Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics

• Main Authors: Huang, Hantao, Khalid, Rai Suleman, Yu, Hao
• Other Authors: Pedrycz, Witold
• Format: Book Chapter
• Language: English
• Published: Springer International Publishing 2017
• Subjects: Computational intelligence; Smart home
• Online Access: https://hdl.handle.net/10356/86077; http://hdl.handle.net/10220/43936
• Institution: Nanyang Technological University

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.