The Journal of Edge Computing (JEC, J. Edge Comp., J. Edge Comput.) is a Diamond Open Access peer-reviewed journal covering the science, theories, and practice of IoT, distributed systems, and edge computing. JEC considers scientific research on using and applying edge computing in various fields: education, science, medicine, architecture, etc. The journal also considers the publication of work on systematic reviews and meta-analyses, the structure of which meets the journal's requirements.
Academy of Cognitive and Natural Sciences
Object detection method based on instance segmentation of satellite image obtained in the conditions of cloud cover
Modern achievements in the space industry, combined with the continuous development of remote sensing technologies, form the basis for solving problems in various areas. Medium- and high-resolution satellite imagery often plays a key role in decision-making during crises in hard-to-reach areas. In the process of processing remote sensing data, a significant and still unresolved problem is the reconstruction of clouded images. This article analyses various approaches to cloud removal and data quality improvement. The traditional approaches considered have certain limitations associated with the loss of useful information. Particular attention is paid to deep learning methods, which are gaining popularity in solving cloud removal problems because they produce good results. The article discusses different DNN architectures (convolutional neural networks (CNN), conditional generative adversarial networks (cGAN)) and their modifications, identifies their advantages and disadvantages. A significant advantage of neural networks is their ability to adapt to various conditions and image types. The analysis of the disadvantages of fusing purely optical data led to the conclusion that the best solution to the problem of cloud removal from satellite images is to combine optical and radar data. As a result, the architecture of a model for removing clouds from optical satellite imagery using generative adversarial networks, combined with radar imagery, was developed. The theoretical hypotheses were confirmed by testing the model on the SEN12MS-CR dataset.
Copyright (c) 2026 Serhii Kovbasiuk, Mykola Romanchuk, Olena Naumchak, Leonid Naumchak https://creativecommons.org/licenses/by/4.0
Performance analysis of localised large language models in resource-constrained edge for Python and Rust APIs
Edge deployments of large language models (LLMs) often suffer from significant latency due to the overhead of high-level client runtimes on resource-constrained hardware. To address this challenge, we conducted a side-by-side performance analysis of four quantised LLMs – Llama 3.2:1b, Gemma 3:1b, Granite 3.1-MoE:1b, and Qwen 2.5:0.5b – on a Raspberry Pi 4 Model B (8 GB LPDDR4, quad-core ARM Cortex-A72) using both Python and Rust API clients. Each model was served via a local Ollama inference server, and a fixed suite of twenty prompts – covering factual retrieval, arithmetic reasoning, translation, code synthesis, and creative generation – was executed sequentially with a two-second inter-request delay, yielding 160 measurements per client. Rust markedly reduces cold-start delays: mean model load times fall from 1 648.7 ms (Python) to 52.8 ms (Rust) for Llama 3.2:1b, and from 607.0 ms to 171.3 ms for Qwen 2.5:0.5b. Corresponding end-to-end latencies decrease by 1.4-2.0 s across models. In warm-start conditions, both clients deliver nearly identical decoding throughput – ≈2.7 tokens/s for Llama 3.2:1b, 4.4 tokens/s for Gemma 3:1b, 7.4 tokens/s for Granite 3.1-MoE, and 8.6 tokens/s for Qwen 2.5:0.5b – indicating that runtime overhead is negligible once models are loaded. Rigorous statistical testing, including paired t-tests, Mann-Whitney U tests, and bootstrap confidence intervals, confirms that Rust’s coldstart advantages are highly significant (p < 0.01). At the same time, throughput differences in steady-state inference are not statistically meaningful. We discuss limitations in platform specificity, quantisation approaches, and prompt diversity, and outline future work on heterogeneous accelerators, adaptive scheduling, and ondevice fine-tuning. Finally, we highlight practical applications in smart agriculture, healthcare monitoring, industrial IoT, autonomous robotics, and offline educational tools. This benchmark furnishes actionable guidelines for selecting client languages and quantised models in edge AI scenarios.
Copyright (c) 2026 Partha Pratim Ray, Mohan Pratap Pradhan https://creativecommons.org/licenses/by/4.0
Comparative and edge-hybrid modeling of EfficientNetV2 and MobileNetV2 for multi-classcrop disease classification with statistical validation
Crop disease classification is critical for global food security, yet deploying accurate deep learning models on resource-constrained edge devices remains challenging. This study systematically compares EfficientNetV2 and MobileNetV2 while proposing an edge-optimised hybrid architecture integrating both with Vision Transformers (ViT). Evaluated on PlantVillage and field-collected images, MobileNetV2 demonstrated superior edge compatibility with 99.0% accuracy, 0.0938 s/image inference speed, minimal resources (30.38MB size), and statistical superiority (z-test p=0.0071). The hybrid model combines MobileNetV2’s texture analysis and Efficient-NetV2’s multiscale detection through a dual-branch architecture enhanced with SE blocks, ViT (16×16 patches), and attention-guided fusion. It achieved 99.5% test accuracy with real-time performance (0.15 s/image) and 97.97% field accuracy via Android deployment. Statistical validation confirmed robustness: Kruskal-Wallis H=597.40 (p<0.05), near-perfect AUC (0.999998), and minimal confidence variance (0.000010). Ablation studies verified architectural efficacy (98.68% accuracy with SE/gating modules). This work advances precision agriculture through a scalable framework unifying hybrid deep learning with edge-compatible deployment.
