The Internet of Everything (IoE) is the paradigm of intelligent services that supports a ubiquitous and always-connected world of smart sensors and actuators, human and non-human in a cyber-physical lifestyle. Recent years have witnessed the fast proliferation of smart devices. In addition, these functional smart sensors possess powerful computation and communication capabilities and contribute to intelligent services outcomes of IoE-based applications. Then by adopting a knowledge-based approach on sensors smartness, we make a comparative assessment and classify smart sensors (image sensors, temperature sensors, proximity sensors, speed sensors, and sound sensors) regarding intelligence levels. We extracted valuable conclusions and inferences from this classification, providing insights and future directions towards promised benefits offered by distilled intelligence in smart sensors applications. (Abstract)

With a growing demand for new technologies, concepts such as the Internet of Everything (IoE) – in which smart sensors (humans and machines) connect, communicate, and share information from the surrounding environment – are gaining notoriety. Thus, this study focuses on developing a knowledge flow model in the IoE scenario for Smart Cities, including how knowledge is disseminated in these intelligent environments in human-to-machine interactions. The study considers a knowledge-based taxonomy defined for IoE environments and supports the full realization of IoE applications through a knowledge management strategy. The proposed IoE Knowledge Flow Model contributes to identification of knowledge flows related to IoE architecture layers in smart city applications.

The paradigm of the Internet of Everything (IoE) adds value to Internet of Things (IoT) in connections among people, processes, data, and things. However, knowledge creation process in IoE context still requires concerns to what extent connected sensors and actuators collaborates in value created and outcomes of IoE applications. This study focuses on the requirement and design approach for identification of critical knowledge within IoE domain though a knowledge ranking approach. Existing solutions for semantic definitions and taxonomies in IoE are reviewed for this purpose. In addition, this work proposes the use of a knowledge-based taxonomy as the main driver for ranking knowledge in smart sensors as IoE enablers with a qualitative approach of its dimensions. To the best of our knowledge, this is the first work that comprehensively integrate a taxonomy concerning ranking knowledge in IoE applications.