IoT Applications Research Articles

Live Data Monitoring in Industry

Abstract: In contemporary industrial environments, real-time monitoring of critical parameters is vital for ensuring safety, efficiency, and compliance with regulations. This thesis details the development and implementation of an advanced live data monitoring system designed to track temperature, humidity, fire presence, and oxygen levels. Key components of the system include a DHT sensor for temperature and humidity measurement, a fire sensor for fire detection, an oxygen sensor for monitoring atmospheric oxygen, and a buzzer for immediate auditory fire alerts. Central to the system is the ESP8266 WiFi module, which facilitates the wireless transmission of sensor data to ThingSpeak, a cloud-based data collection and visualization platform. This setup allows for continuous, remote monitoring of environmental conditions, providing critical insights and alerts to industrial operators. The project delves into the hardware configuration, sensor calibration, data acquisition processes, and wireless communication protocols essential for effective real-time monitoring. It also addresses the challenges of integrating multiple sensors and ensuring reliable data transmission in an industrial setting. The results demonstrate the system's capability to provide accurate, timely data, enhancing safety protocols and operational efficiency. By utilizing IoT technology, the proposed system offers a scalable solution for industrial environments aiming to implement advanced monitoring and alert systems. This work contributes to the field of industrial automation by illustrating a practical application of IoT in improving monitoring and safety measures, highlighting both the technical achievements and practical implications of the implemented system.

Multi-Agent Deep Reinforcement Learning-Based Inference Task Scheduling and Offloading for Maximum Inference Accuracy under Time and Energy Constraints

The journey towards realizing real-time AI-driven IoT applications is facing a significant hurdle caused by the limited resources of IoT devices. Particularly for battery-powered edge devices, the decision between performing task inference locally or by offloading to edge servers, all while ensuring timely results and conserving energy, is a critical issue. This problem is further complicated when an edge device houses multiple local inference models. The challenge of effectively allocating inference models to tasks between local models and edge server models under strict time and energy constraints while maximizing overall accuracy is recognized as a strongly NP-hard problem and has not been addressed in the literature. Therefore, in this work we propose MASITO, a novel multi-agent deep reinforcement learning framework designed to address this intricate problem. By dividing the problem into two sub-problems namely task scheduling and edge server selection we propose a cooperative multi-agent system for addressing each sub-problem. MASITO’s design allows for faster training and more robust schedules using cooperative behavior where agents compensate for each other’s sub-optimal actions. Moreover, MASITO dynamically adapts to different network configurations which allows for high-mobility edge computing applications. Experiments on the ImageNet-mini dataset demonstrate the framework’s efficacy, outperforming genetic algorithms (GAs), simulated annealing (SA), and particle swarm optimization (PSO) in scheduling times by providing lower times ranging from 60% up to 90% while maintaining comparable average accuracy in worst-case scenarios and superior accuracy in best-case scenarios.

Low-Cost 3D Indoor Visible Light Positioning: Algorithms and Experimental Validation

Visible light technology presents significant advancement for indoor IoT applications. These systems offer enhanced bit rate transmission, enabling faster and reliable data transfer. Moreover, optical-based visible light systems facilitate improved location services within indoor environments. However, many of these systems still exhibit limited accuracy within several centimeters, even when relying on costly high-resolution cameras. This paper introduces a novel low-cost visible light system for 3D positioning, designed to enhance indoor positioning accuracy using low-resolution images. Initially, we propose a non-integer pixel (NI-P) algorithm to enhance precision without the need for higher-resolution images. This algorithm allows the system to identify the precise light spot coordinates on the low-resolution images, enabling accurate positioning. Subsequently, we present an algorithm leveraging the precise coordinate data from the previous step to determine the 3D position of objects even in front of errors in the measures. Benefiting from high accuracy, reduced cost, and low complexity, the proposed system is suitable for implementation on low-end hardware platforms, thereby increasing the versatility and feasibility of visible light technologies in indoor settings. Experimental results show an average 2D positioning error of 1.08 cm and 3D error within 1.4 cm at 2.3 m separation between the object and camera, achieved with an average positioning time of 20 ms on a low-end embedded device. Consequently, the proposed system offers fast and highly accurate indoor positioning and tracking capabilities, making it suitable for applications like mobile robots, automated guided vehicles, and indoor parking management. Furthermore, it is easy to deploy and does not require re-calibration.