Copyright (c) 2025 Thomas Njoroge, Dr. Rachael Kibuku, Dr. Kevin Mugoye https://creativecommons.org/licenses/by/4.0
Edge of arXiv 2025: bibliometrics, themes, time trends, and networks
This study conducts a comprehensive bibliometric, thematic, temporal, and network analysis of 2000 edge computing preprints published on arXiv during 2025. Drawing from a corpus authored by 8683 researchers across 124 categories, the analysis reveals a highly collaborative field with an average of 4.86 authors per paper. Thematic modelling identifies 10 core topics, led by energy-efficient computing (659 weighted occurrences), data management frameworks (608), and AI model deployment (509), while research types emphasise systems (28.2%), machine learning (20.0%), and theory (19.2%). Temporal patterns reveal consistent growth, averaging 333 papers per month, with peaks in July (434) and October (415), likely influenced by conference cycles. Network analysis reveals 1074 communities with a modularity of 0.847, highlighting specialised clusters in federated learning and AI at the edge, although security remains underrepresented (3.4%). The field demonstrates strong AI integration (91.25%) and identifies 292 emerging topics, signalling a rapid evolution toward sustainable, quantum-enhanced, and neuromorphic paradigms. Findings underscore gaps in security, real-world evaluation, and sustainability, while proposing directions for interdisciplinary advancement.
Copyright (c) 2025 Serhiy O. Semerikov https://creativecommons.org/licenses/by/4.0
Acoustic Doppler localisation and tracking in 3D space with retardation correction
This study extends Doppler-based acoustic localisation from 2D to a complete 3D framework for UAV tracking. A key methodological contribution is the explicit correction for the retardation effect (signal propagation delay), solved numerically using the Newton-Raphson method. This correction proved essential, as the average localisation error was reduced from approximately 50 m (without correction) to about 15 m (with correction), confirming the practical necessity of including retardation in 3D acoustic models. The VarPro method was implemented to exploit the separable structure of the least-squares problem. Under stabilised conditions (with velocity fixed to resolve the V/f identifiability issue), the method demonstrated convergence and provided reasonable trajectory estimates with mean trajectory errors below 5%. At the same time, the diagnostic analysis revealed fundamental limitations of the VarPro method. First, the strong correlation between source velocity (V) and source frequency (f) makes unconstrained optimisation unstable and prone to divergence. Second, even in stabilised runs, the simplified analytical trajectory models (straight-line or their 7-parameter extension) are structurally inadequate for representing stochastic UAV motion, thereby forcing the optimiser toward non-physical solutions. These findings suggest that while VarPro can be applied successfully to simplified scenarios with constrained parameters, it is not a suitable general solution for localizing UAVs with complex, random trajectories. Future research will therefore focus on recursive state estimation methods such as the extended Kalman filter, which are better suited for dynamic stochastic motion and time-varying source frequencies.
Copyright (c) 2025 Andrii V. Riabko, Tetiana A. Vakaliuk, Oksana V. Zaika, Roman P. Kukharchuk https://creativecommons.org/licenses/by/4.0
Edge intelligence unleashed: a survey on deploying large language models in resource-constrained environments
Edge computing environments face unprecedented challenges in deploying large language models due to severe resource constraints, latency requirements, and privacy concerns that traditional cloud-based solutions cannot address. Current approaches struggle with the fundamental mismatch between LLMs' computational demands - requiring gigabytes of memory and billions of operations - and edge devices' limited capabilities, resulting in either degraded performance or infeasible deployments. This survey presents a systematic analysis of emerging techniques that enable efficient LLM deployment at the edge through four complementary strategies: model compression via quantisation and pruning that reduces memory footprint by up to 75% while maintaining accuracy, knowledge distillation frameworks achieving 4000× parameter reduction with comparable performance, edge-cloud collaborative architectures like EdgeShard delivering 50% latency reduction through intelligent workload distribution, and hardware-specific optimisations leveraging specialised accelerators. Extensive evaluation across multiple real-world testbeds demonstrates that hybrid edge-microservices architectures achieve 46% lower P99 latency and 67% higher throughput compared to monolithic approaches, while supporting 10,000 concurrent users with 100 ms latency constraints and reducing bandwidth consumption by 99.5% through selective cloud offloading. These advancements enable transformative applications in healthcare monitoring, autonomous systems, real-time IoT analytics, and personalised AI services, fundamentally reshaping how intelligence is delivered at the network edge while preserving privacy and ensuring responsiveness critical for next-generation computing paradigms.
