Sahraoui Dhelim

TL;DR: A three-layer task offloading framework named DCC is proposed, which consists of the device layer, cloudlet layer and cloud layer, and a greedy task graph partition offloading algorithm, where the tasks scheduling process is assisted according to the device computing capabilities following a greedy optimization approach to minimize the tasks communication cost.

TL;DR: A service-oriented architecture for aggregating ontological information from distributed data nodes for internet of things using semantic technologies to handle problems of heterogeneity and serve as the foundation to support different applications is provided.

TL;DR: Experimental results show, in the context of IoT, that incorporating the users’ social relationships in service recommendation increases the accuracy and diversity of the offered services.

TL;DR: Experimental results show that the proposed Meta-Interest method can increase the precision and recall of the recommendation system, especially in cold-start settings.

TL;DR: This paper presents and evaluates an FRS based on the big-five personality traits model and hybrid filtering, in which the friend recommended process is based on personality traits and users’ harmony rating and shows that PersoNet performs better than collaborative filtering (CF)-based FRS in terms of precision and recall.