Interoperability In Internet Of Things Research Articles

Proposed fog computing-enabled conceptual model for semantic interoperability in internet of things

Semantic interoperability has emerged as a key barrier amidst the major developments and challenges brought about by the rapid expansion of internet of things (IoT) applications. Establishing interoperability is essential for IoT systems to function optimally, especially across diverse organizations. Despite extensive research in achieving semantic interoperability, dynamic interoperability, a vital facet, remains inadequately addressed. This paper addresses this gap by presenting a fog-based conceptual model designed to facilitate dynamic semantic interoperability in IoT. The model incorporates a single-tier fog layer, providing the necessary processing capabilities to achieve this goal. The study conducts a comprehensive literature review on semantic interoperability, emphasizing latency, bandwidth, total cost, and energy consumption. Results demonstrate the proposed double skin façade (DSF) model’s remarkable 88% improvement in service delay over IoT-SIM and Open IoT, attributed to its efficient load-offloading mechanism and optimized fog layer, offering a 50% reduction in service delay, power consumption, and 86% reduction in network usage compared to existing approaches through data redundancy elimination via pre-processing at the fog layer.

A trust based secure access control using authentication mechanism for interoperability in internet of things

The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.

Enabling privacy-aware interoperable and quality IoT data sharing with context

Sharing Internet of Things (IoT) data across different sectors, such as in smart cities, becomes complex due to heterogeneity. This poses challenges related to a lack of interoperability, data quality issues and lack of context information, and a lack of data veracity (or accuracy). In addition, there are privacy concerns as IoT data may contain personally identifiable information. To address the above challenges, this paper presents a novel semantic technology-based framework that enables data sharing in a GDPR-compliant manner while ensuring that the data shared is interoperable, contains required context information, is of acceptable quality, and is accurate and trustworthy. The proposed framework also accounts for the edge/fog, an upcoming computing paradigm for the IoT to support real-time decisions. We evaluate the performance of the proposed framework with two different edge and fog–edge scenarios using resource-constrained IoT devices, such as the Raspberry Pi. In addition, we also evaluate shared data quality, interoperability and veracity. Our key finding is that the proposed framework can be employed on IoT devices with limited resources due to its low CPU and memory utilization for analytics operations and data transformation and migration operations. The low overhead of the framework supports real-time decision making. In addition, the 100% accuracy of our evaluation of the data quality and veracity based on 180 different observations demonstrates that the proposed framework can guarantee both data quality and veracity.

Interoperable IoT Devices and Systems for Smart Homes: A Data Analytics Approach to Enhance User Experience and Energy Efficiency

The Internet of Things (IoT) has revolutionised the way people live and work, and its increasing popularity has led to the widespread adoption of smart homes. However, the lack of interoperability among IoT devices and systems used in smart homes limits their capacity to enhance the user experience and improve energy efficiency. Therefore, this study focuses on improving interoperability between various IoT systems and devices. It was anticipated that the research would contribute to the creation of a more cohesive and integrated smart home environment by helping to develop a data analytics framework that enables the integration and analysis of data from various devices. The study then produces an intended user design strategy for smart homes that takes into account users' wants, preferences, and behaviours during the design phase. The goal of this project was to create smart houses that are more intelligent, environmentally green, and user-friendly and that will benefit both individuals and the whole society.

Design and development of dynamic Agri‐ontology for IoT interoperability

SummaryIn recent years, the usage and applications of Internet of Things (IoT) have increased exponentially. IoT connects multiple heterogeneous devices like sensors, micro controllers, actuators, smart devices like mobiles, watches, etc. IoT contributes the data produced in the context of data collection, including the domains like military, agriculture, healthcare, etc. The diversity of possible applications at the intersection of the IoT and the web semantics has prompted many research teams to work at the interface between these two disciplines. This makes it possible to collect data and control various objects in transparent way. The challenge lies in the use of this data. Ontologies address this challenge to meet specific data needs in the IoT field. This paper presents the implementation of a dynamic agriculture ontology‐building tool that parses the ontology files to extract full data and update it based on the user needs. The technology is used to create the angular library for parsing the OWL files. The proposed ontology framework would accept user‐defined ontologies and provide an interface for an online updating of the owl files to ensure the interoperability in the agriculture IoT.

Monitoring and Control Framework for IoT, Implemented for Smart Agriculture.

