Edge-Native Software: Designing Resilient Apps for Low-Latency, Distributed Environments

Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results.

The advent, popularity and widespread adoption of Cloud-Native architectures and platforms have revolutionized the way in which applications are being built and deployed in the Cloud. Applications designed for Cloud-Native architectures are being migrated from traditional monolithic approach to a Microservices architecture, in which an application is broken down into a collection of small, independent and collaborative services. These applications are dynamically scheduled and managed by Cloud Native platforms. Container Orchestrators (CO), such as Docker, Kubernetes, Cloud Foundry, enable the deployment and management of stateless and scalable microservices. However, as more and more stateful applications and workloads are being deployed in Cloud-Native platforms, these platforms must support the workloads by providing persistent storage to the microservices so that data can be protected and accessed beyond the lifecycle of an ephemeral container. In this paper, we present the design and development of a Cloud-Native storage service orchestration platform by leveraging capabilities of the IBM Ubiquity framework. Our ultimate goal is to provide persistent storage across different container orchestrators and supporting multi-protocol access for Volume management within the storage systems, such as SSH, REST, GPFS native protocol using CLI, NFS and Object. We demonstrate the effectiveness of the storage service orchestration development vision by providing a prototype implementation using Docker and General Parallel Filesystem (GPFS), aiming to provide shared storage volumes to containers orchestrated by Docker. As the volume and variety of microservices continue to grow and to dominate the development and deployments of Cloud applications, we conjecture that the proposed storage service orchestration framework will be a critical enabling building block for supporting elastic, reliable and effective microservice architectures in Cloud datacenters.

In recent years, the popularity of containerization technologies has been growing. When they are used, computational tasks are placed in lightweight containers that can be easily moved between different computing nodes. Containerization using Docker is especially popular at the moment. The use of these solutions opens up enormous opportunities for building distributed and cluster computing systems. To maintain the operability of such systems, special tools are used, and one of them is an orchestrator. However, existing orchestrators are focused on not-so-large computing systems in which performance can be maintained by simply moving computational tasks from non-working nodes to working ones. In large systems with many nodes and a huge number of computational tasks, it is also necessary to take into account the uneven consumption of resources by various tasks. This article proposes a system architecture that can solve the problem of container orchestration using machine learning methods and given the uneven consumption of resources by

The convergence of edge computing and microservices architecture represents a transformative shift in how organizations process and manage distributed data systems. This article explores the limitations of traditional cloud-centric approaches and presents edge computing as a solution to address these challenges. Through a detailed analysis of real-world implementations across various sectors, including autonomous vehicles, smart cities, and industrial automation, the article examines how edge computing integrated with microservices architecture improves system performance, reduces latency, and optimizes resource utilization. The article investigates deployment challenges and security considerations and provides insights into effective monitoring strategies. Furthermore, the article explores emerging technologies and future research directions that will shape the evolution of edge computing and microservices integration.

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With the convergence of information and telecommunication technologies, the vision of the `Smart City' is fast becoming a reality. City governments in a growing number of countries are capitalizing on these advances to enhance the lives of their citizens and to increase efficiency and sustainability. In this paper, we elaborate on smartCityRA, a reference architecture for Smart City projects, which serves as the design language for creating smart cities blueprints. Such a blueprint caters for diverse stakeholders, devices, platforms, and technologies. We report on our experience in carrying out a proof-of-concept use case with a major telecommunication provider in the UAE. In doing so, we refined our multiple-view model of the initial smartCityRA reference architecture. We show that Data in smart city applications drive the entire development lifecycle and should be considered early in the development cycle. In addition, Data affects all the other views in the smartCityRA and hence the Data View needs to be at the heart of the entire smartCityRA. Realizing the Data view using a component like a Data Hub helped in creating a central integration location for disparate data from different sources, thus reliving developers from dealing with several entities individually. Finally, we show that any smart city reference architecture, like smartCityRA, should be at the right level of abstraction to enable the flexibility of adoption and adaptation by different stakeholders and components.

The Internet of Things (IoT) has become a paradigm shifter, connecting an enormous number of smart devices and facilitating seamless data exchange for a diverse array of applications. The availability and effective use of the IoT ecosystem's resources are key factors in determining how its practical applications will develop as they mature. The IoT resources and their practical application across several areas are thoroughly explored in this paper. The paper begins by classifying and describing the various sensor types, their applications in various fields, and IoT resources, highlighting their contributions to real-time data collection, processing, and transmission. It then goes on to demonstrate a wide range of real-world uses for these resources, such as smart cities, education, agriculture, business, healthcare, environment monitoring, transportation, and industrial automation. However, utilizing IoT resources effectively is not without difficulties. Critical difficulties such as resource allocation, scalability, security, interoperability, and privacy concerns are identified and discussed in the paper. Furthermore, the paper also highlights future directions and emerging trends in IoT resource management, including edge computing, cloud computing, human machine integration, and compatibility with other systems. These developments aim to increase the dependability of IoT applications in diverse settings and optimize resource allocation. This paper's conclusion highlights the crucial role that IoT resources play in advancing real-world applications across a variety of areas. Researchers, practitioners, policymakers, and other stakeholders may collaborate together to effectively leverage the full potential of IoT resources to build intelligent, effective ecosystems that meet the needs of contemporary society by solving difficulties and utilizing developing trends.

