IoT-based Systems Research Articles

Energy Efficient Real-Time E-Healthcare System Based on Fog Computing

The rapid development of Internet of Things (IoT) enabled systems in public and private spaces offers consumers numerous conveniences. Among different internet-connected systems, the use of e-health systems is growing rapidly. The utilization of IoT devices and cloud-fog network technologies have made e-healthcare provision more convenient. While providing valuable services to the healthcare sector, like any other IoT-enable systems it is putting pressure on energy, an essential element of life. Therefore, it is imperative to know the energy consumption model of e-health systems. Considering the importance of energy consumption in IoT-based systems, this article develops a cloud-fog-based e-health system and makes it energy efficient by understanding energy consumption at different layers of communication. Moreover, how fog integration with the cloud reduces energy consumption and delays at different stages of communication is discussed.

Design, Implementation, and Practical Evaluation of a Voice Recognition Based IoT Home Automation System for Low-Resource Languages and Resource-Constrained Edge IoT Devices: A System for Galician and Mobile Opportunistic Scenarios

Systems with voice control are an attractive option for increasing technological integration, not only for people with little knowledge on technology or constrained Internet access, but also for people with certain disabilities. In addition, devices based on Alexa or Google Home provide an interesting alternative for interacting with Internet of Things (IoT) devices, but they usually rely on an Internet connection to a cloud server for their full operation. Furthermore, many voice-recognition systems are only available in a limited number of languages, which tend to be those with the highest number of speakers, thus excluding minority-language speakers. To address the previously mentioned issues, this article presents a solution based on Edge Computing and voice commands that carries out offline voice processing and that is able to interact with IoT-based systems. The proposed system performs local speech inference, providing a communication interface with IoT devices in a Bluetooth mesh, all in a fast way and without the need for an Internet connection. In addition, the proposed solution can be adapted easily for voice recognition of languages with few resources. Such a feature is demonstrated with the Galician language, which is spoken by less than 3 million people worldwide. In particular, different Automatic Speech Recognition (ASR) models based on three of the most popular ASR development frameworks (wav2vec2, DistilHubert, Whisper) were developed to transcribe short speech and to translate it into IoT commands that perform specific home-automation actions. Such models were fine-tuned for Galician with a corpus of approximately 20 hours and were evaluated in static and mobile opportunistic scenarios in terms of accuracy, energy consumption and latency on an embedded platform (that acts as an edge device) and on a cloud server. The obtained results show that inference is performed in less than 2 seconds on a Raspberry Pi 4 for the two smallest models and in less than 500 ms on a high-end Android smartphone when processing all data locally with CPU-only inference (i.e., without hardware acceleration or external processing). The results of the transcriptions are accurate enough to be able to use simple text distance algorithms to detect keywords in the speech and perform commands on IoT devices. In particular, a maximum success rate of 92% was achieved for detecting the indicated commands when using models optimized for being executed on embedded devices. For selected home scenarios, command actions were sent via Bluetooth with average response times of up to 113 ms.

A Study on Artificial Intelligence-based Security Techniques for IoT-based Systems

A Study on Artificial Intelligence-based Security Techniques for IoT-based Systems

Implementation of the IoT-Based Technology on Patient Medication Adherence: A Comprehensive Bibliometric and Systematic Review

The dynamic field of the Internet of Things (IoT) is constantly increasing, providing a plethora of potential integration across various sectors, most notably healthcare. The IoT represents a significant technological leap in healthcare management systems, coinciding with the rising preference for personalized, proactive, cost-effective treatment techniques. This review aimed to thoroughly assess the existing literature through a systematic review and bibliometric analysis, identifying untapped research routes and possible domains for further exploration. The overarching goal was to provide healthcare professionals with significant insights into the impact of IoT technology on Patient Medication Adherence (PMA) and related outcomes. An extensive review of 314 scientific articles on the deployment of IoT within pharmaceutical care services revealed a rising trend in publication volume, with a significant increase in recent years. Pertinently, from the 33 publications finally selected, substantial data support the potential of the IoT to improve PMA, particularly among senior patients with chronic conditions. This paper also comments on various regularly implemented IoT-based systems, noting their unique benefits and limitations. In conclusion, the critical relevance of PMA is highlighted, arguing for its emphasis in future discussions. Furthermore, the need for additional research endeavors is proposed to face and overcome existing constraints and establish the long-term effectiveness of IoT technologies in maximizing patient outcomes.

