Supporting Decision Making for Large-Scale IoTs: Trading Accuracy with Computational Complexity

  • Kostas SioziosEmail author
  • Panayiotis Danassis
  • Nikolaos Zompakis
  • Christos Korkas
  • Elias Kosmatopoulos
  • Dimitrios Soudris


As systems continue to evolve they rely less on human decision-making and more on computational intelligence. This trend in conjunction to the available technologies for providing advanced sensing, measurement, process control, and communication lead towards the new field of Internet-of-Things (IoT). IoT systems are expected to play a major role in the design and development of future engineering platforms with new capabilities that far exceed today’s levels of autonomy, functionality, and usability. Although these systems exhibit remarkable characteristics, their design and implementation is a challenging issue, as numerous (heterogeneous) components and services have to be appropriately designed. The problem of designing efficient IoT becomes far more challenging in case the target system has to meet also timing constraints. This chapter discusses an advanced framework for implementing decision-making mechanisms for large-scale IoT platforms. In order to depict the efficiency of introduced framework, it was applied to customize the building’s cooling and heating in a smart-grid environment. For this purpose, a number of connected smart thermostats are employed, which should facilitate intelligent control to fulfill occupants’ needs, such as the energy consumption and the comfort level in a building environment. Towards this direction, appropriate mechanisms that enable smart thermostats to have the capability to monitor their own performance, to classify, to learn, and to take proper actions, were developed in a systematic way. Experimentation with various configuration setups highlights the superior of introduced solution compared to static temperature values, as well as existing control techniques. Additionally, the significant low computational complexity enables the sufficient implementation of this mechanism as part of a low-cost embedded system, which can be integrated into existing smart thermostats.


Decision making Control Trade-off HVAC Smart thermostats Embedded system 



The research leading to these results has been partially funded by the European Commission FP7-ICT-2013.3.4, Advanced computing, Embedded Control Systems, under the contract #611538 (Local4Global, Also, this work presented is partially supported by the FP7-2013612069-2013-HARPA EU project.


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Kostas Siozios
    • 1
    Email author
  • Panayiotis Danassis
    • 2
  • Nikolaos Zompakis
    • 2
  • Christos Korkas
    • 3
  • Elias Kosmatopoulos
    • 3
  • Dimitrios Soudris
    • 2
  1. 1.School of PhysicsAristotle University of ThessalonikiThessalonikiGreece
  2. 2.School of ECENational Technical University of AthensAthensGreece
  3. 3.Department of ECEDemocritus University of ThraceXanthiGreece

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