. Given current and historical patient load data as inputs, the model outputs future predicted patient loads. Furthermore, we propose re-assigning excess patient loads to nearby facilities that have minimal load as a way to control overcrowding and reduce the number of patients that leave health facilities without receiving medical care as a result of overcrowding. The re-assigning of patients will imply a need for transportation for the patient to move from one facility to another. To avoid putting a further strain on the already fragmented ambulatory services, we assume the existence of a scheduled bus system and propose an Internet of Things (IoT) integrated smart bus system. The developed IoT system can be tagged on buses and can be queried by patients through representation state transfer application program interfaces (APIs) to provide them with the position of the buses through web app or SMS relative to their origin and destination stop. The back end of the proposed system is based on message queue telemetry transport, which is lightweight, data efficient and scalable, unlike the traditionally used hypertext transfer protocol.

On the challenges novice programmers experience in developing IoT systems: A Survey

The co-existence of various kinds of devices, protocols, architectures, and applications make Internet of Things (IoT) systems complex to develop, even for experienced programmers. When novice programmers are learning to implement these systems, they are required to deal with areas in which they do not have a deep knowledge. Furthermore, besides becoming proficient in these areas separately, they should integrate them and build a system whose components are heterogeneous from both software and hardware perspectives.The accurate understanding of the most challenging issues that novices face is fundamental to envision strategies aimed at easing the development of IoT systems. This paper focuses on identifying such issues in terms of software development tasks that novice programmers encounter when working on IoT systems. To this end, a survey was conducted among 40 novice developers that worked in groups developing IoT systems during several years of a university course. Based on their own experiences, individually and as a group, the most challenging development tasks were identified and prioritized over a common architecture, in terms of difficulty level and efforts. In addition, qualitative data about the causes of these issues was collected and analyzed. Finally, the paper offers critical insights and points out possible future work.

An automated low cost IoT based Fertilizer Intimation System for smart agriculture

This paper presents an Internet of Things (IoT) based system by designing a novel Nitrogen-Phosphorus-Potassium (NPK) sensor with Light Dependent Resistor (LDR) and Light Emitting Diodes (LED). The principle of colorimetric is used to monitor and analyze the nutrients present in the soil. The data sensed by the designed NPK sensor from the selected agricultural fields are sent to Google cloud database to support fast retrieval of data. The concept of fuzzy logic is applied to detect the deficiency of nutrients from the sensed data. The crisp value of each sensed data is discriminated into five fuzzy values namely very low, low, medium, high and very high during fuzzification. A set of If-then rules are framed based on individual chemical solutions of Nitrogen (N), Phosphorous (P) and Potassium (K). Mamdani inference procedure is used to derive the conclusion about the deficiency of N, P and K available in soil chosen for testing and accordingly an alert message is sent to the farmer about the quantity of fertilizer to be used at regular intervals. The proposed hardware prototype and the software embedded in the microcontroller are developed in Raspberry pi 3 using Python. The developed model is tested in three different soil samples like red soil, mountain soil and desert soil. It is observed that the developed system results in linear variation with respect to the concentration of the soil solution. A sensor network scenario is created using Qualnet simulator to analyze the performance of designed NPK sensor in terms of throughput, end to end delay and jitter. From the different variety of experiments conducted, it is noticed that the developed IoT system is found to be helpful to the farmers for high yielding of crops.

Design and Development of an IoT Application with Visual Analytics for Water Consumption Monitoring

Water is imperative in the regular day to day existence of the human race. Household people use water to sustain life. Nowadays, household people excessively use water without knowing how much water do they already consume in real-time and are unaware on which faucet in the household has the highest water consumption. With the use of the internet of things (IoT) platform with visual analytics (VA), a real-time water consumption monitoring system has been developed for household and commercialized building applications. In order to get the water consumption data, a Hall Effect water flow sensor is attached to every faucet, which is then processed by a NodeMCU, then pushed to the Heroku cloud database. A Liquid Crystal Display (LCD) alphanumeric screen displays the total volume consumed and the real-time water flow rate for each faucet. To further visualize the water consumption data, a comprehensible chart report about the overall household water consumption from the cloud database can be accessed through a web browser or a mobile device. To achieve an accurate water flow measurement, an initial calibration is being performed for every sensor in the faucet. A test was conducted to determine how accurate the system is in monitoring the correct water consumption. Ten trials of filling up a 10-liter bottle using one faucet, two 10-liter bottles using two faucets, and three 10-liter bottles using three faucets at a time was conducted. The study came up with an overall average accuracy of 99.38%. The study concluded that the developed IoT system can make an accurate water consumption monitoring and can also identify which faucet has the highest consumption using visual analytics.

Multi-Layer Agent Based Architecture for Internet of Things Systems

Nowadays, the Internet of things (IoT) is becoming a promising technology which revolutionizes and simplifies our daily life style. It allows interaction and cooperation between a large variety of pervasive objects over wireless and wired connections, in order to achieve specific goals. Moreover, it provides a concise integration of physical world into computer systems through network infrastructure. This paper provides an agent-based architecture for developing IoT systems. The proposed architecture is multi-layer and generic. It encompasses four layers: Physical Component Management, Local Management -Coordination, Global Management-Coordination and Specialized Operative Management Layers. The first one can be seen as a smart layer that ensures connection and communication between things and the system. The second one constitutes the intelligent core of the system which acts locally to ensure coordination and further internal functioning. The third layer ensures coordination between the local system and the externals ones. The last layer supports additional behaviors which are domain dependent. The architecture is illustrated by an IoT system diagnosis.