The team is currently working on the following open source software, application, and platform:
- Open source community development of RIOT (available online) is a nano operating system for the Internet of Things. While requiring as low as 1,5kB of RAM and 5kB or ROM, RIOT oers real time and energy efficiency capabilities, as well as a single API (partially POSIX compliant) across heterogeneous 8-bit, 16-bit and 32-bit low-hardware. This API is developer-friendly in that it enables multithreading, standard C and C++ application programming and the use of standard debugging tools (which was not possible so far for embedded programming). On top of this, RIOT includes several network stacks, such as a standard IPv6/6LoWPAN stack and a information-centric network stack (based on CCN). RIOT is developed by an international community of open-source developers that was co-founded by INRIA and Freie Universitaet Berlin. The goal of RIOT is to provide a powerful, free, open-source IoT software platform that can be used like Linux is for less constrained machines, both (i) in the context of research and/or teaching, as well as (ii) in industrial contexts. Open source implementation of P2P-RPL (RFC 6997). P2P-RPL is an extension to RPL (i.e., the routing protocol specied in RFC 6550) that enables an IPv6 router in a lowpower, lossy network (LLN) to discover routes to one or more IPv6 routers in the LLN “on demand”. The discovered routes may not be the best available but are guaranteed to meet the specied routing metric constraints. Thus, such routes are considered “good enough” from the application’s perspective, which may not have been the case with paths provided by basic RPL.
- Open source implementation of OSPF-MPR (RFC 5449). OSPF-MPR is an extension of OSPFv3 (i.e., the routing protocol specied in RFC5340) that is adapted to mobile ad hoc networks and based on mechanisms providing (i) flooding-reduction, whereby only a subset of all routers will be involved in (re)transmissions during a
flooding operation, (ii) topology-reduction: only a subset of links are advertised, hence both the number and the size of Link State Advertisements (LSAs) are decreased, and (iii) adjacency-reduction: adjacencies are brought up only with a subset of neighbors for lower database synchronization overhead.
- Open source implementation DragonCast. It is a generic framework for network coding in wireless networks. It is a initially result of the GETRF project of the Hipercom2 team. It is based on intra-flow coding where the source divides the flow in a sequence of payloads of equal size (padding may be used). The design keys of DragonNet are simplicity and universality; DragonNet does not use explicit or implicit knowledge about the topology (such as the direction or distance to the source, the loss rate of the links, …). Hence, it is perfectly suited to the most dynamic wireless networks. The protocol is distributed and requires minimal coordination. DragonNet architecture is modular, it is based on 5 building blocks (LIB, SIG, Protocol, SEW and DRAGON). Each block is almost independent. This makes DragonNet generic and hence adaptable to many application scenarios. DragonNet derives from a prior protocol called DRAGONCAST. Indeed, DragonNet shares the same principles and theoretical overview of DRAGONCAST. It enriches DRAGONCAST by the information base and signaling required to perform broadcast in wireless networks and in wireless sensor networks in particular.
- The team is implicated in the implementation and deployment of MACACOapp application, which is developped in the context of the EU CHIST-ERA MACACO project. It consists in a mobile phone application that periodically samples phone’s information on the mobility (through, e.g., GPS sensor, accelerometer and WiFi/Bluetooth/Cellular environment, connectivity type) and on the data trac it generates (through, e.g., Internet browser history and applications data consumption). The (1) the collected information will allow us studying the correlation between mobility and content demand patterns and that (2) the results of this analysis will allow us inferring the best times and places to transfer content from/to users’ phones location and/or from/to the wireless infrastructure closest to the users’ phones location. Users will be also invited to fill a non-mandatory questionnaire relevant to this study. Our questionnaire collects information about the personality traits and application preferences of people. The information collected from questionnaire will allow us to analyse the correlation between users personality traits and their application preferences and interests.
- The team is involved in the FIT IoT-LAB platform deployment, developped as part of the Equipex FIT. The platform is built to help foster the development, tuning and experimentation of protocols and applications for the Internet of Things and wireless sensor networks. IoT-LAB provides both dedicated IoT hardware deployments, a front-end webportal and backend management software. Using these elements, IoT-LAB enables users to share access to this IoT hardware, set-up and manage experiments. Remote use, and large scale experiments on concrete IoT deployments are thus made possible. The INFINE team is now managing the IoT-LAB site currently located in Rocquencourt, and which was publically opened in November 2014.