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ABSTRACT:Concerning energy consumption and monitoring architectures, our goal is to develop a sustainable declarative monitoring architecture for lower energy consumption taking into account the monitoring system itself. Our second is to develop theoretical and practical tools to model, explore and exploit heterogeneous data from various sources in order to understand a phenomenon like energy consumption of smart building vs inhabitants' social behaviours. We focus on a generic model for data acquisition campaigns based on the concept of generic sensor. The concept of generic sensor is centered on acquired data and on their inherent multidimensional structure, to support complex domain-specific or field-oriented analysis processes. We consider that a methodological breakthrough may pave the way to deep understanding of voluminous and heterogeneous scientific data sets. Our use case concerns energy efficiency of buildings to understand relationship between physical phenomena and user behaviors. The aim of this paper is to give a presentation of our methodology and results concerning architecture and user-centric tools.

In the recent years, the Building Information Modelling (BIM) has become one of the hottest research area in computer science. It attracts many researchers from huge various research domains. Not only the researchers from computer science but also researchers from environmental health, archeology and cultural heritage started working in BIM and its integration with Wireless Sensor Networks (WSN). In this paper, we focus on the integration of BIM and WSN. We tackle one of the major challenges of this integration: how to manage sensor streams by using a digital model of the environment and how to integrate sensor data with the environmental data. Even there are few studies that manage multi sensor data streaming, these approaches are often fitted to a single application and mostly they are independent from the building model. To overcome this problem, we propose a novel real-time environmental monitoring system based on the BIM principles. In this study, we present a multi-application monitoring system architecture. As a testbed environment, the university campus is preferred and for the prototype chosen offices and classrooms are equipped with sensor devices. Real data are enriched with the simulated data to get a large database. The 3D digital model introduces the numerical model of the building and a sensor integration is achieved on the given digital model.

Proliferation of wearable devices with wide spectrum of sensing capabilities together with commercial availability has increased the applicability of ambient intelligence concepts in practical system designs. Being wearable enforces extra constraints in terms of form factor and weight that limit the computational properties and the battery lifetime. There has been increasingly many number of studies for the energy efficiency of embedded and mobile hardware platforms. Due to the known techniques, increasing the energy consumption of an embedded system inherently requires some components to go into the low energy modes with a certain pattern, which in turn entails performance penalties at the application level. Existing solutions for increasing energy efficiency mainly focus only on a certain component of the system, such as hardware, networking firmware and try to achieve energy efficiency without considering the state the application is dynamically in. In this study, the critical balance between energy efficiency and application performance is handled. Application feedback is merged with energy efficiency and according to the application performance, duty cycle mechanism can be configured dynamically. A memory unit (FIFO) of the sensing component is also involved into the dynamic sleep scheduling mechanism in order to process latest sampled data while microprocessor and radio module of the sensor devices are in sleep mode. In this context, one of the fundamental implementations of ambient application which is based on triaxial accelerometer signal, pedometer is performed. Experiments realized on the dataset proved that it exists an interval where energy efficiency is obtained without degrading application performance under critical level and also usage of FIFO showed a significant impact on application performance and energy gain.

Abstract. High energy consumption is a major problem in smart building systems. Existing studies focus on energy consumption of building not the deployed wireless sensors. These approaches are often fitted to a single monitoring application, and lead to static configurations for sensor devices. Moreover, immense raw data generated by the smart building should be used in terms of service. In this paper, we focus on the energy of the monitoring architecture itself and services that use this dataset. We study impacts of services on the energy consumption of monitoring architecture. We consider a monitoring system as a set of applications that exploit sensor measures in real-time, where these applications are declaratively expressed as (service-oriented) continuous queries over sensor data streams. We focalise on multi-application, tackle the optimization of application requirements to manage energy consumption. We introduce a Smart-Service Stream-oriented Sensor Management (3SoSM) that optimizes sensor configurations and manages sensor data streams.