IoT-based Applications Research Articles

Implementation Of IoT-Based Optical Sensors For Real-Time Monitoring In Palm Oil Processing

Optical sensors are widely used in various industrial sectors, including petrochemical and medical industries, with applications ranging from oxygen level measurement, concentration of substances in a liquid, object detection, and more. Optical sensors can be used to detect the water content in oil, such as palm oil or other oils. In an industrial process, especially in the palm oil industry, the water content in oil must meet a maximum standard of 4.5%. Optical sensors can measure water content using various methods, such as Total Internal Reflection (TIR) or Mid-Infrared (MIR), and can be monitored in real-time using IoT-based applications. The utilization of sensitive optical technology takes advantage of changes in the refractive index caused by variations in water content in palm oil. The method employed involves the development of an optical sensor based on the Mid-Infrared principle, which measures the amount of light absorbed by water and the light reaching the sensor for comparison. The results of water content measurements in palm oil that have been conducted can only be implemented for water content measurements of 0-5% in palm oil, with a sensor response that is less than linear above 5% water content. By correcting the non-linearity error of the sensor, the data is reduced to produce measurement results that are closer to the actual water content in palm oil, which will be displayed on local monitoring and real-time or continuous monitoring through IoT using smartphones and PCs wirelessly via IoT-based applications

Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.

Low Network Power Challenges in IoT-Based Applications in Smart Cities

The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

Securing data and preserving privacy in cloud IoT-based technologies an analysis of assessing threats and developing effective safeguard

The Internet of Things (IoT) is a powerful technology adopted in various industries. Applications of the IoT aim to enhance automation, productivity, and user comfort in a cloud and distributive computing environment. Cloud computing automatically stores and analyzes the large amounts of data generated by IoT-based applications. Cloud computing has become a crucial component of the information age through easier data administration and storage. Currently, government agencies, commercial enterprises, and end users are rapidly migrating their data to cloud environments. This may require end-user authentication, greater safety, and data recovery in the event of an attack. A few issues were discovered by authors after analysis and assessments of various aspects of the published research papers. The research analysis reveals that the existing methods need to be further improved to address the contemporary dangers related to data security and privacy. Based on the research reports, it can be stated that safe end-to-end data transmission in a cloud-IoT environment requires modifications and advancements in the design of reliable protocols. Upcoming technologies like blockchain, machine learning, fog, and edge computing mitigate data over the cloud to some level. The study provides a thorough analysis of security threats including their categorization, and potential countermeasures to safeguard our cloud-IoT data. Additionally, the authors have summarized cutting-edge approaches like machine learning and blockchain technologies being used in the data security privacy areas. Further, this paper discusses the existing problems related to data privacy and security in the cloud-IoT environment in today’s world and their possible future directions.

Implementation of a Cuckoo Search intelligent simulation system for node placement problem in Wireless Mesh Networks: Performance evaluation for tuning hyperparameters [formula omitted] and [formula omitted]

In recent years, Internet of Things (IoT) is the hottest topics in the research and implementation of wireless networks. Most of the research of IoT-based applications is related with the data collecting and processing using sensors and actuators. As the network environment for connecting IoT devices are considered WLAN or Bluetooth. The Wireless Mesh Networks (WMNs) are the most suitable networks for the Internet of Things (IoT) due to their numerous benefits. However, they have several issues related to wireless communications. One solution is the optimization of position and location of mesh routers. But finding the best location of mesh routers is an NP-hard problem. To deal with this issue, we propose and implement an intelligent simulation system based on the Cuckoo Search (CS) algorithm called WMN-CS. In the implemented system, the hyperparameters should be tuned to find better solutions. This paper evaluates the WMN-CS performance for various combination of hyperparameters by tuning host bird discovery rate (pa) and scale parameter (γ). The simulation results show that a better performance is achieved when the scale parameter (γ) is between 0.07 and 0.09 and the host bird recognition rate (pa) is between 0.900 and 0.925.