Copyright (c) 2025 Serhiy O. Semerikov, Tetiana A. Vakaliuk, Olga B. Kanevska, Oksana A. Ostroushko, Andrii O. Kolhatin https://creativecommons.org/licenses/by/4.0
Method of adaptive knowledge distillation from multi-teacher to student deep learning models
Transferring knowledge from multiple teacher models to a compact student model is often hindered by domain shifts between datasets and a scarcity of labeled target data, degrading performance. While existing methods address parts of this problem, a unified framework is lacking. In this work, we improve multi-teacher knowledge distillation by developing a holistic framework, enhanced multi-teacher knowledge distillation (EMTKD), that synergistically integrates three components: domain adaptation within teacher training, an instance-specific adaptive weighting mechanism for knowledge fusion, and semi-supervised learning to leverage unlabeled data. On a challenging cross-domain cardiac MRI benchmark, EMTKD achieves a target domain accuracy of 88.5% and an area under the curve of 92.5%, outperforming state-of-the-art techniques by up to 5.0%. Our results demonstrate that this integrated, adaptive approach yields significantly more robust and accurate student models, enabling effective deep learning deployment in data-scarce environments.
Copyright (c) 2025 Oleksandr Chaban, Eduard Manziuk, Pavlo Radiuk https://creativecommons.org/licenses/by/4.0
Akka as a tool for modelling and managing a smart grid system
The article addresses big data processing challenges, fault tolerance, and consumer interaction in a smart grid network management system. It analyses the use of Akka for agent-based modelling of smart grid systems, which enables the creation of distributed, resilient systems that can efficiently scale and recover from failures. The article describes how the hierarchical structure of Akka actors can improve the management of disparate components within a smart grid system, allowing developers to create complex interaction and management models. The approach ensures multithreading and asynchronous operation, geographic distribution of management nodes, and enhanced system security, allowing the simulation of system hierarchy down to the level of the end device or user, which is crucial for overall system reliability.
Copyright (c) 2025 Mykola Yaroshynskyi, Arsentii Prymushko, Ivan Puchko, Oleksii Sirotkin, Dmytro Sinko https://creativecommons.org/licenses/by/4.0
Edge of excellence: JEC's Scopus achievement, doors 2025 breakthroughs, and Volume 4 Issue 1 (2025) highlights
This editorial highlights recent edge computing advancements from the doors 2025 workshop and JEC Volume 4, Issue 1, while celebrating the journal's acceptance into Scopus. The paper examines innovative solutions addressing distributed systems challenges, including IoT-based monitoring systems, intelligent traffic prediction algorithms, and security implementations for resource-constrained environments. Architectural frameworks like Akka for smart grids and edge-deployed large language models represent significant technological developments. The editorial acknowledges Scopus indexing as recognition of the journal's scientific integrity and the collaborative efforts of the editorial community. It demonstrates how edge computing continuously evolves to optimise resources, reduce latency, and enhance security across diverse networked ecosystems.
Copyright (c) 2025 Serhiy O. Semerikov, Tetiana A. Vakaliuk https://creativecommons.org/licenses/by/4.0
Innovative resource-saving security strategies for IoT devices
The astounding trend of increasing the number of connected IoT devices reflects the growing importance of this technology in industry, healthcare, the domestic sphere, and other sectors. However, with the expansion of IoT capabilities, the number of challenges also rises, particularly regarding the security of these devices, many of which are characterised by limited resources such as memory, power consumption, computational power, and network bandwidth. This article examines the key challenges associated with ensuring the security of IoT devices and proposes potential solutions and optimisation strategies that consider these limitations. The primary focus is developing and analysing lightweight cryptographic algorithms capable of providing robust data protection with minimal resource usage. The article also discusses efficient energy management strategies and optimising memory usage in IoT devices. Emphasis is placed on developing adaptive security mechanisms that can effectively respond to dynamic operational conditions and resource constraints. It is noted that further research and development should focus on creating integrated solutions that combine hardware, software, and managerial aspects to optimise the overall efficiency and security of IoT systems.
