Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics
Khalid, Rai Suleman
Date of Issue2017
School of Electrical and Electronic Engineering
Computational intelligence techniques are intelligent computational methodologies such as neural network to solve real-world complex problems. One example is to design a smart agent to make decisions within environment in response to the presence of human beings. Smart building/home is a typical computational intelligence based system enriched with sensors to gather information and processors to analyze it. Indoor computational intelligence based agents can perform behavior or feature extraction from environmental data such as power, temperature, and lighting data, and hence further help improve comfort level for human occupants in building. The current indoor system cannot address dynamic ambient change with a real-time response under emergency because processing backend in cloud takes latency. Therefore, in this chapter we have introduced distributed machine learning algorithms (SVM and neural network) mapped on smart-gateway networks. Scalability and robustness are considered to perform real-time data analytics. Furthermore, as the success of system depends on the trust of users, network intrusion detection for smart gateway has also been developed to provide system security. Experimental results have shown that with a distributed machine learning mapped on smart-gateway networks real-time data analytics can be performed to support sensitive, responsive and adaptive intelligent systems.
© 2017 Springer International Publishing. This is the author created version of a work that has been peer reviewed and accepted for publication by Data Science and Big Data: An Environment of Computational Intelligence, Springer International Publishing. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1007/978-3-319-53474-9_11].