# Embracing the Future Internet of Things +
## 3 IoT Platform Capabilities
### Resource and Context Management  
In the future IoT framework, the resource-context management refers to the _contextualization process_ from data to services which requires the capabilities of _resource-entity mapping_ and _semantic-based discovery_. In the traditional IoT framework, multiple entities which are objects in the real world (e.g., sensors, actuators, and cloud services) are considered together to provide resources (e.g., such as data and context) for a single purpose. For example, sensing readings from the temperature and humidity sensors in a smart home are only used to trigger the heating system. However, the future IoT platforms consist of multiple interdependent systems that collaborate with each other in a symbiotic manner to share all available resources. As shown in Figure 5, multiple single-purpose IoT deployments (e.g., a smart home, video-surveillance) owned by different parties (e.g., private home owner, homeland security), depicted as silos, have their own sensors and/or devices, computation capabilities to analyse data locally (e.g., Complex Event Processing), and storage capabilities with a context management that handles data. Silos are then handled by a domain administration in the cloud that allows interconnectivity with other domains and therefore other silos. Sharing resources allow, for instance, temperature and humidity readings in smart homes to contribute a city-scale monitoring system. Thus, the capability of resource-entity mapping enables omnidirectional information flows across devices, edge, cloud, and systems to collaboratively leverage these resources. Here, \"resources\" are not limited  
Figure 4: Combining the context management interface standard OMA NGSI with semantics offered by the FIESTA-IoT ontology. to physical sensing data, but they can be high-level contextual information shared among multiple entities in the real world. The whole interconnection of resources creates a next generation of context, resembling a global distributed graph of information, that can be seamlessly accessed by any actors of the Internet-of-Things. Data requests are brokered by specific components, namely Next Generation Brokers (NG Brokers), that dispatch data requests to providers of the requested data and aggregate the responses before to return back to the requester achieving a transparent global IoT since the IoT topology is kept hidden from data producers and data consumers perspectives. Semantic-based discovery removes the need for human involvement and assistance and allows worldwide IoT applications to have fully automated reconfiguration and information mash-ups. High-level contextual information are computed by processes of _knowledge extractions_ that combine sensed resources mapping them to common entity models and information model. This capability generates new metadata and attributes necessary for linking information into a context mash-ups. For instance, crowdsensed measurements of noise through smartphones [22] are aggregated to assess the noise level of a city neighbourhood level. The produced data and knowledge might be not of much value if those are not interpreted for understanding a situation of the real world. For example, some indexes of high values of temperature and CO2 together with detected hectic crowd patterns, if correctly interpreted, would permit the identification of an ongoing fire break. This interpretation is done by _situation classification_ analytics components, of which output is either signalled to a human or automatically triggers actions. In addition to these specific contextualization computation, other _stream analytics_ processing are executed for various purposes such as privacy preservation, data polishing, sensor fusion. As we have seen in Section 3.1, we envision the data analytics as topologies of atomic _analytics functions_ (or tasks) dynamically instantiated within the _Artificial Intelligence (AI)_ layer federated among systems (see Figure 5).