Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Interoperability in IoT

Interoperability refers to the ability of IoT systems and components to communicate and share information among them. This crucial feature is key to unlock all of the IoT paradigm´s potential, including immense technological, economic, and social benefits. Interoperability is currently a major challenge in IoT, mainly due to the lack of a reference standard and the vast heterogeneity of IoT systems. IoT interoperability has also a significant importance in big data analytics because it substantively eases data processing. This chapter analyzes the critical importance of IoT interoperability, its different types, challenges to face, diverse use cases, and prospective interoperability solutions. Given that it is a complex concept that involves multiple aspects and elements of IoT, for a deeper insight, interoperability is studied across different levels of IoT systems. Furthermore, interoperability is also re-examined from a global approach among platforms and systems.González-Usach, R.; Yacchirema-Vargas, DC.; Julián-Seguí, M.; Palau Salvador, CE. (2019). Interoperability in IoT. Handbook of Research on Big Data and the IoT. 149-173. http://hdl.handle.net/10251/150250S14917

Human Activity Recognition using a Semantic Ontology-Based Framework

In the last years, the extensive use of smart objects embedded in the physical world, in order to monitor and record physical or environmental conditions, has increased rapidly. In this scenario, heterogeneous devices are connected together into a network. Data generated from such system are usually stored in a database, which often shows a lack of semantic information and relationship among devices. Moreover, this set can be incomplete, unreliable, incorrect and noisy. So, it turns out to be important both the integration of information and the interoperability of applications. For this reason, ontologies are becoming widely used to describe the domain and achieve efficient interoperability of information system. An example of the described situation could be represented by Ambient Assisted Living context, which intends to enable older or disabled people to remain living independently longer in their own house. In this contest, human activity recognition plays a main role because it could be considered as starting point to facilitate assistance and care for elderly. Due to the nature of human behavior, it is necessary to manage the time and spatial restrictions. So, we propose a framework that implements a novel methodology based on the integration of an ontology for representing contextual knowledge and a Complex Event Processing engine for supporting timed reasoning. Moreover, it is an infrastructure where knowledge, organized in conceptual spaces (based on its meaning) can be semantically queried, discovered, and shared across applications. In our framework, benefits deriving from the implementation of a domain ontology are exploited into different levels of abstrac- tion. Thereafter, reasoning techniques represent a preprocessing method to prepare data for the final temporal analysis. The results, presented in this paper, have been obtained applying the methodology into AALISABETH, an Ambient Assisted Living project aimed to monitor the lifestyle of old people, not suffering from major chronic diseases or severe disabilities

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Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries