No abstract

The internet of things (IOT) is a phenomenon of connected devices over the internet to ease human life. It is a system where a separate computing device embedded with sensors is connected to other devices or to the cloud through the different infrastructures of the Internet. The implication of the IOT is still challenging in a geographically distributed environment. Particularly, the main challenges are associated with data privacy and security. In this study, we investigate in the report the risks/issue related to IoT data privacy and security from the existing literature for the last two years and provide a review. We identify a total of seven issues related to IoT data privacy and security. The findings revolved that Privacy, Security, confidentiality, and integrity are the most significant issues for IoT in the current era. The findings of this study provide the researchers with a body of knowledge about the critical issues faced by the users and practitioners of IOT across the globe. Contribution/Originality: In this paper, we conducted the literature review to find out the main challenges that are being faced by challenges related to privacy and security mainly, authentication and access control, confidentiality and integrity IOT devices users and as well as for IOT manufacturer. We highlighted seven, privacy, trust on the device and conducted a questionnaire survey from different organizations and from different research experts and ranked it accordingly.

Context modeling is often used to relate the context in which a system will operate to the entities of interest in the problem domain. It remains the case that context models are inadequate in emerging computing paradigms (e.g., smart spaces and the Internet of Things), in which the relevance of context is shaped dynamically by the changing needs of users. Formal models are required to fuse and interpret contextual information obtained from the heterogeneous sources. In this paper, we propose an integrated and formal context modeling approach for intelligent systems operating in the context-sensitive environments. We introduce a goal-driven, entity-centered identification method for determining which context elements are influential in adapting the system behavior. We then describe a four-layered framework for metamodeling the identification and management of context. First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github.com/metamodeler/ CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.