To mitigate the effects of the lack of IoT standardization, including scalability, reusability, and interoperability, we propose a domain-agnostic monitoring and control framework (MCF) for the design and implementation of Internet of Things (IoT) systems. We created building blocks for the layers of the five-layer IoT architecture and built the MCF's subsystems (monitoring subsystem, control subsystem, and computing subsystem). We demonstrated the utilization of MCF in a real-world use-case in smart agriculture, using off-the-shelf sensors and actuators and an open-source code. As a user guide, we discuss the necessary considerations for each subsystem and evaluate our framework in terms of its scalability, reusability, and interoperability (issues that are often overlooked during development). Aside from the freedom to choose the hardware used to build complete open-source IoT solutions, the MCF use-case was less expensive, as revealed by a cost analysis that compared the cost of implementing the system using the MCF to obtain commercial solutions. Our MCF is shown to cost up to 20 times less than normal solutions, while serving its purpose. We believe that the MCF eliminated the domain restriction found in many IoT frameworks and serves as a first step toward IoT standardization. Our framework was shown to be stable in real-world applications, with the code not incurring a significant increase in power utilization, and could be operated using common rechargeable batteries and a solar panel. In fact, our code consumed so little power that the usual amount of energy was two times higher than what is necessary to keep the batteries full. We also show that the data provided by our framework are reliable through the use of multiple different sensors operating in parallel and sending similar data at a stable rate, without significant differences between the readings. Lastly, the elements of our framework can exchange data in a stable way with very few package losses, being able to read over 1.5 million data points in the course of three months.

DynO-IoT: a dynamic ontology for provisioning semantic interoperability in internet of things

DynO-IoT: a dynamic ontology for provisioning semantic interoperability in internet of things

Exploring the Fusion of Blockchain and AI for Enhanced Practices in IoT Ecosystems: Opportunities and Challenges

The rapid expansion of the Internet of Things (IoT) has ushered in an era of unprecedented data generation, offering transformative potential across industries. Yet, this vast data landscape brings forth challenges related to security, privacy, trust, and intelligent data analysis. In response to these challenges, the fusion of blockchain technology and artificial intelligence (AI) within IoT ecosystems has emerged as a promising solution. This paper embarks on a comprehensive exploration of this fusion, delving into its opportunities and challenges. We provide an overview of IoT's evolution, blockchain technology's fundamental principles, and the significance of AI in data analysis and decision-making. Our focus lies in elucidating how the integration of blockchain fortifies data security, trust, and transparency in IoT applications, while AI augments data analysis, predictive maintenance, and automation. Furthermore, we discuss the challenges and considerations that accompany the integration of AI and blockchain in IoT environments, including scalability, privacy concerns, interoperability, and ethical considerations. By examining the intricate interplay of these technologies, this paper contributes to a deeper understanding of how the fusion of blockchain and AI can usher in a new era of secure, intelligent, and efficient IoT practices.

Applications of Ontology in the Internet of Things: A Systematic Analysis

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions.

Cross-Domain Security and Interoperability in Internet of Things

The Internet of Things advancements has enabled smart city scenarios worldwide. A tight nit communication, on the one hand, is required for device management and monitoring, but on the other hand, it has raised risks in information exchange in cross-domain scenarios. Keeping in view the above issues, this article considered well-known cross-domain access control protocols, i.e., Shibboleth, xDAuth, and OAuth to ensure user’s security. And, the existing work focused specifically on their implementations without verifying if they are effective for the security scenarios. We aim to verify their claim of fulfilling the claimed security requirements or not. We did a Cyber Attack Analysis to highlight the possible security attacks in smart city scenarios. Then, the protocols are verified against those issues to critically analyze if they are the best fit for the said scenario. The Z3-solver and Satisfiability Modulo Theories Library have been in use for protocol verification purposes. Results reveal that the protocol models are functioning correctly in terms of confidentiality, availability, and integrity. Verifying these protocols in terms of other security properties is part of our future work.

IoT Middleware Platforms for Smart Energy Systems: An Empirical Expert Survey

Middleware platforms are key technology in any Internet of Things (IoT) system, considering their role in managing the intermediary communications between devices and applications. In the energy sector, it has been shown that IoT devices enable the integration of all network assets to one large distributed system. This comes with significant benefits, such as improving energy efficiency, boosting the generation of renewable energy, reducing maintenance costs and increasing comfort. Various existing IoT middlware solutions encounter several problems that limit their performance, such as vendor locks. Hence, this paper presents a literature review and an expert survey on IoT middleware platforms in energy systems, in order to provide a set of tools and functionalities to be supported by any future efficient, flexible and interoperable IoT middleware considering the market needs. The analysis of the results shows that experts currently use the IoT middleware mainly to deploy services such as visualization, monitoring and benchmarking of energy consumption, and energy optimization is considered as a future application to target. Likewise, non-functional requirements, such as security and privacy, play vital roles in the IoT platforms’ performances.

