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Kolomvatsos, K., Oikonomou, P., Koziri, M., & Loukopoulos, T.
IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1651-1658). IEEE.
Publication year: 2018

The limited computational and storage capabilities of the devices interconnected in Internet of Things (IoT) make them to host only a sub-set of the the collected data. Every device, i.e., an IoT node, should keep only the necessary data locally, thus, it can be able to process them and provide responses in limited time. Nodes can act as a team and cooperate to store the data close to the processing of tasks defined in the form of queries. In this paper, we propose a model for deciding the allocation of data in a set of IoT nodes. Every node decides if the observed data are correlated with the available datasets or they are outliers. We propose an ensemble scheme for multidimensional outliers detection that results, in real time, the final decision. When data are accepted to be locally stored, nodes select their peers where data will be replicated. This way, we keep the data in multiple locations in the network aiming to reduce latency in the provision of responses and support a fault tolerant mechanism. The replication decision is based on the correlation of the incoming data with the present datasets. We analytically describe our model and evaluate it through extensive simulations presenting its pros and cons.