Job offer

Collecting environmental remote sensing data is key to quantifying and better understanding the impacts of global change. Collecting data in remote ecosystems such as mangrove estuaries, rainforests, deep-water habitats and river flowstreams requires high coverage and spatiotemporal precision. While satellite systems enable high coverage, they lack precise spatiotemporal data collection. Swarms of drones have emerged as a promising way to improve the precision of data collection. However, designing drone swarms as fault- tolerant remote sensing platforms remains challenging. This doctoral project addresses a key research issue for these platforms: fault- tolerant data dissemination. The project will investigate and propose mission-oriented dissemination techniques to enable adaptive sensing data collection ranging from low-latency, fast data collection to partition-tolerant, delay-tolerant remote sensing applications.

Context and proposed research

Effective environmental data collection and dissemination in drone swarms relies heavily on distributed synchronisation mechanisms that allow nodes to exchange state, maintain a consistent view of shared data, and support collective decision making. This requirement is particularly challenging in mobile settings, where communication links are intermittent, network topology changes frequently due to mobility, and available bandwidth and energy resources are limited. Additionally, the CAP Theorem [Gilbert and Lynch 2012] states that a distributed system can simultaneously satisfy at most two of the following properties: consistency, availability, and partition tolerance. In highly dynamic swarm networks, network partitions are frequent and largely unavoidable, which makes it difficult for traditional strongly consistent systems to maintain high availability. As a result, such systems are hard to deploy efficiently in drone swarms, where continuous operation and timely coordination are essential. Multicast primitives [Janic 2005] are fundamental for information dissemination in networked systems. These primitives allow drones to synchronise their collected data and to enable fault tolerance through replication. As drones communicate through a sparse, wireless ad hoc network, we assume that state synchronisation messages are exchanged through unreliable channels to reduce synchronisation costs. To explore novel primitives suitable for drones swarms, this project has three main goals. Firstly, we aim to survey data dissemination techniques for environmental monitoring. Secondly, we aim to design novel, adaptive protocols for drone swarms based on the ecosystems to be monitored. Thirdly, we expect to validate the techniques with a proof of concept.

References Gilbert, S. and Lynch, N. (2012). Perspectives on the cap theorem. Computer, 45(2):30-36. Janic, M. (2005). Multicast in network and application layer. Dissertation (tu delft), Delft University of Technology. Johnson, D., Ntlatlapa, N. S., and Aichele, C. (2008). A simple pragmatic approach to mesh routing using BATMAN. In 2nd IFIP International Symposium on Wireless Communications and Information Technology in Developing Countries. Guillen-Perez, A., Montoya, A.-M., Sanchez-Aarnoutse, J.-C., and Cano, M.-D. (2021). A comparative performance evaluation of routing protocols for flying ad-hoc networks in real conditions. Applied Sciences, 11(10):4363. Shah, D. (2009). Gossip algorithms. Foundations and Trends in Networking, 3(1):1-125. Drabkin, V., Friedman, R., Kliot, G., and Segal, M. (2007). Rapid: Reliable probabilistic dissemination in wireless ad-hoc networks. In 2007 26th IEEE International Symposium on Reliable Distributed Systems (SRDS 2007), pages 13-22. IEEE. Levis, P., Brewer, E., Culler, D., Gay, D., Madden, S., Patel, N., Polastre, J., Shenker, S., Szewczyk, R., and Woo, A. (2008). The emergence of a networking primitive in wireless sensor networks. Communications of the ACM, 51(7):99-106. Herrmann, C., Mager, F., and Zimmerling, M. (2018). Mixer: Efficient many-to-all broadcast in dynamic wireless mesh networks. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems., Mobility Rule: Applicants must not have resided or conducted their primary activity (work, studies, etc.) in the France for more than 12 months in the 36 months preceding the call deadline. Periods of stay as a tourist, refugee, or time spent in national service, are not considered as residency. Selection process

To apply, please send the following information to rs-resco-recruitment@lists.recherche.enac.fr(Subject=PhD position [ENAC-UFPB-PhD27-BEST]: distributed computing for CPSswams):

• Curriculum Vitæ
• Letter of motivation that should describe the applicant’s background in the areas of the project, reason for interest in the project, and future plans
• A list of courses and grades of the last three years of study (an informal transcript is OK).
• Names and contact details of at least two people who can write you references, whom we will contact directly.
• If relevant, a link to your publications and/or open-source developments.

The selection process is three-fold:

• First, all application will be evaluated by both project’s partners
• Second, selected applicants will be interviewed,
• Finally, the ranking positions of selected candidates will be communicated as well as final hiring guidelines

Additional comments

About ENAC and UFPB

The ENAC, National School of Civil Aviation, is located in Toulouse, France, the centre of the European aerospace industry (e.g., AirBus, Thales, and CNES). It offers an ideal working environment, where researchers can focus on developing new ideas, collaborations and projects. Our research topics at ENAC Lab include emerging CPS design (e.g., drones, underwater robots and nanosatellites), aviation safety and security, sustainable transportation development, and aeronautical computer-human interactions. For further information, please consult our site. The proposed research will be developed in the ENAC research laboratory, ENAC Lab, in close cooperation with in close cooperation with the Laser research group of the UFPB in Brazil.

Website for additional job details https://recherche.enac.fr/~silvestre/jobs.html

Master Degree or equivalent

Research Field Mathematics » Algorithms

Education Level Master Degree or equivalent

Research Field Computer science » Informatics

Education Level Master Degree or equivalent, To explore both fundamental and applied aspects of this project, applicants should hold a Master’s degree in Computer Science or Informatics, Physics, Mathematics or a related field by the starting date of the doctoral project. They must be excited by research in distributed computing, distributed algorithms, remote sensing, applied computing to environmental sciences, and/or swarm robotics/intelligence, and should have an excellent academic record in one of these areas. Familiarity with formal specification and verification (e.g., TLA+), queue theory, computational fluid dynamics, and graph theory/algorithms would be greatly appreciated. Teamwork and communication skills are key to this position, and industrial experience is a plus. Specific Requirements

Excellent proficiency in English is required (CECR : C1; IELTS : 7.0; Cambridge English Scale :185; or equivalent). Knowledge of French is not required for this position.

Languages ENGLISH

Level Excellent

Research Field Computer science » Systems designMathematics » AlgorithmsComputer science » InformaticsEnvironmental science » Global changeEngineering » Aerospace engineering

Years of Research Experience 1 - 4, Academic Requirements: By the call deadline, candidates must hold a master’s degree or equivalent diploma; and must not hold a doctoral degree.

This fully-funded PhD starts in September 2027 and the duration of the contract/scholarship is 3 years. Benefits include:

• French government strongly subsidizes its higher education system, therefore therefore the tuition fees are among the more competitive in Europe. Moreover, our program grants tuition fee waiver at UFPB in Brazil.
• Social security coverage included.
• Dedicated funding to support research, training, networking and international mobility activities, including allowance to cover flights and living expenses for up to 12 months in Brazil.
• Subsidized meals.
• Partial reimbursement of public transport costs.
• Social, cultural and sports events and activities.

Eligibility criteria