E-ticket System as an example of Internet of Things application

We are entering a new revolution of technology called Internet of Things (IoT). It enables machines to be connected and exchange data. It brings a huge potential to different areas of technology. It is being used in several applications in different fields: building smart city, smart home, health care and agriculture. This thesis summarizes current IoT application development and the development of an E-ticket system as a demonstration of IoT application. The literature research was taken place prior to the implementation phase. Several studies on existing scientific articles were done about current IoT applications and its use cases. After that, current IoT technologies were studied and the best technologies were chosen to implement the E-ticket system. It involves Amazon web services (AWS) [8], Java Spring framework [9] and REST [10] calls over 3G [11] connections. As the result of the research and implementation, the E-ticket system was developed and demonstrated to the teacher. The solution provides police officers a quick and easy way to issue fine tickets and automates the fine payment process. Moreover, it also allows people to give feedback about performance of police officers. It is being sold to the government to solve cash payment issues in developing countries. The research shows that IoT can be utilized in many different existing applications. It will improve the scalability of the application and allow collecting a huge amount of data, which will be the resource for big data analysis, machine learning, etc. IoT is a part of a big technology revolution we are going through at the moment

Approach in the development of lightweight microservice architecture for small data center monitoring system

In the past decade there is a significant trend of implementing IoT technologies and standards in different industries. This trend brings cost reductions to the companies and other benefits as well. One of the main benefits is real-time and uniform data collection. The data are transferred using diverse communication protocols, from the sensor nodes to the centralized application. So far, current approaches in developing applications are not proved itself to be efficient enough in scenarios when a significant amount of data needs to be stored and analyzed. The focus of this paper is on development of software architecture suitable for usage in Internet of Things (IoT) systems where the larger amount of data can be processed in real-time. The software architecture is developed in order to support the sensor network for monitoring the small data center and it is based on microservices. Besides the system and its architecture, this paper presents the method of analysis of system performances in real-time environment. The proposal for lightweight microservice architecture, presented in this paper, is developed with .NET Core and RabbitMQ, with the utilization of MongoDB and SQLite databases systems for storing data collected with IoT devices. In this paper, the system evaluation and research results in different stress scenarios are also presented. Because of its complexity, only the most significant segments of architecture will be presented in this paper. The proposed solution showed that proposed lightweight architecture based on microservices could deal with the larger amount of sensor data in the case of using MongoDB. On the other hand, the usage of SQLite database is not recommended due to the lower performances and test results

Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

An elastic software architecture for extreme-scale big data analytics

This chapter describes a software architecture for processing big-data analytics considering the complete compute continuum, from the edge to the cloud. The new generation of smart systems requires processing a vast amount of diverse information from distributed data sources. The software architecture presented in this chapter addresses two main challenges. On the one hand, a new elasticity concept enables smart systems to satisfy the performance requirements of extreme-scale analytics workloads. By extending the elasticity concept (known at cloud side) across the compute continuum in a fog computing environment, combined with the usage of advanced heterogeneous hardware architectures at the edge side, the capabilities of the extreme-scale analytics can significantly increase, integrating both responsive data-in-motion and latent data-at-rest analytics into a single solution. On the other hand, the software architecture also focuses on the fulfilment of the non-functional properties inherited from smart systems, such as real-time, energy-efficiency, communication quality and security, that are of paramount importance for many application domains such as smart cities, smart mobility and smart manufacturing.The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the ELASTIC Project (www.elastic-project.eu), grant agreement No 825473.Peer ReviewedPostprint (published version

Cloud Cyber Security: Finding an Effective Approach with Unikernels

Achieving cloud security is not a trivial problem to address. Developing and enforcing good cloud security controls are fundamental requirements if this is to succeed. The very nature of cloud computing can add additional problem layers for cloud security to an already complex problem area. We discuss why this is such an issue, consider what desirable characteristics should be aimed for and propose a novel means of effectively and efficiently achieving these goals through the use of well-designed unikernel-based systems. We have identified a range of issues, which need to be dealt with properly to ensure a robust level of security and privacy can be achieved. We have addressed these issues in both the context of conventional cloud-based systems, as well as in regard to addressing some of the many weaknesses inherent in the Internet of things. We discuss how our proposed approach may help better address these key security issues which we have identified

Orchestration in the Cloud-to-Things Compute Continuum: Taxonomy, Survey and Future Directions

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems has been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, and heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis.Comment: Journal of Cloud Computing Pages: 2

Software Components for Smart Industry Based on Microservices: A Case Study in pH Control Process for the Beverage Industry

[EN] Modern industries require constant adaptation to new trends. Thus, they seek greater flexibility and agility to cope with disruptions, as well as to solve needs or meet the demand for growth. Therefore, smart industrial applications require a lot of flexibility to be able to react more quickly to continuous market changes, offer more personalized products, increase operational efficiency, and achieve optimum operating points that integrate the entire value chain of a process. This requires the capture of new data that are subsequently processed at different levels of the hierarchy of automation processes, with requirements and technologies according to each level. The result is a new challenge related to the addition of new functionalities in the processes and the interoperability between them. This paper proposes a distributed computational component-based framework that integrates communication, computation, and storage resources and real-time capabilities through container technology, microservices, and the publish/subscribe paradigm, as well as contributing to the development and implementation of industrial automation applications by bridging the gap between generic architectures and physical realizations. The main idea is to enable plug-and-play software components, from predefined components with their interrelationships, to achieve industrial applications without losing or degrading the robustness from previous developments. This paper presents the process of design and implementation with the proposed framework through the implementation of a complex pH control process, ranging from the simulation part to its scaling and implementation to an industrial level, showing the plug-and-play assembly from a definition of components with their relationships to the implementation process with the respective technologies involved. The effectiveness of the proposed framework was experimentally verified in a real production process, showing that the results scaled to an industrial scale comply with the simulated design process. A qualitative comparison with traditional industrial implementations, based on the implementation requirements, was carried out. The implementation was developed in the beverage production plant "Punta Delicia", located in Colima, Mexico. Finally, the results showed that the platform provided a high-fidelity design, analysis, and testing environment for cyber information flow and their effect on the physical operation of the pH control.This work has been supported by for research cooperation between Universidad de Colima (Mexico), Universidad Autonoma de Occidente (Colombia), Universitat Politecnica de Valencia (Spain) and the juice production plant Punta Delicia located in Colima, Mexico.Serrano-Magaña, H.; González-Potes, A.; Ibarra-Junquera, V.; Balbastre, P.; Martínez-Castro, D.; Simó Ten, JE. (2021). Software Components for Smart Industry Based on Microservices: A Case Study in pH Control Process for the Beverage Industry. Electronics. 10(7):1-21. https://doi.org/10.3390/electronics1007076312110

Performance Evaluation Metrics for Cloud, Fog and Edge Computing: A Review, Taxonomy, Benchmarks and Standards for Future Research

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments