# Embracing the Future Internet of Things +
## 4 Realization of Hyper-Connected IoT realm
### Smart-City Magnifier: Smart Cities Enabled by Future Hyper-Connected IoT  
As an example of future application that makes use of cross-cloud interoperation, we have implemented a smart city application, named Smart-City Magnifier. The application purpose is to analyse the situation of generic geographic areas and compute indicators representing the health of the urban and natural environments. The outcome of the indicators are then showed into a dashboard that highlights the critical situations such as emergencies (e.g., fire breaks), high crowd densities, and urban and rural pollution. The data is harvested from data sources belonging to different IoT domains hosted by separated IoT clouds and going through a process of associating observations to real-world things (e.g., a city or a neighbourhood) and then analyse the situations related to those things. Figure 6 shows the concrete implementation architecture of the Smart-City Magnifier application. Data are acquired by many Semantic Mediation Gateways (SMGs) that have the purpose of semantically translating from a format (protocol and data model) to another format. In this application, SMGs transform data to NGSI format modelled with FIESTA-IoT ontology (see Section 3.2). The integrated IoT deployments are either coming from the FIESTA-IoT system [23; 24], from crowd mobility IoT deployments in New Zealand or from the smart city of Murcia offered by the SMARTIE EU project [25]. The deployments behind the FIESTA-IoT framework are exposing the IoT data either through the FIESTA-IoT historical triple store accessible via SPARQL Protocol and RDF Query Language (SPARQL) queries or via exposed IoT endpoints, discoverable again through a SPARQL query, for every available sensors. In both cases, the data format is plain JSON-Linked Data (JSON-LD) annotated with the FIESTA-IoT ontology. Through the FIESTA-IoT platform, more than 7500 sensors or resources could be accessed from 10 testbeds reporting environmental data (e.g., outdoor temperature, humidity, particles concentration, luminosity, noise level), road traffic monitoring (e.g., vehicle speed, traffic intensity), car and bike parking spots, public transportation status (e.g., bus estimated arrival times, vehicle localization), garbage management, soil and trees monitoring (urban parks and garden, rural areas), pedestrian presence detector, electromagnetic outdoor exposure, smart building/office information (e.g., human presence, power consumption, heating ventilation and air conditioning-HVAC system, solar panels), signal power and power consumption of wireless sensors, and sea water quality (e.g., pH, Ammonium). The crowd mobility deployments from New Zealand are instead exposing the data via an ad hoc JSON format. The Murcia smart city data, comprehensive of 430 city wide urban mobility sensors (e.g., traffic sensors, bike and car parking spot sensors, public transportation status sensors) and 25 smart building sensors in the university campus, can be accessed via the NGSI interface, which is following an ad hoc data format defined by the specific IoT silo owner  
The interoperable data is then, on the one hand, handled on multiple IoT Broker instances federated in a topology of peers, and, on the other hand, orchestrated by the _FogFlow_[1] framework which offers a service-defined analytics layer (see Section 3.1). The federation of brokers is meant both to enhance the scalability of the whole system, since the data coming from the IoT deployments are sharded among them, and to differentiate IoT domains. Any of the brokers is able to provide all the available data in the federation through a single NGSI request, necessitating sometimes of a multi-hop connection, transparently executed, if data are not directly provided by the contacted broker. The data analytics instead is made of atomic tasks handled and instantiated by the FogFlow framework which manages the available computation resources and orchestrates the data stream between tasks.