A Semantic Collaboration Method Based on Uniform Knowledge Graph

The Semantic Internet of Things is the extension of the Internet of Things and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the Internet of Things. However, the Semantic Internet of Things has the characteristics of both the Internet of Things and the Semantic Web environment, and the corresponding semantic data presents many new data features. In this study, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph, allowing us to be applied to the environment of the Semantic Internet of Things better. Here, we design a semantic collaboration method based on a uniform knowledge graph. It can take the uniform knowledge graph as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the Semantic Internet of Things. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes

Semantic-driven Configuration of Internet of Things Middleware

We are currently observing emerging solutions to enable the Internet of Things (IoT). Efficient and feature rich IoT middeware platforms are key enablers for IoT. However, due to complexity, most of these middleware platforms are designed to be used by IT experts. In this paper, we propose a semantics-driven model that allows non-IT experts (e.g. plant scientist, city planner) to configure IoT middleware components easier and faster. Such tools allow them to retrieve the data they want without knowing the underlying technical details of the sensors and the data processing components. We propose a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of automated context-aware configuration of filtering, fusion, and reasoning mechanisms in IoT middleware according to the problems at hand. We incorporate semantic technologies in solving the above challenges. We demonstrate the feasibility and the scalability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized into any other middleware platform. We evaluate CASCoM in agriculture domain and measure both performance in terms of usability and computational complexity.Comment: 9th International Conference on Semantics, Knowledge & Grids (SKG), Beijing, China, October, 201

Semantic Gateway as a Service architecture for IoT Interoperability

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between things. This paper proposes a gateway and Semantic Web enabled IoT architecture to provide interoperability between systems using established communication and data standards. The Semantic Gateway as Service (SGS) allows translation between messaging protocols such as XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of broadly accepted specifications such as W3C's Semantic Sensor Network (SSN) ontology for semantic annotations of sensor data provide semantic interoperability between messages and support semantic reasoning to obtain higher-level actionable knowledge from low-level sensor data.Comment: 16 page

C Minor: a Semantic Publish/Subscribe Broker for the Internet of Musical Things

Semantic Web technologies are increasingly used in the Internet of Things due to their intrinsic propensity to foster interoperability among heterogenous devices and services. However, some of the IoT application domains have strict requirements in terms of timeliness of the exchanged messages, latency and support for constrained devices. An example of these domains is represented by the emerging area of the Internet of MusicalThings.InthispaperweproposeCMinor,aCoAP-based semantic publish/subscribe broker speci\ufb01cally designed to meet the requirements of Internet of Musical Things applications, but relevant for any IoT scenario. We assess its validity through a practical use case

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Semantic reasoning on the edge of internet of things

Abstract. The Internet of Things (IoT) is a paradigm where physical objects are connected with each other with identifying, sensing, networking and processing capabilities over the Internet. Millions of new devices will be added into IoT network thus generating huge amount of data. How to represent, store, interconnect, search, and organize information generated by IoT devices become a challenge. Semantic technologies could play an important role by encoding meaning into data to enable a computer system to possess knowledge and reasoning. The vast amount of devices and data are also challenges. Edge Computing reduces both network latency and resource consumptions by deploying services and distributing computing tasks from the core network to the edge. We recognize four challenges from IoT systems. First the centralized server may generate long latency because of physical distances. Second concern is that the resource-constrained IoT devices have limited computing ability in processing heavy tasks. Third, the data generated by heterogeneous devices can hardly be understood and utilized by other devices or systems. Our research focuses on these challenges and provide a solution based on Edge computing and semantic technologies. We utilize Edge computing and semantic reasoning into IoT. Edge computing distributes tasks to the reasoning devices, which we call the Edge nodes. They are close to the terminal devices and provide services. The newly added resources could balance the workload of the systems and improve the computing capability. We annotate meaning into the data with Resource Description Framework thus providing an approach for heterogeneous machines to understand and utilize the data. We use semantic reasoning as a general purpose intelligent processing method. The thesis work focuses on studying semantic reasoning performance in IoT system with Edge computing paradigm. We develop an Edge based IoT system with semantic technologies. The system deploys semantic reasoning services on Edge nodes. Based on IoT system, we design five experiments to evaluate the performance of the integrated IoT system. We demonstrate how could the Edge computing paradigm facilitate IoT in terms of data transforming, semantic reasoning and service experience. We analyze how to improve the performance by properly distributing the task for Cloud and Edge nodes. The thesis work result shows that the Edge computing could improve the performance of the semantic reasoning in IoT

Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the 16th International Middleware Conference (Middleware Doct Symposium 2015), Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US