Copyright (c) 2025 Inna Rozlomii, Andrii Yarmilko, Serhii Naumenko https://creativecommons.org/licenses/by/4.0
IoT-based home control system using NodeMCU and Firebase
Integrating Internet of Things (IoT) technology in home control systems has led to a significant advancement in the control and monitoring of home appliances and environmental conditions. This study presents the development of an IoT-based home control system that uses the NodeMCU ESP8266 microcontroller to manage several home appliances. This system connects to a Firebase real-time database to store or retrieve data in real-time. The user interacts with the IoT system through a mobile application named My Home, developed using Java programming language, and this mobile application is also interfaced with the Firebase cloud server. The developed IoT system allows users to control home appliances such as lights, sockets, fans, and cookers, and it also provides real-time updates of environmental parameters, such as temperature and humidity, which are measured by a DHT11 sensor. In addition, a PIR motion sensor was integrated into the system to enhance the home's security by detecting intrusion. The system's hardware functionality is based on a written Arduino code, which establishes Wi-Fi connectivity to a predefined network, communicates with the Firebase database, handles appliance control and manages sensor data. This IoT-based home control system shows the potential of integrating microcontrollers with cloud services to create a smart, responsive and user-friendly home control platform. The developed IoT system offers a foundation for advancements, thereby making it a valuable contribution to the growing field of smart home technology.
Copyright (c) 2024 Paul Oluwatobiloba Ayeni, Olayiwola Charles Adesoba https://creativecommons.org/licenses/by/4.0
Managing energy consumption in FPGA-based edge computing systems with soft-core CPUs
Edge computing, characterized by processing data closer to its source, has emerged as a promising paradigm to address the challenges of latency, bandwidth, and privacy in the Internet of Things (IoT) era. At the same time, Field-Programmable Gate Arrays (FPGAs) have gained significant attention in edge computing due to their ability to reconfigure design, low power consumption, and high performance. However, the energy consumption of FPGA-based edge computing systems remains a critical concern, particularly in resource-constrained environments where power efficiency is crucial. This paper presents an energy-efficient edge computing system focusing on job scheduling and power management optimization. We review existing techniques and methodologies for optimizing energy consumption in computing systems, including FPGA-based edge devices, identify key challenges and opportunities for future enhancement and propose a flexible, low-power system design with soft-core CPUs.
Copyright (c) 2025 Oleksandr Hryshchuk, Sergiy Zagorodnyuk https://creativecommons.org/licenses/by/4.0
A comprehensive systematic review of intrusion detection systems: emerging techniques, challenges, and future research directions
The role of Intrusion Detection Systems (IDS) in the protection against the increasing variety of cybersecurity threats in complex environments, including the Internet of Things (IoT), cloud computing, and industrial networks. This study evaluates the existing state-of-the-art IDS methodologies using Deep Learning (DL) approaches, and advanced feature engineering techniques. This research also highlights the success of models such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Explainable AI (XAI) in improving detection accuracy as well as computational efficiency and interoperability. Blockchain and quantum computing technologies are explored to improve data privacy, resilience, and scalability in decentralized and resource-constrained environments. This work primarily identifies key challenges, including real-time anomaly detection, adversarial robustness, and imbalance datasets, to assist researchers in investigating further research opportunities. Focusing on future research in filling these gaps, proceeds toward developing lightweight, adaptive, and ethical IDS frameworks that can operate in real-time across dynamic and heterogeneous environments. In this paper, existing IDS approaches, research opportunities, and advanced cybersecurity strategies are critically synthesized to create a useful resource for academics, researchers, and industry practitioners.
Copyright (c) 2025 Arjun Kumar Bose Arnob, Rajarshi Roy Chowdhury, Nusrat Alam Chaiti, Sudipta Saha, Ajoy Roy https://creativecommons.org/licenses/by/4.0
Ambience: an operating system for IoT microservices
Increasingly, the heterogeneity of devices and software that comprise the Internet of Things (IoT) is impeding innovation. IoT deployments amalgamate compute, storage, networking capabilities provisioned at multiple resource scales, from low-cost, resource constrained microcontrollers to resource rich public cloud servers. To support these different resource scales and capabilities, the operating systems (OSs) that manage them have also diverged significantly. Because the OS is the “API” for the hardware, this proliferation is causing a lack of portability across devices and systems, complicating development, deployment, management, and optimization of IoT applications. To address these impediments, we investigate a new, “clean slate” OS design and implementation that hides this heterogeneity via a new set of abstractions specifically for supporting microservices as a universal application programming model in IoT contexts. The operating system, called Ambience, supports IoT applications structured as microservices and facilitates their portability, isolation, and deployment time optimization. We discuss the design and implementation of Ambience, evaluate its performance, and demonstrate its portability using both microbenchmarks and end-to-end IoT deployments. Our results show that Ambience can scale down to 64MHz microcontrollers and up to modern x86_64 servers, while providing similar or better performance than comparable commodity operating systems on the same range of hardware platforms.