Design of a Stub-Loaded Coupled Line Diplexer for IoT-Based Applications

Design of a Stub-Loaded Coupled Line Diplexer for IoT-Based Applications

A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things

Global population growth and increasing pollution levels are directly related. The effect does not just apply to outdoor spaces. Likewise, the low indoor air quality is also having a negative impact on the health of the building residents. According to the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Tackling this public health issue, due to the direct relationship between air pollution levels and mortality and morbidity rates as well as overall comfort, is mandatory. Many companies have begun to build inexpensive sensors for use in Internet of Things (IoT)-based applications to pollution monitoring. The research highlights design aspects for sustainable monitoring systems including sensor types, the selected parameters, range of sensors used, cost, microcontrollers, connectivity, communication technologies, and environments. The main contribution of this systematic paper is the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. Firstly, the IEEE database had the highest contribution to this research (48.51%). The results showed that 87.1%, 66.3%, and 36.8% of studies focused on harmful gas monitoring, thermal comfort parameters, and particulate matter levels pollution, respectively. The most studied harmful gases were CO2, CO, NO2, O3, SO2, SnO2, and volatile organic compounds. The cost of the sensors was suitable for people with limited incomes and mostly under USD 5, rising to USD 30 for specific types. Additionally, 40.35% of systems were based on ESP series (ESP8266 and ESP32) microcontrollers, with ESP8266 being preferred in 34 studies. Likewise, IoT cloud and web services were the preferred interfaces (53.28%), while the most frequent communication technology was Wi-Fi (67.37%). Indoor environments (39.60%) were the most studied ones, while the share for outdoor environments reached 20.79% of studies. This is an indication that pollution in closed environments has a direct impact on living quality. As a general conclusion, IoT-based applications may be considered as reliable and cheap alternatives for indoor and outdoor pollution monitoring.

ZSGEEHCP: Zonal Stable Gateway Based Energy Efficient Heterogeneous Clustering Protocol For WSN- Based IoT.

In future most of the devices on the earth connected to the internet for making world of intelligent networks. The internet of things (IoT) and Industrial IoT play a very significant role for implementing such kind of smart networks. So, in that case, the transmission of packets of data sent to the base station from sensor nodes with minimum energy consumption in a homogeneous or heterogeneous environment to hike the stability of the network appears as a big challenge for IoT-based networks. But, such kind of problem can be overcome by using a proper clustering algorithm. In this work, different kinds of classical clustering algorithms are studied precisely. Most of the traditional clustering algorithm protocols presume that all the nodes have the same amount of energy, they are unable to fully exploit the presence of node heterogeneity and the same problem will be faced with IoT-based networks. In this specific piece of work, we recommend a gateway based heterogeneous energy-based clustering algorithm which is crucial for many wireless sensor networks along with IoT-based applications. In this concept geographical area is divided into three zone, Advanced, Intermediate and normal zone. Here, advance nodes send data through gateway to the base station while intermediate node use clustering algorithm for transferring data and normal nodes communicate data to the base station directly. ZSGHCP is driven by the mounded election protocol probabilities of each node elected as a cluster head depending on the rest of the energy. By simulation, we show that ZSGHCP invariably extends the stability time as compared to current clustering procedures. Finally, we investigate the sensitivity of our ZSGHCP protocol to network heterogeneity characteristics that capture energy imbalance, throughput, and no of alive nodes. We construct that ZSGHCP payout extends the durability region for excessive values of additional energy brought by extra higher-powered nodes.

A Lightweight Symmetric Cryptography based User Authentication Protocol for IoT based Applications

The utilization of IoT is expanding across various domains, including telecare, intelligent home systems, and transportation networks. In these environments, IoT devices generate data gathered on remote servers, requiring external users to authenticate themselves to access the data. However, existing authentication protocols for IoT must meet the crucial requirements of speed, security against multiple attacks, and ensuring user anonymity and un-traceability. The main objective of this work is to find lightweight symmetric cryptography-based user authentication protocol tailored for IoT-based applications, focusing on MIM (Man-in-the-Middle) attack prevention, enhanced anonymity, and secure communication between IoT nodes and remote servers via IoT gateways. Existing protocols often lack sufficient defenses against MIM attacks and do not adequately address the need for enhanced user anonymity and secure communication channels within the IoT framework. Our research has identified that authentication techniques based on pairing are susceptible to attacks targeting temporary session-specific data, impersonation, privileged insiders, and offline password guessing. Furthermore, using bilinear pairing in these techniques requires significant computational and communication resources to address the security as mentioned above concerns. A new authentication mechanism must be proposed and designed explicitly for IoT scenarios. The proposed approach exclusively utilizes hash and exclusive-or operations to ensure suitability within the IoT context; thoroughly evaluated the recommended protocol against existing authentication protocols, employing both informal and formal analytical routines like BAN logic, ROR model, and AVISPA simulation. Our findings suggest protocol not only enhances performance but also enhances security. The proposed approach is a tried-and-true strategy for improving security rules in practical Internet of Things (IoT) settings addressing the inherent challenges posed by authentication requirements in IoT environments. The accuracy 98.93%, and Node detection rate 46.57% were improved which is a better outcome.

