Despite the advantages that the Internet of Things (IoT) will bring to our daily life, the increasing interconnectivity, as well as the amount and sensitivity of data, make IoT devices an attractive target for attackers. To address this issue, the recent Manufacturer Usage Description (MUD) standard has been proposed to describe network access control policies in the manufacturing phase to protect the device during its operation by restricting its communications. In this paper, we define an architecture and process to obtain and enforce the MUD restrictions during the bootstrapping of a device. Furthermore, we extend the MUD model with a flexible policy language to express additional aspects, such as data privacy, channel protection, and resource authorization. For the enforcement of such enriched behavioral profiles, we make use of Software Defined Networking (SDN) techniques, as well as an attribute-based access control approach by using authorization credentials and encryption techniques. These techniques are used to protect devices’ data, which are shared through a blockchain platform. The resulting approach was implemented and evaluated in a real scenario, and is intended to reduce the attack surface of IoT deployments by restricting devices’ communication before they join a certain network.
DNN processing on image streams has opened the possibility for new and innovative applications. Some of those would benefit from performing the computation locally, avoiding incurring into latencies due to data travelling to image processing services in the cloud, and thus allowing for faster response times. New devices like the GPU-accelerated NVIDIA Jetson family, such as the Jetson Nano, are capable of running modern DNN image processing models, offering an affordable, powerful and scalable local alternative to cloud processing. Performing local image processing can also benefit security and even GDPR compliance, potentially easing the deployment of this solutions. Not only that, but local image processing can also bring the possibility of applying these techniques in areas with reduced connectivity, where cloud-based solutions are unfeasible . In this work, we propose an architecture for the orchestration of DNN accelerated image processing on IoT devices, based on FogFlow; an orchestration platform capable of leveraging cloud and edge resources. FogFlow is part of the FIWARE initiative and is based in the NGSI family of standards, widely applied in Smart City, Smart Building and Smart Home solutions, making it an easy-to-integrate technology.INDEX TERMS DNN image processing, IoT, orchestration.
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