The Internet of Things (IoT) generates massive streams of data which call for ever more efficient real time processing. Designing and implementing a big data service for the real time processing of such data requires an extensive knowledge of both input load and data distribution in order to provide a service which can cope with the workload. In this context, we study in this paper the challenges inherent to the real time processing of massive data flows from the IoT. We provide a detailed analysis of traces gathered from a well-known healthcare sport-oriented application in order to illustrate our conclusions from a big data perspective.
Structured Peer to Peer overlays have shown to be a very good solution for building very large scale distributed information systems. Most of them are based on Distributed Hash Tables (DHTs) that provide an easy way to manage replicas, thus facilitating high availability of data as well as fault tolerance. However, DHTs can also be affected by some well known Distributed Denial of Services attacks that can lead to almost complete unavailability of the stored objects. Very few powerful solutions exist for this kind of security weakness, and increasing the number of replicas for a given object seems to be the best known one. In this paper, we show how a recursive replicating schema can provide a good solution for this kind of attack. This work is part of a CNRS/CONICYT international cooperation project between France and Chile.
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.