The paper presents an approach and case study of a distributed driver monitoring system. The system utilizes smartphone sensors for detecting dangerous states for a driver in a vehicle. We use a mounted smartphone on a vehicle windshield directed towards the driver’s face tracked by the front-facing camera. Using information from camera video frames as well as other sensors, we determine drowsiness, distraction, aggressive driving, and high pulse rate dangerous states that can lead to road accidents. We propose a cloud system architecture to capture statistics from vehicle drivers, analyze it and personalize the smartphone application for the driver. The cloud service provides reports on driver trips as well as statistics to developers. This allows to monitor and improve the system by developing modules for personification and taking into account context situation. We identified statistically that the driver eye closeness is related to the light brightness and drowsiness recognition should be adjusted accordingly.

Cyber-Physical-Social Systems integrate various resources from physical network, cyberspace, and social world.These systems rely on communication, computation and control infrastructures commonly consisting of several levels for the three worlds with various resources as sensors, actuators, computational resources, services, humans, etc. Efficient interaction of these resources is essential for cyber-physical-social systems operation. The paper presents multi-level self-organization approach for cyber-physical-social systems that distinguishes three main level for the system resource interaction: physical level, planning level, and strategic level. Smart home cleaning scenariois proposedas an example of multi-level self-organization approach. Robot vacuum cleaner creates room map using the light-sensitive sensors and clean room using the map. For the efficient cleaning, it requires the lights switch on in the cleaning room. For this purposes the vacuum cleaner has to interact with the adaptive illumination control system.Manipulating robot move light furniture (e.g. chairs, or coffee table) or decorative elements (pots with flowers, vases, etc.). It interacts with the vacuum cleaner for synchronization of moving light furniture.For the strategic level, a special strategic planning service has been identified that determines general strategy of resources behavior in the scenario based on interaction with personal mobile device and current situation in smart home. Example of general strategies can be for example reduction in energy consumption, excellent cleaning. For the planning level, a special room management services have been proposed that provide general behavior the cyber-physical-social systems resources in physical level. Example of these strategies can be clean room 1 completely, after that clean room 2 or vice versa. For the physical level a vacuum cleaner, manipulating, and light control services have been proposed. The paper present ontology-based information model for smart home devices interaction. It allows the services of physical level linked to the appropriate smart room devices interact in cyber space while physical devices interact in physical space. Every resource in physical level of cyber-physical-social system is described by the ontology. Appropriate ontologies of vacuumcleanerrobot, adaptive illumination control system, and manipulating robothave been proposed in the paper.

The advantage of human-machine collective intelligence for decision support systems is the ability to make better decisions due to the mitigation of human biases in the generation of potential solutions and their evaluation. So far, the potential of human-machine collective intelligence was used only in few decision support systems, however, teamwork between humans and machines has not been achieved. This is partly due to the lack of interoperability in these systems. In earlier works, the authors proposed the apparatus of multi-aspect ontologies implying the integration of multiple domain ontologies to provide interoperability between humans and machines and coordinate interrelated processes going on in the systems of the considered type. Such ontologies have proved efficient for systems that require intensive information and knowledge exchange between loosely-related dynamic autonomous domains (e.g., enterprise knowledge management, product lifecycle management, or human-machine collective intelligence systems). However, existing ontology development methodologies fail to recommend a process that would support cross-domain knowledge integration during the multi-aspect ontology development. Moreover, the structure of the multi-aspect ontology imposes some restrictions on the integration approach. The paper proposes such a methodology for the multi-aspect ontology development that incorporates the aspect integration approach at multiple levels. The methodology is applied to develop a multi-aspect ontology for decision support based on human-machine collective intelligence. An example from the “e-tourism” domain demonstrates the applicability of the proposed methodology as well as the usage of the multi-aspect ontology for a human-machine environment aimed at solving real-world problems. The proposed methodology can facilitate the development of ontologies for complex knowledge-based systems that operate with knowledge from multiple loosely-connected domains.