IoT-based Solution Research Articles

Auto Temperature and Mask Scanning Entry System

Now that many shops, offices and institutions are re-opening again after the Corona lockdown, many businesses are faced with the need to provide the best possible protection for their staff and customers. Face masks and body temperature checks play an important part in the protection effort. While this is already done routinely and at a large scale at airports or railway stations, many businesses and institutions are struggling to meet the challenge. Face mask monitoring often requires additional staff resources. At the same time, body temperature checks by staff come with certain risks in terms of hygiene and data privacy. In this project, we introduce an affordable IoT-based solution aiming to increase COVID-19 indoor safety, covering several relevant aspects: 1) contactless temperature sensing 2) mask detection. Contactless temperature sensing subsystem relies on Arduino Uno using infrared sensor or thermal camera, while mask detection and social distancing check are performed by leveraging computer vision techniques on camera- equipped computer.

Integrated urban water management with micro storages developed as an IoT-based solution – The smart rain barrel

Low cost sensors are emerging alongside innovative data transfer technologies, allowing the integration of smart solutions for the decentralisation of water infrastructure. In this work, the smart rain barrel (SRB) concept is introduced as an IoT-based solution for advanced rainwater harvesting. The SRB consists of a rain barrel extended by a discharge valve, which is centrally controlled. This concept offers (1) individual control of each implemented SRB with its incorporation into the integrated water system management and (2) a simple large-scale implementation of additional storage units as an alternative to future expansion of existing infrastructure. The open-source software “Smartin” is developed by hypothetically retrofitting an Alpine municipality with SRBs to evaluate the effects of the latter on the urban water infrastructure. Compared to uncontrolled rain barrels, a simple coordinated control strategy already clearly improves the performance of the integrated system by reducing combined sewer overflow and addressing drinking water demand.

MyGreen: An IoT-Enabled Smart Greenhouse for Sustainable Agriculture

This article presents the potential of Internet-of-Things (IoT) in the area of greenhouse farming and leading to the smart agriculture. The different parameters, such as humidity, water nutrients solution level, pH and electrical conductivity (EC) value, temperature, UV light intensity, CO2 level, mist, and amount of insecticides or pesticides, are monitored through various sensors so that significant knowledge can be captured and early fault detection and diagnosis can be done. A decision support system (DSS) acts as the central operating system that governs and coordinates all the activities. Furthermore, this work also accounts for the different challenges of greenhouse rose farming and highlights a new IoT-based solution, which is smart and sustainable. The model presented in this work is well adapted to the changing environment, thereby redefining the terms of sustainability.

IoT based Smart Water Quality Prediction for Biofloc Aquaculture

Traditional fish farming faces several challenges, including water pollution, temperature imbalance, feed, space, cost, etc. Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The objective of the research is to propose an IoT-based solution to aquaculture that increases efficiency and productivity. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed system has been implemented and tested to validate and achieve a satisfactory result.

Development of an IoT-Based Solution Incorporating Biofeedback and Fuzzy Logic Control for Elbow Rehabilitation

The last few years have seen significant advances in neuromotor rehabilitation technologies, such as robotics and virtual reality. Rehabilitation robotics primarily focuses on devices, control strategies, scenarios and protocols aimed at recovering sensory, motor and cognitive impairments often experienced by stroke victims. Remote rehabilitation can be adopted to relieve stress in healthcare facilities by limiting the movement of patients to clinics, mainly in the current COVID-19 pandemic. In this context, we have developed a remote controlled intelligent robot for elbow rehabilitation. The proposed system offers real-time monitoring and ultimately provides an electronic health record (EHR). Rehabilitation is an area of medical practice that treats patients with pain. However, this pain can prevent a person from positively interacting with therapy. To cope with this matter, the proposed solution incorporates a cascading fuzzy decision system to estimate patient pain. Indeed, as a safety measure, when the pain exceeds a certain threshold, the robot must stop the action even if the desired angle has not yet been reached. A fusion of sensors incorporating an electromyography (EMG) signal, feedback from the current sensor and feedback from the position encoder provides the fuzzy controller with the data needed to estimate pain. This measured pain is fed back into the control loop and processed to generate safe robot actions. The main contribution was to integrate vision-based gesture control, a cascade fuzzy logic-based decision system and IoT (Internet of Things) to help therapists remotely take care of patients efficiently and reliably. Tests carried out on three different subjects showed encouraging results.

