Multiagent approach for resource management in Smart Environments

The dynamic nature of Smart Environments, enabled thanks to the Internet of Things (IoT) paradigm, requires the application of innovative resource discovery/selection services. Recommender systems provide useful and customized information, properly selected and filtered, for users and services. This paper proposes a multiagent approach for building a recommendation system in smart environments. Smart objects, enhanced IoT objects interacting among them and with users, are mapped through real-valued vectors obtained by word embedding techniques. The vectors are assigned to bio-inspired agents that, working autonomously, bring out a set of similarity grouped entities - clusters of smart objects with similar features - thus enabling an efficient and dynamic recommendation/selection system. Experiment results on synthetic and real datasets prove the effectiveness of the approach.