Enhancing IoT Data and Semantic Interoperability Based on Entity Tree Embedding Under an Edge–Cloud Framework

Background and Aim: To understand the difficulties of elderly in natural listening situations, the present study assessed the ability of participants to extract phonetic information and semantic information in noise. The main objective was to evaluate the relationship between age and performance in Semantic Information Processing (SIP) and Phonetic Information Processing (PIP) tasks. Methods: Fifty-three normal hearing participants aged 40 to 65 years were categorized into five age groups. Participants underwent information processing assessments using standardized semantic and phonetic questions from Kannada stories under Signal-to-Noise Ratios (SNR) i.e., quiet, 0 dB SNR, and –4 dB SNR conditions. Results: Younger participants outperformed older participants in both SIP and PIP tasks. Performance was optimal in quiet conditions, followed by 0 dB and –4 dB SNR. Except at –4 dB, PIP scores exceeded SIP scores. A significant differences was observed between the groups 1–4 and group 5 at 0 dB SNR in PIP condition only. A moderate negative correlations indicated that performance in both SIP and PIP declined with age, with linear regression revealing reductions of 0.216 and 0.210 each year in SIP and PIP respectively. Conclusion: Age-related declines in PIP were noted at one SNR, while SIP remained stable, aligning with the Transmission Deficit Hypothesis (TDH). Nonetheless, regression analyses indicated a general decline in both SIP and PIP with age, supporting the Inhibitory Deficit Hypothesis (IDH).

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

The Internet of Things (IoT) connects people with real‐world objects to exchange data. It is performed using services and devices present in the user's activities based on the IoT. Based on a common vocabulary or mappings, knowledge and information can be exchanged by different agents, services, and devices in this scenario. The heterogeneous sources can be represented and integrated by an ontology. The Semantic Web offers semantic interoperability that facilitates communication between heterogeneous devices and technology platforms. The state‐of‐the‐art of IoT semantic interoperability is assessed and reviewed in this work. The importance and challenges of interoperability are discussed in detail with an in‐depth analysis of the requirements. IoT based semantic interoperability model is discussed with semantic annotations of data required for heterogeneous IoT devices. Additionally, it has been presented which Semantic Web technologies are incorporated, and the challenges have been studied in this research area. Agriculture is a domain where IoT applications have a lot of potentials. The market is filled with several devices that collect data from the farms and send it to the cloud. Semantic Web technology for applications in agriculture has been discussed in detail with data integration. Semantic resources for agriculture have been enlisted with linked data hubs and semantic data standards. The article aims to address the need and requirement of IoT with semantic interoperability in the field of agriculture. The use of IoT interoperability in agriculture can bring long term benefits to the farmers and increase productivity while reducing the overall costs incurred.

The Internet of Things (IoT) is driving the technology and advances of the Internet and every day it is becoming more popular to talk about IoT systems in multiple domains: Smart Cities, Agriculture, and Industrial Internet, etc. Increasingly, IoT systems will need to interact and be interconnected for offering the always-promoted everything-connected paradigm. Current IoT systems rely on semantic web technologies for integrating data and ensure web services interoperability. However there are yet a gap to ensure semantic interoperability among IoT systems. Most of the existing proposed (open) approaches and solutions lack on formal methodologies for interoperability in technology and standard format of the data. We studied and analyzed most available semantic-based IoT approaches to identify the main requirements hindering IoT semantic interoperability. In this paper, we present SEG 3.0 a methodology to federate, unify and provide semantic interoperability. SEG 3.0 emerges from methodologies for ontology engineering and the idea of unification and federated systems. We propose SEG 3.0 and apply it to Internet of Things (IoT) and particularly on use cases for smart cities as proof of concept. Firstly, we define characteristics required for the methodology. Secondly, we describe the processes and the different formal steps. Thirdly, we provide a proof of concept framework and architecture applying this methodology, thus the benefits of using SEG 3.0 methodology in IoT domains are described. Finally, we demonstrate that the SEG 3.0 methodology is applied to three use cases: (1) the M3 framework to assist developers in designing semantic-based IoT applications, (2) the VITAL EU project for smart cities, and (3) the FIESTA-IoT EU project for IoT semantic interoperability. SEG 3.0 is a formal methodology generic enough to be applied to other domains than IoT and smart cities, since the main benefit of the SEG 3.0 is integrating heterogeneous data and adding value to it to build innovative applications.