Application of Rough Concept Lattice Model in Construction of Ontology and Semantic Annotation in Semantic Web of Things

In order to solve the problem of interoperability in Internet of Things, the Semantic Web technology is introduced into the Internet of Things to form Semantic Web of Things. Ontology construction is the core of Semantic Web of Things. Firstly, this paper analyzes the shortcomings of ontology construction methods in the Semantic Web of Things. Then, this paper proposes construction of semantic ontology based on improved rough concept lattice, which provides theoretical basis for semantic annotation of the sensing data attributes. In addition, this paper describes the semantic annotation system for the Internet of Things based on semantic similarity of ontology. The system consists of three steps: ontology mapping integration module, information extraction module, and semantic annotation of sensing data. Finally, the experimental results show that this semantic annotation method effectively improves the flexibility of sensor information and data attributes and effectively enhances the expression ability of sensor information and the use value of data.

WoTemu: An emulation framework for edge computing architectures based on the Web of Things

The edge computing model is an approach to Internet of Things (IoT) architectures based on the redistribution of services and infrastructure from centralized clouds to locations closer to IoT devices. The Web of Things (WoT) is another important IoT trend, currently led by the W3C, which aims at solving the IoT interoperability problem by adopting proven technologies and patterns from the Web. The design and validation of IoT deployments based on these paradigms is a complex task that involves multiple services, heterogeneous hardware and diverse communication technologies. Testing such projects in real world conditions usually requires a significant investment of resources. There are simulation tools that can assist in this process with much lower barriers of entry, however, they involve the designer making modelling assumptions that are not always representative of the real systems. This work presents an emulation tool for IoT projects based on the edge computing model that is able to seamlessly scale horizontally by leveraging container orchestration (Docker swarm mode). Furthermore, the W3C WoT model is included as a first-class citizen, enabling the designer to model all actors in the system as Things. The tool can run the real production code with minimal modifications and provides meaningful insights into the behaviour of the proposed architecture. This knowledge serves to rapidly iterate the optimization process, simplifying design issues and the detection of bottlenecks before committing to a real deployment in the field. A real-world scenario is also emulated in order to demonstrate its capabilities and validate its contribution.

Hub-OS: An interoperable IoT computing platform for resources utilization with real-time support

The Internet of Things (IoT) is a model where devices and applications interact together to provide smart services in different contexts like smart homes, or smart vehicles. One of the challenges of IoT is the diversity of vendors who provide the IoT devices and the IoT applications. Such diversity leads to reduced utilization of the provided computing resources within IoT computing platforms, due to the different hardware requirements of each vendor for his developed applications, and their corresponding devices. Thus, the same computing resources could be duplicated in the same IoT platform, with minor variations, to fit different vendors needs. Such duplication revsults in extra costs on the application vendors, and reduced utilization of those computing resources. Another consequence of such diversity is the lack of a common interface for communication across IoT devices and applications from different vendors. Such common interface should present a level of abstraction that would hide both the vendor-specific and the IoT platform-specific implementation details, to provide seamless integration of new IoT devices and applications within an IoT platform. This paper presents a novel architecture for an IoT computing platform, namely Hub-OS, for managing devices and applications from different vendors with efficient utilization of computing resources. IoT devices and applications could be easily integrated without the need to modify either Hub-OS software components, or the vendor-specific applications and devices. Furthermore, Hub-OS could host IoT applications that demand real-time processing if needed. To evaluate Hub-OS, we implemented one case study for a smart vehicle, and conducted a quantitative evaluation using varied configurations. Our quantitative evaluation shows that Hub-OS can integrate applications and devices from different vendors, with reduced latency, reduced CPU usage, efficient IoT application startup and migration times.

Importance of semantic interoperability in smart agriculture systems

AbstractThe 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.

EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX

With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices. EdgeX Foundry is a representative open-source-based IoT gateway platform, providing various IoT protocol services and interoperability between them. However, due to the absence of container orchestration technology, such as automated deployment and dynamic resource management for application services, EdgeX Foundry has fundamental limitations of a potential edge computing platform. In this paper, we propose EdgeX over Kubernetes, which enables remote service deployment and autoscaling to application services by running EdgeX Foundry over Kubernetes, which is a product-grade container orchestration tool. Experimental evaluation results prove that the proposed platform increases manageability through the remote deployment of application services and improves the throughput of the system and service quality with real-time monitoring and autoscaling.