EPOPTIS: A Monitoring-as-a-Service Platform for Internet-of-Things Applications.

The technology landscape has been dynamically reshaped by the rapid growth of the Internet of Things, introducing an era where everyday objects, equipped with smart sensors and connectivity, seamlessly interact to create intelligent ecosystems. IoT devices are highly heterogeneous in terms of software and hardware, and many of them are severely constrained. This heterogeneity and potentially constrained nature creates new challenges in terms of security, privacy, and data management. This work proposes a Monitoring-as-a-Service platform for both monitoring and management purposes, offering a comprehensive solution for collecting, storing, and processing monitoring data from heterogeneous IoT networks for the support of diverse IoT-based applications. To ensure a flexible and scalable solution, we leverage the FIWARE open-source framework, also incorporating blockchain and smart contract technologies to establish a robust integrity verification mechanism for aggregated monitoring and management data. Additionally, we apply automated workflows to filter and label the collected data systematically. Moreover, we provide thorough evaluation results in terms of CPU and RAM utilization and average service latency.

ADVANCED METHODS FOR MAINTAINING AND MANAGING THE LIFE CYCLE OF CLOUD ENVIRONMENTS: SURVEY

Resource management is a fundamental concept in cloud computing and virtualization, encompassing the allocation, release, coordination, and monitoring of cloud resources to optimize efficiency. The complexity arises from the virtualized, heterogeneous, and multi-user nature of these resources. Effective governance is challenging due to uncertainty, large-scale infrastructures, and unpredictable user states. This paper presents a comprehensive taxonomy of resource management technologies, offering a detailed analysis of design architecture, virtualization, and cloud deployment models, along with capabilities, objectives, methods, and mechanisms. In a cloud computing environment, deploying application-based resource management techniques necessitates understanding the system architecture and deployment model. This paper explores centralized and distributed resource management system architectures, providing a review of effective resource management techniques for both, accompanied by a comparative analysis. The evolution of cloud computing from a centralized to a distributed paradigm is examined, emphasizing the shift towards distributed cloud architectures to harness the computing power of smart connected devices at the network edge. These architectures address challenges like latency, energy consumption, and security, crucial for IoT-based applications. The literature proposes various methods for distributed resource management, aligning with the distributed nature of these architectures. Resource management in cloud computing involves discovery, provisioning, allocation, and monitoring functions, with sub-functions like mapping and scheduling. Integrated approaches to consolidation and resource management have been explored in numerous studies. This paper summarizes and analyzes existing research on resource management functions, focusing on identification, provisioning, allocation planning, and monitoring, based on their objectives and methods.

Digital twin-assisted intelligent anomaly detection system for Internet of Things

The proliferation of Internet of Things (IoT) devices within the cyber–physical infrastructure has raised privacy and security concerns. This is because processing large amounts of data and extracting anomalous data cannot be imprecisely identified with the limited storage and processing capabilities of IoT devices. Against this backdrop, edge computing is a networking paradigm to reshape the next generation of IoT-based applications, which brings computation and data storage closer to the data sources. However, since edge servers are difficult to obtain security protections like in centralized data centers, this makes them more vulnerable to security attacks. To address this issue, we propose a novel digital twin system architecture in edge computing-based Internet of Things (IoT) networks, which allows efficient data processing for anomaly detection at the network edge. Digital twin is a replica of the real world in a virtual environment with continuous information exchange. Our digital twin-assisted solution gives a data-driven strategy against the cell outage and simulates the behavior and patterns of the physical assets in the digital world. Powered by data processing capabilities at the network edge, the proposed digital twin-assisted model can help extract features and behaviors, reduce noise from data generated in the network physical model, and filter out redundant data for a better characterization of the network physical model in the anomaly detection service. To this extent, the contributions in this paper are threefold: We first propose a novel digital twin-assisted anomaly detection framework that processes high-dimensional data efficiently and improves the system performance despite the imbalanced classes. Second, we define an autoencoder-based threshold algorithm and evaluate the eXtreme Gradient Boosting (XGBoost) classifier to implement self-learning capability into the digital twin for an accurate prediction. Third, we extract the features, dependencies, and relationships to apply the learned model to the new and unseen dataset for real-time monitoring and optimization and evaluate the performance of the proposed model with both real-time and historical data. Finally, we analyze the performance of different classifiers to validate our model. We use four different datasets and demonstrate that our solution outperforms the state-of-the-art techniques in terms of accuracy performance, AUC score, and ROC curve.

