Summary
The Internet of Things (IoT) supports many users and context‐aware applications controlling heterogeneous IoT devices. This differs from traditional networks, in which a single entity manages each device. Thus, new access control models must be created in order to support more responsive, scalable, secure, and autonomous management. This article presents an attribute‐based access control model, which applies conflict resolution and access delegation in a multiuser and multiapplication environment. With scalability in mind, we propose the caching of access permissions, as well as a split policy processing model in which the devices with enough computational power perform part of the processing. The proposed model was implemented as part of the ManIoT architecture an d evaluated in experiments on a testbed to demonstrate its efficiency. Results show that our model accelerates the processing of access management policies from 51% by up to 79%.
This work presents a large margin learning algorithm for singlehidden layer feedforward networks (SLFNs) with random weightsfor the hidden neurons, called RP-IMA. This algorithm, applied tobinary classification problems, proposes randomly assignedweightsto the hidden layer and the use of an incremental margin algorithmto calculate the weights of the output neuron of the SLFN. Theresults showed that the proposed algorithm is capable to obtaina large separation margin in the feature space and has its performanceless sensitive to variations in the network architecture, whencompared to Extreme Learning Machines.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.