Semantic models for IoT sensing to infer environment–wellness relationships

The apparent growth of the internet of things (IoT) has allowed its deployment in many domains. The IoT devices sense their surroundings and transmit the data via the Web. According to statistics, due to the proliferation of smart devices, the number of active IoT devices is expected to exceed 25.4 billion by 2030.1 A large number of IoT objects gather an enormous amount of raw data. The data generated by various IoT objects and sensors are heterogeneous, with varying types and formats. Therefore, it is difficult for IoT systems to share and reuse raw IoT data, which causes the problem of lack of interoperability. The lack of interoperability in IoT systems creates a problematic issue that prevents IoT systems from performing well. To address this issue, data modeling and knowledge representation using semantic web technologies may be an appropriate solution to give meaning to raw IoT data and convert it to an enriched data format. The primary goal of this research section is to highlight the best outcomes for semantic interoperability among IoT systems, which can serve as a guideline for future studies via the presentation of a literature review on semantic interoperability for Internet of Things systems, including challenges, prospects, and recent work. The paper also provides an overview of the application of semantic web technologies in IoT systems, such as specific ontologies, frameworks, and application domains that use semantic technologies in the IoT areas to solve interoperability and heterogeneity problems.

Due to the rapid growth of the Internet of Things (IoT), researchers have demonstrated various IoT solutions, which are used to interconnect a wide range of IoT devices through the Internet. However, IoT stumbled into vertical silos; the available solutions provide specific IoT infrastructure, devices, protocols, data formats and models. This diversity and heterogeneity lead to interoperability issues. Heterogeneity happens at all IoT layers, especially at the application layer; devices often adopt mutually incompatible application-layer communication protocols to connect devices to IoT services. Furthermore, in order to integrate semantics to raw data, each system uses its one domain-specific ontology to make data more understandable and interpretable by adding semantic annotations. Working in isolation reduces the interoperability among IoT devices and systems, things across domains need to internetwork and collaborate to provide high level IoT services. Therefore, to alleviate the problem of both communication protocol interoperability and semantic interoperability across vertical silos of systems at the application layer, this paper proposes a semantic gateway (SGIoT) that acts as a bridge between heterogeneous sink nodes at the physical level and IoT services. SGIoT enables interconnectivity between communication protocols such as CoAP and MQTT regardless of their communication model, meanwhile it enables semantics integration throu gh cross-domain ontology (CDOnto) for semantic annotation, in order to provide interpretation of messages among IoT applications across domains. Our approach focuses on modularity and extensibility.

Introducing semantics into the Internet of Things (IoT) has been attracting increasing attention from researchers and industrial practitioners. Interoperability stays a critical weight to the engineers of the Internet of Things Systems. This is because of the way that the IoT gadgets are exceptionally heterogeneous as far as fundamental correspondence protocol, information organizations, and advances. Our spotlight in this section is on information semantics, liable for definition, the board, and preparing of information. The Internet of Things (IoT), true to form the foundation for the imagined idea of Smart structure, brings new conceivable outcomes for the structure of the executives. IoT vision presents promising and practical answers for gigantic information assortment and its investigation which can be applied in numerous areas thus cause them to work all the more productively. End-user development programming environments in the IoT enable end-users to customize their IoT objects’ joint behavior, typically via IFTTT trigger-action rules. Aim of the chapter to propose an IoT based Semantic Interoperability Model with EUPont Semantic Web ontology to provide Semantic Interoperability among heterogeneous IoT devices for control of end-user applications.

Latest innovations in Internet of Things (IoT) technologies as well as the new paradigms in Artificial Intelligence systems are opening up opportunities to create smart computing infrastructures for the Healthcare Facility Management. However, the current scenario of hospital buildings maintenance management is strongly characterized by slow, redundant, and not integrated processes, which lead to loss of money, resources, and time. On the other hand, lack of data and information in as-built digital models considerably limits the potential of Building Information Modelling in Facility Maintenance Management. Consequently, optimization of data collection process and management is required. In this light, this paper presents a review of embedding AI (Artificial Intelligence) in BIM-IoT integration for the process of healthcare Facility Maintenance Management (FMM) in order to conquer the current challenges. The first challenge in front of integrating IoT– BIM, is the lack of information; the second challenge is BIM’s sematic information that has not been able to display indoor conditions’ elements which should be reconsidered; and the third challenge is the data size which is stored in systems as well as the eligibility of individuals to apply the related data. Additionally, some emerging trends in IoT are reviewed such as the combination of Machine Learning and Artificial Intelligence in order to exploit their advantages and complement their limitations, which enable new promising IoT applications.

The Internet of Things (IoT) is unanimously identified as one of the main technology enablers for the development of future intelligent environments. However, the current IoT landscape is suffering from large fragmentation with many platforms and vendors competing with their own solution. This fragmented scenario is now jeopardizing the uptake of the IoT, as investments are not carried out partly because of the fear of being captured in lock-in situations. To overcome these fears, interoperability solutions are being put forward in order to guarantee that the deployed IoT infrastructure, independently of its manufacturer and/or platform, can exchange information, data and knowledge in a meaningful way. This paper presents a Global IoT Services (GIoTS) use case demonstrating how semantic interoperability among five different smart city IoT deployments can be leveraged to develop a smart urban mobility service. The application that has been developed seamlessly consumes data from them for providing parking guidance and mobility suggestions at the five locations (Santander and Barcelona in Spain and Busan, Seoul and Seongnam in South Korea) where the abovementioned IoT deployments are installed. The paper is also presenting the key aspects of the system enabling the interoperability among the three underlying heterogeneous IoT platforms.