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

Packaging Environmental Sensors for Monitoring Urban-Microclimates

Abstract An internet-of-things (IoT)-based low-cost sensor network can be used to collect the data necessary to study both Urban Heat Island (UHI) and air pollution. There are several key challenges associated with an IoT-based solution to environmental data monitoring, including packaging and deployment. This study explores these challenges by looking at effects the packaging has on the deployed environmental sensors. Several packaging designs are numerically studied using a computation fluid dynamics (CFD) model. Two sensor designs are chosen using results obtained from CFD modeling and then experimentally deployed. The findings conclude that the IoT sensors chosen for this study are not significantly affected by flow velocities or require advanced packaging designs when paired with street-side outdoor digital displays.

Connected Bike-smart IoT-based Cycling Training Solution.

The Connected Bike project combines several technologies, both hardware and software, to provide cycling enthusiasts with a modern alternative solution for training. Therefore, a trainer can monitor online through a Web Application some of the important parameters for training, more specifically the speed, cadence and power generated by the cyclist. Also, the trainer can see at every moment where the rider is with the aid of a GPS module. The system is built out of both hardware and software components. The hardware is in charge of collecting, scaling, converting and sending data from sensors. On the software side, there is the server, which consists of the Back-End and the MQTT (Message Queues Telemetry Transport) Broker, as well as the Front-End of the Web Application that displays and manages data as well as collaboration between cyclists and trainers. Finally, there is the Android Application that acts like a remote command for the hardware module on the bike, giving the rider control over how and when the ride is monitored.

Air Flow Control of a Smart Electric Fan using IoT Solutions

Remote control of electric fans by the application of IoT technology addresses the restrictions of “push button” controls that are usual in conventional fans. This paper explains a new control method for air flow control of a Smart Electric Fan (SEF), in this case fans mainly used in the household. In this new method the supply voltage that is applied to the motor terminals of the SEF is precisely changed with the mobile application developed by using IoT solutions. As this proposed method integrates with the conventional control methods referred to as the Firing Angle Control method or Phase Angle Control method with a new IoT-based solution, the controlling of any device becomes simpler. We developed a mobile application that enables the fan to be remotely controlled using a smartphone or tablet computer. The phase or delay angle of the voltage waveform is changed by giving as an input from the smart phone mobile application, and this results in minimizing the power loss caused due to switching in case of push button system. We demonstrated that by using this app, implementing the IoT-based solution, the air flow rate of the SEF was increased and the performance of the SEF exceeded the air flow rate of the original, conventionally controlled fan with the speed of the fan being significantly increased. Applying this control method has thus been verified to be advantageous and appropriate for the remote control of the air flow and fan speed, resulting in the efficient Smart Electric Fan. The main focus of this research was to implement the integrated control method in the residential fans.

Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service.

Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models’ and Gaussian Mixture Models’ topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.

A Smart Glove to Track Fitness Exercises by Reading Hand Palm

Medical studies have intensively demonstrated that sports activity can enhance both the mental and the physical health of practitioners. In recent years, fitness activity became the most common way to motivate and engage people in sports activity. Recently, there have been multiple attempts to elaborate on the “ideal” IoT-based solution to track and assess these fitness activities. Most fitness activities (except aerobic activities like running) involve one or multiple interactions between the athlete’s hand palms and body or between the hand palms and the workout materials. In this work, we present our idea to exploit these biomechanical interactions of the hand palms to track fitness activities via a smart glove. Our smart glove-based system integrates force-sensitive resistor (FSR) sensors into wearable fitness gloves to identify and count fitness activity, by analyzing the time series of the pressure distribution in the hand palms observed during fitness sessions. To assess the performance of our proposed system, we conducted an experimental study with 10 participants over 10 common fitness activities. For the user-dependent activity recognition case, the experimental results showed 88.90% of the F score for overall activity recognition. The result of leave-one-participant-out cross-validation showed an F score ranging from 58.30% to 100%, with an average of 82.00%. For the exercise repetition count, the system achieved an average counting error of 9.85%, with a standard deviation of 1.38.

Smart integrated IoT healthcare system for cancer care

The emergence of the internet of things (IoT) has drastically influenced and shaped the world of technology in the contexts of connectivity, interconnectivity, and interoperability with smart connected sensors, objects, devices, data, and applications. In fact, IoT has brought notable impacts on the global economy and human experience that span from industry to industry in a variety of application domains, including healthcare. With IoT, it is expected to facilitate a seamless interaction and communication of objects (devices) with humans in the environment. Therefore, it is imperative to embrace the potentials and benefits of IoT technology in healthcare delivery to ensure saving lives and to improve the quality of life using smart connected devices. In this paper, we focus on the IoT based healthcare system for cancer care services and business analytics/cloud services and also propose the adoption and implementation of IoT/WSN technology to augment the existing treatment options to deliver healthcare solution. Here, the business analytics/cloud services constitute the enablers for actionable insights, decision making, data transmission and reporting for enhancing cancer treatments. Furthermore, we propose a variety of frameworks and architectures to illustrate and support the functional IoT-based solution that is being considered or utilized in our proposed smart healthcare solution for cancer care services. Finally, it will be important to understand and discuss some security issues and operational challenges that have characterized the IoT-enabled healthcare system.

An IoT-Based Solution for Monitoring a Fleet of Educational Buildings Focusing on Energy Efficiency

Raising awareness among young people and changing their behaviour and habits concerning energy usage is key to achieving sustained energy saving. Additionally, young people are very sensitive to environmental protection so raising awareness among children is much easier than with any other group of citizens. This work examines ways to create an innovative Information & Communication Technologies (ICT) ecosystem (including web-based, mobile, social and sensing elements) tailored specifically for school environments, taking into account both the users (faculty, staff, students, parents) and school buildings, thus motivating and supporting young citizens’ behavioural change to achieve greater energy efficiency. A mixture of open-source IoT hardware and proprietary platforms on the infrastructure level, are currently being utilized for monitoring a fleet of 18 educational buildings across 3 countries, comprising over 700 IoT monitoring points. Hereon presented is the system’s high-level architecture, as well as several aspects of its implementation, related to the application domain of educational building monitoring and energy efficiency. The system is developed based on open-source technologies and services in order to make it capable of providing open IT-infrastructure and support from different commercial hardware/sensor vendors as well as open-source solutions. The system presented can be used to develop and offer new app-based solutions that can be used either for educational purposes or for managing the energy efficiency of the building. The system is replicable and adaptable to settings that may be different than the scenarios envisioned here (e.g., targeting different climate zones), different IT infrastructures and can be easily extended to accommodate integration with other systems. The overall performance of the system is evaluated in real-world environment in terms of scalability, responsiveness and simplicity.

IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems

The Internet of Things (IoT) is entering the daily operation of many industries; applications include but are not limited to smart cities, smart grids, smart homes, physical security, e-health, asset management, and logistics. For example, the concept of smart cities is emerging in multiple continents, where enhanced street lighting controls, infrastructure monitoring, public safety and surveillance, physical security, gunshot detection, meter reading, and transportation analysis and optimization systems are being deployed on a city-wide scale. A related and cost-effective user-level IoT application is the support of IoT-enabled smart buildings. Commercial space has substantial requirements in terms of comfort, usability, security, and energy management. IoT-based systems can support these requirements in an organic manner. In particular, power over Ethernet, as part of an IoT-based solution, offers disruptive opportunities in revolutionizing the in-building connectivity of a large swath of devices. However, a number of deployment-limiting issues currently impact the scope of IoT utilization, including lack of comprehensive end-to-end standards, fragmented cybersecurity solutions, and a relative dearth of fully-developed vertical applications. This paper reviews some of the technical opportunities offered and the technical challenges faced by the IoT in the smart building arena.

