Summary In the era of the Internet of Things (IoT), we are witnessing to an unprecedented data production because of the massive deployment of wireless sensor networks (WSNs). Typically, a network of several hundred sensors is created to ensure the interactions between the cyber world and the physical world. Unfortunately, the intensive use of this kind of networks has raised several security issues. Indeed, many WSN‐based applications require secure communication in order to protect collected data. This security is generally ensured by encryption of communication between sensors, which requires the establishment of many cryptographic keys. Managing these keys, within a protocol, is an important task that guarantees the effectiveness of the security mechanism. The protocol should be intelligently adaptable not only to intrusion events but also to the security level needed by some applications. An efficient protocol optimizes also sensors energy and consequently increases the network life cycle. In this paper, we propose, a smart and dynamic key management scheme for hierarchical wireless sensor networks (SKWN). Our protocol offers three subschemes to deal with key establishment, key renewal, and new node integration. Regarding existing schemes, SKWN does not only provide reliable security mechanisms, but it also optimizes energy consumption and overheads related to the communication and memory usage. Furthermore, our approach relies on a machine learning approach to monitor the state of the network and decide the appropriate security level. We provide a formal approach and its implementation, together with simulations allowing to compare resources usage with respect to existing approaches.

Abstract. In this paper, we combine query rewriting and configuration to provide a new semantic-based approach to service composition, featuring a two-stage process that relies on 1) a simple formalization of semantic Web services that supports query rewriting, and 2) a clear separation between constraints and service/domain knowledge description. Given a user query and a set of service descriptions, query rewriting is used to decompose the query into sets of services that implement the required functionalities (discovery phase). At the orchestration phase, configuration is used to capture dependencies between services, and to generate a set of composite Web services ranked according to user preferences, while maintaining validity with respect to business rules organized into different levels (composition, service and user). We provide a formal framework and a complete implementation of the proposed approach, together with experiments by considering services from different domains.

International audienceIn the era of bigdata, with a massive set of digital information of unprecedented volumes being collected and/or produced in several application domains , it becomes more and more difficult to manage and query large data repositories. In the framework of the PetaSky project (http://com.isima.fr/Petasky), we focus on the problem of managing scientific data in the field of cosmology. The data we consider are those of the LSST project (http://www.lsst.org/). The overall size of the database that will be produced is expected to exceed 60 PB [28]. In order to evaluate the performances of existing SQL On MapReduce data management systems, we conducted extensive experiments by using data and queries from the area of cosmology. The goal of this work is to report on the ability of such systems to support large scale declarative queries. We mainly investigated the impact of data partitioning, indexing and compression on query execution performances