Iot application for energy poverty detection based on thermal comfort monitoring

The development of a datalogger for identifying Energy Poverty (EP) using thermal comfort monitoring is described in this work. There is not a uniform definition of EP, and no global recommendations indicating the thermal comfort characteristics that should be utilized to identify EP. Most Internet of Things (IoT)-based systems designed for EP identification measure energy consumptions (electricity and gas). There is a lack of works that use IoT-based systems to identify EP through the monitoring of thermal comfort parameters. To address the deficiencies discovered in the identification of EP from the perspective of thermal efficiency, an IoT-based monitoring system was designed, developed, and tested. A first pilot was installed in a household in Getafe. A full month of temperature, relative humidity, and CO2 concentration measurements were utilized to evaluate the system, which was then compared to a commercial system. The results revealed that the new IoT-based approach was very dependable and may be used to accurately monitor EP-related parameters.

Prerequisites for the transition to a knowledge-based economy in the context of structural transformation, quality of life and economic growth

The modern world is undergoing a rapid structural transformation driven by the growth of intelligent systems and environments. These AI and IoT-based systems provide new opportunities to increase productivity, optimize resources and improve the quality of life. However, the transition to an economy of intelligent systems and environments also introduces challenges and requires a strategic approach. Artificial intelligence is becoming a key driver of the intelligent systems economy. This section of the article analyzes the impact of AI on factory automation, data management and decision making. It also discusses the need to develop an appropriate regulatory and legal framework for the use of AI. The Internet of Things (IoT) allows objects and devices to be connected and exchange data. The article examines the impact of IoT on industry, healthcare, transport and urban infrastructure. The role of standardization and security in the development of IoT is discussed. The transition to an economy of intelligent systems is often accompanied by the automation of routine work tasks. The article examines the impact of automation on the labor market and proposes measures to prepare the workforce for changes in the economy. The rise of the smart system economy provides an opportunity to improve the quality of life of citizens. However, it is necessary to take into account the issues of privacy, security and accessibility of technologies for all segments of the population. This section of the article analyzes the relationship between economic growth and living standards.On the basis of the author’s model of increasing the efficiency of the regional economy, a mechanism for increasing economic growth in the economies of the North Caucasus republics and the Stavropol Territory has been identified. The prevailing role of such factors as: the number of employees, gross regional product, investment, per capita income, etc., factors in ensuring economic growth in the Republic of Dagestan, the Stavropol Territory is proved. At the same time, these factors did not have a decisive influence on the dynamics of the socio-economic development of the Kabardino-Balkarian Republic. Elasticity between factors is calculated, which shows the different architecture of regional economies. Based on them, resource replacement matrices are constructed, the manipulation of which allows you to control the development path of the regional system.

Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection

The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimization-based machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches.

IoT-Based Systems for Soil Nutrients Assessment in Horticulture.

Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management.

Microservice Communication for IoT-based Systems. Architecture Review and Performance Test

One of the most important things in IoT system development is the right communication technologies and protocols. Communication of modern IoT systems can be divided into two main parts: device-to-cloud communication and communication between cloud microservices (application level). In this study, the authors designed a test-system environment for evaluating the performance of the existing transmitting protocols for the cloud microservices communication. The proposed environment allows emulate of IoT systems with low network latency which allows evaluating and comparing protocols performance. The authors provide tests for the most popular application-level protocols: HTTP, MQTT, AMQP, and GRPC. The performance evaluation was performed based on such metrics: throughput, concurrency, scalability, transmitting size, and init connection time. The obtained experimental results and testing environment can be used for the efficient design of microservice communication.

