Streaming Analytics in User-centric Internet of Things Domains: A Fog-enabled System Architecture for Smart Home Applications

A smart home is an apartment or a house in which smart devices communicate with each other to improve key areas of daily life, such as comfort, security or energy consumption. Therefore, the smart home domain is user-centric and exhibits characteristics that distinguish it from other application domains of the Internet of Things. Specifically, this concerns the existence of different 'regular' and 'smart' devices, but also the basic arrangement of each household, which is highly individual. As a result, the realization of analytics scenarios in the smart home domain is influenced by household-specific requirements regarding the configuration, composition and execution of analytics tasks. Existing approaches in scientific literature cover the resulting architectural challenges only insufficiently. With the emergence of new computing paradigms, architectural concepts and technologies, new opportunities for analytics approaches, which enable individual household insights, become evident. For this reason, the objective of this work is the design of an Internet of Things analytics architecture for smart home applications, which supports the flexible deployment of analytics pipelines, therefore enabling the generation of individual household insights. In order to achieve this goal, challenges for Internet of Things analytics architectures are identified and analyzed by conducting a literature review. Based on the resulting challenges catalog, an architectural model is designed that facilitates the processing and analysis of streaming data from smart devices of different kinds. The developed architecture utilizes the fog computing paradigm, therefore allowing the deployment and execution of analytics pipelines in the cloud as well as at edge of the network. The architectural model is the foundation for a prototype, which is implemented to evaluate the proposed solution. The evaluation is performed by conducting several experiments, which are designed in order to validate the prototypes feasibility to address the found challenges. The main contributions of this work are a challenges catalog for Internet of Things analytics architectures, an architectural model for analytics in smart home applications as well as a prototype, which is based on it

A cloud-based Analytics-Platform for user-centric Internet of Things domains – Prototype and Performance Evaluation

Data analytics have the potential to increase the value of data emitted from smart devices in user-centric Internet of Things environments, such as smart home, drastically. In order to allow businesses and end-consumers alike to tap into this potential, appropriate analytics architectures must be present. Current solutions in this field do not tackle all of the diverse challenges and requirements, which were identified in previous research. Specifically, personalized, extensible analytics solutions, which still offer the means to address big data problems are scarce. In this paper, we therefore present an architectural solution, which was specifically designed to address the named challenges. Furthermore, we offer insights into the prototypical implementation of the proposed concept as well as an evaluation of its performance against traditional big data architectures

Streaming Analytics in User-centric Internet of Things Domains: A Fog-enabled System Architecture for Smart Home Applications

A smart home is an apartment or a house in which smart devices communicate with each other to improve key areas of daily life, such as comfort, security or energy consumption. Therefore, the smart home domain is user-centric and exhibits characteristics that distinguish it from other application domains of the Internet of Things. Specifically, this concerns the existence of different 'regular' and 'smart' devices, but also the basic arrangement of each household, which is highly individual. As a result, the realization of analytics scenarios in the smart home domain is influenced by household-specific requirements regarding the configuration, composition and execution of analytics tasks. Existing approaches in scientific literature cover the resulting architectural challenges only insufficiently. With the emergence of new computing paradigms, architectural concepts and technologies, new opportunities for analytics approaches, which enable individual household insights, become evident. For this reason, the objective of this work is the design of an Internet of Things analytics architecture for smart home applications, which supports the flexible deployment of analytics pipelines, therefore enabling the generation of individual household insights. In order to achieve this goal, challenges for Internet of Things analytics architectures are identified and analyzed by conducting a literature review. Based on the resulting challenges catalog, an architectural model is designed that facilitates the processing and analysis of streaming data from smart devices of different kinds. The developed architecture utilizes the fog computing paradigm, therefore allowing the deployment and execution of analytics pipelines in the cloud as well as at edge of the network. The architectural model is the foundation for a prototype, which is implemented to evaluate the proposed solution. The evaluation is performed by conducting several experiments, which are designed in order to validate the prototypes feasibility to address the found challenges. The main contributions of this work are a challenges catalog for Internet of Things analytics architectures, an architectural model for analytics in smart home applications as well as a prototype, which is based on it

Streaming Analytics in User-centric Internet of Things Domains: A Fog-enabled System Architecture for Smart Home Applications

A smart home is an apartment or a house in which smart devices communicate with each other to improve key areas of daily life, such as comfort, security or energy consumption. Therefore, the smart home domain is user-centric and exhibits characteristics that distinguish it from other application domains of the Internet of Things. Specifically, this concerns the existence of different 'regular' and 'smart' devices, but also the basic arrangement of each household, which is highly individual. As a result, the realization of analytics scenarios in the smart home domain is influenced by household-specific requirements regarding the configuration, composition and execution of analytics tasks. Existing approaches in scientific literature cover the resulting architectural challenges only insufficiently. With the emergence of new computing paradigms, architectural concepts and technologies, new opportunities for analytics approaches, which enable individual household insights, become evident. For this reason, the objective of this work is the design of an Internet of Things analytics architecture for smart home applications, which supports the flexible deployment of analytics pipelines, therefore enabling the generation of individual household insights. In order to achieve this goal, challenges for Internet of Things analytics architectures are identified and analyzed by conducting a literature review. Based on the resulting challenges catalog, an architectural model is designed that facilitates the processing and analysis of streaming data from smart devices of different kinds. The developed architecture utilizes the fog computing paradigm, therefore allowing the deployment and execution of analytics pipelines in the cloud as well as at edge of the network. The architectural model is the foundation for a prototype, which is implemented to evaluate the proposed solution. The evaluation is performed by conducting several experiments, which are designed in order to validate the prototypes feasibility to address the found challenges. The main contributions of this work are a challenges catalog for Internet of Things analytics architectures, an architectural model for analytics in smart home applications as well as a prototype, which is based on it