Copyright (c) 2024 Fatih Bakir, Sierra Wang, Tyler Ekaireb, Jack Pearson, Chandra Krintz, Rich Wolski https://creativecommons.org/licenses/by/4.0
Enhanced deep learning model architecture for plant disease detection in Chilli plants
A new deep-learning model for classifying and detecting plant diseases in chilli plants is described. It is built on a modified version of the MobileNet architecture. The model overcomes conventional diagnostic tools’ high computing costs and restricted adaptability by combining sophisticated optimisation models and reliable training procedures. The model considerably reduces the time and resources needed for an accurate diagnosis while effectively managing complicated illness presentations, with a diagnostic accuracy of 97.18%. Using the chilli leaf picture dataset, data augmentation, and finetuning techniques, the model shows promise for real-time disease diagnosis in agricultural environments. The study underscores the importance of high-quality image data and extensive training datasets, calling for further evaluation across various climatic and environmental conditions to ensure robustness and adaptability. This research opens new opportunities for AI-based models in diverse agricultural contexts, potentially leading to significant advancements in precision farming.
Copyright (c) 2024 Sultanul Arifeen Hamim, Akinul Islam Jony https://creativecommons.org/licenses/by/4.0
The sweet taste of IoT deception: an adaptive honeypot framework for design and evaluation
The rapid proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges for critical infrastructure systems. Honeypots and honeynets have emerged as promising deception technologies for detecting, deflecting, and investigating IoT-specific threats. In this paper, we propose an integrated framework for the design, implementation, and evaluation of adaptive honeypots in IoT environments. The framework consists of two key components: (1) an adaptive honeypot architecture that dynamically adjusts its behaviour based on observed attack patterns and (2) an evaluation methodology with quantitative metrics to assess the effectiveness of IoT honeypots. We discuss the current usage and future potential of this integrated framework in the context of critical infrastructure protection, highlighting challenges and opportunities for collaborative defence against evolving cyber threats.
Copyright (c) 2024 Dmytro S. Morozov, Andrii A. Yefimenko, Tetiana M. Nikitchuk, Roman O. Kolomiiets, Serhiy O. Semerikov https://creativecommons.org/licenses/by/4.0
Edge computing research: a bibliometric analysis and JEC Volume 3 Issue 2 (2024) highlights
This editorial presents a bibliometric analysis of edge computing research published in IEEE Access from 2018 to 2023, followed by an overview and discussion of the papers featured in the current issue of the Journal of Edge Computing (JEC), Volume 3, Issue 2 (2024). The IEEE Access analysis reveals key trends and themes in edge computing research, including IoT integration, AI and machine learning applications, resource management, wireless networks, and security concerns. The most cited authors, countries, and collaboration patterns are also identified. The JEC papers align well with these findings, addressing challenges and proposing novel solutions in areas such as IoT interoperability, plant disease detection using deep learning, wireless technologies in IoT projects, efficient operating systems, and adaptive IoT honeypot frameworks. The research in both IEEE Access and JEC demonstrates the rapid advancements and growing importance of edge computing in various domains.
Copyright (c) 2024 Tetiana A. Vakaliuk https://creativecommons.org/licenses/by/4.0
Use of wireless technologies in IoT projects
When it comes to creating projects based on the use of the Internet of Things (IoT), wireless sensor networks are often used. Edge computing in IoT technology reduces system response delays to sensor output signals and increases the network throughput. At the same time, a short-range sensor network can work locally without access to the Internet, while long-range networks, as a rule, require access to the Internet and use both edge and cloud computing. The paper shows the features of the application of both peripheral and cloud computing in the practical implementation of IoT projects with various methods of wireless data transmission. Specific examples show the possibility of data transmission over a short distance using ESP-NOW technology, nRF24L01 radio modules and creating a local Wi-Fi access point. The range of sensor data transmission between microcontrollers is practically determined for each proposed option. The calculation of the range of the LoRa radio line is carried out for the actual sensitivity values of the receiver. The Okamura-Hata radio wave propagation model is proposed to estimate the LoRa radio line’s total loss. The operational range of LoRa modules with different types of signal modulation is practically determined. The essence of edge computing combined with digital and analogue sensors is shown. Peculiarities of peripheral computing during the implementation of IoT projects for remote control of blood saturation of patients using wireless technologies are considered. An example of data transmission of the air quality control system through a gateway based on an ESP8266 microcontroller with a graphical display of measurements in the IoT cloud service ThingSpeak and Blynk was provided.
Copyright (c) 2024 Tetiana A. Vakaliuk, Oleksandr V. Andreiev, Oleksandr F. Dubyna, Oksana L. Korenivska, Yevheniya O. Andreieva https://creativecommons.org/licenses/by/4.0
Advancing IoT interoperability: dynamic data serialization using ThingsBoard
Benchmarking leading data serialization protocols such as schemaless JSON with binary serialization formats demonstrates the superior performance of the latter in Internet of Things (IoT) ecosystems. However, ease of integration and maintenance are equally important factors for real-world applications. IoT developers choose schemaless JSON formats for primary serialization because of their user-friendliness. However, interest in using Protocol Buffers directly at the device level in Internet of Things ecosystems is growing. Many IoT devices now transfer data exclusively via Protobuf, while others are switching to this format to improve efficiency and reduce network load. However, the static nature of Protobuf requires constant developer intervention, which undermines the scalability and versatility of the platforms, especially in cloud deployments. We explore the challenges of integrating devices that communicate exclusively through Protobuf into IoT platforms using the ThingsBoard as an example. Our study proposes a dynamic method for integrating new Protobuf-compatible devices by automating the compilation of the scheme into the platform’s code base. This approach aims to simplify integration and maintenance, which, in addition to productivity, are key factors in operating efficiency in IoT environments.
Copyright (c) 2024 Dmytro I. Shvaika, Andrii I. Shvaika, Volodymyr O. Artemchuk https://creativecommons.org/licenses/by/4.0
Reliable distributed systems: review of modern approaches
Called on to refine service-oriented architecture, a new architectural pattern named microservices emerged in the early two-thousands promising to speed up the delivery of new features. All of this is achieved by allowing many teams to work on separate autonomous parts of an application independently, though steepening the learning curve and introducing infrastructure-related issues. Overtime, while gaining more popularity, the initial meaning, as well as peculiarities of distributed systems development and reason for the use of the pattern, has turned out to be less prominent, instead becoming a sliver bullet for monolithic applications, which irreversibly leads to a tremendous increase in complexity of a system as well as other inherent problems. The absence of a single definition as well as the misleading name of the microservices pattern has all contributed to the development of the notoriously known distributed monolith. In this document, a review was conducted to resurrect the notion of microservices, understand their nature, and how and when the pattern should be applied. This is done by firstly understanding the difference between monolithic and distributed systems including their strengths and weaknesses, and defining the single most important reason for microservices to be used. The main part focuses on reducing the coupling in the system, which is the main obstacle during the development of a reliable and agile distributed system. Firstly, by modeling boundaries following domain teams and architectural needs, we ensure autonomous deployable units are created that provide independence during development, isolation, and reliability. Later by analyzing approaches to sharing data and communication, which are two major forces structuring a distributed system, we secure the previously established boundaries.
Copyright (c) 2023 Oleh V. Talaver, Tetiana A. Vakaliuk https://creativecommons.org/licenses/by/4.0
Responding to challenge call for machine learning model development in diagnosing respiratory disease sounds
The normal and abnormal sounds from the respiratory system shed great light on medical science by revealing the quality, diseases and changes in people’s lungs. In medicine, this easy and old method, realized by the stethoscope, facilitates the diagnosis of diseases by specialists. This manual method can sometimes lead to wrong decisions in terms of sound detection due to different audibility. Detailed sound analysis is crucial for accurately detecting lung diseases with high mortality rates. As technology advances, the development of automated approaches based on machine learning is of great interest as they provide modern and highly accurate analysis. In today’s most popular topic, i.e., the COVID-19 disaster, the conflict between early detection of respiratory disease and machine learning for sound signal processing is extremely important. In this study, a machine learning model was developed to detect respiratory system sounds such as sneezing/coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine learning model was developed in this study, which was a strong response to the challenge called the “Pfizer digital medicine challenge” on the “OSFHOME” open access platform. In the database provided in this challenge, which consists of 3 parts, features that effectively showed coughing/sneezing sound analysis were extracted from training, testing and validating samples. Based on the Mel frequency cepstral coefficients (MFCC) feature extraction method, mathematical and statistical features were prepared. The sequential forward selection (SFS) feature selection method was used to select the relevant and dominant variables among the obtained features to represent the model fully and accurately. Three different classification techniques were considered for successful respiratory sound classification in the dataset containing more than 3800 sounds. Support vector machine (SVM) with radial basis function (RBF) kernels, decision tree and ensemble aggregation classification methods were used as classification techniques. In an attempt to classify coughing/sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83% success.
Respiratory sounds, COVID-19, Coughing/Sneezing, Feature extraction, Classification.