Sensors on Internet of Things Systems for the Sustainable Development of Smart Cities: A Systematic Literature Review.

The Internet of Things (IoT) is a critical component of smart cities and a key contributor to the achievement of the United Nations Sustainable Development Goal (UNSDG) 11: Sustainable Cities and Communities. The IoT is an infrastructure that enables devices to communicate with each other over the Internet, providing critical components for smart cities, such as data collection, generation, processing, analysis, and application handling. IoT-based applications can promote sustainable urban development. Many studies demonstrate how the IoT can improve smart cities' sustainable development. This systematic literature review provides valuable insights into the utilization of the IoT in the context of smart cities, with a particular focus on its implications for sustainable urban development. Based on an analysis of 73 publications, we discuss the role of IoT in the sustainable development of smart cities, focusing on smart communities, smart transportation, disaster management, privacy and security, and emerging applications. In each domain, we have detailed the attributes of IoT sensors. In addition, we have examined various communication technologies and protocols suitable for transmitting sensor-generated data. We have also presented the methods for analyzing and integrating these data within the IoT application layer. Finally, we identify research gaps in the literature, highlighting areas that require further investigation.

Impact of Internet of Things Adoption on Organizational Performance: A Mediating Analysis of Supply Chain Integration, Performance, and Competitive Advantage

Companies, in emerging economies and beyond, struggle to develop integration mechanisms that deliver supply chain performance and competitive advantages. Those striving for organizational performance by streamlining supply chain processes must assess the challenges and benefits of employing technologies for supply chain integration. This study’s objective is to analyze the supply chain integration antecedents required to enable supply chain performance, competitive advantages, and organization performance. Although prior research indicates that the Internet of Things (IoT), as one of the enabling technologies, plays an instrumental role in enhancing organization performance through supply chain integration, no empirical tests have been performed before. Therefore, this study examines the direct and indirect effects of the IoT’s impact on organizational performance through different mediating variables—supply chain integration, supply chain performance, and competitive advantages. This study uses survey data from Saudi Arabian companies that adopted IoT-based applications in their supply chains. The results from partial least squares structural equation modeling indicate that the benefits and challenges of IoT adoption significantly affect supply chain integration. Moreover, supply chain integration partially mediates the relationship between IoT, supply chain performance, and competitive advantages while supply chain performance and competitive advantages mediate the relationship between supply chain integration and organization performance. This study offers theoretical implications regarding how dynamic capabilities are associated with supply chain integration and how they influence organizational performance; it also reveals valuable managerial insights.

QEMCGAN: Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN for Medical Image Translation.

Generative adversarial networks (GANs) have become popular in medical imaging because of their remarkable performance and ability to translate images across different domains. However, GANs face several issues in image-to-image translation, including training instability, lack of diversity, and mode collapse. These issues become even more complex when using cyclic GANs. Additionally, collecting paired images required for GANs may be costly, especially in the medical domain. Cyclic GANs are a favorable choice for addressing this issue, as they can convert cross-domain images. However, no pre-existing technique or algorithm is comprehensive enough to handle diverse datasets and applications. To address these issues, we propose a novel Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN (QEMCGAN) that employs evolutionary computation, multiobjective optimization, and an intelligent selection scheme. We use simulated annealing and Pareto ranking selection using three fitness criteria to address local optima stagnation. Additionally, we use model quantization because of its suitability for low-cost IoT-based applications. Extensive trials reveal that EMCGAN and QEMCGAN produces more visually realistic images than other approaches while preserving both background information and salient features. In addition, QEMCGAN performs on par with the baseline approach even when the model size is halved, making it more efficient.

