Towards a Situation-Aware Architecture for the Wisdom Web of Things

  • Akihiro Eguchi
  • Hung Nguyen
  • Craig ThompsonEmail author
  • Wesley Deneke
Part of the Web Information Systems Engineering and Internet Technologies Book Series book series (WISE)


Computers are getting smaller, cheaper, faster, with lower power requirements, more memory capacity, better connectivity, and are increasingly distributed. Accordingly, smartphones became more of a commodity worldwide, and the use of smartphones as a platform for ubiquitous computing is promising. Nevertheless, we still lack much of the architecture and service infrastructure we will need to transition computers to become situation aware to a similar extent that humans are. Our Everything is Alive (EiA) project illustrates an integrated approach to fill in the void with a broad scope of works encompassing Ubiquitous Intelligence (RFID, spatial searchbot, etc.), Cyber-Individual (virtual world, 3D modeling, etc.), Brain Informatics (psychological experiments, computational neuroscience, etc.), and Web Intelligence (ontology, workflow, etc.). In this chapter, we describe the vision and architecture for a future where smart real-world objects dynamically discover and interact with other real or virtual objects, humans or virtual humans. We also discuss how the vision in EiA fits into a seamless data cycle like the one proposed in the Wisdom Web of Things (W2T), where data circulate through things, data, information, knowledge, wisdom, services, and humans. Various open research issues related to internal computer representations needed to model real or virtual worlds are identified, and challenges of using those representations to generate visualizations in a virtual world and of “parsing” the real world to recognize and record these data structures are also discussed.


Virtual World Smart Object Real World Object Computer Mediate Communication Electronic Product Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Akihiro Eguchi
    • 1
  • Hung Nguyen
    • 2
  • Craig Thompson
    • 2
    Email author
  • Wesley Deneke
    • 3
  1. 1.Oxford Centre for Theoretical Neuroscience and Artificial IntelligenceUniversity of OxfordOxfordUK
  2. 2.Department of Computer Science and Computer EngineeringUniversity of ArkansasFayettevilleUSA
  3. 3.Department of Computer Science and Industrial TechnologySoutheastern Louisiana UniversityHammondUSA

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