Improved Jellyfish Algorithm-based multi-aspect task scheduling model for IoT tasks over fog integrated cloud environment

Corporations and enterprises creating IoT-based systems frequently use fog computing integrated with cloud computing to harness the benefits offered by both. These computing paradigms use virtualization and a pay-as-you-go strategy to provide IT resources, including CPU, memory, network and storage. Resource management in such a hybrid environment becomes a challenging task. This problem is exacerbated in the IoT environment, as it generates deadline-driven and heterogeneous data demanding real-time processing. This work proposes an efficient two-step scheduling algorithm comprising a Bi-factor classification task phase based on deadline and priority and a scheduling phase using an enhanced artificial Jellyfish Search Optimizer (JS) proposed as an Improved Jellyfish Algorithm (IJFA). The model considers a variety of cloud and fog resource parameters, including speed, capacity, task size, number of tasks, and number of virtual machines for resource provisioning in a fog integrated cloud environment. The model has been tested for the real-time task scenario with the number of tasks considering both the smaller workload and the relatively higher workload scenario matching the real-time situation. The model addresses the Quality of Service (QoS) parameters of minimizing the batch’s make-span time, lowering the batch execution costs, and increasing the resource utilization. Simulation results prove the effectiveness of the proposed model.

A Green Hybrid Congestion Management Scheme for IoT-Enabled WSNs

In the modern era, the Internet of Things (IoT) plays a vital role in connecting physical objects to the Internet. Therefore, an IoT technology can monitor and control any physical object from remote locations. In IoT-based systems, Wireless Sensor Network (WSN) is used to monitor a large number of heterogeneous physical objects. The IoT-enabled WSNs suffer from congestion problems that significantly reduce the network lifetime and performance of the network. This paper present a green hybrid congestion control mechanism for IoT-enabled WSNs. It uses an unequal clustering mechanism which saves the energy of battery-limited sensor nodes and resolves energy hole issues. Furthermore, a novel two-class priority-based congestion avoidance mechanism is proposed which significantly reduces communication delay. Extensive simulations and real-life experiments are performed to demonstrate the effectiveness of the proposed scheme. Experimental result shows that the proposed scheme outperforms the state-of-the-art algorithms in terms of network lifetime, successful packet delivery, average throughput, and other parameters. Furthermore, the proposed scheme is also tested in a real testbed that demonstrates its effectiveness for disaster management in smart city applications.

Modeling Automatic Room Temperature and Humidity Monitoring System with Fan Control on the Internet of Things

The Internet of Things (IoT) aims to expand the benefits of being connected to the Internet network continuously. It functions as a control system that has been widely applied in various fields because in certain case people are not allowed in certain rooms for security reasons. The research aimed to create a temperature and humidity monitoring system using as many fan controls as expected by utilizing IoT. The model used input in the form of temperature and humidity sensors. The output was a motor driver that drove a fan and used a microcontroller as the main processor. IoT-based systems consisted of hardware and software. Hardware included NodeMCU ESP8266 V3, DHT22 sensor, L298N motor driver module, fan, and computer. Meanwhile, the microcontroller software was made using Arduino IDE. From the test results, the system model works well. Fan control is set manually based on desired room temperature and humidity monitoring based on IoT. A mobile phone can also monitor temperature and humidity and control fans. The DHT22 sensor can read temperature and humidity every two seconds so that the resulting data is stable to display. Then, the L298N motor driver can adjust the fan speed with Pulse Width Modulation (PWM) using analog data ranging from 1 to 1.024.

Designing humanitarian logistics network for managing epidemic outbreaks in disasters using Internet-of-Things. A case study: An earthquake in Salas-e-Babajani city

