A Comparative Analysis of IoT Protocols for Resource Constraint Devices and Networks

Communication standards and protocols are detrimental to the success of any Internet of Things (IoT) system or application. Selecting a communication standard and a suitable middleware or messaging protocol for IoT connectivity is challenging due to the heterogeneous resource-constrained IoT devices and their messaging requirements. Recently, several messaging/middleware protocols in the IoT field were developed and adopted in the industry. However, to date, there is no specific messaging protocol that can support all messaging use cases and fulfil the overall requirements of IoT systems. Therefore, it is critical to understand the application layer messaging and communication protocols of IoT systems to identify the most appropriate protocol that could fit and be applied in various contexts. This paper provides a comparative analysis of the MQTT, CoAP, and AMQP messaging protocols including their security. © 2022 ACM.

AN APPROACH TO SEMANTIC EDUCATIONAL CONTENT MINING USING NLP

The COVID-19 epidemic had caused one of the most significant disruptions to the global education system. Many educational institutions faced sudden pressure to switch from face-to-face to online delivery of courses. The conventional classes are no longer the primary means of delivery;instead, online education and resources have become the prominent approach. With the increasing demand for supplementary course materials to fulfill the needs of each area of study, students began to use search engines and online resources that contain discussions, practical demonstrations, and tutorial videos to aid students in their studies and course work. This study addresses the underlying challenges of retrieving relevant online educational materials by introducing an intelligent agent for semantic data mining. It works as middleware infrastructure that allow context-aware data processing and mining. YouTube was used to assess the consistency of the proposed model since it returns a large number of results in its search pool. The results showed that using the extraction of topics method, the similarities scores with the proposed model provided favorable results. Furthermore, an improvement in video ranking and sorting was realized. According to the findings, using this method provided users with a more productive and reliable study experience. © Proceedings of the International Conference on E-Learning 2022, EL 2022 - Part of the Multi Conference on Computer Science and Information Systems 2022, MCCSIS 2022. All rights reserved.

Sensor Networks with Edge Intelligence for Reliable Air Quality Monitoring in the Covid-19 Era

Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to capture the high variability of personal exposure to air pollution during daily life with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of these novel technologies for scientific and policy purposes due to their lack of reliability. The aim of this work is the development of three types of portable air quality devices that monitor particulate matter, differential pressure and outdoor emissions (CO, CO2, O3 and VOCs) with high reliability using low-cost sensors and communicating measurements to the cloud in real time. Reliability is strengthened in all three places: at the sensor level, the device/edge level and at the cloud, cashing data until network connectivity is restored. In order to evaluate their efficiency, two case studies were deployed: (a) in a modern industrial setting and (b) in an IT office space in Greece and the findings are reported.

Enabling Healthcare 4.0 applications development through a middleware platform

Smart healthcare is a promising direction for healthcare services and can be facilitated by adopting the Healthcare 4.0 vision, which utilizes new technologies to provide value-added healthcare services to patients. Developing and integrating Healthcare 4.0 applications is challenging in terms of design complexity, architectural choices, support services, reliability, security, and privacy. This chapter discusses these challenges and identifies the requirements for enabling the Healthcare 4.0 vision. It also proposes H4Ware, a service-oriented middleware, for Healthcare 4.0. H4Ware helps utilize new and emerging technologies to relax some of these challenges and provides a coherent collection of support and advanced services, enabling simpler development, integration, and deployment of healthcare applications. An example application illustrating how these services can be integrated is discussed in addition to introducing the core services in H4Ware and a prototype implementation. H4Ware will enable the fast development of healthcare applications that will help handle diseases such as COVID-19. © 2022 Elsevier Inc. All rights reserved.

Design and Implementation of OPC UA-Based VR/AR Collaboration Model Using CPS Server for VR Engineering Process

In order to cope with the changing era of the innovative management paradigm of the manufacturing industry, it is necessary to advance the construction of smart factories in the domestic manufacturing industry, and in particular, the 3D design and manufacturing content sector is highly growthable. In particular, the core technologies that enable digital transformation VR (Virtual Reality)/AR (Augmented Reality) technologies have developed rapidly in recent years, but have not yet achieved any particular results in industrial engineering. In the manufacturing industry, digital threads and collaboration systems are needed to reduce design costs that change over and over again due to the inability to respond to various problems and demands that should be considered when designing products. To this end, we propose a VR/AR collaboration model that increases efficiency of manufacturing environments such as inspection and maintenance as well as design simultaneously with participants through 3D rendering virtualization of facilities or robot 3D designs in VR/AR. We implemented converting programs and middleware CPS (Cyber Physical System) servers that convert to BOM (Bill of Material)-based 3D graphics models and CPS models to test the accuracy of data and optimization of 3D modeling and study their performance through robotic arms in real factories.