With the increasing use of Internet of Things (IoT) devices in various industries, the challenge of efficient use of energy and reducing the amount of device downtime is a key concern in sustainable and reliable systems usage. The paper deals with energy consumption optimization and the forecasting of device failures in the IoT systems in industrial automation. More precisely, we suggest the hybrid solution consisting of reinforcement learning (RL) to optimize energy consumption, Long Short-Memory (LSTM) networks to predictive maintenance, and edge-cloud offloading solutions to improve the performance of devices and their sustainability. We use the modeling of heterogeneous IoT devices in our methodology, but with different energy profiles, different workloads, and operational modes. We use RL to dynamically tweak devices modes, which saves energy and does not impact task completion. We combine predictive maintenance, in which we use LSTM to forecast device failures using historical data to give timely warnings on maintenance. Moreover, the edge-cloud offloading decisions are also integrated into our system to further optimize the energy consumption and optimize the efficiency of performing tasks. The principal results are that our method is highly energy efficient and device uptime, and reinforcement learning is the most energy saving method. The predictive maintenance model is an effective way of cutting on the downtime, as the correct prediction of the occurrence of failure results in the taking of preventive measures. Task offloading, especially when there are resource constraints, improves system reliability without affecting the performance. Such findings indicate that adaptive IoT systems could be used to optimize energy use and system performance and, therefore, make industrial automation more sustainable. Our solution is informative to the design of future IoT systems in which energy efficiency and predictable stability will be the most crucial attributes. Keywords: IoT Energy Optimization, Predictive Maintenance, Reinforcement Learning, LSTM Networks, Edge-Cloud Offloading.

The rapid expansion of the Internet of Things (IoT) has significantly increased the demand for robust, scalable, and reliable wireless communication systems. As billions of interconnected devices generate vast volumes of data in real-time, ensuring efficient communication while maintaining system integrity has become a major challenge. This review explores the critical role of computer science in addressing these issues, focusing on key strategies and technologies that enhance the scalability and reliability of wireless IoT networks. The paper investigates algorithmic solutions for efficient routing, congestion control, and resource allocation, alongside architectural innovations such as edge computing, fog networking, and cloud-IoT integration. It also examines the role of artificial intelligence and machine learning in predictive maintenance, fault detection, and adaptive network optimization. Furthermore, the review discusses communication standards and protocols (e.g., LoRaWAN, Zigbee, NB-IoT) and their suitability for large-scale deployments. By integrating insights from distributed systems, cybersecurity, and software engineering, the paper offers a comprehensive overview of how computer science contributes to the development of resilient and scalable IoT wireless communication frameworks. This synthesis aims to guide future research and deployment strategies across smart cities, healthcare, agriculture, and industrial automation.

Plug & Produce is an important part of the 4th industrial revolution (I4.0) that focuses on product development and production scenarios, demanding rapid adaptation to customer needs, with minimum effort, and no business interruption. Achieving this requires the use of flexible and agile automation systems. Microservices Architecture can be an opportunity to bring such flexibility and agility to industrial automation systems. Since Microservices Architecture is a new paradigm in service-oriented systems, an in-depth analysis from the industrial automation perspective is necessary in order to figure out why and how this new architecture could be helpful to build flexible automation systems. In this paper we first clearly describe and characterize the Microservices Architecture. We next discuss an existing paradox in microservice granularity, and suggest an approach to solve the mentioned paradox. Then we compare this new approach to the Service Oriented Architecture, highlight the key (dis)advantages of utilizing Microservice Architecture in building flexible automation systems that fits in 4th industrial revolution needs, and describe the challenges to its future adoption.

The rise of Microservices architectures has introduced a surge in the demand for containerized software provisioning and orchestration. Kubernetes is a leading container orchestration platform widely adopted for its efficient management of containerized applications within cloud datacenters. However, the proliferation of containerized applications, particularly in resource-constrained environments like Internet of Things (IoT) edge devices, has prompted numerous software vendors to release lightweight Kubernetes-derived platforms, tailored for streamlined deployment in such infrastructures. Despite this, selecting the most suitable distribution for edge-based IoT applications remains a challenge due to a lack of comprehensive studies in this domain. This paper presents a comprehensive model for evaluating the performance of lightweight Container Orchestration Distributions (CODs), incorporating resource usage, control-plane performance, and data-plane performance metrics. Also, we conduct a comparative empirical analysis of four prominent CODs (i.e., k0s, k3s, Microk8s, and Microshift) by focusing on their suitability for edge environments. Our findings reveal that k3s is the overall best-performant COD.

