A Cost Model for Data Discovery in Large-Scale IoT Networks of Smart Cities

Abstract

A smart city with huge numbers of physical (e.g., sensors and actuators) and non-physical (e.g., external databases) data sources will continuously produce high amounts of massive city-data. Distributed data storage across the city may store the produced city-data. City managers through different update mechanisms may send the produced city-data from distributed data storage to centralized data storage (e.g., Cloud data storage). Hence, the data discovery issues are in a vital position in the smart city concepts because the produced city-data may exist in different data storage platforms from distributed to centralized data. In this paper, we will first present our proposed Distributed-to-Centralized Information and Communications Technology (D2C-ICT) architecture for the Zero Emission Neighborhoods (ZEN) center. This proposed D2C-ICT architecture can provide multiple facilities from the joined benefits of distributed and centralized technologies in smart cities. Second, we will show how the Multi-Attribute Utility Theory (MAUT) cost model can be beneficial to find the appropriate data for building city services across the different storage platforms on the city scale as well as can be applied in the ZEN center and its pilots.