Along with the destructive effects of catastrophes throughout the world, the COVID-19 outbreak has intensified the severity of disasters. Although the global aid organizations and philanthropists aim to alleviate the adverse impacts, many employed actions are not impactful in dealing with the epidemic outbreak in disasters. However, there is a gap in controlling the epidemic outbreak in the aftermath of disasters. Therefore, this paper proposes a novel humanitarian location-allocation-inventory model by focusing on preventing COVID-19 outbreaks with IoT-based technology in the response phase of disasters. In this study, IoT-based systems enable aid and health-related organizations to monitor people remotely, suspect detection, surveillance, disinfection, and transportation of relief items. The presented model consists of two stages; the first is defining infected cases, transferring patients to temporary hospitals promptly, and accommodating people in evacuation centers. Next, distribution centers are located in the second stage, and relief items are transferred to temporary hospitals and evacuation centers equally regarding shortage minimization. The model is solved by the LP-metric method and applied in a real case study in Salas-e-Babajani city, Kermanshah province. Then, sensitivity analysis on significant model parameters pertaining to the virus, relief items, and capacity has been conducted. Using an IoT-based system in affected areas and evacuation centers reduces the number of infected cases and relief item's shortages. Finally, several managerial insights are obtained from sensitivity analyses provided for healthcare managers.

An IoT-based resource utilization framework using data fusion for smart environments

Nowadays, many communities are emerging towards smart environments, requiring the communication and collaboration of diverse Internet-of-Things (IoT) devices. A smart environment exploits the use of IoT technology to share and process data among such devices for a better living. However, this comes with additional costs, such as the exponential growth of IoT devices, the heterogeneity of IoT use cases, and the new complex features encountered by IoT data, which complicate their processing and analysis using the traditional techniques. This causes a dramatic performance degradation of the used processing resources, which directly affects the overall efficiency and performance of IoT-based systems. Although different studies have presented resource utilization approaches for IoT systems, but they were not evaluated from different resource utilization perspectives. Besides, no efforts have been directed to investigate their effectiveness to process the unprecedented IoT data features that inevitably impact the accuracy and efficiency of resource utilization. In this paper, the Triple Phases Resource Utilized Data Fusion (TPRUDF) framework is proposed as the first IoT-based cost-aware resource utilization using data fusion. It exclusively considers different IoT data features by employing three phases of data fusion: (1) data in – data out, (2) data in – feature out, and (3) feature in – decision out. TPRUDF fuses the raw IoT data by maintaining the complex IoT data features, independent of the IoT domain or the computing model, using the spatiotemporal data fusion (STDF) IoT-based data fusion approach. TPRUDF then fuses the uncorrelated data features via the Principal Component Analysis. Finally, it employs two different resource utilization techniques: (1) Genetic Algorithms and (2) Particle Swarm Optimization, fusing their results using the voting logic fusion technique. A public edge-computing simulator is used to evaluate TPRUDF via three real-world smart cities datasets. The experimental results of the proposed TPRUDF framework indicate that it: (1) achieves an average accuracy level of resource utilization equal to 91%, (2) increases the resource utilization throughput by an average of 40% and eventually minimizes the processing delay, (3) boosts the resource utilization availability by 60%, and (4) decreases the energy consumption by 35%.

An Efficient Spam Detection Technique for IoT Devices Using Machine Learning

Abstract: The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. The volume of data released from these devices will increase many-fold in the years to come. In such an environment, machine learning algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this project, we propose the security of the IoT devices by detecting spam using machine learning. In this framework, five machine learning models are evaluated using various metrics with a large collection of inputs features sets. Each model computes a spam score by considering the refined input features. This score depicts the trustworthiness of IoT device under various parameters. The results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.

UAV and IoT-Based Systems for the Monitoring of Industrial Facilities Using Digital Twins: Methodology, Reliability Models, and Application.

This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components—sensors, means of communication, processing, and presentation—in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles.

A Systematic Survey on Fog and IoT Driven Healthcare: Open Challenges and Research Issues

Technological advancements have made it possible to monitor, diagnose, and treat patients remotely. The vital signs of patients can now be collected with the help of Internet of Things (IoT)-based wearable sensor devices and then uploaded on to a fog server for processing and access by physicians for recommending prescriptions and treating patients through the Internet of Medical Things (IoMT) devices. This research presents the outcome of a survey conducted on healthcare integrated with fog computing and IoT to help researchers understand the techniques, technologies and performance parameters. A comparison of existing research focusing on technologies, procedures, and findings has been presented to investigate several aspects of fog computing in healthcare IoT-based systems, such as increased temporal complexity, storage capacity, scalability, bandwidth, and latency. Additionally, strategies, tools, and sensors used in various diseases such as heart disease, chronic disease, chikungunya viral infection, blood pressure, body temperature, pulse rate, diabetes, and type 2 diabetes have been compared.