Copyright (c) 2024 Negin Melek https://creativecommons.org/licenses/by/4.0
An IoT system based on open APIs and geolocation for the prevention of human health disorders
The article presents a study devoted to improving the developed Internet of Things system based on open APIs and geolocation, which is aimed at analyzing data about the state of the environment using an expert approach and data visualization for possible prevention of human health disorders. Based on the developed Internet of Things system, open APIs, geolocation using intelligent gadgets, and the Meteorological Geographic Information System, the study generates a message about the danger to human health associated with meteorological factors. Accordingly, a person is informed promptly about potential risks and threats, particularly about the presence of pollen in the air, indicating the level of its concentration in the air, and about problems with air quality. What is the “anthropo-geo-sensory-digital” prerequisite for making effective real-time decisions to prevent human health disorders? New features were added to the developed system to analyze data about potential risks and threats that could lead to human health disorders, in particular, about the presence of temperature problems, under the condition that this indicator goes beyond the normative and optimal zone; the presence of relative humidity problems, under the condition that this indicator go beyond the normative and optimal zone; the presence of wind speed problems, if the air wind speed exceeds the permissible standards. Effective decision-making based on providing timely information about potential risks and threats to human health, in addition to preventive, has significant methodological and technological potential that can be used to improve the effectiveness of health care, both in extreme conditions and in conditions of sustainable existence. The system developed and improved by us can also be considered as one of the ways of introducing innovations in health care, the IT field, the educational process in institutions of higher education and conducting further research in this field, in particular, in the direction of data processing in health care systems based on machine learning.
meteorological geographic information system
Copyright (c) 2024 Oksana Klochko, Vasyl Fedorets https://creativecommons.org/licenses/by/4.0
Editorial for JEC Volume 3 Issue 1 (2024)
This editorial introduces the first issue of the third volume of the Journal of Edge Computing (JEC). It provides an overview of the five articles featured in this issue, which cover diverse applications of edge computing technologies in domains such as cybersecurity, healthcare, and distributed systems. The first article summarizes the 4th Edge Computing Workshop (doors 2024), highlighting research advances in edge computing. The second article proposes an LSTM-based model for detecting cyber attacks in IoT systems using the CIC-IoT2023 dataset. The third article presents a machine-learning model for classifying respiratory system sounds to aid in the early diagnosis of respiratory diseases. The fourth article describes an IoT system that analyzes environmental data using geolocation to generate alerts about potential health risks. The fifth article explores the use of telemetry for dynamic analysis of distributed systems to identify architectural smells and anomalies. The editorial highlights the potential of edge computing technologies in addressing various challenges and expresses gratitude to the authors, reviewers, and editorial team for their contributions.
Copyright (c) 2024 Serhiy O. Semerikov https://creativecommons.org/licenses/by/4.0
Telemetry to solve dynamic analysis of a distributed system
In the modern software development world, implementing distributed solutions has become quite common due to the flexibility it brings to big companies. The downside is that when developing such systems, especially in many teams, global design problems may not be obvious and lead to a slowdown in the development process or even problems with the location of errors or degradation of overall system performance. In addition, the timely reaction to system degradation is complicated by the distributed nature of the architecture; while manually configuring rules for reporting problematic situations can be time-consuming and still incomplete, automatic detection of possible system anomalies will give engineers (especially Software Reliability Engineers) the focus on problems. For this reason, applications that can dynamically analyse the system for problems have great potential. Currently, the topic of using telemetry for system analysis is actively studied and gaining traction, so further research is valuable. The work aims to theoretically and practically prove the possibility of using telemetry to analyse a distributed information system and detect harmful architectural practices and anomalous events. To do this, firstly, a detailed overview of the problems related to the topic and the feasibility of using telemetry is provided; the next section briefly describes the history of the development of monitoring systems and the key points of the latest OpenTelemetry standard, reviews popular application performance monitoring systems, and defines innovative features to be further researched. The main part includes an explanation of the approach used to collect and process telemetry, a reasoning behind the usage of Neo4j as a data storage solution, a practical overview of graph theory algorithms that help in the analysis of the collected data, and a description outlining how the PCA algorithm is employed to detect unusual situations in the whole system instead of individual metrics. The results provide an example of using the software presented with Neo4j Bloom to visualise and analyse the data collected over several hours from the OpenTelemetry Demo test system. The last section contains additional remarks on the results of the study.
Copyright (c) 2024 Oleh V. Talaver, Tetiana A. Vakaliuk https://creativecommons.org/licenses/by/4.0
Empowering the Edge: Research advances from doors 2024
The 4th Edge Computing Workshop (doors 2024) held in Zhytomyr, Ukraine, aimed to bring together researchers to share advances and applications in edge computing, a critical technology for enabling the Internet of Things and analytics close to data sources. This report summarises the event, highlighting the peer-reviewed papers covering topics from edge hardware to AI and security. The nine accepted papers, selected from 19 submissions, propose innovative approaches to advance the field. Common themes include leveraging edge computing for sensing applications and addressing resource utilisation and management challenges. The workshop stimulated valuable discussion and collaboration around these important research directions.
