Fault-Tolerant Remote Sensing Data Dissemination Protocols For Climate Change And Sustainability

About this position

Keywords: distributed computing, dissemination protocols, swarm intelligence, global change, remote sensing, fault tolerance, intermittent computing

Abstract

• 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

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