Research on the Time Drift Stability of Differential Inductive Displacement Sensors with Frequency Output.

An edge displacement sensor is one of the key technologies for building large segmented mirror astronomical optical telescopes. A digital interface is one novel approach for sensor technologies, digital transformation and the Internet of Things (IoT) in particular. Frequency output sensors and inductance-to-digital converter (LDC) demonstrated significant advantages in comparison with conventional sensors with analog-to-digital converter (ADC) interfaces. In order for the differential inductive frequency output displacement (DIFOD) sensor to meet the high-stability requirements of segmented mirror astronomical telescopes, it is important to understand the factors for time drift of the sensor. This paper focuses on the investigation of key factors of sensor structure and material, signal conditioning and interface, and fixtures for time drift to permanently installed applications. First, the measurement principle and probe structural characteristics of the sensor are analyzed. Then, two kinds of signal conditioning and digitalization methods using resonance circuits and LDC chips are implemented and compared. Finally, the time drift stability experiments are performed on the sensors with different signal conditioning methods and fixtures under controlled temperature. Experimental results show that the magnetic shield ring effectively improves the sensitivity and quality factor of the sensors, the time drift stability of the sensor using the signal conditioning based on resonance circuits is better than that of the sensors using LDC chips, and the root mean square (RMS) of the sensor time drift meets the requirement of 0.01 μm/24 h. This study will help further development of high-stability of frequency output sensors and IoT-based systems for scaled-up applications in the future.

Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms

Efficient resource use is a very important issue in wireless sensor networks and decentralized IoT-based systems. In this context, a smooth pathfinding mechanism can achieve this goal. However, since this problem is a Non-deterministic Polynomial-time (NP-hard) problem type, metaheuristic algorithms can be used. This article proposes two new energy-efficient routing methods based on Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO) algorithms to find optimal paths. Moreover, in this study, a general architecture has been proposed, making it possible for many different metaheuristic algorithms to work in an adaptive manner as well as these algorithms. In the proposed methods, a new fitness function is defined to determine the next hop based on some parameters such as residual energy, traffic, distance, buffer size and hop size. These parameters are important measurements in subsequent node selections. The main purpose of these methods is to minimize traffic, improve fault tolerance in related systems, and increase reliability and lifetime. The two metaheuristic algorithms mentioned above are used to find the best values ​​for these parameters. The suggested methods find the best path of any length for the path between any source and destination node. In this study, no ready dataset was used, and the established network and system were run in the simulation environment. As a result, the optimal path has been discovered in terms of the minimum cost of the best paths obtained by the proposed methods. These methods can be very useful in decentralized peer-to-peer and distributed systems. The metrics for performance evaluation and comparisons are i) network lifetime, ii) the alive node ratio in the network, iii) the packet delivery ratio and lost data packets, iv) routing overhead, v) throughput, and vi) convergence behavior. According to the results, the proposed methods generally choose the most suitable and efficient ways with minimum cost. These methods are compared with Genetic Algorithm Based Routing (GAR), Artificial Bee Colony Based routing (ABCbased), Multi-Agent Protocol based on Ant Colony Optimization (MAP-ACO), and Wireless Sensor Networks based on Grey Wolf optimizer. (GWO-WSN) algorithms. The simulation results show that the proposed methods outperform the others.

Home Automation System Using Based on IoT

Abstract: This paper has shown an automation proposal that represents how IoT-based systems are making human life easier with the help of Google Assistant. It helps to control the automation of lighting, air conditioning, and CCTV security, as well as home appliances. Automation systems also control the excessive usage of electricity. Google Assistant is one of the greatest home companions that controls every IoT-based home appliance with the good interaction of Wi-Fi modules. It also provides good coverage of Wi-Fi-connected devices. Wi-Fi is frequently used for the facility of remote monitoring. The remotely monitored and controlled home devices connected via the Internet is a part of the Internet of things(IoT). So overall, we can perceive that developing our home smart that also provides a better management system without any human intervention. This document describes the implementation of such systems