Development a novel design of miniaturized heptagonal Koch fractal wide band antenna for 5G mm wave and IoT applications

The object of the study is a compact heptagonal broadband antenna specially designed for use in the 5G millimeter band using Koch fractals to improve performance. As a result of the study, the most important problem of achieving higher gain, improving bandwidth and reducing interference at higher frequencies, which is necessary for the effective functioning of 5G networks, was solved. As a result, the maximum realized gain of 5 dB was obtained at a frequency of 27.58 GHz with an impressive bandwidth in the range from 26.5 to 40 GHz. The use of Koch fractal geometry and defective ground planes significantly improves impedance matching and expands bandwidth, which explains the excellent antenna performance compared to traditional designs. The features and distinguishing features of the results obtained, thanks to which they allowed solving the problem under study, are its compact dimensions (only 9 mm by 9 mm) and the ability to maintain VSWR at a level of less than 2 in the entire frequency spectrum. These features make the antenna particularly suitable for millimeter-band integration and flexible applications such as portable devices and wearable home appliances. The field of practical application of the results includes integration into portable and wearable devices, improving the performance and connectivity of Internet of Things applications. The conditions of practical use require compliance with 5G network standards and compatibility with millimeter-wave technologies. This characterizes the antenna as a significant achievement in antenna technology, demonstrating its potential for widespread adoption in next-generation wireless communication systems and paving the way for more reliable and high-performance wireless networks

Editorial

Dear Readers, It gives me great pleasure to announce the sixth regular issue of 2024. In this issue, 6 papers cover various topical aspects of computer science by 19 authors from 7 countries: Brazil, Chile, France, India, Saudi Arabia, Tunisia, and Turkey. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. As always, I would like to thank all authors for their sound research and the editorial board and our guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, help to maintain the quality of our journal.  In this regular issue, I am very pleased to introduce the following 6 accepted articles: George Marsicano, Edna Dias Canedo, Glauco V. Pedrosa, Cristiane S. Ramos, and Rejane M. da C. Figueiredo from Brazil look in their study into digital transformation of public services in a startup-based environment by 23 focus groups and 175 participants in total. Mauricio Solar and Pablo Aguirre from Chile discuss their research on 3D chest CT processes applying a ResNet-50 model to which a new dimension of information has been added, namely a simple autoencoder. In a collaborative work between researchers from Tunisia and France, Rakia Saidi, Fethi Jarray, and Didier Schwab propose in their article a cross-encoder neural network (Cross-BERT-GRU) to deal with the semantic similarity of Arabic sentences that benefits from both the strong contextual understanding of BERT and the sequential modeling capabilities of GRU. Also in a collaborative research between institutions from Tunisia and Saudi Arabia, Nozha Jlidi, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika present their research on MediaPipe with GNN for human activity recognition. G.V.Vidya Lakshmi and S. Gopikrishnan from India look into missing values research for the IoT domain and in particular present IMD-MP technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations. Last but not least, F. Didem Alay, Nagehan İlhan, and M. Tahir Güllüoğlu address in their article a comparative study of data mining methods for solar radiation and temperature forecasting models. Enjoy Reading Cordially,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Scalable solutions for implementing satellite-based sensor networks

The importance of sensor networks in the field of IoT cannot be overstated, especially with the growing demand for IoT solutions. The new Internet protocols such as IPv6 and 5G mobile provide high-capacity communication networks, demonstrating the relevance and potential applications of IoT in the future. However, with new demands come new challenges. This paper aims to explore the challenges of Satellite-based Sensor Networks, a new topic with various applications. This article explores the challenges and constraints of implementing a scalable satellite-based sensor network. We examined the hardware and software limitations of the Iridium satellite SBD protocol and proposed tailored solutions using design flow algorithms to tackle flexibility, power consumption, bandwidth, and scalability issues. Satellite-based sensor networks face challenges in data transmission and sensor lifespan. In this study, we introduce event-driven and compression algorithms to address these challenges. By implementing clustering, we achieved a significant increase in the number of nodes within each cluster. Our proposed methods were validated using simulated datasets derived from temperature and location data. A unique data transmission method, based on event count, was introduced. Notably, the Differential Huffman compression algorithm resulted in an 81 % reduction in data transmission compared to traditional methods. This demonstrates about 62 % improvement in efficiency over existing techniques. Our approach not only paves the way for future advancements in this domain but also highlights the untapped potential of satellite-based sensor networks.