Securing Smart Cities using LSTM algorithm and lightweight containers against botnet attacks

In the rapidly evolving landscape of financial technology (FinTech), the seamless integration of various systems has emerged as a critical factor in optimizing transaction volumes and enhancing overall operational efficiency. This review explores the key strategies and technological innovations that drive successful systems integration within FinTech platforms, focusing on the implications for transaction scalability, security, and user experience. With the increasing complexity of financial ecosystems, FinTech companies face the challenge of integrating disparate systems, including payment gateways, data analytics platforms, customer relationship management (CRM) tools, and regulatory compliance modules. Effective integration not only ensures the smooth flow of information across these systems but also enhances transaction processing capabilities, enabling FinTech companies to handle larger transaction volumes with minimal latency and errors. This study highlights the role of advanced integration technologies, such as Application Programming Interfaces (APIs), microservices architecture, and cloud computing, in facilitating real-time data exchange and processing across platforms. Additionally, the integration of machine learning algorithms and artificial intelligence (AI) enhances predictive analytics and decision-making processes, further boosting transaction efficiency and scalability. The review also discusses the importance of maintaining robust security protocols during systems integration to protect against cyber threats and ensure compliance with financial regulations. By adopting a holistic approach to systems integration, FinTech companies can achieve a higher level of operational agility, thereby meeting the growing demands of digital consumers and capitalizing on the expanding digital economy. Keywords: FinTech, Systems Integration, Transaction Volumes, APIS, Microservices Architecture, Cloud Computing, Machine Learning, Artificial Intelligence, Cybersecurity, Operational Efficiency.

Edge computing, integrated with Industrial Control Systems (ICS), is transforming the landscape of industrial automation, providing a decentralized computing model that enables processing data closer to the source. This technology is crucial in optimizing the performance and security of industrial operations, enhancing the efficiency of processes by reducing latency, and mitigating cybersecurity risks. ICS environments are increasingly vulnerable to cyberattacks, and the shift to edge computing offers a promising solution by allowing real-time data processing, which reduces the dependency on centralized data centers and strengthens data security. This paper explores the role of edge computing in ICS, particularly in relation to its security implications. It examines how the proximity of computation to the source of data minimizes exposure to external threats, addresses issues such as data privacy, and enhances response times in critical industrial systems. Additionally, it investigates the combination of edge computing with machine learning, AI, and blockchain technologies, which provide sophisticated anomaly detection, predictive maintenance, and encryption techniques, to further secure the industrial ecosystem. The paper concludes by outlining the advantages, challenges, and future developments of integrating edge computing with ICS for enhanced security and operational efficiency. KEYWORDS: Edge Computing, Industrial Control Systems, Cybersecurity, Data Privacy, Latency Reduction, Machine Learning, Blockchain.

This research presents a cutting-edge approach that combines chemical engineering principles with artificial intelligence (AI) to enhance battery maintenance in Internet of Things (IoT) applications. The study develops predictive maintenance models capable of accurately forecasting battery degradation, thereby optimizing usage patterns and extending battery life. By integrating electrochemical data with AI-driven predictive analytics, the project enables real-time monitoring of battery health, achieving over 90% accuracy in anticipating maintenance needs, which reduces unplanned downtime and lowers operational costs. Employing machine learning frameworks, data analysis tools, and chemical engineering simulation software, the focus is on lithium-ion and solid-state batteries, commonly used in IoT networks across sectors such as smart cities, agriculture, and healthcare. Key results include a 20−30% increase in battery lifespan and a 15% improvement in energy efficiency, significantly reducing electronic waste and environmental footprint. This interdisciplinary approach provides a foundation for sustainable battery technology in IoT technology, paving the way for future advancements in AI-augmented predictive maintenance and chemical engineering models for next-generation applications. The integration of AI with chemical engineering principles allows for the development of models that adapt to the unique electrochemical behavior of different battery types, such as lithium-ion and solid-state batteries. By analyzing lifecycle data and device usage patterns, predictive models can account for variables that influence battery performance over time, including temperature fluctuations and charging cycles. This project utilizes neural networks and reinforcement learning algorithms to train models capable of learning from historical data, enhancing their predictive capabilities. A significant aspect of this research involves the preprocessing of battery lifecycle data to ensure high data quality, which is critical for accurate model training and validation.