An IoT-Based Solution for Control and Monitoring of Additive Manufacturing Processes

Day-by-day, industries are looking for digitizing their manufacturing processes, as well as introducing control and monitoring systems along the shop floor. Internet functionalities and data-based services have been introduced on processes in order to innovate the production based on the Industry 4.0 paradigm. This new concept, also named advanced manufacturing, digital factory or industrial Internet brings benefits for production processes and machines towards a complete value chain transformation with all, or most, information stored on cloud based services. This work presents a proof of concept system additive manufacturing machines management, where an Internet integration of beacon technology on manufacturing environment allows the communication of production data extracted from the machines with mobile devices in a fast and intuitive way. Information can be obtained through applications installed on mobile devices, providing real time indexes of productivity to managers or operators when they simply approach to the machines. The proposed solution aims to show the application of this technology in the manufacturing environment, where IoT (Internet of Things) linked to systems integration and cloud architecture, presents the benefits of the Industry 4.0 concepts. Finally, the gains in productivity and innovation obtained with the concepts of advanced manufacturing have been presented, to add value in term of growth and competitiveness for the companies in their manufacturing processes.

A Cloud to the Ground: The New Frontier of Intelligent and Autonomous Networks of Things

The Internet of Things paradigm is supporting - and will support - an ever increasing number of services and applications impacting on almost every aspect of our everyday life. The current trend is forecasting IoT to connect tens of billions of objects by 2020 yielding a very high volume of data to be acquired, transmitted, and processed. IoT typically relies on cloud computing to process, analyze, and store the data acquired by IoT entities. Unfortunately, the need to transmit all data from the information producing objects to the cloud for a subsequent processing/analysis phase would require a large bandwidth and increase the latency in the decision making process whenever decisions/reactions must be promptly made by the IoT units. The fog computing paradigm aims to address these problems by extending cloud computing toward the edge of the network. In this direction, this article introduces a novel FC-IoT paradigm designed to move computing, storage, and applications/services close to IoT objects so as to reduce communication bandwidth and energy consumption as well as decision making latency. The proposed IoT-based solution has been designed to have intelligent and autonomous IoT objects that are integrated with an FC and fog networking approach. The distinguishing features of the intelligent FC-IoT platform are low latency, self-adaptation, low energy consumption, and spectrum efficiency.

Toward end-to-end biomet rics-based security for IoT infrastructure

The IoT is the next generation of innovation in the smart world, which has the potential to improve safety, security, and the quality of our daily lives through pervasive communication and computation. Currently, we have observed that a plethora of interconnected smartphones, devices, and sensors are deployed for providing personalized services (e.g., social media, smart home, health monitoring) at any time from anywhere. The personalized services offered by IoT, although enhancing the quality of our lives, have serious challenges of securing networks and data in transit, as every day a myriad of devices and services are connected to the IoT. However, existing security solutions, such as two-factor authentication based on passwords along with second-level protection may not be efficient and reliable for providing end-to-end secure communication solutions among different devices and services connected to the IoT. To this end, this article proposes an end-to-end secure IoT-based solution using biometrics and pairing-based cryptography. Because of the uniqueness of one’s biometric traits (e.g., face, fingerprint, palm, iris, voice, heartbeat), a biometric-based security solution is less vulnerable to security breaches for IoT systems or infrastructure. We present a biometric- based IoT infrastructure comprising four layers, and for each layer, we discuss possible security challenges along with the corresponding countermeasures. Finally, we provide a case study of face-based biometric recognition, where sensors or smartphones capture a face image and securely transmit it to the IoT platform to provide end-to end security.

Modeling IoT-based solutions using human-centric wireless sensor networks.

The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at the design time. Therefore, the process of designing these systems is ad hoc and its real impact is evaluated once the solution is already implemented, which is risky and expensive. This paper proposes a modeling approach that uses human-centric wireless sensor networks to specify and evaluate models of IoT-based systems at the time of design, avoiding the need to spend time and effort on early implementations of immature designs. It allows designers to focus on the system design, leaving the implementation decisions for a next phase. The article illustrates the usefulness of this proposal through a running example, showing the design of an IoT-based solution to support the first responses during medium-sized or large urban incidents. The case study used in the proposal evaluation is based on a real train crash. The proposed modeling approach can be used to design IoT-based systems for other application scenarios, e.g., to support security operatives or monitor chronic patients in their homes.