BBS: An energy efficient localized routing scheme for query processing in wireless sensor networks

A wireless sensor network (WSNET) can support various types of queries. The energy resource of sensors constrains the total number of query responses, called query capacity received by the sink processing in There are four problems in the existing approaches for energy-efficient query WSNETs: 1. the fact that sensors near the sink drain their energy much faster than distant sensors has been overlooked, 2. routing trees (RT) are rooted at the sink, and therefore, aggregative queries are less energy-efficient, 3. data reception cost has been ignored, and 4. flooding is used in query distribution or RT construction. In this paper, we propose a Broadcasting-Based query Scheme (BBS) to address the above problems. BBS reduces the energy depletion rate of sensors near the sink, builds different localized RTs for different query types, and eliminates the flooding cost of query distribution. Compared to the existing approaches, simulation studies show that BBS produces significant improvement in the query capacity for non-holistic queries (10\%-100\% capacity improvement) and holistic queries (tip to an order of magnitude of capacity improvement)

Big Data Management in the Cloud: Evolution or Crossroad?

International audienceIn this paper, we try to provide a synthetic and comprehensive state of the art concerning big data management in cloud environments. In this perspective, data management based on parallel and cloud (e.g. MapReduce) systems are overviewed, and compared by relying on meeting software requirements (e.g. data independence, software reuse), high performance, scalability, elasticity, and data availability. With respect to proposed cloud systems, we discuss evolution of their data manipulation languages and we try to learn some lessons should be exploited to ensure the viability of the next generation of large-scale data management systems for big data applications

Database Consistency Models

International audienceA data store allows application processes to put and get data from a shared memory. In general, a data store cannot be modelled as a strictly sequential process. Applications observe non-sequential behaviours, called anomalies. The set of possible behaviours, and conversely of possible anomalies, constitutes the consistency model of the data store