Abstract
Internet of Things (IoT) solutions are becoming irreplaceable in various application domains. IoT enables control over many systems in a smart environment, such as the heating, ventilation and air conditioning (HVAC) system, lighting, and appliances in a smart home or office. By enhancing IoT solutions with a cognitive capability it becomes possible to, for example, adjust ambient conditions according to user preferences without the need for direct user intervention. This functionality constitutes a fore-coming phase in IoT evolution—Cognitive IoT. In this paper, we propose an agent-based smart environment system and compare it to a centralized implementation. In both approaches, feed-forward artificial neural networks are trained under supervision and used to adjust the lighting conditions to the specific user. The agent-based approach offers better preference prediction precision as each user is supported by one agent with a neural network specialized only for his preferences as opposed to the centralized approach where all user preferences are predicted by one neural network. Additionally, the agent-based approach enables easier addition of new users.
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