Custom Network Protocol Stack for Communication Between Nodes in a Cloudlet System

With the advent of internet of things (IoT), new network paradigms have emerged. One such technology is cloudlets. Cloudlets are being increasingly used in various IoT-based applications such as smart homes, smart cities, healthcare, and industrial automations. Cloudlets have an advantage of proximity to the end-device while offering services similar to the cloud. Existing cloudlets use IEEE 802.11 for communication between nodes. In this paper, the authors present a protocol customized for usage in cloudlets, which also considers various limitations of the node that constitute the cloudlet. The nodes on the cloudlet are generally constrained in terms of power and memory when compared to nodes on a cloud. The custom protocol also incorporates fault-tolerance, time synchronization, and factors such as task affinities for communication. The protocol proposed in this paper gave an excellent packet delivery ratio, the lowest being 91% even with increased bandwidth usage when compared to IEEE 802.11.

Monitoring Smart Applications for Monitoring and Controlling of IoT-Based Strawberry Hydroponic Plants

Hydroponic farming has become an attractive option in strawberry cultivation as it provides better control over the growing environment of the plants. However, proper management and continuous monitoring are challenges often faced by farmers in hydroponic farming practices. Therefore, in this study, we designed an IoT-based application that aims to monitor and control strawberry hydroponic farming through smartphone devices. The method used in this research involves developing an application that connects with various sensors installed in the hydroponic farm environment. The data obtained from the sensors is transmitted in realtime through the IoT network. In addition to the monitoring function, the application is also equipped with a control system that can be customized according to the needs of farmers. The result of this research is an application that can work from a short distance (local) or a long distance (cloud). The application is equipped with a hazard alarm notification system. The feature allows farmers to take action to maintain the health and quality of strawberry plants. The use of this application is expected to help make it easier for farmers to monitor and control hydroponic farming to be more efficient and optimize crop yields.

A Smart Machine Learning Model for Detection of Brain Hemorrhage Using IoT

Abstract: For the most part, the development of Web of Things empowered applications propelled the world during the most recent couple of years, giving cutting edge and novel-based answers for various issues. This developmental field is fundamentally lead by remote sensor organization, radio recurrence ID, and brilliant versatile advances. The Internet of Things, or IoT, plays a crucial role in smart medical devices and wearables that can collect a wide range of longitudinal patient-generated health data while also providing options for preliminary diagnosis. As far as endeavors made for aiding the patients utilizing IoT-based arrangements, specialists exploit capacities of the AI calculations to give proficient arrangements in drain conclusion. To decrease the demise rates and propose precise treatment, this paper presents a savvy IoT-based application utilizing AI calculations for the human cerebrum drain finding. In view of the modernized tomography check pictures for intracranial dataset, the help vector machine and feedforward brain network have been applied for the grouping purposes. Generally, characterization consequences of 80.67% and 86.7% are determined for the help vector machine and feedforward brain organization, separately. It is finished up from the resultant examination that the feedforward brain network beats in ordering intracranial pictures. The classification tool's output provides information about the kind of brain hemorrhage that, in the end, helps validate the expert's diagnosis. It is used as a learning tool for trainee radiologists to reduce system errors.

IoT Based Early Flood Detection System with Arduino and Ultrasonic Sensors in Flood-Prone Areas

IoT is one of the focuses of application development carried out by various developers today. The aim is to enable various devices and work independently to meet the various needs of their users. The flood early warning system is one of the much-needed IoT-based applications, enabling users to quickly obtain water level information in an area. This application can help people to be more aware of flood disasters, especially during the rainy season. This research develops a flood early warning system application by utilizing Arduino and ultrasonic sensors installed in flood-prone areas. The sensor is used to measure the water level at a time based on the distance from the water surface to the sensor. When the distance between the water surface and the sensor is less than or equal to the set threshold, the sensor will send data and alerts to the user via email. This research applies the IoT design and development method. In addition, this research also used the C and Python programming language for application prototypes and the MySQL database to store the data. the application in this study was tested using the blackbox method and the results showed that all application functions could run properly.