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Ambient Intelligence System (AmIS) can be constructed using data collected from Internet of Things (IoT). In this paper, the IoT data collection problem is studied for AmIS with dynamic structure and dynamic behavior of participants (devices), where constraints on resources consumption and performance are essential. A novel technology is proposed, which includes the following steps: (1) definition of the data collection (DC) problem (considering the model of the observed system, DC conditions, etc.); (2) DC policy assignment; (3) construction of DC models; (4) evaluation and presentation of the data processing results. The proposed DC technology supports the development of data collecting subsystems in AmIS. Such subsystems provide data that reflect the changes in structure, state, situation, and behavior of participants in their IoT environment in time. Therefore, we show how this “cognitive” function of the DC process increases the intelligence level of IoT environment.

Practically all human activities depend on technical systems, which consists of a multitude of dynamically distributed objects. In order to control these systems, it is necessary to build and periodically rebuild models of objects, which consist of elements and connections between them and describes the object's state in time and space. Due to a large amount of monitoring data, the problem of automation of object model synthesis arises. By now the most work is done by experts. Analysis of the works from the related areas has shown that methods for the automated synthesis of object models based on link discovering do not exist. An approach for the automated synthesis of object models based on content extracted from messages received from monitoring systems is proposed. A context describing synthesis process conditions is supposed to be considered. The approach is illustrated with an example.