Ultra8T: A sub-threshold 8T SRAM with leakage detection

In energy-constrained scenarios such as IoT applications, the primary requirement for System-on-Chips (SoCs) is to increase battery life. However, when performing the sub/near-threshold operations, the relatively large leakage current hinders Static Random Access Memory (SRAM) from normal read/write functionalities at the lowest possible voltage (VDDMIN). In this work, we first propose a model that describes a specific relationship between read current and leakage noise in a given column. Based on the model, Ultra8T SRAM is designed to aggressively reduce VDDMIN by using a leakage detection strategy where the safety sensing time on bitlines is quantified without any additional hardware overhead. We validate the proposed Ultra8T using a 256 × 64 array in 28 nm CMOS technology. Post-simulation results show successful read operation at 0.25 V with 1.11 μs read delay, and the minimum energy required is 1.69 pJ at 0.4 V

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms.

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced.

Blockchain‐based secure multifunctional data aggregation for fog‐IoT environments

SummaryData aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain‐based secure multifunctional data aggregation method for IoT‐Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions.

Systems Interoperability Types: A Tertiary Study

Interoperability has been a focus of attention over at least four decades, with the emergence of several interoperability types (or levels), diverse models, frameworks, and solutions, also as a result of a continuous effort from different domains. The current heterogeneity in technologies such as blockchain, IoT and new application domains such as Industry 4.0 brings not only new interaction possibilities but also challenges for interoperability. Moreover, confusion and ambiguity in the current understanding of interoperability types exist, hampering stakeholders’ communication and decision-making. This work presents an updated panorama of software-intensive systems interoperability with particular attention to its types. For this, we conducted a tertiary study that scrutinized 37 secondary studies published from 2012 to 2023, from which we found 36 interoperability types associated with 117 different definitions, besides 13 interoperability models and six frameworks in various domains. This panorama reveals that the concern with interoperability has migrated from technical to social-technical issues going beyond the software systems’ boundary and still requiring solving many open issues. We also address the urgent actions and also potential research opportunities to leverage interoperability as a multidisciplinary research field to achieve low-coupled, cost-effective, and interoperable systems.

Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

With the rapid development of the Internet of Things (IoTs), wearable sensors are playing an increasingly important role in daily monitoring of personal health and wellness. The signal-to-noise-ratio has become the most critical performance factor to consider. To enhance it, on the one hand, good sensing materials/devices have been employed; on the other hand, signal amplification and noise reduction circuits have been used. However, most of these devices and circuits work in an active sampling mode, requiring frequent data acquisition and hence, entailing high-power consumption. In this scenario, a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great interest. Here, we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor (PBJT) that acts as an autonomous and highly sensitive, current- and/or voltage-mediated pressure sensor. The PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collector-base diodes. Upon force exertion on the emitter side, as a result of the piezoelectric effect, the emitter-base diode is forward biased while the collector-base diode is reverse biased. Due to the inherent BJT amplification effect, the PBJT achieves record-high sensitivities of 139.7 kPa-1 (current-based) and 88.66 kPa-1 (voltage-based) in sensing mode. The PBJT also has a fast response time of <110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger touch. Therefore, the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous, making it promising for pressure sensing and other IoT applications.

