Wireless Personal Communications

, Volume 75, Issue 4, pp 2289–2322 | Cite as

Cognitive Management of Devices in the Wireless World

  • V. Stavroulaki
  • N. Koutsouris
  • Y. KritikouEmail author
  • P. Demestichas


The success of mobile networks has been driven by the services offered, i.e. voice in second generation and multimedia services in third generation networks. Similarly, a key issue for the success of future generation networks is considered to be the provision of enhanced, always available, personalised services. At the same time, the complexity and heterogeneity of the infrastructure of mobile network operators increases as Radio Access Technologies continue to evolve and new ones emerge. All these issues call for self-management and learning capabilities in future generation network systems. Cognitive, reconfigurable systems encompassing self-management and learning capabilities have been devised as a solution in this direction. Cognitive systems determine their behaviour, in a self-managed way. This is done reactively or proactively, based on goals, policies, knowledge and experience obtained through learning. This paper focuses on the user device and presents a Cognitive Device Management System that comprises mechanisms for dynamically selecting the optimal device configuration, taking into account user preferences, device environment characteristics (context), policies, and knowledge established through machine learning functionality.


Bayesian statistics Cognitive device Cognitive systems  Device management Learning User preferences 



Beyond the third generation


Bit rate


Cognitive device management system


Graphical user interface


Joint radio resource management


Multi-attribute decision making


Megabits per second


Mobile network operator(s)




Quality of service


Radio access technology(-ies)


Round trip time



This work was performed in the project E\(^{3}\) ( which had received research funding from the Community’s Seventh Framework programme. The contributions of colleagues from the E\(^{3}\) consortium are hereby acknowledged. This work was evolved in the context of the OneFIT (Opportunistic networks and Cognitive Management Systems for Efficient Application Provision in the Future InterneT, and UniverSelf ( Projects. Furthermore, evolved versions of this work have supported training activities in the context of the ACROPOLIS (Advanced coexistence technologies for Radio Optimisation in Licenced and Unlicensed Spectrum -Network of Excellence) project ( This paper reflects only the authors’ views and the Community is not liable for any use that may be made of the information contained therein.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • V. Stavroulaki
    • 1
  • N. Koutsouris
    • 1
  • Y. Kritikou
    • 1
    Email author
  • P. Demestichas
    • 1
  1. 1.University of PiraeusPiraeusGreece

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