Enabling Self-learning in Dynamic and Open IoT Environments

The next big thing in computing is the Internet of Things (IoT), referring to dynamic and ever-evolving environments, generating high-volume streams of heterogeneous yet correlated contextual information of varying quality and complexity. Moreover, the increase in user mobility and unreliable sensor availability in IoT, necessitates the context-aware applications to dynamically adapt their behavior at run time. In this paper, we elicit the need for different self-learning techniques to tackle the openness of the IoT environments and propose enabling algorithms to achieve them. First, we present possible application scenarios which can benefit from both supervised and unsupervised self-learning. Later, we propose correlation mining algorithms based on Kullback-Leibler (KL) divergence and frequent set mining that exploits correlated contexts to enable unsupervised self-learning. These algorithms help to identify alternate sources for a context and semantically describe the previously unseen contexts in terms of already known contexts. We have realized the proposed algorithms on top of a Bayesian framework (HARD-BN) which supports autonomous learning. Our experiments demonstrate the applicability of the proposed correlation mining algorithms and their feasibility to enable self-learning in open and ever-evolving IoT environments.