The DEWI High-Level Architecture: Wireless Sensor Networks in Industrial Applications

This paper presents the High-Level Architecture (HLA) of the European research project DEWI (Dependable Embedded Wireless Infrastructure). The objective of this HLA is to serve as a reference framework for the development of industrial Wireless Sensor and Actuator Networks (WSANs) based on the concept of the DEWI Bubble. The DEWI Bubble constitutes a set of architecture design rules and recommendations that can be used to integrate legacy industrial sensor networks with a modern, interoperable and flexible IoT (Internet-of-Things) infrastructure. The DEWI Bubble can be regarded as a high-level abstraction of an industrial WSAN with enhanced interoperability (via standardized interfaces), dependability, technology reusability and cross-domain development. The DEWI Bubble aims to resolve the issue on how to integrate commercial WSAN technology to match the dependability, interoperability and high criticality needs of industrial domains. This paper details the criteria used to design the HLA and the organization of the infrastructure internal and external to the DEWI Bubble. The description includes the different perspectives, models, or views of the architecture: the entity model, the layered perspective of the entity model and the functional model. This includes an overview of software and hardware interfaces. The DEWI HLA constitutes an extension of the ISO/IEC 29182 SNRA (Sensor Network Reference Architecture) towards the support of wireless industrial applications in different domains: aeronautics, automotive, railway and building. To improve interoperability with existing approaches, the DEWI HLA also reuses some features from other standardized technologies and architectures. The DEWI HLA and the concept of Bubble allow networks with different industrial sensor technologies to exchange information between them or with external clients via standard interfaces, thus providing consolidated access to sensor information of different industrial domains. This is an important aspect for smart city applications, Big Data, Industry 4.0 and the Internet-of-Things (IoT). The paper includes a non-exhaustive review of the state of the art of the different interfaces, protocols and standards of this architecture. The HLA has also been proposed as the basis of the European projects SCOTT (Secure Connected Trustable Things) for enhanced security and privacy in the IoT and InSecTT (Intelligent Secure Trustable Things) for the convergence of artificial intelligence (AI) and the IoT.

RML based ontology development approach in internet of things for healthcare domain

Purpose A huge amount of diverse data is generated in the Internet of Things (IoT) because of heterogeneous devices like sensors, actuators, gateways and many more. Due to assorted nature of devices, interoperability remains a major challenge for IoT system developers. The purpose of this study is to use mapping techniques for converting relational database (RDB) to resource directory framework (RDF) for the development of ontology. Ontology helps in achieving semantic interoperability in application areas of IoT which results in shared/common understanding of the heterogeneous data generated by the diverse devices used in health-care domain. Design/methodology/approach To overcome the issue of semantic interoperability in healthcare domain, the authors developed an ontology for patients having cardio vascular diseases. Patients located at any place around the world can be diagnosed by Heart Experts located at another place by using this approach. This mechanism deals with the mapping of heterogeneous data into the RDF format in an integrated and interoperable manner. This approach is used to integrate the diverse data of heart patients needed for diagnosis with respect to cardio vascular diseases. This approach is also applicable in other fields where IoT is mostly used. Findings Experimental results showed that the RDF works better than the relational database for semantic interoperability in the IoT. This concept-based approach is better than key-based approach and reduces the computation time and storage of the data. Originality/value The proposed approach helps in overcoming the demerits of relational database like standardization, expressivity, provenance and supports SPARQL. Therefore, it helps to overcome the heterogeneity, thereby enabling the semantic interoperability in IoT.

Multi-Layer Semantic Middleware for Cross-Domain Internet of Things

Heterogeneous access is an issue caused by different standards of devices and systems in the Internet of Things(IoT), which makes cross-domain devices difficult to connect and communicate with each other. In this paper, to solve the interoperability problem for cross-domain IoT, a multi-layer semantic middleware framework using a WoT-based knowledge graph is proposed. Firstly, the knowledge graphs of sub-domains are built respectively, and then integrate them through a middleware framework based on WoT. We evaluated the effectiveness of this proposed approach through a cross-domain Smart Building project. We find that our approach can effectively enable cross-domain IoT interoperability.

Data Spine: A Federated Interoperability Enabler for Heterogeneous IoT Platform Ecosystems.

Today, the Internet of Things (IoT) is pervasive and characterized by the rapid growth of IoT platforms across different application domains, enabling a variety of business models and revenue streams. This opens new opportunities for companies to extend their collaborative networks and develop innovative cross-platform and cross-domain applications. However, the heterogeneity of today’s platforms is a major roadblock for mass creation of IoT platform ecosystems, pointing at the current absence of technology enablers for an easy and innovative composition of tools/services from the existing platforms. In this paper, we present the Data Spine, a federated platform enabler that bridges IoT interoperability gaps and enables the creation of an ecosystem of heterogeneous IoT platforms in the manufacturing domain. The Data Spine allows the ecosystem to be extensible to meet the need for incorporating new tools/services and platforms. We present a reference implementation of the Data Spine and a quantitative evaluation to demonstrate adequate performance of the system. The evaluation suggests that the Data Spine provides a multitude of advantages (single sign-on, provision of a low-code development environment to support interoperability and an easy and intuitive creation of cross-platform applications, etc.) over the traditional approach of users joining multiple platforms separately.