Science gateway adoption using plug-in middleware for evidence-based healthcare data management

There is a growing need for next-generation science gateways to increase the accessibility of emerging large-scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID-19-related challenges. Such science gateways that enable access to distributed computing resources for large-scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio-technical approach for developing a next-generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID-19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence-based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug-in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot-assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. © 2022 John Wiley & Sons, Ltd.

AI-JasCon: An Artificial Intelligent Containerization System for Bayesian Fraud Determination in Complex Networks

The post-COVID-19 era will create major financial losses in organizational resources as a result of fraudulent activities by malicious agents existing at the edge and cloud domains. Most transactional systems from the edge-to-cloud layers lack the robust platform integration (such as application program interface (API) microservices) needed for fraud mitigation in networks and systems. This paper presents an AI containerization API system based on JAVA-SQL container (JSR-233) for fraud prediction and prevention in telecommunication networks. Pipeline modeling involving the Bayesian software implementation paradigm is introduced using the AI-JasCon model. A demonstration of how the AI engine works with a complex network system for observation of some calls, call frequency, and hidden activities for predictive classification (analytics) at the network backend is discussed. Robust network architecture is introduced for deterministic data mining while creating Bayesian computation to determine fraud potentials through prior, posterior, and joint probability distributions. AI-JasCon framework achieves predictive fraud detection with containerization and modularization using class models and data structures. At the network core layer, an enterprise management backend uses linear discriminant via fog controllers that processes identified fraud subscribers in the network. Also, a standard Java middleware container for distributed transaction management, directory services, and messaging is used to test the application. AI-JasCon framework provides a successful standard for determining fraudulent interactions in edge-to-cloud networks while providing a pipeline application programming model for continuous integration and continuous delivery (CI/CD). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Toward Crowdsourced Drug Discovery: Start-Up of the Volunteer Computing Project SiDock@home

In this paper, we describe the experience of setting up a computational infrastructure based on BOINC middleware and running a volunteer computing project on its basis. We characterize the first series of computational experiments and review the project’s development in its first six months. The gathered experience shows that BOINC-based Desktop Grids allow to efficiently aid drug discovery at its early stages. © 2021, Springer Nature Switzerland AG.

IoT Cloud Computing Middleware for crowd monitoring and evacuation

Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the contrary, wireless sensor networks (WSNs) are distributed systems that scale and feature large numbers of small, computationally limited, low-power, unreliable nodes. In this article, we provide a top-down approach explaining the architecture, implementation and rationale of a distributed fault-tolerant IoT middleware. Specifically, this middleware consists of multiple mini-computing devices (Raspberry Pi) connected in a WSN which implement the Map-Reduce algorithm. First, we explain the tools used to develop this system. Second, we focus on the Map-Reduce algorithm implemented to overcome common network connectivity issues, as well as to enhance operation availability and reliability. Lastly, we provide benchmarks for our middleware as a crowd tracking application for a preserved building in Greece (i.e., M. Hatzidakis’ residence). The results of this study show that IoT middleware with low-power and low-cost components are viable solutions for medium-sized cloud computing distributed and parallel computing centres. Potential uses of this middleware apply for monitoring buildings and indoor structures, in addition to crowd tracking to prevent the spread of COVID-19. © 2021, North Atlantic University Union NAUN. All rights reserved.

Health 4.0: On the Way to Realizing the Healthcare of the Future.

Health 4.0 establishes a new promising vision for the healthcare industry. It creatively integrates and employ innovative technologies such as the Internet of Health Things (IoHT), medical Cyber-Physical Systems (medical CPS), health cloud, health fog, big data analytics, machine learning, blockchain, and smart algorithms. The goal is to deliver improved, value-added and cost-effective healthcare services to patients and enhance the effectiveness and efficiency or the healthcare industry. Health 4.0 (adapted from the Industry 4.0 principles) changes the healthcare business model to enhance the interactions across the healthcare clients (the patients), stakeholders, infrastructure, and value chain. This effectively will improve the quality, flexibility, productivity, cost-effectiveness, and reliability of healthcare services in addition to increasing patients' satisfaction. However, building and utilizing healthcare applications that follow the Health 4.0 concept is a non-trivial and complex endeavor. In addition, advanced potential applications based on Health 4.0 capabilities are not yet being investigated. In this paper we define the main objectives of Health 4.0 and discuss advanced potential Health 4.0 applications. To have a clear understanding of these applications, we categorize them in 4 groups based on the primary beneficiary of these applications. Thus we have patient targeted applications, applications supporting healthcare professionals, resource management applications and high-level healthcare systems management applications. In addition, as we studied the different applications, we realized that these is a certain collection of services that these most of them need regardless of their goals or business context. Services supporting data collection and transfer, security and privacy, reliable operations are some examples. As a result we propose creating a service-oriented middleware framework to offers the common services to the applications developers and facilitate the integration of different services to build applications under the Health 4.0 umbrella.