Page 268 - ITU Kaleidoscope 2016
P. 268

P.3    Systematic analysis of geo-location and spectrum sensing as access methods to TV white space.
                      Hope Mauwa; Antoine Bagula (University of The Western Cape, South Africa); Marco Zennaro;
                      Ermanno Pietrosemoli (The Abdus Salam International Centre for Theoretical Physics, Italy); Albert
                      A. Lysko (Council for Scientific and Industrial Research, South Africa); Timothy X Brown (Carnegie
                      Mellon University, Rwanda)


                      Access to the  television white space by white  space devices  comes with a major technical
                      challenge: white space devices can potentially interfere with existing television signals. Two
                      methods have been suggested in the  literature to help white  space devices  identify unused
                      channels in the TV frequency  band so  that they can avoid causing harmful  interference to
                      primary services legally protected to run on the band. These methods are geo-location spectrum
                      database and spectrum sensing. Discussions in the literature have placed much emphasis on the
                      limitations of the spectrum sensing approach  and  mainly based on the developed world
                      environment ignoring the performance requirements of the geo-location database approach and
                      how the absence of these requirements in a developing region could affect its performance. This
                      paper considers a broader analysis of the approaches by looking at factors that can affect the
                      performance of each approach and how the presence or absence of these factors in a developed
                      region or developing region can affect their performance. In so doing, the paper highlights the
                      need to conduct more research on the performance of spectrum sensing in developing regions
                      where there are plenty of white spaces to ascertain its use in these regions.


               P.4    Task-based process modeling for policy making in smart cities.
                      Leonidas Anthopoulos  (University of Applied Science  (TEI) of  Thessaly, Greece);  George
                      Giannakidis (Center for Renewable Energy Sources and Saving, Greece)
                      Several competitive standards have been introduced for smart city quite recently, which define the
                      architecture and its components or city key performance indicators. However, these standards do not
                      discuss smart service formulation, nor policy making process modeling. Standardization assists in
                      achieving process automation by introducing “best practices” as standard process models. Policy
                      making mainly follow non-standardized procedures, even if it is supported by various tools (i.e.,
                      Multi-Criteria Decision Methods (MCDM)). Inspired by the Task-Based Modeling method (TBM)
                      this paper focuses on policy making process standardization for smart cities. It utilizes the case-study
                      of the InSmart (Integrative Smart City Planning) coordination action in the smart city of Trikala,
                      Greece, in an attempt to define and introduce a model for such a process.


               P.5    CleanWiFi: the wireless network for air  quality monitoring, community  Internet access and
                      environmental education in smart cities.
                      Carlos Andrés Gómez Ruíz (Universitaria Agustiniana, Colombia)


                      This work presents a new development model for wireless community networks, framed within the
                      sustainable development goals, SDG 11, to achieve the transformation of cities to environmentally
                      sustainable areas and to take measures to fight climate change; this model is based on the use of
                      renewable energies,  mesh  routing protocols, the  monitoring of air quality and  environmental
                      variables, IoT, and the application of educational methodologies in order to reward less polluted
                      areas. There are proposed some ideas about standardization of building networks for measuring air
                      quality in smart cities, which provide a great source of information for location-based services (LBS)
                      that promote environmental awareness services. The CleanWiFi network constantly monitors the air
                      for pollutant gases, uses that information to feed a Big Sensor Data system, and uses the same data
                      for the automatic configuration of the public WiFi service, displaying information about the quality
                      of the air to the user, and rewarding less oolluted areas with a better service. That way it raises public
                      awareness about state of air pollution and how important it is to reduce it; promotes the use of
                      renewable energies and brings WiFi connectivity to the people.







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