Gérôme Bovet

Offering web-of-things connectivity to building networks

Building management systems (BMS) are nowadays present in new and renovated buildings, relying on dedicated networks. The presence of various building networks leads to problems of heterogeneity, especially for developing BMS. In this paper, we propose to leverage on the Web-of-Things (WoT) framework, using well-known standard technologies of the Web like HTTP and RESTful APIs for standardizing the access to devices seen from an application point of view. We present the implementation of two gateways using the WoT approach for exposing KNX and EnOcean device capabilities as Web services, allowing a fast integration in existing and new management systems.

Distributed Semantic Discovery for Web-of-Things Enabled Smart Buildings

Nowadays, our surrounding environment is more and more scattered with various types of sensors. Due to their intrinsic properties and representation formats, they form small islands isolated from each other. In order to increase interoperability and release their full capabilities, we propose to represent devices descriptions including data and service invocation with a common model allowing to compose mashups of heterogeneous sensors. Pushing this paradigm further, we also propose to augment service descriptions with a discovery protocol easing automatic assimilation of knowledge. In this work, we describe the architecture supporting what can be called a Semantic Sensor Web-of-Things. As proof of concept, we apply our proposal to the domain of smart buildings, composing a novel ontology covering heterogeneous sensing, actuation and service invocation. Our architecture also emphasizes on the energetic aspect and is optimized for constrained environments.

Machine learning with the internet of virtual things

Peripheral devices working in the context of the Internet of Things, specifically sensors, produce large amounts of data that can be used to infer knowledge. In this area, machine learning technologies are increasingly used to establish versatile models. In this article, we present a new architecture capable of running machine learning algorithms in a sensor network. This approach has advantages in terms of confidentiality and energy efficiency-related data transfer. First, we argue that some types of machine learning algorithms are consistent with this approach, particularly those based on the use of generative algorithms. Subsequently we detail our proposed architecture based on Internet of Things and Web of Things paradigms facilitating the integration in sensor networks. The convergence of generative models and Web Objects leads to the concept of virtual sensors exposing high-level knowledge using data from various sensors. Finally, we demonstrate the feasibility and performance of our proposal using a real scenario.

Virtual Things for Machine Learning Applications

Internet-of-Things (IoT) devices, especially sensors are producing large quantities of data that can be used for gathering knowledge. In this field, machine learning technologies are increasingly used to build versatile data-driven models. In this paper, we present a novel architecture able to execute machine learning algorithms within the sensor network, presenting advantages in terms of privacy and data transfer efficiency. We first argument that some classes of machine learning algorithms are compatible with this approach, namely based on the use of generative models that allow a distribution of the computation on a set of nodes. We then detail our architecture proposal, leveraging on the use of Web-of-Things technologies to ease integration into networks. The convergence of machine learning generative models and Web-of-Things paradigms leads us to the concept of virtual things exposing higher level knowledge by exploiting sensor data in the network. Finally, we demonstrate with a real scenario the feasibility and performances of our proposal.

A Scalable Cloud Storage for Sensor Networks

Data storage has become a major topic in sensor networks as large quantities of data need to be archived for future processing. In this paper, we present a cloud storage solution benefiting from the available memory on smart things becoming data nodes. In-network storage reduces the heavy traffic resulting of the transmission of all the data to an outside central sink. The system built on agents allows an autonomous management of the cloud and therefore requires no human in the loop. It also makes an intensive use of Web technologies to follow the clear trend of sensors adopting the Web-of-Things paradigm. Further, we make a performance evaluation demonstrating its suitability in building management systems.

Energy-Efficient Optimization Layer for Event-Based Communications on Wi-Fi Things

Abstract The Web-of-Things or WoT offers a way to standardize the access to services embedded on everyday objects, leveraging on well accepted standards of the Web such as HTTP and REST services. The WoT offers new ways to build mashups of object services, notably in smart buildings composed of sensors and actuators. Many things are now taking advantage of the progresses of embedded systems relying on the ubiquity of Wi-Fi networks following the 802.11 standards. Such things are often battery powered and the question of energy efficiency is therefore critical. In our research, we believe that several optimizations can be applied in the application layer to optimize the energy consumption of things. More specifically in this paper, we propose an hybrid layer automatically selecting the most appropriate communication protocol between current standards of WoT. Our results show that indeed not all protocols are equivalent in terms of energy consumption, and that some noticeable energy saves can be achieved by using our hybrid layer.

Will web technologies impact on building automation systems architecture

Abstract Offices, factories and even private housings are more and more endowed with building management systems (BMS) targeting an increase of comfort as well as lowering energy costs. This expansion is made possible by the progress realized in pervasive computing, providing small sized and affordable sensing devices. However, current BMS are often based on proprietary tech- nologies, making their interoperability and evolution more difficult. For example, we observe the emergence of new applications based on intelligent data analysis able to compute more complex models about the use of the building. Such applications rely on heterogeneous sets of sensors, web data, user feedback and self-learning algorithms. In this position paper, we discuss the role of Web technologies for standardizing the application layer, and thus providing a framework for developing advanced building applications. We present our vision of TASSo, a layered Web model facing actual and future challenges for building management systems.

An Energy Efficient Layer for Event-Based Communications in Web-of-Things Frameworks

Leveraging on the Web-of-Things (WoT) allows standardizing the access of things from an application level point of view. The protocols of the Web and especially HTTP are offering new ways to build mashups of things consisting of sensors and actuators. Two communication protocols are now emerging in the WoT domain for event-based data exchang, namely WebSockets and RESTful APIs. In this work, we motivate and demonstrate the use of a hybrid layer able to choose dynamically the most energy efficient protocol.