Search

Summary: Geo‐related data, also known as spatial data, is represented using a vector used for representing longitude, elevation, and latitude. Specially built systems, well known as Geographical Information Systems (GIS), made use of such data for querying, manipulating, navigation, and analyzing. In the current era of data, science needs to involve smart interactive investigation involving Internet of Data (IoD) on predicting upcoming changes and spatial updates on the map is growing rapidly. To resolve issues concerning real‐time spatial data, transformation using semantic annotation can provide a better way to translate spatial relationships. These spatial relationships will support spatial analysis by linking different cause and effect with the help of reasoning mechanism. This research's major focus is on a data transformation methodology for geo‐related semantic annotation. Spatial dataset gets stored in a database and then transformed into Extensible Markup Language (XML) and Resource Description Framework (RDF). Even for bi‐directional transformation to work properly, we need to map different schema level transformations. A deep research is conducted to consider available mappings, implementations, and updates to further improving data fusion for having better compatibility. Then, transformed data as results get analyzed and discussed based on the data mapping rules formulated. It is aimed to show the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data. [ABSTRACT FROM AUTHOR]

Interoperability remains a major burden to the developers of Internet of Things systems. It is due to IoT devices are extremely heterogeneous regarding basic communication protocols, data formats, and technologies. Furthermore, due to the absence of worldwide satisfactory standards, Interoperability tools remains imperfect. In this paper, we have proposed Semantic Interoperability Model for Big-data in IoT (SIMB-IoT) to deliver semantic interoperability among heterogeneous IoT devices in health care domain. This model is used to recommend medicine with side effects for different symptoms collected from heterogeneous IoT sensors. Two datasets are taken for the analysis of big-data. One dataset contains diseases with drug details and the second dataset contains medicines with side effects. Information between physician and patient are semantically annotated and transferred in a meaningful way. A Lightweight Model for Semantic annotation of Big-data using heterogeneous devices in IoT is proposed to provide annotations for big data. Resource Description Framework (RDF) is a semantic web framework that is recycled to communicate things using Triples to make it semantically significant. RDF annotated patients’ data and made it semantically interoperable. SPARQL query is used to extract records from RDF graph. Tableau, Gruff-6.2.0, and Mysql tools are used in simulation in this article. [ABSTRACT FROM AUTHOR]

Data and information security is considered to be an important and challenging task for any field of life. But it becomes more critical especially when it deals with the medical field due to life and health hazards. The ratio of internal security threats to external threats always remains high. A huge number of efforts and technical expertise are required in the case of attacking the system from the external environment. But it requires fewer efforts if a system is attacked internally by the stakeholders of the system. This article presents an access control model that secures the medical data of patients against internal cybersecurity threats. It allows only the legitimate users, that is, authorized patients and doctors to communicate despite the fact of physical boundaries. The proposed model implements authorization in combination with permissions and roles instead of roles only for medical staff. It removes the discrepancies in the existing access control models. The proposed model ensures communication among doctors and patients in a secure, private, and efficient manner. The model is demonstrated by using mathematical modeling along with implementation examples. The proposed model outperformed in comparison with state-of-the-art access control models. [ABSTRACT FROM AUTHOR]

Big IoT data is inherently distributed, high-dimensional, irregular, and sparse in nature. Fog computing model in its original form is by no means the optimal solution for mining big IoT data. However, utilizing the network edge for mining tasks, such as enabling edge and IoT devices to mine locally frequent patterns can significantly improve the mining performance. Additionally, edge devices capable of performing distributed job processing could utilize the model to the fullest. But resource poorness of edge and IoT devices needs lightweight pattern mining algorithms. This paper presents Distributed HARnessing the Power of Powersets for Mining Frequent Itemsets (D-HARPP), a spark-based distributed algorithm to mine frequent co-occurring itemsets in big IoT data. Unlike state-of-the-art distributed algorithms, D-HARPP makes a single pass over the data and does not create candidate itemsets; thus, achieves significantly better runtime and consumes the least memory. Moreover, performance of D-HARPP is not deteriorated at lower minimum support thresholds. These distinguishing characteristics make D-HARPP an optimal choice for Spark-enabled edge and IoT devices. D-HARPP has outperformed Spark-Apriori, another distributed algorithm by significant margins, both in terms of runtime and memory consumption, particularly on sparse datasets. [ABSTRACT FROM AUTHOR]