To address the issue of insufficient real-time capability in existing Internet search engines within the Internet of Things environment, this research investigates the architecture of Internet of Things search systems. It proposes a data dynamic prediction algorithm tailored for the process of entity information search in the Internet of Things. The study is based on the design of a data compression algorithm for the Internet of Things entity information search process using the Rotating Gate Compression Algorithm. The algorithm employs the Least Squares Support Vector Machine to dynamically predict changes in entity node states in the Internet of Things, aiming to reduce sensor node resource consumption and achieve real-time search. Finally, the research introduces an Internet of Things entity information search system based on the data dynamic algorithm. Performance test results indicate that the segmented compression algorithm designed in the study can enhance compression accuracy and compression rate. As compression accuracy increases, errors also correspondingly increase. The prediction algorithm designed in the study shows a decrease in node energy consumption as reporting cycles increase, reaching 0.2 at 5 cycles. At the 5-cycle point, the prediction errors on two research datasets are 0.5 and 7.8, respectively. The optimized data dynamic prediction algorithm in the study effectively reduces node data transmission, lowers node energy consumption, and accurately predicts node state changes to meet user search demands.

The purpose of this study is to examine the degradations of driving and talking performances due to interferences of semantic and spatial information processing during dual task performances between cell phone talk and driving. The color and geographical information fed into the ears through the cell phone is classified into semantic and spatial information in cerebral activities, respectively. The information regarding traffic signal and road scene perceived by the eyes in driving task is similarly classified. The interferences due to the combinations of their information were experimentally investigated using a driving simulator. Traveling instability and time lag of braking operation were measured as well as reaction time of listening task while driving. As a result, degradations of driving performances and reactions to listening task became more distinguished in case the same kind of information both from visual and auditory organs was processed simultaneously.

Recent years have seen enormous growth in the number of devices connected to the Internet. With such rapid development in the Internet of Things (IoT) domain, data generated by these devices is ever increasing. Data is considered as one of the most crucial entities in the Internet of Things, and this data may be homogeneous as well as heterogeneous in nature. Analysis of homogeneous information is a simple process as this will not involve further processing, but analysis of heterogeneous data is a huge task. The heterogeneous nature of data in IoT based healthcare system, an important application, leads to semantic interoperability, which is the ability of systems to exchange data with definite, shared meaning. Semantic Interoperability expedites data interchange unambiguously irrespective of format and source of data. Therefore, a knowledge-based model is required to integrate heterogeneous data-generating devices to expand the applications of IoT. In this paper, an ontology-based model is proposed and implemented to collect the patient’s diverse data using different tools over gateways. Then the generated/collected data is transmitted to the cloud for processing in terms of semantics and structure. This results in a holistic mechanism in the form of an ontology used for the prediction and diagnostics of heart diseases in patients located at distant places by experts based at another location.

The Internet of Things (IoT) primary objective is to make a hyper-connected world for various application domains. However, IoT suffers from a lack of interoperability leading to a substantial threat to the predicted economic value. Schema.org provides semantic interoperability to structure heterogeneous data on the Web. An extension of this vocabulary for the IoT domain (iot.schema.org) is an ongoing research effort to address semantic interoperability for the Web of Things (WoT). To design this vocabulary, a central challenge is to identify the main topics (concepts and properties) automatically from existing knowledge in IoT applications. We designed KE4WoT (Knowledge Extraction for the Web of Things) to automatically identify the most important topics from literature ontologies of 3 different IoT application domains - smart home, smart city and smart weather - based on our corpus consisting of 4500 full-text conference and journal articles to utilize domain-specific knowledge encoded within IoT publications. Despite the importance of automatically identifying the relevant topics for iot.schema.org, up to know there is no study dealing with this issue. To evaluate the extracted topics, we compare the descriptiveness of these topics for the 10 most popular ontologies in the 3 domains with empirical evaluations of 23 domain experts. The results illustrate that the identified main topics of IoT ontologies can be used to sufficiently describe existing ontologies as keywords.

Semantic interoperability is the key technology to enable evolution of the Internet of Things (IoT) from its current state of independent vertical IoT silos to interconnected IoT platform federations. This paper analyzes the possible solution space on how to achieve semantic interoperability and presents five possible approaches in detail together with a discussion on implementation issues. It presents the H2020 symbIoTe project as an example on how semantic interoperability can be achieved using semantic mapping and SPARQL query re-writing. We conclude that the found approaches together with the proposed technologies have the potential to act as corner stone technologies for achieving semantic interoperability.