Copyright (c) 2024 Tetiana A. Vakaliuk, Serhiy O. Semerikov https://creativecommons.org/licenses/by/4.0
A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset
The growth of Internet of Things (IoT) gadgets has ushered in a new era of connectedness and convenience, but it has also sparked worries about security flaws. Long Short-Term Memory (LSTM) networks are used in this research's use of intrusion detection as a novel strategy to strengthen IoT security. The proposed LSTM-based model excels in detecting both known and evolving cyber-attack patterns with an accuracy rate of 98.75% and an F1 score of 98.59% in extensive experimental evaluations using the vast CIC-IoT2023 dataset, representing a varied array of IoT network traffic scenarios. This research contributes significantly to IoT security while addressing the urgent need for adaptable intrusion detection systems to defend against changing cyber threats. It is an essential step toward ensuring IoT technology's long-term development and dependability in a world that is becoming more interconnected.
Copyright (c) 2024 Akinul Islam Jony, Arjun Kumar Bose Arnob https://creativecommons.org/licenses/by/4.0
Editorial for JEC Volume 2 Issue 2 (2023)
This editorial presents innovative research at the intersection of edge computing and various disciplines, demonstrating the transformative potential of edge computing technologies. The issue includes a study on a digital Proportional-Integral-Derivative (PID) regulator model for controlling unmanned aerial vehicles, using digital filtering methods and a genetic algorithm; discusses an autonomous Internet of Things (IoT) system for monitoring classroom microclimates, contributing to the understanding of how microclimate parameters influence the physiological state of students; details the design and implementation of an educational model for a smart home, integrating various subsystems and renewable energy sources. This issue aims to inspire further exploration and innovation in edge computing, driving the field forward and opening up new possibilities for technology and society.
Copyright (c) 2023 Tetiana A. Vakaliuk https://creativecommons.org/licenses/by/4.0
Edge computing in environmental science: automated intelligent robotic platform for water quality assessment
This paper introduces a novel intelligent robotic platform designed to expedite and enhance the process of water quality assessment and bottom relief analysis in reservoirs. The platform, equipped with an array of sensors and actuators, is capable of conducting comprehensive studies over larger areas of the reservoir, thereby overcoming the limitations of traditional water analysis methods. The platform’s advanced design includes a control board, servo motors, a brushless motor, a radio module, a GPS module, and a motor speed controller, all housed within a robust casing. The paper presents a functional diagram of the platform and discusses the results of a system study conducted on a reservoir. The study aimed to verify the system’s operation, evaluate the effectiveness of the research conducted, and calibrate water quality sensors. The platform utilizes an ultrasonic sensor for depth measurement and sensors for water acidity and temperature. The results of the monitoring system experiments led to the creation of a detailed map of the reservoir’s bottom area and provided valuable insights into water quality.
Copyright (c) 2023 Andrii G. Tkachuk, Mariia S. Hrynevych; Tetiana A. Vakaliuk; Oksana A. Chernysh, Mykhailo G. Medvediev https://creativecommons.org/licenses/by/4.0
Design and implementation of an edge computing-based GPS tracking system
This paper presents a comprehensive study on the technical aspects of edge-enabled GPS tracking systems, their evolution, and an analysis of existing problem-solving approaches. It introduces mathematical models that simulate the operation of the hardware and software components of these systems. An adaptive user interface has been developed, enabling usage across various platforms such as smartphones and personal computers. The paper also explores innovative methods for visualizing the trajectory of a moving object on an electronic map. Specialized software for the Atmega162-16PU microcontroller has been developed to control the GSM module and GPS receiver. A novel method for data transmission from a GPS tracker to a web server is proposed. The paper concludes with the successful testing of two experimental GPS-tracker prototypes under uncertain conditions. The developed edge-enabled GPS tracking software and hardware show potential for monitoring moving objects within the coverage of GSM cellular networks.
Copyright (c) 2023 Mykola V. Klymenko, Andrii M. Striuk https://creativecommons.org/licenses/by/4.0
Design and implementation of an IoT-based educational model for smart homes: a STEM approach
This work presents a comprehensive study on the design and implementation of an educational model for a smart home, leveraging the principles of the Internet of Things (IoT). The model encompasses three key levels: command, communication, and management, and integrates several subsystems, including communication, signalling, lighting control, temperature regulation, garbage container filling, nd sensor data monitoring. The hardware components for implementing the Mini Smart House, a practical application of the model, are detailed in the study. The Mini Smart House utilizes a range of technologies for efficient management and incorporates renewable energy sources for power. The model was independently produced by students participating in a STEM project, with research activities encompassing sketching, construction part creation, sensor assembly, Arduino board programming in the Arduino IDE environment, and system functionality testing. The Mini Smart House was validated within the STEM-center of the Physics and Mathematics Faculty of Ternopil Volodymyr Hnatiuk National Pedagogical University during the educational process and numerous trainings and seminars for pupils and teachers of computer science.
Copyright (c) 2023 Nadiia Balyk, Svitlana Leshchuk, Dariia Yatsenyak https://creativecommons.org/licenses/by/4.0