Gérôme Bovet’s research while affiliated with Armasuisse and other places

There is currently an ongoing initiative to improve the interoperability between nations and other partners during common missions through Federated Mission Networking (FMN). So far, the focus of the standardization and profiling work done in FMN has mostly been on static and deployed networks, where networking resources are stable and plentiful. There is however also a need for interoperability at the tactical edge, between mobile units that have limited and often disrupted communications. In a previous study, we compared different protocols for subscription based distribution of information. We concluded that the WS-Notification standard, which is currently used in NATO, has a too large overhead in lower capacity tactical networks, and that for instance the Message Queuing Telemetry Transport (MQTT) protocol could be used instead. In this paper, we expand upon those findings by investigating the applicability of MQTT in tactical networks further. Here, we address one of the main shortcomings in the testbed used in our previous experiments by adding in new and more realistic radio models, which allow us to better assess the performance of MQTT in the tactical domain. Furthermore, we also expand our experiments evaluating MQTT for sensor networks (MQTT-SN) as well. The reason for adding MQTT-SN to the experiments is that this protocol is based on UDP rather than TCP. This work has been performed in the context of the NATO STO/IST-150 «NATO Core Services profiling for Hybrid Tactical Networks» working group.

Simulation or emulation of mobile ad hoc networks (MANET) is used to predict or analyze the performance of MANETs under various scenarios. One challenge is to emulate realistically the MANET's radio performance. Running the Extendable Mobile Ad Hoc Network Emulator (EMANE) framework, we show how to reproduce measured characteristics, namely throughput and round-trip time, of real tactical radios using wideband or narrowband TDMA-based waveforms. Additionally, a solution to simulate rate adaptation is proposed. An introduction to EMANE and the EMANE radio model plugins is also provided.

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.

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.

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.