Federated Learning for Edge Computing in REST APIs: Investigate the Feasibility of Employing Federated Learning Techniques to Train Machine Learning Models Directly on Edge Devices Interacting with REST APIs, Minimizing Data Transfer and Enhancing Privacy

This research explores the feasibility of using federated learning techniques to train machine learning models directly on edge devices interacting through REST APIs. This article designs an architecture that facilitates federated learning using REST APIs for communication between a central server and edge nodes. The architecture ensures efficient model distribution, update aggregation, and iterative global model refinement. This research implements this system in a simulated environment to evaluate its performance. Initial results show that federated learning significantly reduces data transfer and enhances privacy without compromising model accuracy. The REST API-based communication model effectively coordinates updates between the central server and edge devices, and privacy-preserving techniques maintain data security throughout the process. This study confirms that federated learning is a viable solution for edge computing, addressing key challenges in data privacy and communication efficiency, and opens pathways for scalable and effective distributed learning systems in real-world IoT applications. Federated learning offers a decentralized alternative, enabling local model training on edge devices with only model updates shared with a central server. This approach minimizes data transfer and enhances privacy.

A Systematic Review of Blockchain-based Privacy-Preserving Reputation Systems for IoT Applications

A Systematic Review of Blockchain-based Privacy-Preserving Reputation Systems for IoT Applications

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects.

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

Integration of Machine Learning with Fog Computing for Health Care Systems Challenges and Issues: A Review

Fog computing, a distributed cloud computing model, extends the traditional cloud paradigm to the network's edge, reducing latency and alleviating congestion. It addresses challenges in classical cloud architectures exacerbated by real-time IoT applications, which produce massive amounts of data that traditional cloud computing struggles to process due to limited bandwidth and high propagation delays. Fog computing is crucial in latency-sensitive applications like health monitoring and surveillance, where it processes vast volumes of data, minimizing delays and boosting performance. This technology brings computation, storage, monitoring, and services closer to the end-user, enhancing real-time decision-making capabilities. This paper presents the challenges of IoT applications. It also demonstrates the role of two emerging technologies fog computing and machine learning in health care scenarios.

Enhancing IoT Applications with Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) in Heterogeneous Wireless Sensor Networks (HWSN)

Enhancing IoT Applications with Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) in Heterogeneous Wireless Sensor Networks (HWSN)

FogDEFTKube: Standards‐compliant dynamic deployment of fog service containers

AbstractThe traditional cloud‐centric approach in IoT applications lack the speed and efficiency required for time‐critical tasks, resulting in network inefficiencies. To address this, the notions of Edge and Fog computing have emerged as alternatives. Fog computing facilitates the deployment of services and applications closer to the network's edge, lowering latency and allowing real‐time capabilities. It enhances reliability, fault tolerance, and connectivity in areas with spotty network coverage. Despite the fact that fog computing overcomes the limitations of cloud‐centric IoT processing, its adoption faces challenges like platform independence, interoperability, and portability. To tackle these challenges, the FogDEFT (Fog computing out of the box: Dynamic dEployment of Fog service containers with TOSCA) framework was developed. It complies to OASIS‐TOSCA standards and guarantees dynamic deployment of fog services on resource‐constrained devices while leveraging Docker containerization technology to ensure platform independence and interoperability. Due to its tight coupling with Docker Swarm, which is designed for medium‐sized deployments, the fogDEFT framework is constrained by Docker Swarm's limitations, hindering its ability to effectively manage large‐scale, automated, and resource‐efficient microservice deployments. To address these limitations, we propose FogDEFTKube, an extension of the FogDEFT architecture that incorporates Kubernetes for orchestration, Jenkins for continuous integration and deployment, and a comprehensive redefinition of the core capabilities of the FogDEFT architecture. This offers a promising solution that supports Kubernetes for handling scalable and highly available fog applications with ease while offering CI/CD. FogDEFTKube simplifies the modeling and deployment of fog services while abstracting the complexities of underlying fog networks.

A learning-based data and task placement mechanism for IoT applications in fog computing: a context-aware approach

A learning-based data and task placement mechanism for IoT applications in fog computing: a context-aware approach

A DBDCP Antenna With a Helmet-Conformal AMC for Industrial IoT Applications Featuring LHCP and RHCP in the Low and High Bands, Respectively

A DBDCP Antenna With a Helmet-Conformal AMC for Industrial IoT Applications Featuring LHCP and RHCP in the Low and High Bands, Respectively

Reliability and performance of resource efficiency in dynamic optimization scheduling using multi-agent microservice cloud-fog on IoT applications

Reliability and performance of resource efficiency in dynamic optimization scheduling using multi-agent microservice cloud-fog on IoT applications